The Inhabited Body — Volume 2: Feeding, Fixing, and Fighting Your Microbiome

What We Do to Our Microbiome — and What It Does Back

Horst Herb

2026-04-18

© 2026 Horst Herb. All rights reserved.

The Inhabited Body — Volume 2: Feeding, Fixing, and Fighting Your Microbiome

1. The Inhabited Body — Volume 2: Feeding, Fixing, and Fighting Your Microbiome

2. Chapter 18: Antibiotics — Collateral Damage

1. 18.1 The Broad-Spectrum Problem: Carpet-Bombing an Ecosystem

2. 18.2 Short-Term Disruption and Long-Term Scars

3. 18.3 Early-Life Antibiotic Exposure and Lifetime Consequences

4. 18.4 Antibiotic Resistance Genes in the Commensal Microbiome — A Hidden Reservoir

5. 18.5 Candida Blooms and the Kingdom That Fills the Vacuum

6. 18.6 The Recovery Phase — What Actually Happens When Antibiotics Stop

7. 18.7 Towards Precision: Narrow-Spectrum and Microbiome-Sparing Approaches

3. Chapter 19: Phage Therapy — The Comeback

1. 19.1 A History: D’Hérelle, Soviet Medicine, and the Road Not Taken

2. 19.2 Two Patients, Two Infections, Two Worlds

3. 19.3 Landmark Cases: When Compassionate Use Changed the Conversation

4. 19.4 The Limits of the Comeback

4. Chapter 20: Diet — Feeding the Ecosystem

1. 20.1 The Dry Season in Hadzaland

2. 20.2 The Gardener’s Dilemma

3. 20.3 The Currency of the Realm: Fibre and the Microbiota-Accessible Carbohydrates

4. 20.4 Diversity Beats Dose: The Thirty-Plant Rule

5. 20.5 The Mediterranean Pattern, Put to the Test

6. 20.6 Fermented Foods: The Other Living Diet

7. 20.7 Polyphenols: Why It Depends Whose Garden You Have

8. 20.8 The Other Side of the Plate: Protein, Fat, and the Metabolites You Don’t Want

9. 20.9 Ultra-Processed Food: When the Package Is the Problem

10. 20.10 When You Eat: The Circadian Microbiome

11. 20.11 Personalised Nutrition: Why “What Should I Eat?” Is the Wrong Question

12. 20.12 The Controversies, Met Head-On

13. 20.13 What This Means for Tuesday Dinner

14. 20.14 Looking Ahead

15. Key References

5. Chapter 21: The Other Levers — How the Rest of How We Live Shapes the Ecosystem Inside Us

1. 21.1 The Garden Has Weather

2. 21.2 The Forgotten Clock: Artificial Light and the Diurnal Microbiome

3. 21.3 Twenty-Two Degrees All Year: The Thermostat and the Unfelt Season

4. 21.4 The Invisible Garden in the Air: HEPA Filters, UVC Lamps, and the Post-Pandemic Reflex

5. 21.5 Dirt, Biodiversity, and the Old Friends We Stopped Meeting

6. 21.6 The Dog in the Doorway: Household Pets and the Infant Microbiome

7. 21.7 The Household as a Microbial Unit: What We Share With the People We Live With

8. 21.8 Moving the Body: Exercise as a Microbial Intervention

9. 21.9 Sleep and Stress: The Nervous System’s Dialogue With the Gut

10. 21.10 The Medicine Cabinet: Non-Antibiotic Drugs and the Microbiome

11. 21.11 The Water We Drink: Chlorine, Hardness, and the Microbes That Aren’t There

12. 21.12 Taking Stock: The Modern Environment as a Microbiome Experiment

13. Key References

6. Chapter 22: Drugs Beyond Antibiotics — The Quiet Pharmacology of the Gut

1. 22.1 The Quietest Side Effect

2. 22.2 Reading the Evidence: A Note Before We Start

3. 22.3 Closing the Acid Gate: PPIs and Their Cousins

4. 22.4 The Drug That Works Through Microbes: Metformin

5. 22.5 The Quiet Erosion: NSAIDs and the Small Bowel You Cannot Feel

6. 22.6 Statins, Cholesterol, and the Bact2 Enterotype

7. 22.7 The Mind-Altering Drugs: Antipsychotics, Antidepressants, and the Microbial Cost of Calm

8. 22.8 Opioids and the Bowel That Forgets to Move

9. 22.9 The Forgotten Effect of Laxatives

10. 22.10 Hormones in a Pill: Contraceptives, HRT, and the Estrobolome

11. 22.11 The Supplement Aisle: Iron, Vitamin D, Fish Oil, and What’s Real About Berberine

12. 22.12 Polypharmacy: The Cumulative Tax

13. 22.13 Pharmacomicrobiomics: When Your Microbes Decide What Your Medicine Does

14. 22.14 What This Means for the Person Reading This Book

15. References for Chapter 22

7. Chapter 23: Probiotics — Promise, Hype, and Hard Truths

1. 23.1 What “Probiotic” Actually Means — and the Gap Between Definition and Marketplace

2. 23.2 The Evidence Hierarchy: Which Strains, for Which Conditions, Actually Work?

3. 23.3 The Colonisation Problem — Most Probiotics Don’t Establish Residence

4. 23.4 The Personalisation Problem — Mucosal Colonisation Varies Dramatically Between Individuals

5. 23.5 When Probiotics Backfire: The Post-Antibiotic Paradox

6. 23.6 The Competition Framework: “Beneficial” Species as Ecological Disruptors

7. 23.7 The Infant Probiotic Question — NEC Prevention vs Ecological Disruption

8. 23.8 Probiotics and the Resistome: Can Probiotics Expand Antibiotic Resistance?

9. 23.9 Next-Generation Probiotics: Akkermansia, Faecalibacterium, Engineered Strains

10. 23.10 Regulatory Grey Zones, the Supplement Industry, and What the Consumer Should Know

11. 23.11 A Measured Conclusion: Not “Probiotics Are Bad” but “Probiotics Are Drugs We’re Prescribing Without a Diagnosis”

12. Key Chapter References

8. Chapter 24: Faecal Microbiota Transplantation — The Nuclear Option

1. 24.1 A Title That Has to Be Earned

2. 24.2 Four Centuries of Yellow Soup, and What We Can Honestly Claim About It

3. 24.3 Amsterdam, 2013: The Trial That Stopped Itself

4. 24.4 What Actually Happens: Inside a Modern FMT Clinic

5. 24.5 Beyond C. difficile: The Frontier That Does Not Quite Work Yet

6. 24.6 The Concept of the Super-Donor

7. 24.7 The Safety Signal We Have to Take Seriously

8. 24.8 The Underground: DIY-FMT and What It Tells Us

9. 24.9 From Stool to Standard Product: The End of FMT As We Know It?

10. Key Chapter References

9. Chapter 25: Precision Microbiome Engineering — From Nuclear Option to Scalpel

1. 25.1 The Opposite of a Transplant

2. 25.2 Four Kinds of Precision

3. 25.3 What Already Works: The Defined Consortia

4. 25.4 The Engineered Commensals: Synlogic and the Bacteria That Eat Poisons

5. 25.5 The CRISPR Antimicrobials: Killing One Species at a Time

6. 25.6 The Regulatory Landscape: Why Approval Is Slower Than the Science

7. 25.7 The Risks Specific to Precision Engineering

8. 25.8 What Is Plausibly Near, and What Is Not

9. 25.9 Scalpel, Not Nuclear: A Final Framing

10. Key Chapter References

10. The Inhabited Body — Bibliography by Chapter

1. Ch. 1 — You Are Never Alone

2. Ch. 2 — Built Together: How Microbes Shaped Human Evolution

3. Ch. 3 — Your First Inheritance: Colonisation Across a Lifetime

4. Ch. 4 — Tools of Discovery

5. Ch. 5 — The Gut

6. Ch. 6 — The Skin

7. Ch. 7 — The Respiratory Tract

8. Ch. 8 — The Urogenital Microbiome

9. Ch. 9 — The Oral Microbiome

10. Ch. 10 — The Mycobiome

11. Ch. 11 — The Brain Microbiome

12. Ch. 13 — The Gut-Brain Axis

13. Ch. 14 — Behavioural Modulation

14. Ch. 15 — Metabolism, Hormones, and the Endocrine Microbiome

15. Ch. 16–17 — Bacteriophages

16. Ch. 18 — Phage Therapy

17. Ch. 19 — Antibiotics: Collateral Damage

18. Ch. 20 — Diet: Feeding the Ecosystem

19. Ch. 21 — Environment, Lifestyle, and the Modern World

20. Ch. 22 — Drugs Beyond Antibiotics

21. Ch. 23 — Probiotics: Promise, Hype, and Hard Truths

22. Ch. 24 — Faecal Microbiota Transplantation

23. Ch. 25 — Precision Microbiome Engineering

24. Ch. 26 — Inflammatory and Autoimmune Disease

25. Ch. 27 — Metabolic Disease and Obesity

26. Ch. 28 — Cancer

27. Ch. 29 — Neurological and Psychiatric Disease

28. Ch. 31 — Ethical, Social, and Philosophical Questions

29. Notable Observations

30. Ch. 13 — The Gut-Brain Axis: Microbes and the Mind

31. Ch. 17 — Phages, Immunity, and the Tripartite Relationship

1. Title Page

2. Cover

3. Table of Contents

The Inhabited Body — Volume 2: Feeding, Fixing, and Fighting Your Microbiome

What We Do to Our Microbiome — and What It Does Back

Horst Herb

⚠ PROOF COPY — 2026-04-18 — NOT FOR DISTRIBUTION

Chapter 18: Antibiotics — Collateral Damage

18.1 The Broad-Spectrum Problem: Carpet-Bombing an Ecosystem

On the evening of 11 December 1945, Alexander Fleming stood at a lectern in Stockholm to deliver his Nobel Lecture. He had already said what the audience came to hear — the story of the contaminated Petri dish, the ring of inhibition around the Penicillium colony, the miracle drug that had just finished saving Allied soldiers from wound infections in Europe and the Pacific. But he ended his lecture with a warning that the audience, exhausted by war and intoxicated by the promise of antibiotics, was in no mood to take seriously.

“It is not difficult,” Fleming said, “to make microbes resistant to penicillin in the laboratory by exposing them to concentrations not sufficient to kill them, and the same thing has occasionally happened in the body.” He went on to describe, with a kind of clinical premonition, how a man who bought penicillin at the chemist’s and took it for a sore throat might give his streptococci enough exposure to select for resistant mutants, then pass those mutants to his wife, who might die of a resistant pneumonia — “and who is responsible for that lady’s death? Why, the man who negligently played with penicillin” [REF:fleming1945].

It was the first public warning about antibiotic resistance, delivered by the discoverer himself, in the year of penicillin’s greatest triumph. It was ignored. For the next seventy years, antibiotics were prescribed as if the supply were infinite, the pathogens static, and the ecosystem they passed through empty.

That ecosystem, of course, was not empty. By the time a course of amoxicillin reaches the small bowel, it enters a community of hundreds of bacterial species, living in structured relationships with the mucus layer, with each other, and with the host. The drug does not distinguish between the pathogen the physician was worrying about and the commensals the physician never thought about. It kills what it can reach, in the concentrations it can reach, and it leaves the survivors to fill the vacancies. What happens next is the subject of this chapter.

The Ecologist’s Objection

If an ecologist were asked to describe modern antibiotic therapy in ecological terms, they would probably reach for a metaphor from forestry. Imagine a mature, old-growth forest — centuries in the making, with thousands of species arranged in layered niches, each holding the others in check through competition, predation, and mutualism. Now imagine treating that forest for a bark-beetle outbreak by aerial spraying with a broad-spectrum herbicide. You would certainly kill the bark beetles’ host trees, and thereby the beetles, but you would also kill most of the other trees, most of the understory, most of the fungi, most of the insects, and — indirectly — most of the animals that depend on any of these. The bark-beetle problem would be solved. The forest would be gone.

This is not a strained analogy. When the oncologist Martin Blaser, working at New York University, began asking in the mid-2000s whether the antibiotic era had been quietly producing an ecological catastrophe inside human bodies, the forest metaphor was one he returned to repeatedly [REF:blaser2014]. His argument, developed over a decade of research and eventually published as the book Missing Microbes, was that broad-spectrum antibiotics do not merely treat an infection — they depopulate a habitat, and the recolonisation that follows is not always complete. Some species come back. Some do not. The forest that regrows is not the forest that was there before.

The empirical question behind Blaser’s hypothesis is simple: when you finish a course of antibiotics, does your microbiome really return to baseline? For decades, the assumption was yes. Bowel flora were thought to be resilient, self-restoring, and more or less interchangeable between individuals. The few studies that looked at the question used culture methods that could see only a fraction of the community, and they generally reported that things looked fine after a week or two. The molecular methods we described in Chapter 4 changed that picture completely.

What a Single Course Actually Does

The landmark study on this question was published in 2018 by Albert Palleja and colleagues at the University of Copenhagen. The design was elegant and unsettling. Twelve healthy young men — average age twenty-four, no recent antibiotic use, no underlying illness — were given a four-day course of three broad-spectrum antibiotics: meropenem, gentamicin, and vancomycin. This combination was chosen to mimic the regimen a patient might receive for a serious hospital-acquired infection. The volunteers provided stool samples at baseline, at the end of treatment, and then at eight time points over the following six months. The samples were analysed by deep shotgun metagenomic sequencing, the technique that allows researchers to identify every microbial species present and estimate its abundance without the biases of culture [REF:palleja2018].

The headline result, repeated in news coverage at the time, was reassuring: most of the bacterial species present at baseline had returned to detectable levels by day forty-two. The microbiome was “resilient.” The word resilient did a lot of work in that coverage. What it obscured was everything in the paper that came after the headline.

First, “most” was not “all.” Nine common species that had been present in all twelve subjects at baseline were still undetectable in most subjects six months later. These were not obscure organisms. They included species of Bifidobacterium adolescentis, Coprococcus eutactus, and Methanobrevibacter smithii — species associated with short-chain fatty acid production, immune regulation, and hydrogen metabolism in the colon. The researchers’ wording was cautious: these species had not recovered “during the observation period.” The implication, unstated but clear, was that they might not recover at all.

Second, the community that did come back was not the community that had left. Overall diversity — the number of species present, weighted by their relative abundances — remained below baseline at six months. More importantly, the functional composition of the community had shifted. Genes involved in virulence, in biofilm formation, and in antibiotic resistance were enriched in post-treatment samples. The bacteria that had survived the antibiotic onslaught and expanded into the vacated niches were, on average, more aggressive and less cooperative than the ones they had replaced. The forest had regrown, but it was a forest of weeds.

Third — and this was the finding that made microbiome researchers most uncomfortable — the post-treatment communities contained forty-seven new genes conferring resistance to a variety of antibiotics, including some that the volunteers had never received. These were not novel mutations. They were resistance genes that had been present all along, at low frequency, in minority members of the community. The antibiotic treatment had acted as a selective sieve, enriching for the carriers. When the community regrew, it regrew from a founder population that was already more antibiotic-resistant than the one it replaced.

One four-day course. Twelve healthy young men. A six-month shadow.

The Concentration Gradient That Nobody Prescribes

To understand why antibiotics do this much collateral damage, it helps to think about where the drug actually goes when you swallow it. When a patient takes an oral antibiotic, the drug is absorbed across the small intestinal wall into the bloodstream, from which it is distributed to the site of infection — a lung, a kidney, a surgical wound. But absorption is never complete, and excretion is never instantaneous. A substantial fraction of the dose passes through the small intestine unabsorbed and arrives in the large intestine, where the densest part of the microbiome lives. A further fraction, already absorbed, is secreted back into the gut via the bile — the so-called enterohepatic circulation. For some antibiotics, the concentration that reaches the colon is a small fraction of the blood level; for others, it is much higher than anywhere else in the body.

This means that the dense bacterial populations of the colon — Bacteroides, Faecalibacterium, Roseburia, Bifidobacterium, and the hundreds of less-abundant species that live alongside them — are exposed to antibiotic concentrations they did not need, in pursuit of a pathogen that is almost never in the colon. The “target” of the prescription is a streptococcus in the throat, a Staphylococcus aureus in a wound, an E. coli in the bladder. The victims of the prescription are the trillions of bystanders who happen to share the same body.

Pharmacologists have a phrase for this kind of exposure: off-target effect. It is the same phrase used when a cancer drug damages healthy tissue, or when a psychiatric drug disturbs sleep. But there is an important difference. When a chemotherapy drug damages the bone marrow, clinicians monitor the damage and stop if it becomes too severe. When an antibiotic damages the gut microbiome, nobody is looking. The prescription is written for the infection. The bystander community is neither monitored before nor counted after. In the vocabulary of clinical medicine, the microbiome is not yet an organ; it is collateral.

18.2 Short-Term Disruption and Long-Term Scars

The phrase “collateral damage” suggests something transient, the way a bruise is transient. And in the short term, that is how antibiotic disruption feels to most patients — a few days of loose stools, perhaps a bit of bloating, occasionally a yeast infection, and then back to normal. The “back to normal” part is an illusion, but it is a convincing one, because the host-level symptoms usually resolve even when the community underneath has not.

To see what the damage actually looks like in time, and how long it lasts, we need to follow three time-scales: hours, months, and years.

Hours and Days: The First Hit

Within twelve to twenty-four hours of the first oral dose of a broad-spectrum antibiotic, bacterial density in the colon begins to fall. By day three, in most adults, total faecal bacterial load has dropped by one to two orders of magnitude — that is, by a factor of ten to one hundred. Species diversity, measured by any of the standard ecological indices, drops sharply. The composition becomes dominated rather than distributed: a few surviving species, often organisms that were present as minority members before treatment, expand into the empty niches and come to represent a large share of the total community. Enterococcus species are classic examples. So are certain Enterobacteriaceae, including the kinds of E. coli and Klebsiella that we would prefer not to see multiplying unchecked in the gut [REF:dethlefsen2011].

This early phase is also when patients notice the clinical side-effects. Antibiotic-associated diarrhoea affects somewhere between five and thirty per cent of patients, depending on the drug; for clindamycin and broad-spectrum beta-lactams, the rate is at the high end. Most cases are mild and self-limiting, caused by loss of the normal fermentative community that processes undigested carbohydrates in the colon. Without that community, carbohydrates reach the distal bowel intact, draw water osmotically, and produce diarrhoea. The same loss removes a protective function: the commensals that normally outcompete pathogens for nutrients and attachment sites are no longer there to do it.

The most severe version of this failure is Clostridioides difficile colitis — an infection that, almost uniquely among gut pathogens, is essentially a disease of antibiotic use. C. difficile spores are ingested from the environment all the time, but they cannot establish in a healthy, competitive gut community. When antibiotics remove the competition, the spores germinate, the bacterium colonises the damaged mucosa, and it releases toxins that cause severe inflammation and diarrhoea. In its worst form, C. difficile colitis produces life-threatening bowel perforation and requires emergency surgery. It kills approximately thirty thousand people per year in the United States alone, and almost every case is traceable to a prior course of antibiotics — often antibiotics prescribed for a trivial or questionable indication [REF:lessa2015]. The irony is stark: a drug prescribed to treat an infection sometimes causes one that is considerably worse.

Months: Partial Recovery

If the patient survives the acute phase — as the great majority do, with or without symptoms — what happens next is a long, gradual, partial return toward baseline. The Palleja study captured this phase in detail. Bacterial density recovers fairly quickly; within two to three weeks of the last dose, stool samples again look “full” under the microscope. Diversity recovers more slowly, usually taking one to two months. And composition — the precise identity and proportion of the species present — recovers most slowly of all, and often incompletely.

A different landmark paper, published by David Relman’s group at Stanford in 2011, followed three adults through two courses of ciprofloxacin, spaced six months apart. The first course reduced bacterial diversity substantially; by the time of the second course, the community had recovered — but not fully, and not to exactly the same configuration it had before. The second course produced a similar disruption, and the recovery from that course was less complete than the first. Each insult left a residue [REF:dethlefsen2011].

This raises an important point about how recovery should be measured. If we ask, “Did the bacterial community return to something that looks like a normal human microbiome?”, the answer is usually yes. The returning community has human-like numbers and human-like proportions of the dominant phyla. If we ask instead, “Did the community return to the specific configuration that existed in this individual before treatment?”, the answer is often no. The post-antibiotic community is a normal microbiome, but it is a different normal. It belongs to the individual’s post-treatment life, not to their pre-treatment life.

For a young, healthy adult with a single course of antibiotics for a real infection, this distinction may be academic. The new microbiome is different, but probably functions well enough. For a child, or a chronically ill patient, or someone who has had many courses of antibiotics over a lifetime, the distinction matters a great deal.

Years: The Cumulative Shadow

The long-term consequences of antibiotic use — the ones that show up years or decades later — are the hardest to study and, not coincidentally, the ones Blaser has spent most of his career worrying about. The fundamental problem is that you cannot run a randomised controlled trial in which one group of children receives antibiotics and the other does not, and then follow both groups for thirty years to see who develops asthma, obesity, inflammatory bowel disease, or diabetes. The ethical and logistical barriers are insurmountable. What researchers can do, and have increasingly done, is look at very large observational cohorts — datasets containing millions of children, linked to national prescribing records and subsequent health outcomes — and ask whether the children who received antibiotics at particular ages went on to develop particular diseases more often than those who did not.

These studies cannot prove causation. They can only show association. An association between antibiotic exposure in the first year of life and a later diagnosis of asthma could, in principle, reflect a confounder: perhaps children who wheeze are more likely to be given antibiotics, and are also more likely to be diagnosed with asthma later. This kind of “reverse causation” is a real concern, and the best studies have gone to considerable lengths to address it — by excluding respiratory antibiotics from the analysis, by controlling for infection history, by comparing siblings who differ in their antibiotic exposure, and by applying causal inference techniques designed to strip out bias.

The associations that survive this scrutiny are uncomfortable. Large cohort studies have linked antibiotic exposure in the first two years of life to increased risks of childhood asthma, allergic rhinitis, atopic dermatitis, obesity, inflammatory bowel disease, juvenile idiopathic arthritis, and autism spectrum disorder — each association modest in magnitude, each of them repeatedly replicated in different populations [REF:aversa2021]. The effect sizes are not alarming for any one condition: a fifteen-to-thirty per cent increase in risk is typical, and a child who receives a single course of antibiotics for a genuine infection is not in a medical emergency. What is alarming is the cumulative picture, and the dose-response relationship. More courses produce more risk. Earlier exposure produces more risk than later. Broad-spectrum exposure produces more risk than narrow-spectrum. The pattern is exactly what you would expect if some fraction of the modern epidemic of childhood immune and metabolic disease were, in part, a consequence of ecosystem disruption during the developmental window when the microbiome is still being assembled.

This is the window we turn to next.

18.3 Early-Life Antibiotic Exposure and Lifetime Consequences

In Chapter 3, we described the assembly of the infant microbiome as a developmental process — a sequence of ecological successions that begins with the birth canal, continues through breastmilk, and settles into something resembling an adult community somewhere around the third birthday. This is one of the most consequential developmental processes in human biology. The microbes that establish themselves during the first thousand days educate the immune system, calibrate the metabolism, and occupy the niches that will be hardest to reclaim later. Disrupt the process once, early enough and severely enough, and you may shape the rest of the life.

The Kenneth Ley Experiments

The strongest evidence for this claim comes from experiments that could only be done in animals. In 2012, a team led by Ilseung Cho and Martin Blaser at NYU reported a series of experiments on young mice given subtherapeutic doses of antibiotics — the kind of low-dose, continuous exposure used for decades in the livestock industry to promote growth. The mice were given penicillin, vancomycin, chlortetracycline, or a combination, in doses designed to mimic agricultural exposure rather than to treat any specific infection. The intervention began at weaning and continued for a few weeks [REF:cho2012].

The mice gained weight. More precisely, they developed altered body composition: more fat, different fat distribution, changes in hepatic lipid metabolism, altered expression of genes involved in lipid handling. The antibiotic-exposed animals were not sick — they looked like normal, healthy mice who happened to be slightly fatter than expected. The microbiome changes that accompanied these metabolic changes were modest in taxonomic terms but functionally significant: shifts in the proportions of the dominant phyla, changes in genes involved in carbohydrate metabolism and short-chain fatty acid production.

A follow-up study, published two years later, went further. The team showed that giving the antibiotic exposure only during the early-life window — the first few weeks after weaning — produced lasting metabolic changes, even though the microbiome eventually recovered its pre-antibiotic composition [REF:cox2014]. The gut community healed. The body did not. Something about the community’s state during the early-life window had programmed the animal’s later physiology, and once the window closed, the programming was set. You could restore the microbiome and still have the altered animal.

Transplant experiments confirmed the causal direction. When the researchers transplanted microbiota from antibiotic-exposed young mice into germ-free recipients, the recipients developed the same metabolic changes. When they transplanted microbiota from control mice, the recipients did not. The altered community was not just a marker of altered physiology; it was the cause of it. And the critical window — the period during which the alteration had to happen in order to produce the lasting effect — was early, brief, and easily missed.

The Human Parallel

Extrapolating from mice to humans is a dangerous business, and nowhere more dangerous than in microbiome research, where species differences in gut community structure, immune development, and metabolism are substantial. But the mouse experiments were designed to be informative about the human question, and their results lined up suspiciously well with what observational studies of children had been suggesting for years. Children given antibiotics before age two are, on average, modestly heavier than their peers later in childhood. Children given multiple courses of broad-spectrum antibiotics in the first year of life are at increased risk of obesity, metabolic disturbance, and immune disorders, as noted above. The human evidence is correlational, but the mouse evidence is experimental, and the two converge on the same conclusion: the developing microbiome is vulnerable in a way the adult microbiome is not, and the vulnerability has lifelong consequences.

One detail deserves emphasis. The agricultural practice that the mouse experiments modelled — feeding low-dose antibiotics continuously to young livestock — was not adopted because someone had a theory about microbiomes. It was adopted empirically, in the 1950s, when farmers noticed that young chickens and pigs given antibiotic-laced feed grew faster and produced more meat per unit of feed. The practice spread worldwide and was, for decades, one of the main uses of antibiotics in volume terms: more antibiotic by weight went into livestock feed than into human medicine. We have, in other words, accidental empirical evidence from billions of animals that early-life, low-dose antibiotic exposure alters growth. Blaser’s contribution was to point out the uncomfortable implication: if it does it in farm animals, there is no obvious reason it would not do it in children.

The Caesarean Complication

A full account of early-life microbiome disruption must also mention a procedure that is not usually thought of as an antibiotic exposure but effectively always includes one: the elective caesarean section. In most high-income countries, caesarean delivery is performed with prophylactic antibiotics given to the mother shortly before the skin incision, to reduce the risk of post-surgical wound infection. These antibiotics cross the placenta and reach the baby before the umbilical cord is cut. The first substance the newborn encounters is not the vaginal microbiome, but ampicillin or a cephalosporin.

We described the consequences of bypassing the birth-canal microbial transfer in Chapter 3: caesarean babies acquire a starter community dominated by skin and environmental organisms rather than the maternal vaginal and intestinal flora that vaginally-born babies inherit. Add to this the additional disruption of intrapartum antibiotic exposure, and the result is a newborn whose first microbiome is both incomplete and partially sterilised. The epidemiological signal is consistent with this mechanism: caesarean-born children show modestly elevated risks for many of the same conditions that are associated with early-life antibiotic use — asthma, allergy, obesity, immune disorders [REF:chu2017].

We do not yet know how to fully correct for this disruption. Experiments with “vaginal seeding” — swabbing the newborn with maternal vaginal fluid after caesarean delivery — have shown partial restoration of the expected microbial profile, but the clinical effects on later health outcomes are still being evaluated [REF:dominguezbello2016]. The point is not that caesarean sections should be avoided — they save lives, and in many cases are the only safe option for mother or baby. The point is that modern delivery practice has routinely imposed a double microbial insult on newborns without appreciating that either one was happening.

18.4 Antibiotic Resistance Genes in the Commensal Microbiome — A Hidden Reservoir

Most of what the public hears about antibiotic resistance concerns pathogens: MRSA in hospitals, resistant E. coli in urinary tract infections, multi-drug-resistant tuberculosis. The implicit mental model is that “resistance” is a property of “bad” bacteria — the ones that cause disease — and that the solution is to prescribe antibiotics more cautiously, develop new drugs, and contain the resistant pathogens in hospitals. This model is not wrong, exactly, but it is radically incomplete. It misses the fact that the largest reservoir of antibiotic resistance genes in most human bodies is not in the pathogens but in the commensals.

The Resistome

Microbiome researchers use the term resistome to describe the complete collection of antibiotic resistance genes in a community — all of them, whether in pathogenic or commensal bacteria, whether currently active or dormant, whether on chromosomes or on mobile genetic elements that can be transferred between cells. The resistome is, in some sense, a geological record of the community’s antibiotic history: every gene in it is there because, at some point in the evolutionary past, an ancestor of the current community acquired it and found it useful enough to keep.

The surprise, when researchers first began characterising human gut resistomes systematically, was how large they were. Even in individuals with no recent antibiotic exposure, the gut microbiome carries hundreds to thousands of resistance genes, distributed across dozens of bacterial species [REF:hu2013]. Resistance to tetracyclines and beta-lactams is near-universal. Resistance to macrolides, aminoglycosides, and fluoroquinolones is common. Some of these genes are very old — older than the antibiotics we now use to treat human infections. The first antibiotic resistance genes evolved in the soil, where antibiotic-producing organisms have been making antibiotics, and competing organisms have been evolving resistance to them, for hundreds of millions of years. When humans began extracting antibiotics from soil organisms and using them as medicine, we imported, along with the drugs, the resistance infrastructure that had evolved alongside them.

What antibiotic treatment does to the resistome is not to create resistance genes — they are already there — but to enrich them. The sensitive bacteria die; the resistant bacteria survive and multiply. Genes that were present in one per cent of the community can be in thirty per cent a week later. And because many resistance genes sit on mobile genetic elements — plasmids, integrons, transposons, and the prophages we discussed in Chapter 16 — they can jump from one bacterial species to another. A resistance gene that was quietly present in a commensal Bacteroides can end up, after a few rounds of conjugation, in an opportunistic E. coli or a nascent C. difficile strain.

The Pool Contamination Problem

This dynamic produces an uncomfortable implication. Every course of antibiotics a patient takes not only treats the target infection but also updates the patient’s personal resistome. The update is usually modest, but it is cumulative, and the gene pool of resistance genes is shared — through the family, through the workplace, through the healthcare system — with everyone the patient contacts. Antibiotic resistance, in this view, is not just a property of individual pathogens; it is a property of human populations.

A 2019 study by Yassour and colleagues followed Finnish children from birth to age three, sampling their gut microbiomes monthly and tracking their antibiotic exposures. The researchers found that resistance genes accumulated in the children’s microbiomes over time, with each course of antibiotics producing a detectable uptick. Even more striking, some of the resistance genes detected in the children were genes for antibiotics they had never received — genes that had presumably been acquired by commensals through horizontal transfer, carried along as passengers, and then selected for when any antibiotic was given [REF:yassour2016]. The children were accumulating a resistance portfolio. They were not the patients for whom these resistance genes would matter, but they were the carriers who would pass them on.

This is the hidden cost of antibiotic use that traditional resistance metrics miss. When a hospital reports its rates of MRSA or VRE, it is counting clinically apparent resistant infections. It is not counting the slow accumulation of resistance determinants in the gut flora of every patient, every visitor, every healthcare worker, and every patient’s family at home. The hospital’s resistance ledger looks manageable. The population’s looks considerably less so.

18.5 Candida Blooms and the Kingdom That Fills the Vacuum

There is a group of organisms in the gut that we have discussed at length in this volume but have not yet mentioned in this chapter: the fungi. The oversight is appropriate, because for most healthy people, the gut mycobiome is a minor, well-regulated component of the microbial community — a minority by numbers, a minority by metabolic contribution, and normally kept in check by the bacterial majority. Antibiotics change that.

We touched on this phenomenon in Chapter 10, when we described how Candida albicans — the most common commensal yeast in the human gut — undergoes dramatic expansion after antibiotic treatment. The mechanism is straightforward: bacteria and fungi compete for the same resources — nutrients, attachment sites, and growth factors — and in a healthy gut, the bacteria usually win, because they are more numerous, they grow faster, and they produce metabolic by-products that suppress fungal proliferation. Remove the bacterial majority with a broad-spectrum antibiotic, and the fungi, which are intrinsically resistant to antibacterial drugs, find themselves in a newly empty environment with abundant nutrients and no competition. Candida populations in stool samples can increase by two or three orders of magnitude within a week of starting antibiotic treatment [REF:dollive2013].

For most patients, this fungal bloom is asymptomatic or produces only mild symptoms — oral thrush, vaginal candidiasis, diaper rash in infants. These are the visible consequences of an invisible shift in the gut ecosystem. For immunocompromised patients, the consequences can be much more serious, including invasive candidiasis and bloodstream infection. But the more interesting question, for the purposes of this chapter, is not what happens to the patient during the Candida bloom. It is what happens to the ecosystem, and what happens when the patient stops taking the antibiotic.

This is where the interkingdom dynamics we described in Chapter 10 come back into play. Candida is not a passive opportunist. Its expansion in the post-antibiotic gut actively remodels the environment in ways that make bacterial recolonisation harder. Fungal hyphae provide physical surfaces that can be colonised by opportunistic bacteria, creating mixed biofilms. Candida metabolism acidifies the local environment and produces signalling molecules that influence bacterial gene expression. Most importantly, Candida primes the host immune system in ways that favour inflammation — a pattern we discussed in Chapter 10 in the context of inflammatory bowel disease, where gut fungal expansion is a consistent feature. When the antibiotic is stopped and the bacterial community attempts to regrow, it must do so in an environment that has been reshaped by the intervening fungal bloom.

The clinical implications of this are still being worked out. It is clear, from observational studies, that patients with persistent gut Candida expansion after antibiotic treatment have worse microbiome recovery and higher rates of subsequent bacterial infections, including C. difficile [REF:markey2018]. It is also clear that the question of what to do about it is unresolved. Antifungal drugs such as fluconazole suppress Candida but introduce their own ecological disruption to the fungal community. Saccharomyces boulardii, a probiotic yeast we will return to in the probiotics chapter, has been used empirically to displace Candida and promote microbiome recovery, with mixed but generally encouraging results. The idea that one fungus might be used as a defence against another’s expansion is ecological medicine in its most literal sense.

For now, the key point is that the “kingdom blind spot” we discussed in Chapter 10 is not just a research problem. It is a clinical one. A clinician who prescribes antibiotics is implicitly prescribing a fungal expansion. The prescription note mentions neither.

18.6 The Recovery Phase — What Actually Happens When Antibiotics Stop

Imagine you could look into the gut of a patient who has just finished a course of antibiotics, with a time-lapse camera capable of following bacteria day by day. What you would see, over the following weeks, is one of the great dramas of ecological biology: a recolonisation race, played out in real time, with the outcome depending largely on who gets there first.

The Vulnerability Window

In the first week after the last dose, the gut community is at its minimum. Total bacterial density is low. Diversity is collapsed. The survivors — organisms that happened to be antibiotic-resistant, or that occupied sanctuary niches where the drug did not reach, or that existed as spores or other dormant forms — are scattered through an environment that is, from their perspective, unusually empty and unusually resource-rich. The normal competitive pressures that constrain any individual species in a healthy microbiome have been temporarily lifted.

This is the vulnerability window, and it lasts, depending on the antibiotic and the individual, somewhere between one and four weeks. During this time, the gut is abnormally permissive. Species that would normally struggle to establish, because they cannot compete with the incumbent community, can now find empty niches. Species that enter the gut in small numbers — from food, from the environment, from another person’s skin — have a better chance of taking hold than they would in a healthy gut. The race is on.

The race has several competitors. There are the survivors of the antibiotic insult, regrowing from whatever remnant populations they managed to preserve. There are the organisms flowing in from upstream — from the mouth, from food, from the environment. There are the organisms in the healthcare environment, if the patient is in hospital, including some that are notorious for exploiting exactly this window: Clostridioides difficile, vancomycin-resistant Enterococcus, carbapenem-resistant Enterobacteriaceae. And, in a growing number of clinical studies, there are the organisms deliberately introduced by the clinician: probiotics, fermented foods, or — in the most radical version — transplanted faecal communities from a healthy donor.

What determines the outcome? Ecologically, the answer is: who arrives first, and in what numbers. This is the principle of priority effects in community ecology. When a habitat has been cleared and is being recolonised, early arrivers have a disproportionate influence on the final community structure, because they establish the conditions under which later arrivers must compete. A niche filled early by one species is not available to another. A metabolite produced early by one organism shapes the environment for everything that follows. The species that win the recolonisation race are not necessarily the ones that would dominate a mature, equilibrium community; they are the ones that happen to be abundant during the first days of the vulnerable window.

This insight has substantial therapeutic implications, which we will return to in the probiotics chapter and in our discussion of faecal microbiota transplantation. For now, the point is that the recovery phase is not a passive process of the old community reassembling itself. It is an active, competitive, ecologically contingent process in which the starting conditions matter enormously. The “natural” recovery that happens when a patient stops their antibiotics and does nothing else is one possible outcome. It is not the only one.

Why Recovery Is So Often Incomplete

Return now to the Palleja study. Why did those nine bacterial species fail to recover at six months? Four possibilities, none of them reassuring, can be read from the data.

First, some species may have been locally extinct. If the antibiotic killed every cell of a given species in the gut, that species can only return if it re-enters from outside — from food, from contact with another person, from the environment. In an ancestral human living in close contact with dirt, family members, and livestock, re-entry would be relatively easy. In a modern adult living in a clean apartment and eating processed food, it may be nearly impossible. Extinction at the individual level becomes effectively permanent.

Second, some species may have depended on cooperative interactions with other species that were themselves eliminated. A bacterium that consumes the metabolic by-products of another bacterium cannot thrive if its partner is gone. A species that requires a specific vitamin produced by another species has the same problem. The gut microbiome contains extensive networks of such metabolic dependencies, and disrupting several species at once can cause cascading losses that are hard to reverse even after the antibiotic is withdrawn.

Third, some species may have been displaced by successor communities that now occupy the niches they used to hold. Once another organism has settled into a niche and established itself, the original occupant faces a competitive disadvantage in trying to return. The ecological door has closed.

And fourth, some species may still be present at very low, undetectable levels and may yet recover on a time-scale longer than the study measured. The six-month cut-off of the Palleja paper was dictated by practical constraints, not biological ones. We do not yet know the true time-scale of complete recovery — if it happens at all.

These four mechanisms are not mutually exclusive. They act together, and they act differently in different individuals, which is part of why recovery trajectories after antibiotic treatment are so variable. Some people seem to bounce back to essentially the same community they started with. Others do not. The factors that distinguish these groups — age, baseline diversity, diet, environmental exposure, genetics, and the specific antibiotic regimen — are still being untangled.

This is the background against which Chapter 19 opened with Sarah’s story. When Sarah received her third antibiotic course for recurrent urinary tract infection, her gut had not fully recovered from the second, and the community that regrew was progressively less able to provide the colonisation resistance that would normally keep opportunistic E. coli from proliferating. Each course had worked clinically, in the short term. Each course had left her ecosystem in a more vulnerable state than the one before. By the time the clinicians had run out of empiric options, Sarah’s problem was no longer an infection. It was an ecosystem that had been blunted by the very therapy meant to help it, and was now unable to defend itself.

18.7 Towards Precision: Narrow-Spectrum and Microbiome-Sparing Approaches

If the argument of this chapter is correct — that antibiotics, particularly broad-spectrum antibiotics, cause substantial collateral damage to the commensal microbiome, and that this damage has real consequences for patients — then it follows that the correct response is not to abandon antibiotics, but to use them better. The word “better” in this context means something more specific than “prescribing less.” It means matching the therapy more precisely to the target, so that the off-target damage is minimised.

This is not a new idea. The physicians who first used antibiotics in the 1940s understood the principle perfectly well: treat the specific bacterium causing the specific infection, with the most targeted drug available. What changed over the following decades was not the principle but the practice. As diagnostic laboratories became slower relative to clinical urgency, as culture-and-sensitivity results began to arrive too late to guide the initial prescription, and as broad-spectrum drugs became available that made the question of “which organism is it?” seem almost obsolete, empiric broad-spectrum therapy became the default. It was faster, easier, and “safer” in the sense that it was less likely to miss the pathogen. The ecological cost of that safety was invisible, so it was ignored.

Reversing this drift requires progress on several fronts.

Faster Diagnostics

The first front is diagnostic speed. The traditional workflow for bacterial identification — culture the specimen, wait one to three days for growth, test antibiotic susceptibility, wait another day for results — is too slow to guide the initial prescription in most acute infections. The physician has to start treatment before the laboratory can say what organism is present, and by the time the results are back, the empiric therapy is usually either working (in which case nobody bothers to change it) or not working (in which case switching is complicated by the partial treatment already received).

This workflow is finally changing. Rapid molecular diagnostics — PCR-based tests, mass spectrometry (MALDI-TOF), and metagenomic sequencing — can now identify bacterial pathogens from a clinical specimen in hours rather than days, and the same technologies can increasingly detect resistance genes directly, without waiting for the bacteria to grow. In some settings, a urine sample can be analysed for pathogen identity and antibiotic susceptibility in under an hour. In others, a bloodstream infection can be characterised overnight rather than over days [REF:goswami2023]. These advances have not yet reached most primary care settings, where the great majority of antibiotic prescriptions are written, but they are reaching there — slowly, patchily, and driven more by cost considerations than by microbiome concerns. The ecological consequences of faster diagnostics have been a pleasant side-effect rather than a primary motivation, but they are real. A prescription written against a known organism can use a narrower drug, administered for a shorter duration, than a prescription written against a statistical guess.

Narrow-Spectrum Drugs

The second front is the revival of interest in narrow-spectrum antibiotics — drugs that target specific bacterial families rather than the broad sweep that current broad-spectrum agents cover. For much of the antibiotic era, narrow-spectrum agents were considered commercially unattractive. A drug that works only against Streptococcus pyogenes has a smaller market than one that works against streptococci, staphylococci, and a range of gram-negative bacilli. Pharmaceutical companies developing new antibiotics naturally preferred broader-spectrum candidates. The result was a pipeline biased toward exactly the kind of drugs that do the most collateral damage.

This bias is now being reconsidered, partly because broad-spectrum drugs are increasingly being held in reserve as last-line therapies in the face of resistance, and partly because microbiome research has made the collateral damage concrete. A narrow-spectrum antibiotic that leaves the bulk of the commensal community intact is now, for the first time, being valued for what it does not do. Drugs such as fidaxomicin — a narrow-spectrum macrocyclic antibiotic that targets Clostridioides difficile with minimal effect on the wider gut community — represent the kind of microbiome-sparing agent that was previously considered a curiosity and is now considered a template [REF:louie2011].

The logic of narrow-spectrum therapy is, however, only as good as the diagnostic precision that makes it possible. If you do not know what organism you are treating, a narrow-spectrum drug is a liability: it might miss. The diagnostic and pharmacological fronts advance together or not at all.

Shorter Courses

The third front is duration. For most infections, the duration of antibiotic therapy was set decades ago by expert opinion rather than by controlled trials. Ten days of penicillin for a streptococcal sore throat. Fourteen days of treatment for a urinary tract infection. Twenty-one days for a complicated pyelonephritis. These numbers were chosen to be safely long enough that the infection would be eradicated in everyone, including the slow responders. They were not chosen to minimise collateral damage, because at the time no one was thinking about that.

A growing body of modern trial evidence shows that shorter courses — sometimes much shorter courses — achieve the same clinical cures with less collateral damage. Three days of antibiotics for uncomplicated cystitis is now standard in many guidelines, replacing the traditional seven-to-ten-day course. Five days of antibiotics for community-acquired pneumonia produces outcomes indistinguishable from ten days. Seven days of treatment for bacteraemia is, in carefully selected patients, as effective as fourteen [REF:spellberg2016]. The pattern is consistent: the long courses that were standard for decades were longer than necessary, and the shortening delivers benefits both to the individual patient (fewer side-effects, less disruption) and to the community (less resistance selection). The guideline updates are happening slowly, because changing clinical practice is always slow, but the direction of travel is clear.

Protecting the Bystanders

The fourth front is the most speculative, but perhaps the most interesting. It asks: is there a way to protect the commensal microbiome during antibiotic therapy, so that the target infection is treated while the bystander community is shielded from collateral damage? The answers coming from research laboratories are beginning to include ideas that would have sounded absurd a decade ago.

One approach is to deliver antibiotics in a form that keeps them out of the colon. Oral beta-lactamases, taken alongside oral antibiotics, can degrade any drug residue that reaches the lower gut before it can do damage to the commensal community. A drug called ribaxamase was developed specifically for this purpose — to preserve the colonic microbiome during intravenous beta-lactam therapy — and early trials have shown that it substantially reduces the disruption associated with such treatment, including a reduction in C. difficile infection rates [REF:kokai2019]. The approach is still experimental, but the principle — selective decontamination of the contamination — is novel and promising.

Another approach is to use bacteriophages, which brings us back to where this chapter began and forward to where the next chapter goes. Phages, as we have seen, are exquisitely specific. A phage that targets a particular strain of E. coli does not touch the rest of the community. In principle, phage therapy is the ultimate narrow-spectrum antibacterial treatment: it is a treatment for one strain of one species, and nothing else. The ecological consequences for the commensal community are, by design, minimal. We will explore what this means clinically in Chapter 19.

And a third approach, at the frontier of what is currently possible, is to combine antibiotic therapy with deliberate microbial reconstitution — either by giving probiotics during the antibiotic course to maintain a defensive population, or by giving faecal microbiota transplants afterwards to repopulate the damaged community. These are ideas we will develop in later chapters of this volume. For now, the point is that the mental model is shifting. For seventy years, antibiotic therapy was conceptualised as a duel between the drug and the pathogen, with the microbiome as inert background. That model is now being replaced by one in which the microbiome is an active, consequential participant — an ecosystem whose protection is part of the treatment plan rather than an afterthought.

A Different Kind of Stewardship

The standard response to the antibiotic crisis has been “antibiotic stewardship” — the set of institutional programmes designed to ensure that antibiotics are prescribed only when necessary, at the right dose, for the right duration, with the right drug. Stewardship is good. It is also, as currently practised, almost entirely concerned with preventing the emergence of resistant pathogens. The ecological damage to the commensal microbiome — the nine species that do not come back, the resistance genes that accumulate in healthy children’s guts, the metabolic shifts that persist for years — is not part of most stewardship programmes. It is not measured, not reported, and therefore not optimised against.

A broader form of stewardship is beginning to emerge. It treats the commensal microbiome as a patient-level asset worth protecting, measures antibiotic exposure in terms of ecological cost as well as pharmacological effect, and values narrow-spectrum, short-duration, microbiome-sparing therapy even when the broader-spectrum alternative would be clinically adequate. The call is not for less antibiotic prescribing but for better antibiotic prescribing: prescribing that accounts for the full cost of the treatment, including the cost to the organ that has, so far, been asked to bear that cost in silence.

Seventy-five years after Fleming’s Nobel Lecture, we are beginning to understand the dimensions of the warning he issued. It was not only that bacteria would evolve resistance to the drugs we use against them, although they have. It was also that those drugs, used the way they came to be used, would reshape the ecosystems we carry inside us — silently, cumulatively, and for keeps. The forest analogy is not an exaggeration. The cost, counted properly, is not negligible. And the question — what is a better way? — takes us directly into the next chapter, where we meet the oldest answer to that question, revived just in time.

Key References — Chapter 18

- [REF:fleming1945] Fleming A (1945). Penicillin. Nobel Lecture, 11 December 1945. Nobel Foundation, Stockholm.

- [REF:blaser2014] Blaser MJ (2014). Missing Microbes: How the Overuse of Antibiotics Is Fueling Our Modern Plagues. Henry Holt and Company, New York.

- [REF:palleja2018] Palleja A, Mikkelsen KH, Forslund SK et al. (2018). Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat. Microbiol., 3(11), 1255–1265. doi:10.1038/s41564-018-0257-9

- [REF:dethlefsen2011] Dethlefsen L & Relman DA (2011). Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc. Natl. Acad. Sci. USA, 108(Suppl 1), 4554–4561. doi:10.1073/pnas.1000087107

- [REF:lessa2015] Lessa FC, Mu Y, Bamberg WM et al. (2015). Burden of Clostridium difficile infection in the United States. N. Engl. J. Med., 372(9), 825–834. doi:10.1056/NEJMoa1408913

- [REF:aversa2021] Aversa Z, Atkinson EJ, Schafer MJ et al. (2021). Association of infant antibiotic exposure with childhood health outcomes. Mayo Clin. Proc., 96(1), 66–77. doi:10.1016/j.mayocp.2020.07.019

- [REF:cho2012] Cho I, Yamanishi S, Cox L et al. (2012). Antibiotics in early life alter the murine colonic microbiome and adiposity. Nature, 488(7413), 621–626. doi:10.1038/nature11400

- [REF:cox2014] Cox LM, Yamanishi S, Sohn J et al. (2014). Altering the intestinal microbiota during a critical developmental window has lasting metabolic consequences. Cell, 158(4), 705–721. doi:10.1016/j.cell.2014.05.052

- [REF:chu2017] Chu DM, Ma J, Prince AL et al. (2017). Maturation of the infant microbiome community structure and function across multiple body sites and in relation to mode of delivery. Nat. Med., 23(3), 314–326. doi:10.1038/nm.4272

- [REF:dominguezbello2016] Dominguez-Bello MG, De Jesus-Laboy KM, Shen N et al. (2016). Partial restoration of the microbiota of cesarean-born infants via vaginal microbial transfer. Nat. Med., 22(3), 250–253. doi:10.1038/nm.4039

- [REF:hu2013] Hu Y, Yang X, Qin J et al. (2013). Metagenome-wide analysis of antibiotic resistance genes in a large cohort of human gut microbiota. Nat. Commun., 4, 2151. doi:10.1038/ncomms3151

- [REF:yassour2016] Yassour M, Vatanen T, Siljander H et al. (2016). Natural history of the infant gut microbiome and impact of antibiotic treatment on bacterial strain diversity and stability. Sci. Transl. Med., 8(343), 343ra81. doi:10.1126/scitranslmed.aad0917

- [REF:dollive2013] Dollive S, Chen YY, Grunberg S et al. (2013). Fungi of the murine gut: episodic variation and proliferation during antibiotic treatment. PLoS ONE, 8(8), e71806. doi:10.1371/journal.pone.0071806

- [REF:markey2018] Markey L, Shaban L, Green ER et al. (2018). Pre-colonization with the commensal fungus Candida albicans reduces murine susceptibility to Clostridium difficile infection. Gut Microbes, 9(6), 497–509. doi:10.1080/19490976.2018.1465158

- [REF:goswami2023] Goswami C, Fox S, Holden MTG & Connor TR (2023). Genomic approaches to rapid diagnosis of antimicrobial resistance. Clin. Microbiol. Infect., 29(7), 841–847. doi:10.1016/j.cmi.2023.02.024

- [REF:louie2011] Louie TJ, Miller MA, Mullane KM et al. (2011). Fidaxomicin versus vancomycin for Clostridium difficile infection. N. Engl. J. Med., 364(5), 422–431. doi:10.1056/NEJMoa0910812

- [REF:spellberg2016] Spellberg B (2016). The new antibiotic mantra — “shorter is better”. JAMA Intern. Med., 176(9), 1254–1255. doi:10.1001/jamainternmed.2016.3646

- [REF:kokai2019] Kokai-Kun JF, Roberts T, Coughlin O et al. (2019). Use of ribaxamase (SYN-004), a beta-lactamase, to prevent Clostridium difficile infection in beta-lactam–treated patients: a double-blind, phase 2b, randomised placebo-controlled trial. Lancet Infect. Dis., 19(5), 487–496. doi:10.1016/S1473-3099(18)30731-X

Chapter 19: Phage Therapy — The Comeback

19.1 A History: D’Hérelle, Soviet Medicine, and the Road Not Taken

In 1919, two years after he named the bacteriophage and published his discovery in the Comptes Rendus of the French Academy of Sciences, Félix d’Hérelle did something that no modern drug regulator would countenance. He drank a phage preparation himself, waited to see if anything happened, then gave it to his family members. When nobody fell ill, he administered phages to a twelve-year-old boy dying of severe dysentery at the Hôpital des Enfants-Malades in Paris. The boy recovered. D’Hérelle published the result and moved on to treating cholera patients in India, plague victims in Egypt, and dysentery outbreaks wherever he could find them. By the early 1920s, he was the most controversial microbiologist in the world — a self-taught outsider making extraordinary claims about a therapy that the scientific establishment could not decide was real [REF:dherelle1917].

We told part of this story in Chapter 16, when we described the discovery of phages and the bitter dispute between d’Hérelle and Jules Bordet over whether phages were truly living viruses or merely bacterial enzymes. But the story of phage therapy — the deliberate use of phages to treat human infections — has a trajectory of its own, and it is a trajectory shaped as much by geopolitics as by science. It is a story of a promising therapy that flourished in the East while the West forgot it existed, and of the extraordinary circumstances that are now bringing it back.

The Georgian Connection

The pivotal figure in the survival of phage therapy was a Georgian microbiologist named George Eliava. Born in 1892, Eliava trained in bacteriology in Tbilisi and Odessa before travelling to Paris in the early 1920s, where he met d’Hérelle at the Pasteur Institute. The two became collaborators and friends. Eliava was captivated by phage biology and by d’Hérelle’s vision of therapeutic application. When he returned to Tbilisi, he carried that vision with him, and in 1923 he established a bacteriological research institute dedicated to studying and producing phages for clinical use [REF:kutateladze2010].

D’Hérelle visited Tbilisi twice, in 1933 and 1934, and spent approximately eighteen months working alongside Eliava — his longest sustained collaboration. Stalin himself had endorsed the project, recognising the strategic value of a therapy that could treat battlefield infections. The Tbilisi institute became the world’s first purpose-built centre for phage research and production, and the two men began building a phage library: a collection of bacteriophages active against the most common human pathogens [REF:kutateladze2010].

The collaboration ended with a knock on the door. In 1937, during Stalin’s Great Terror, George Eliava was arrested by the NKVD — Lavrenty Beria’s secret police — and executed as an “enemy of the people.” The exact charge remains disputed; some accounts suggest political disfavour, others that Beria held a personal grudge. All of Eliava’s papers and photographs were destroyed. He became a non-person, virtually unknown outside Georgia for forty years. D’Hérelle, devastated, returned to France and never went back to the Soviet Union [REF:kutateladze2010].

But the institute survived. Eliava’s colleagues continued the work — quietly, under conditions of political repression, with whatever resources they could secure. Throughout the Soviet era, the Tbilisi institute (officially renamed the George Eliava Institute of Bacteriophage, Microbiology and Virology in 1988) produced phage preparations on an industrial scale. Soviet soldiers received phage cocktails as routine prophylaxis. Phage preparations were available in Georgian pharmacies without a prescription — as commonplace as aspirin. The science was real, the clinical use was widespread, and the results, while inconsistently documented by Western standards, were often impressive [REF:abedon2011].

Why the West Walked Away

Meanwhile, the West abandoned phage therapy almost entirely. The reasons were partly scientific and partly historical.

The scientific problems were genuine. Early phage preparations were often crude — poorly purified, inconsistently potent, and contaminated with bacterial debris that could cause fevers and inflammatory reactions. Clinical trials in the 1920s and 1930s produced mixed results, and the rigour of those trials, by modern standards, was poor. Some researchers used phages against infections that we now know were caused by organisms the phages could not infect. Others failed to account for the extreme specificity of phages — a preparation active against one strain of Staphylococcus aureus might be useless against the strain infecting a particular patient. Without proper strain matching, phage therapy was, in effect, a lottery.

But the decisive blow was not scientific. It was pharmacological. In 1928, Alexander Fleming discovered penicillin. By the early 1940s, industrial production of penicillin had begun, and the results were transformative. Penicillin worked against a broad range of bacteria. It did not require strain matching. It did not need to be individually prepared for each patient. It could be manufactured in vast quantities, shipped anywhere, stored indefinitely, and prescribed by any physician. It was, in every practical sense, the opposite of phage therapy: a universal solution where phages offered only a bespoke one.

The pharmaceutical industry chose antibiotics, and the funding followed. By the 1950s, phage therapy had virtually disappeared from Western medical literature. It persisted only in the Soviet Union and its satellite states — in Tbilisi, in Warsaw, in Wrocław — where it continued to be used clinically, studied scientifically, and refined practically, largely invisible to the English-speaking world.

The Cold War made this invisibility worse. Soviet-era publications were in Russian or Georgian, rarely translated, and published in journals that Western libraries did not carry. The few Western scientists who visited Tbilisi found an institute that was clinically productive but, after the Soviet collapse in 1991, physically crumbling. During the Georgian Civil War of the early 1990s, researchers reportedly stored phage cultures in their own homes — in personal refrigerators — to prevent the library from being lost. The institute survived, but just barely [REF:kutateladze2010].

The Return

The resurgence of phage therapy in the West was driven by a crisis that d’Hérelle could not have anticipated: the rise of antibiotic resistance. By the 2000s, the miracle drugs that had displaced phages were failing. Multi-drug-resistant bacteria — MRSA, carbapenem-resistant Enterobacteriaceae, extensively drug-resistant Mycobacterium tuberculosis — were causing infections that no available antibiotic could treat. The pipeline of new antibiotics had slowed to a trickle, because the economics of antibiotic development are punishing: a drug that works is used for short courses and then held in reserve, producing far less revenue than a chronic-disease medication taken for years.

In this context, Western medicine began to look, with increasing urgency, at the therapy it had abandoned seventy years earlier. And when it looked, it found the Eliava Institute still standing — still producing phages, still treating patients, still maintaining a library of over six hundred characterised bacteriophages active against the most clinically important human pathogens [REF:zaldastanishvili2021].

19.2 Two Patients, Two Infections, Two Worlds

To understand what phage therapy actually looks like — not as a concept but as a clinical experience — it helps to follow two hypothetical patients through the same infection. Both have the same problem: a chronic, antibiotic-resistant urinary tract infection caused by multi-drug-resistant Escherichia coli, an infection that has recurred despite three courses of antibiotics over the past eighteen months. One patient follows the conventional Western pathway. The other travels to Tbilisi.

These are composite patients — not real individuals, but their stories are assembled from published case reports and documented clinical workflows at the Eliava Phage Therapy Center [REF:zaldastanishvili2021] [REF:leitner2021].

The Antibiotic Pathway: Sarah’s Story

Sarah is fifty-two, a schoolteacher in Melbourne, and she has been battling recurrent UTIs for two years. The first infection responded to trimethoprim. The second did not — her GP switched to nitrofurantoin, which cleared the symptoms for three months before they returned. The third course was a fluoroquinolone, ciprofloxacin, which worked briefly but left Sarah with tendon pain and persistent gastrointestinal disturbance. Her urine culture now grows E. coli resistant to all three classes.

Sarah’s experience is not unusual. Among patients with recurrent UTIs, resistance accumulates with each treatment cycle. A large cohort study of nearly 149,000 UTI patients found that by the sixth infection, sixty-five per cent of causative organisms were resistant to at least one antibiotic class, and twenty per cent were resistant to three or more [REF:dickstein2023]. Each course of empiric therapy — prescribed before culture results are available, targeting the most likely pathogen with the most likely drug — is a gamble. When the gamble fails, the patient has gained nothing except collateral damage to her microbiome.

And the collateral damage is real. A landmark 2018 study by Palleja and colleagues followed twelve healthy young men given a four-day course of three broad-spectrum antibiotics. Using deep metagenomic sequencing, the team tracked the gut microbiome’s recovery over six months. The headline finding was that most bacterial species returned within about forty-five days. The less-publicised finding was that nine common species that were present before treatment remained undetectable in most subjects at the six-month mark. They had not recovered. They may never recover [REF:palleja2018].

Sarah’s GP refers her to a urologist, who orders more cultures and prescribes a fourth antibiotic — meropenem, a carbapenem, one of the last-resort drugs. This requires intravenous infusion in a hospital outpatient unit. The infection clears. Three months later, it returns. The E. coli is now carbapenem-intermediate. Sarah is running out of drugs, and each course has pushed her further down a narrowing corridor of resistance while progressively degrading the microbial community that, as we discussed in Chapter 5, normally provides colonisation resistance against the very pathogens now overwhelming her.

This is the paradox of empiric antibiotic therapy for chronic infections. The treatment is fast, accessible, and requires no specialised laboratory matching — any GP can write the prescription within minutes. But it is also blunt, ecologically destructive, and self-undermining: each course of broad-spectrum antibiotics selects for the resistant organisms that cause the next infection. For acute, first-episode infections in otherwise healthy people, the system works tolerably well. For chronic, recurrent, or resistant infections, it can feel like trying to put out a fire by pouring petrol on it.

The Phage Pathway: Elena’s Story

Elena is fifty-four, a civil engineer from Munich, and she has the same infection — chronic recurrent UTI, multi-drug-resistant E. coli, three failed antibiotic courses. Her urologist, having exhausted the formulary, mentions a possibility she has never heard of: phage therapy at the Eliava Phage Therapy Center in Tbilisi, Georgia.

Elena’s first step is not getting on a plane. It is getting on the phone. The Eliava center offers both in-person treatment and a distance protocol. She sends her most recent urine culture results to the center — crucially, including the live bacterial isolate, shipped in a transport medium via a medical courier service. This step is essential. Unlike antibiotics, which are prescribed based on the class of bacteria causing the infection, phage therapy is prescribed based on the specific strain. The phages that will treat Elena must be tested against her bacteria, in her culture, before a single dose is prepared [REF:zaldastanishvili2021].

At the Eliava laboratory, technicians plate Elena’s E. coli isolate and test it against the center’s standard phage preparations. The Eliava Institute maintains a library of commercial phage cocktails — products with names like Pyo Bacteriophage, Intesti Bacteriophage, and SES Bacteriophage — each containing a mixture of phages active against common pathogens. The testing method is conceptually simple: a drop of phage preparation is spotted onto a lawn of Elena’s bacteria. If the phages kill the bacteria, a clear zone — a plaque — forms in the bacterial lawn. No plaque, no activity. Plaque means the phage can infect and lyse Elena’s strain [REF:zaldastanishvili2021].

In Elena’s case, one of the standard preparations shows good activity against her isolate. This is the best-case scenario. If none of the commercial products worked, the center would move to a second step: screening their broader research library of over six hundred characterised phages. If that also failed, they could prepare a custom phage — a phage specifically isolated and amplified to target Elena’s strain, a process that takes eight to twelve weeks and costs significantly more [REF:zaldastanishvili2021].

Elena decides on in-person treatment and flies to Tbilisi. Her treatment course will last approximately ten to fourteen working days. On arrival, she is evaluated by a physician at the Phage Therapy Center — a clinical consultation that includes a full medical history, review of prior treatments, and fresh specimen collection to confirm the infecting organism has not changed since her samples were shipped.

The treatment itself is multi-route. Elena receives oral phage preparations — taken as a liquid, typically twice daily — for systemic exposure. Because her infection is in the urinary tract, she also receives phages delivered locally: vaginal suppositories containing the same phage preparation, designed to establish high phage concentrations at the infection site. For prostatitis patients, the center uses rectal suppositories; for respiratory infections, nebulised inhalation. The route matches the anatomy of the infection, a principle that mirrors how any careful clinician chooses between oral and topical antibiotics — but taken further, because phages replicate at the site of infection, amplifying their own concentration precisely where the bacteria are [REF:zaldastanishvili2021] [REF:leitner2021].

What happens during the first week is, from Elena’s perspective, somewhat anticlimactic. There is no dramatic crisis, no intensive monitoring. She takes her oral doses, uses the suppositories, and attends periodic check-ups where fresh specimens are collected and cultured. The clinicians are watching for three things. First, is the bacterial load declining? Second, are any new organisms appearing — sometimes, as phages destroy one bacterial population, a previously suppressed species emerges from the biofilm underneath. Third, is the target strain developing phage resistance?

This third question is the critical one, and it is where phage therapy diverges most sharply from the antibiotic model. When bacteria develop antibiotic resistance, the usual response is to switch to a different antibiotic — often a broader-spectrum one, inflicting more collateral damage. When bacteria develop phage resistance, the response is to switch to a different phage — one that targets a different surface receptor on the bacterium. And here is the crucial insight from evolutionary biology: bacteria that evolve resistance to a specific phage often do so by modifying or losing the surface receptor that the phage uses for attachment. But surface receptors are not decorative. They serve essential functions — nutrient uptake, signalling, motility. Losing a receptor to escape a phage frequently comes at a fitness cost. In some well-documented cases, bacteria that evolve phage resistance simultaneously lose their antibiotic resistance, because the same surface structures are involved in both. The bacterium trades one vulnerability for another [REF:chan2016].

This phenomenon — sometimes called the “phage resistance trade-off” — is one of the most exciting features of phage therapy from an evolutionary perspective. It means that phage therapy and antibiotic therapy can, in principle, be used in combination to create an evolutionary trap: bacteria that resist the phage become sensitive to the antibiotic, and bacteria that resist the antibiotic remain susceptible to the phage. The organism cannot escape both pressures simultaneously.

Two Timelines, Two Philosophies

The contrast between Sarah’s and Elena’s experiences illustrates something deeper than a difference in treatment modality. It reflects two fundamentally different philosophies of infectious disease management.

The antibiotic model is empiric. It starts from probability: most UTIs are caused by E. coli, most E. coli are sensitive to trimethoprim, therefore prescribe trimethoprim. It is fast, cheap, and requires no microbiology beyond a standard culture (which, in many healthcare systems, is not even performed for a first uncomplicated UTI). It treats the infection as a generic category — a UTI — rather than as a specific organism in a specific patient. And it works well for first-episode acute infections, which is why it has been the standard of care for decades.

The phage model is personalised. It starts from the individual patient’s bacterium: isolate it, culture it, test the phages against it, select the ones that work, deliver them by the route that makes anatomical sense, monitor for resistance, adjust as needed. It is slower, more labour-intensive, and requires a level of microbiological engagement that most Western healthcare systems abandoned when antibiotics made it unnecessary. But for the patient whose infection has defeated the empiric approach — the patient with a multi-drug-resistant chronic infection, the patient who has cycled through four or five antibiotic courses without lasting benefit — the personalised model offers something the empiric model cannot: a treatment matched to their infection, not to the statistical average.

The Georgian physicians at Eliava describe their approach as “iterative infectious disease management” — a phrase that captures the difference neatly [REF:zaldastanishvili2021]. Phage therapy is not a one-shot cure. It is a process: test, treat, culture, reassess, adjust. It is closer, in philosophy, to how oncologists manage cancer — with serial monitoring, adaptive treatment plans, and an acceptance that the disease may evolve under therapeutic pressure — than to how most infections are currently treated. It is slower and more demanding. But for the patients who need it, the question is not whether they can afford the time. It is whether they have any alternative.

19.3 Landmark Cases: When Compassionate Use Changed the Conversation

The case that brought phage therapy into the Western medical mainstream was not a clinical trial. It was a desperate gamble made by a wife who happened to be an epidemiologist.

The Patterson Case

In November 2015, Tom Patterson — a sixty-nine-year-old psychiatry professor at the University of California, San Diego — fell critically ill while on holiday in Egypt. He had developed a pancreatic pseudocyst, which became infected with Acinetobacter baumannii, a gram-negative bacterium notorious for its ability to acquire resistance genes. Patterson’s isolate was resistant to every available antibiotic. He was medevaced to Frankfurt, then transferred to UC San Diego, where he slipped into septic shock and multi-organ failure. His physicians had nothing left to offer [REF:schooley2017].

His wife, Steffanie Strathdee — a globally recognised HIV epidemiologist — began searching the literature. She found the phage therapy literature and, in a series of frantic emails and phone calls, contacted researchers across the United States. Within weeks, a collaboration had assembled that would have been impossible through normal channels. Teams at Texas A&M University, the United States Navy Medical Research Center, and San Diego State University screened their phage collections against Patterson’s Acinetobacter isolate. They found phages that worked. Forest Rohwer’s laboratory at SDSU purified the preparations for intravenous use [REF:schooley2017].

On March 15, 2016, phage therapy began — administered intravenously, directly into Patterson’s bloodstream. Four days later, he opened his eyes and recognised his daughter. It was the first sign of neurological recovery after weeks of unresponsiveness. By June, Acinetobacter was undetectable in his body. By August, he walked out of the hospital [REF:schooley2017].

The Patterson case was published in 2017 in Antimicrobial Agents and Chemotherapy and became the most widely reported phage therapy story in the Western media. It was not a controlled trial. It was not blinded. Patterson received other treatments alongside the phages. Scientifically, it proved very little in isolation. But it did something more important: it proved that phage therapy could be done — safely, rapidly, in a modern Western hospital, under FDA emergency investigational new drug (IND) authorisation — and that a patient at death’s door could walk out alive.

Strathdee and Patterson later co-authored a book, The Perfect Predator, and their case led directly to the founding of the Center for Innovative Phage Applications and Therapeutics (IPATH) at UC San Diego — the first dedicated phage therapy centre in North America.

The Carnell-Holdaway Case

If the Patterson case demonstrated that phage therapy could work in an emergency, the case of Isabelle Carnell-Holdaway demonstrated something even more remarkable: that phages could be engineered for a patient.

In 2017, Isabelle — a fifteen-year-old British girl with cystic fibrosis — underwent bilateral lung transplantation at Great Ormond Street Hospital in London. Post-transplant, she developed a disseminated infection with Mycobacterium abscessus, an environmental mycobacterium that is intrinsically resistant to most antibiotics and notoriously difficult to treat. The infection spread to her surgical wound, her skin, and her liver. Her transplant team had exhausted all antibiotic options and, as Isabelle’s condition deteriorated, her physicians contacted Graham Hatfull at the University of Pittsburgh [REF:dedrick2019].

Hatfull runs the largest collection of characterised mycobacteriophages in the world — the Science Education Alliance Phage Hunters Advancing Genomics and Evolutionary Science (SEA-PHAGES) programme, built over two decades with the help of thousands of undergraduate students. His team screened Isabelle’s M. abscessus isolate against the collection and identified three candidate phages. One killed the bacteria efficiently. The other two did not — but Hatfull’s team genetically engineered them, deleting the repressor gene that allowed the phages to enter lysogeny rather than killing their hosts, converting them from temperate to obligately lytic. The result was a bespoke three-phage cocktail designed specifically for Isabelle’s infection [REF:dedrick2019].

Treatment began in June 2018, with intravenous infusions twice daily and topical application to skin lesions. Within seventy-two hours, the skin sores began to dry. After six weeks, the infection had substantially resolved. The case was published in Nature Medicine in 2019 and represented the first therapeutic use of genetically engineered phages in a human patient [REF:dedrick2019].

From Anecdotes to Evidence

These cases — and a growing number like them — are not clinical proof that phage therapy works. They are case reports, the lowest rung of the evidence ladder. The patients were desperately ill, received multiple concurrent treatments, and were not compared to matched controls. Any honest assessment must acknowledge that spontaneous improvement, placebo effect, or synergistic effects of co-administered antibiotics could account for some or all of the observed benefit.

But the case reports have accumulated to a point where the pattern is difficult to dismiss. A landmark 2024 retrospective analysed one hundred consecutive patients treated with personalised phage therapy across thirty-five hospitals in twelve countries. Clinical improvement was observed in 77.2 per cent. Bacterial eradication was achieved in 61.3 per cent. Adverse events were rare — seven non-serious suspected reactions in one hundred cases. Importantly, the study found that bacterial eradication was seventy per cent less likely when phages were given without concurrent antibiotics, suggesting that the combination of phages and antibiotics — the evolutionary trap we described earlier — may be more effective than either alone [REF:dedrick2023].

The evidence is not yet at the level that would satisfy a drug regulator for market approval. No phage therapy product has received full approval from the FDA, the European Medicines Agency, or the TGA. All current use in the West occurs under compassionate access pathways — the FDA’s emergency IND mechanism, which can be approved by phone on the same day, or expanded access protocols for individual patients. Approximately ninety phage therapy clinical trials are now active worldwide, including forty-one in the United States, and the first randomised controlled trials are beginning to report results [REF:fda2023].

But the gap between case reports and regulatory approval is being closed from both ends. From the bottom, the case series are growing larger and more systematically documented. From the top, regulators are developing new frameworks: Belgium has established a manufacturing monograph for phage preparations, Israel has created a “Clinical Phage Microbiology” pipeline analogous to clinical antibiotic susceptibility testing, and the FDA has issued guidance documents on phage therapy as a biological product [REF:fda2023] [REF:yerushalmy2023].

19.4 The Limits of the Comeback

It would be irresponsible to write about phage therapy without writing honestly about its limitations, because the history of medicine is littered with therapies that were oversold before they were understood.

The Georgian model at Eliava — the most mature clinical programme in the world — is itself instructive. The 2021 case series from the Eliava Phage Therapy Center described three patients with chronic bacterial infections treated with phages over extended periods. The results were nuanced, not triumphant. Patient 1, a forty-three-year-old man with cystic fibrosis and chronic Pseudomonas aeruginosa lung infection, was treated for four years. He improved — his bacterial load dropped by a factor of ten to one hundred — and he was able to discontinue antibiotics entirely, replacing them with phage monotherapy. But his Pseudomonas was never eradicated. Genetic analysis showed that the bacterial population had diversified under phage pressure, with sensitive and resistant strains coexisting. Patient 2, a sixty-four-year-old woman with bronchiectasis, underwent a similar trajectory: five sequential custom phages over three years, each replacing the last as resistance emerged. Her infection eventually cleared, but the path was long and iterative. Patient 3 — a seventy-two-year-old woman with chronic Klebsiella pneumoniae cystitis — did not respond. Her bacteria cleared initially but returned within months, and the treatment was ultimately unsuccessful [REF:zaldastanishvili2021].

These outcomes reflect the honest reality of phage therapy. It is not penicillin. It does not reliably eliminate an infection in a single course. It requires ongoing microbiological surveillance, willingness to switch phages as resistance emerges, and acceptance that some infections will not respond. For patients whose infections are otherwise untreatable, a seventy-seven per cent clinical improvement rate is substantial. But it is not a cure rate, and the twenty-three per cent who do not improve — and the thirty-nine per cent whose bacteria are not eradicated even when symptoms resolve — are a reminder that phage therapy is a sophisticated tool, not a magic bullet.

There are also practical barriers. Phage therapy requires microbiology infrastructure that many healthcare systems no longer maintain. It requires phage libraries — large, characterised collections — that take decades to build. It requires clinicians who understand phage biology, something that is not currently part of medical training anywhere in the Western world. And it requires a regulatory framework that does not yet exist for a therapy that is, by design, personalised: every patient potentially receives a different product, which does not fit neatly into the pharmaceutical model of standardised manufacturing and batch testing.

The comparison with Elena’s experience in Tbilisi is illuminating. Elena’s treatment cost approximately €4,200 for a ten-to-fourteen-day in-person course. Whether that is cheap or expensive depends entirely on where you live. In the United States, where healthcare costs are among the highest in the world, a single hospital admission for intravenous antibiotics can easily exceed that amount many times over. In Australia, a public hospital might charge an uninsured foreign patient around AUD 1,300 per day — roughly comparable to the Eliava fee for a short IV course, but potentially exceeding it for a longer one. In many European countries with universal public healthcare, the patient’s out-of-pocket cost for hospital-administered antibiotics might be close to zero. The point is not that phage therapy is cheap in absolute terms, but that the cost comparison depends heavily on geography and insurance — and that Elena is paying out of pocket for a treatment that no Western insurer currently covers. She also had to travel to Georgia, wait two to four weeks for an appointment, and entrust her care to a centre that, however experienced, operates outside the regulatory frameworks that Western patients and their physicians are accustomed to. For patients who cannot travel — the elderly, the immunocompromised, those in resource-limited settings — the Georgian model is not accessible. And for healthcare systems that measure quality by standardisation and reproducibility, a therapy that is different for every patient poses challenges that are as much administrative as scientific.

None of these limitations are reasons to dismiss phage therapy. They are reasons to invest in it — in clinical trials, in phage library development, in training programmes, in regulatory innovation. The antibiotic crisis is not going away. The pipeline of new antibiotic classes has produced only a handful of truly novel agents in the past four decades. The bacteria are evolving faster than the pharmaceutical industry can respond. Phage therapy is not the only alternative — antimicrobial peptides, lysins, CRISPR-based approaches, and microbiome engineering (which we will discuss in later chapters) all hold promise. But phage therapy has something the others do not: a century of clinical experience, a living proof-of-concept in Tbilisi, and a growing body of Western case data suggesting that, for the patients who need it most, the viruses that eat bacteria may be the oldest new medicine we have.

In the next section, we will turn to the cutting edge: the engineering of synthetic phages, the design of phage cocktails guided by genomics, and the question of whether phage therapy can ever scale from compassionate use to routine clinical practice.

Key References — Chapter 19

- [REF:dherelle1917] d’Hérelle F (1917). Sur un microbe invisible antagoniste des bacilles dysentériques. Comptes Rendus Acad. Sci. Paris, 165, 373–375.

- [REF:kutateladze2010] Kutateladze M & Adamia R (2010). Bacteriophages as potential new therapeutics to replace or supplement antibiotics. Trends Biotechnol., 28(12), 591–595. doi:10.1016/j.tibtech.2010.08.001

- [REF:abedon2011] Abedon ST, Kuhl SJ, Blasdel BG & Kutter EM (2011). Phage treatment of human infections. Bacteriophage, 1(2), 66–85. doi:10.4161/bact.1.2.15845

- [REF:chan2016] Chan BK, Sistrom M, Wertz JE, Kortright KE, Narayan D & Turner PE (2016). Phage selection restores antibiotic sensitivity in MDR Pseudomonas aeruginosa. Sci. Rep., 6, 26717. doi:10.1038/srep26717

- [REF:schooley2017] Schooley RT, Biswas B, Gill JJ et al. (2017). Development and use of personalized bacteriophage-based therapeutic cocktails to treat a patient with a disseminated resistant Acinetobacter baumannii infection. Antimicrob. Agents Chemother., 61(10), e00954-17. doi:10.1128/AAC.00954-17

- [REF:palleja2018] Palleja A, Mikkelsen KH, Forslund SK et al. (2018). Recovery of gut microbiota of healthy adults following antibiotic exposure. Nat. Microbiol., 3(11), 1255–1265. doi:10.1038/s41564-018-0257-9

- [REF:dedrick2019] Dedrick RM, Guerrero-Bustamante CA, Garlena RA et al. (2019). Engineered bacteriophages for treatment of a patient with a disseminated drug-resistant Mycobacterium abscessus. Nat. Med., 25(5), 730–733. doi:10.1038/s41591-019-0437-z

- [REF:zaldastanishvili2021] Zaldastanishvili E, Leshkasheli L, Dadiani M et al. (2021). Phage Therapy Experience at the Eliava Phage Therapy Center: Three Cases of Bacterial Persistence. Viruses, 13(10), 1901. doi:10.3390/v13101901

- [REF:dickstein2023] Dickstein Y, Jeong JY, Brintz BJ et al. (2023). Antibiotic resistance in urinary tract infections: results from a multi-site cohort. J. Infect. Dis., 230(6), e1344. doi:10.1093/jid/jiad123

- [REF:dedrick2023] Dedrick RM, Smith BE, Cristinziano M et al. (2023). Personalized bacteriophage therapy outcomes for 100 consecutive cases: a multicentre, multinational, retrospective, observational study. Nat. Microbiol. doi:10.1038/s41564-024-01705-x

- [REF:leitner2021] Leitner L, Ujmajuridze A, Chanishvili N et al. (2021). Intravesical bacteriophages for treating urinary tract infections in patients undergoing transurethral resection of the prostate: a randomised, placebo-controlled, double-blind clinical trial. Lancet Infect. Dis., 21(3), 427–436. doi:10.1016/S1473-3099(20)30330-3 [Published online 2020; print March 2021]

- [REF:fda2023] U.S. Food and Drug Administration (2023). Science and Regulation of Bacteriophage Therapy. FDA Guidance Document.

- [REF:yerushalmy2023] Yerushalmy O, Khalifa L, Coppenhagen-Glazer S et al. (2023). Towards Standardization of Phage Susceptibility Testing: The Israeli Phage Therapy Center “Clinical Phage Microbiology” — A Pipeline Proposal. Clin. Infect. Dis., 77(Suppl 5), S337–S351. doi:10.1093/cid/ciad514

- [REF:aswani2021] Aswani VH & Shukla SK (2021). An Early History of Phage Therapy in the United States: Is It Time to Reconsider? Clin. Med. Res., 19(2), 82–89. doi:10.3121/cmr.2021.1605

Chapter 20: Diet — Feeding the Ecosystem

20.1 The Dry Season in Hadzaland

In the acacia woodlands south of Lake Eyasi, in northern Tanzania, a group of people still live in a way that most of humanity abandoned about ten thousand years ago. The Hadza are among the last full-time hunter-gatherers on earth. There are perhaps a thousand of them, and only a few hundred still live almost entirely on what they can hunt, dig, and pick from the landscape around them. They do not keep animals. They do not plant crops. They do not have a word for “farmer.” What they eat today is what they found today.

In 2013 and 2014, a team of researchers from Stanford University and the University of Bologna went to live with the Hadza and do something that had never been done before: follow the same people, through an entire year, and track what happened inside them as the seasons turned. They collected stool samples in the wet season, when the women’s foraging brought back baobab pods, berries, and tubers in abundance, and the men returned with honey stolen from wild bee hives high in the acacia trees. Then they collected samples again in the dry season, when the berries were gone, the tubers were woody and bitter, and the camp subsisted mostly on meat and ekwa, a hard root dug out of the cracked earth with pointed sticks [REF:smits2017].

What they found was astonishing. The gut microbes of the Hadza did not stay the same. They cycled. Entire families of bacteria that were abundant in the wet season almost disappeared in the dry season, and then came back when the rains returned and the fruits and tubers followed. One group, the Bacteroidetes — a family we have met many times in this book — surged when the diet was high in plant fibre and retreated when it was not. Another group, the Firmicutes, moved in the opposite direction. The researchers were watching, in real time, a rainforest of bacteria change species composition with the weather.

Now compare that with the microbes of a person in Melbourne, Manchester, or Minneapolis. Here the seasons, such as they are, do not reach the supermarket. A tomato in January tastes the same as a tomato in July, only worse. The diet does not cycle. The microbes do not cycle. Or rather — and this is the more disturbing finding from a different branch of the same research program — they have already stopped cycling. Many of the taxa that cycle in the Hadza are entirely absent from the Western gut. We are not in a permanent summer. We are in a permanent winter so long that most of us no longer remember anything else [REF:sonnenburg2016] [REF:schnorr2014].

This chapter is about that forgotten summer — and about how, three times a day, you get a vote on whether it returns.

20.2 The Gardener’s Dilemma

In Chapter 18 we talked about antibiotics as something closer to a forest fire than to a scalpel — a tool that treats the infection and sterilises the ground around it. In Chapter 19 we talked about phages, which act more like a sniper than a flame-thrower. Both chapters were about killing things. This chapter is about feeding them.

The distinction matters because almost everything the popular science shelf of a health food store sells you is framed as a killing or a planting intervention. “Cleanses” kill. “Probiotics” plant. Both framings are, for most people most of the time, the wrong way to think about the problem. Your microbiome is not a garden in the sense that you start from bare earth and decide what goes where. By the time you are old enough to read a label on a supplement bottle, the garden is decades old. Some of your microbial species have been with you since you came down the birth canal. Others joined when you were weaned, or when you went to primary school, or when your family moved house. Many of them cannot be bought in a capsule at all. Some of them are probably extinct in most of your neighbours, and you will not get them back by swallowing a pill.

What you can do, every day, is decide what to feed them.

The metaphor to hold in mind for the rest of this chapter is not pharmacology. It is agriculture. A gardener does not usually plant a new lawn every spring; she feeds the one she has. She chooses composts and mulches that favour certain plants and disadvantage others. She waters in patterns that support the soil she wants, and she knows that what she cannot see — the fungi and bacteria and protists underneath — is doing more of the work than the bits she can. Over years, her choices produce a particular kind of garden. Somebody else, making different choices with the same starting soil, produces a different one. Neither of them got to pick the seeds.

This chapter is a practical guide to feeding the garden you have. It is organised around a question the reader is entitled to ask at the end of any chapter on diet: so what should I eat on Tuesday? We will get to a direct answer, tiered by the strength of the evidence, in section 20.12. Before we get there, we have to understand what we are feeding, what the microbes do with what we give them, and why the answers are not always the same for every gardener.

We will work outwards from the strongest and cleanest evidence — fibre — to progressively more complex and contested territory: fermented foods, polyphenols, the Mediterranean pattern, protein and fat, ultra-processed food, meal timing, personalisation, and the controversies (gluten, FODMAPs, lectins, carnivore) that readers will have encountered on social media and are entitled to have addressed directly. Nothing in this chapter is invented. Every claim is tied to a published study, and where the evidence is weak or preliminary I will say so plainly.

One warning before we begin. The history of nutrition science is littered with confident claims that did not survive the next decade. Eggs were bad, then good, then bad, then neither. Fat was the enemy, then sugar was the enemy, then insulin, then inflammation. A reasonable reader, watching that parade go by, might conclude that nutrition science is simply unreliable and the sensible thing to do is ignore it. That conclusion would be wrong, but it would be understandable. What I can promise is this: the evidence we will draw on in this chapter is microbiome-mediated evidence, which is a different kind of evidence from the older generation of “association studies” that drove the older reversals. We can now watch, in human subjects, what happens to specific microbial populations, specific metabolites, specific inflammatory markers, and in some cases specific clinical outcomes when the diet changes. The evidence is not perfect. But it is less likely to reverse, because it is grounded in a mechanism that can be observed directly.

With that, let us look at the most important thing any human being can put on the plate for the ecosystem inside them: fibre.

20.3 The Currency of the Realm: Fibre and the Microbiota-Accessible Carbohydrates

What your body cannot digest, the microbes can

When you eat a piece of wholemeal toast, your digestive enzymes break down most of it. The starches in the flour are split into glucose and absorbed in the small intestine. The proteins are cleaved into amino acids. The fats, if there are any, are emulsified by bile and picked up by the enterocytes that line the small bowel. By the time what remains of the toast reaches the large intestine, the components that your own enzymes know how to handle are gone.

What is left is the part of the plant that your body cannot digest. The botanical name for most of it is dietary fibre. The chemical reality is more interesting. “Fibre” is not one molecule. It is a bag of very different polysaccharides — cellulose from plant cell walls, hemicelluloses from the matrix that holds those walls together, pectins from the soft tissues of fruits, inulin from the roots of chicory and the bulbs of onions, resistant starch from cooked-and-cooled potatoes and unripe bananas, beta-glucans from oats and barley, arabinoxylans from wheat bran. Some of these you have heard of. Most of them you haven’t. What they have in common is that the human genome does not encode the enzymes to take them apart.

The microbes in your large intestine do. This is one of the most important biochemical facts in this entire book. Your genome carries instructions for making roughly seventeen digestive enzymes that act on carbohydrates. The combined genome of the microbes in your colon encodes tens of thousands of such enzymes, organised into complex multi-protein machines called carbohydrate-active enzymes, or CAZymes. The gut is, in effect, a second digestive system — one that runs on different software than your own, and that is capable of metabolising substrates your own system cannot touch [REF:koh2016].

In 2014, Erica and Justin Sonnenburg at Stanford proposed a name for the subset of dietary carbohydrates that reach the large intestine and feed this second digestive system: microbiota-accessible carbohydrates, or MACs [REF:sonnenburg2014]. The name sounds technical, but the concept is the single most important idea in modern dietary microbiology, and once you see it you cannot unsee it. Every meal you eat delivers some amount of MAC to your colon. The higher the MAC content of your meal, the more substrate your microbes have to work with. The lower the MAC content, the more your microbes have to find something else to eat — and as we are about to see, they do not starve quietly.

The mucus is also food

The human large intestine is lined with a layer of mucus that is not decorative. It is a physical and chemical barrier, two layers thick in the colon, that keeps the trillions of resident bacteria in the lumen from touching the epithelial cells underneath. The outer layer is loose and colonised by bacteria that graze on it. The inner layer is dense, highly cross-linked, and — in health — virtually sterile. The cells in the epithelium sit under a shield of what is, for the bacteria that grow above it, the last line between them and host tissue. This matters enormously because the moment bacteria reach the epithelium, the immune system notices, and inflammation follows.

In 2016, a team led by Mahesh Desai, working in the laboratory of Eric Martens at Michigan, put gnotobiotic mice — mice with a defined, human-derived gut community — through a controlled dietary experiment. One group got a diet containing plenty of plant polysaccharides. The other got a diet essentially devoid of fibre but nutritionally complete in every other respect. The team then used fluorescent probes to measure the thickness and composition of the colonic mucus layer, along with metagenomics to see what the bacteria were doing [REF:desai2016].

The finding was so vivid it made the cover of Cell. On the fibre-free diet, the mucus layer thinned dramatically. The bacteria, unable to find the plant polysaccharides they were equipped to degrade, switched their metabolism to the only remaining source of carbohydrate in the bowel — the glycans of the host mucus layer itself. Specific species that carry the enzymatic machinery for mucin degradation, including Akkermansia muciniphila and Bacteroides caccae, bloomed. The bacteria were literally eating the wallpaper. Worse, when the mice were then challenged with a pathogen — Citrobacter rodentium, a rough mouse equivalent of enterohaemorrhagic E. coli — the fibre-deprived animals developed more severe disease than the fibre-fed ones. The thinned mucus layer had become a highway rather than a barrier.

Think about what that finding means. If you stop feeding your microbes fibre, they don’t quietly shrink away. They pivot — and start eating you. The mechanism is not unique to mice. Mucin-degrading bacteria are abundant in the human colon, and the machinery they use to take mucus apart is, in biochemical terms, general-purpose. The experiment is hard to replicate in humans for ethical reasons, but the implication is serious: the thickness of the mucus layer that separates our bacteria from our own cells is, in part, a function of how much fibre we eat.

What your grandchildren will inherit

A second Sonnenburg paper, also in 2016, made the stakes even clearer. Using the same kind of mice carrying a human-derived microbiota, the team put animals on a low-MAC diet for a single generation and measured what happened to the next [REF:sonnenburg2016].

The parents lost some of their bacterial diversity on the low-MAC diet — roughly a 60% reduction in the species richness of their gut community. When they were put back on the high-MAC diet, most of what they had lost came back. Not all of it, but most. But when the mice were bred, the pups inherited only the species that had survived the low-MAC period. The pups never encountered the missing species and therefore never acquired them. By the fourth generation on the cycling diet, many of those species were gone entirely. Even when the grandchildren and great-grandchildren were put back on a high-MAC diet, the species did not come back. They were extinct.

The experiment is in mice, and we should be careful about what it predicts for humans. But the logic of extinction applies to any transmitted microbial community. If a mother carries species A, B, and C, and her child is born and breastfed, the child acquires species A, B, and C. If the mother has lost species C entirely before conception, the child cannot inherit what is not there. Multiple generations of low-fibre eating in a population can, in principle, produce exactly the pattern Sonnenburg’s mice showed: a cumulative, generation-over-generation loss of diversity that does not bounce back even when diet improves, because the species simply are no longer in the transmission pool.

Look at the Western microbiome. Compared with the microbiomes of traditional populations — the Hadza, the Yanomami of the Amazon, the Matses of the Peruvian jungle, rural Malawian children — it is strikingly impoverished. Between a third and a half of the bacterial species that those populations carry are simply not present in people who grew up in industrialised cities. We used to think the explanation was environmental exposure: that hunter-gatherers pick up more microbes from dirt, water, and animals than city dwellers do. The Sonnenburg mouse experiment adds another explanation, one that is harder to hear. We may have eaten some of them into extinction, across generations, without realising it [REF:sonnenburg2016].

This is why fibre deserves its place at the start of this chapter. It is not, as some decades of nutritional advice framed it, merely useful for keeping things moving. It is the substrate that keeps the entire bacterial community alive and operational in its normal configuration. Stop supplying it, and the community does not pause politely. It eats the house, loses members, and in the long run — if the pattern is maintained across generations — it loses them permanently.

What the microbes give back: short-chain fatty acids

When microbes ferment MACs — and “fermentation” here means anaerobic breakdown by bacterial enzymes, not the pickle-jar kind, which we will get to in section 20.6 — they produce a small number of remarkable molecules called short-chain fatty acids, or SCFAs. Three of them dominate: acetate (two carbons), propionate (three carbons), and butyrate (four carbons). They are produced in roughly a 60:20:20 ratio in a healthy adult colon, and between them they account for somewhere between 5 and 10 percent of the total daily energy intake of the host [REF:koh2016].

Each has a distinct job.

Butyrate is the most remarkable, because it is the primary energy source for the cells lining your own large intestine. The colonocytes — the epithelial cells of the colon — get roughly 70% of their energy from bacterial butyrate rather than from the blood supply. This is one of the most intimate metabolic relationships between any two kingdoms of life. You are not running your own colon on your own fuel. The bacteria that sit in the lumen are, quite literally, feeding the cells that face them. When butyrate production drops — because MAC intake drops, because the fibre-fermenting bacteria are not there, because antibiotics have killed them — the colonocytes fall back on less favourable fuels, and the tight junctions between them begin to loosen. The permeability of the gut wall, in other words, is partly a function of how well-fed the colonocytes are, and the colonocytes’ feeding is partly a function of how much fibre you ate yesterday.

Butyrate does more than feed cells. Inside the colonocyte, it acts as a signalling molecule. It inhibits an enzyme called histone deacetylase, or HDAC, which changes how certain genes are read. One of the downstream effects is the differentiation of a particular class of immune cells — regulatory T cells, or Tregs — that keep the colonic immune system from over-reacting to the bacteria it lives with. In 2013, two separate groups published almost simultaneously in Nature and in Science showing that butyrate drives Treg differentiation in the colon and suppresses colitis in mouse models [REF:furusawa2013] [REF:smith2013]. The food you eat, via the microbes that eat it, signals to the cells that decide whether your immune system tolerates what it sees in your gut or attacks it.

Propionate has its own story. Much of what the bacteria produce is absorbed into the portal vein and taken directly to the liver, where it influences gluconeogenesis — the making of new glucose — and lipid synthesis. Propionate appears to reduce appetite in some studies by acting on gut hormones like PYY and GLP-1, the same hormones targeted by the latest generation of weight-loss drugs. It is not a drug. But it is an endogenous molecule produced, by your bacteria, in proportion to the fibre you feed them.

Acetate is the most abundant and the most diffusible. It reaches the systemic circulation and is taken up by peripheral tissues, including muscle and adipose tissue. Its signalling role is less well understood than butyrate’s, but it contributes to the cross-talk between the gut and brain and appears to play a role in appetite regulation.

All three of them — acetate, propionate, butyrate — act, collectively, as chemical messengers that your microbes send to your cells. They are the receipt your microbes give you in exchange for the fibre you supply. Low fibre in, low SCFAs out. And low SCFAs out means a colon that is both hungrier and more inflamed than it should be.

The hard outcomes

If the mechanism were all we had, a cautious reader would be right to ask whether it matters clinically. Cells in petri dishes do all sorts of things they do not do in living people, and mouse experiments do not always translate to the human bowel. The honest answer is that we also have clinical outcome data, and it is striking.

In 2019, a consortium led by Andrew Reynolds at the University of Otago in New Zealand published, in The Lancet, a series of systematic reviews and meta-analyses pooling data from 185 prospective cohort studies and 58 randomised controlled trials — nearly 135 million person-years of observation [REF:reynolds2019]. The question they asked was simple: does carbohydrate quality, and specifically dietary fibre intake, predict hard clinical outcomes?

The answer was yes, and the magnitude was larger than most people realise. For every eight grams per day of additional fibre intake, the analysis found an 8% reduction in all-cause mortality, a 7% reduction in coronary heart disease, a 19% reduction in colorectal cancer, and a 15% reduction in type 2 diabetes. The dose-response was continuous: more fibre, less disease, all the way up to the highest intake categories — about 25 to 29 grams per day — with no plateau in the data at conventional intake levels and suggestions that higher might be better still.

For context, the average daily fibre intake in most Western countries is around 12 to 15 grams. The recommendations from most national dietary guidelines are 25 to 30 grams per day for adults. The intake of traditional populations has been estimated at around 100 grams per day — roughly seven times what most of us manage. The Hadza, in the wet season, eat around 150 to 200 grams of fibre per day, because baobab pods and wild tubers are essentially concentrated fibre delivery devices. You would not enjoy eating what the Hadza eat. But the gap between their intake and ours is the gap between a microbiome that cycles through seasons and one that has forgotten what a season is.

The Reynolds analysis is not the only evidence, but it is the cleanest recent summary, and it gives us a number to carry forward: eight grams of fibre per day buys you roughly an 8% reduction in your risk of dying of anything. That is an extraordinary number for any dietary variable, and it is backed by the largest and most rigorous synthesis in the field.

The many shapes of fibre

One more thing before we move on, because “just eat more fibre” is not a useful instruction without some feel for what the word actually means.

Fibre is not one thing. The commercially useful distinction is between soluble and insoluble fibre, based on whether the fibre dissolves in water, but this distinction is a legacy of food chemistry, not microbial biology, and it maps only loosely onto what the microbes actually care about. A more useful breakdown, for our purposes, is functional:

Fermentable fibres are the ones the microbes can take apart readily and use as fuel. Inulin (in chicory root, Jerusalem artichoke, garlic, onion, leek, asparagus), resistant starch (in cooked-and-cooled potatoes, cooked-and-cooled rice, green bananas, and legumes), beta-glucans (in oats and barley), and pectins (in apples, citrus peel, and most fruits) fall into this category. These are the ones that generate the most SCFAs and feed the most microbes.

Slowly fermentable fibres — arabinoxylans in wheat bran, certain hemicelluloses in whole grains — are broken down more gradually and tend to be fermented further along the colon, producing SCFAs in regions where the first category has mostly been consumed. This matters because cancer risk in the colon is highest in the distal portion, and SCFA production in that area depends on slowly fermentable fibres that survive the early part of the journey.

Poorly fermentable fibres — cellulose and lignin, the structural fibres of plant cell walls and wood — are largely excreted intact. They have important non-microbial effects on bowel transit and stool bulk. But they do not, as such, feed the ecosystem.

A good diet contains all three classes. If you eat a bowl of oats topped with berries and a spoon of ground flax in the morning; some lentils or chickpeas and leafy greens at lunch; and a dinner of whole-grain pasta with vegetables, herbs, olive oil, and a small piece of fish, you have covered the full spectrum without thinking about it. If you eat white toast, a ham sandwich on white bread, and pasta with tomato sauce, you have not.

The 2024 review by Deehan and colleagues is the best single summary of fibre subtypes and their metabolic effects currently available, and readers who want the biochemical detail can find it there [REF:deehan2024]. For our purposes, the single most important take-away from the fibre section of this chapter is this: your microbes run on substrate, the substrate is fibre, and there are a great many different fibres with subtly different effects. The next section turns that insight into an actionable rule — a single number that, of everything in this chapter, is probably the most concretely useful thing the modern microbiome literature has produced.

20.4 Diversity Beats Dose: The Thirty-Plant Rule

A number you can actually use

If this chapter had to offer the reader one number, and only one, the number would be thirty.

It comes from the American Gut Project, a citizen-science study that, between 2012 and 2017, enrolled more than 11,000 volunteers in the United States, the United Kingdom, and Australia. Participants paid a small fee, were mailed a swab kit, sampled their own stool, and filled in a long dietary questionnaire. The samples were sequenced at the laboratory of Rob Knight at the University of California San Diego. The scale of the project — roughly an order of magnitude larger than any comparable microbiome study at the time — allowed the analysts to ask questions that smaller studies cannot answer, including: which dietary variable, out of dozens tracked, best predicts the diversity of the gut microbiome? [REF:mcdonald2018]

The answer was not total fibre grams. It was not whether the participant identified as vegan or vegetarian. It was not meat consumption, carbohydrate consumption, or calorie intake. It was a single variable the research team called “plant types per week”: the number of distinct plant species a person reported eating over the previous seven days.

The result fell cleanly into two groups. People who reported eating thirty or more different types of plants per week had, on average, significantly more diverse gut microbiomes than people who reported ten or fewer. The effect was dose-dependent, consistent across the three countries, and robust to adjustment for fibre intake, age, body mass index, and whether the person called themselves a plant-eater. Eating more species of plants, regardless of how much fibre was in any single serving, predicted a richer ecosystem in the gut.

Thirty plant species a week sounds like a lot. Most people, asked to guess how many they eat, guess something like ten or fifteen. Almost everybody underestimates. The trick is what you count. Every plant counts: fruits, vegetables, whole grains, legumes, nuts, seeds, herbs, and spices. A salad with mixed greens, tomato, cucumber, red onion, parsley, and a dressing of olive oil, lemon, and black pepper contains eight different plants before you have finished chewing. A bowl of muesli with oats, wheat flakes, barley, almonds, walnuts, sunflower seeds, flaxseed, dried cranberries, and raisins is nine. A good Indian dal with lentils, tomato, onion, garlic, ginger, turmeric, cumin, coriander, chilli, and a wedge of lime is ten. A piece of dark chocolate counts. Coffee counts. Green tea counts. Even a sprinkle of basil on top of a dish counts.

Once you start keeping track, thirty a week is not only achievable — it is surprisingly easy for anyone who cooks at home and uses a spice rack. For the person who lives on sandwiches, ready meals, and takeaway, it is almost impossible. That asymmetry is the most honest summary of what the modern microbiome literature is telling us about how food choices matter at the everyday level.

Why diversity, not dose

The why behind the thirty-plants finding is worth pausing over, because it tells us something about the nature of the gut ecosystem that the fibre story alone does not.

Each plant species contains a somewhat different mix of polysaccharides, polyphenols, proteins, and minor bioactive molecules. A carrot and a parsnip look similar on the plate but carry different classes of polysaccharide in their cell walls. Oats deliver beta-glucans that barley has less of; barley delivers arabinoxylans that oats have less of. A blueberry carries a different polyphenol profile from a blackberry, and both differ from a strawberry. Each of these slightly different substrates is best handled by a slightly different subset of gut microbes — different enzymes, different species, sometimes different whole metabolic guilds.

A person who eats the same four or five plants every day has, in effect, been running a one-crop farm in their colon. The microbes that can handle those four or five crops thrive. The microbes that require something else — a different polysaccharide, a different polyphenol, a different trace amount of some obscure glycan — cannot find work, and their populations dwindle. Over months and years, the ecosystem contracts around the narrow diet.

A person who eats thirty or forty plants a week is running something more like a mixed polyculture. Nothing is in huge abundance, but almost every microbial guild finds something to do. The ecosystem holds its diversity not because any one food is magical but because the variety creates niches for a wider range of inhabitants.

This is not speculation. A series of studies has now shown that increasing the variety of fibre types in a diet — rather than the total quantity — changes the microbiome and the metabolome in measurable ways. Ranaivo and colleagues, in a 2022 trial, showed that enriching a daily-consumed bread with a diverse rather than uniform mixture of fibre sources produced larger changes in microbial composition and metabolic markers in subjects at cardiometabolic risk, even when the total fibre dose was held constant [REF:ranaivo2022]. Diversity, in other words, is doing work that quantity alone cannot replicate.

The practical advice that falls out of this is refreshingly unlike most nutritional advice, because it does not require you to eliminate anything. It just asks you to add. When you plan a shopping list, reach for one new vegetable you have not bought before. When you cook, reach for one more herb than the recipe calls for. Keep seeds (sesame, pumpkin, sunflower, flax, chia) in the pantry and sprinkle them on things. Eat the skins of fruits when they are edible; keep the leaves on the beetroot; buy whole spices and grind them yourself. Every species added is a new niche opened inside you.

Two things you can’t see on the plate

There is a further reason to favour variety — two of them, in fact — that the thirty-plants number happens to hedge against even though the American Gut investigators were not looking for them. Both have to do with the quiet fact that the food on the modern plate is not quite what the food on the same plate was a few decades ago.

The first is what agronomists sometimes call the dilution effect. Donald Davis and colleagues at the University of Texas compared United States Department of Agriculture food-composition tables for forty-three garden crops between 1950 and 1999 and found statistically reliable declines in six of thirteen nutrients — protein, calcium, phosphorus, iron, riboflavin and ascorbic acid — on the order of six to thirty-eight per cent. The authors were careful in their own interpretation: the declines, they wrote, are “most easily explained by changes in cultivated varieties between 1950 and 1999, in which there may be trade-offs between yield and nutrient content” [REF:davis2004]. In other words, the same tomato or head of lettuce on today’s supermarket shelf is very likely a different cultivar from the one your grandmother bought, bred over decades to pack more water, more starch, more sheer weight into each fruit, and proportionally less of several minerals and vitamins. The effect is not catastrophic and does not apply to every crop or every nutrient, but it is real enough, and consistent enough across foods, that it is difficult to dismiss. And unlike flashier concerns, it is quietly cumulative: a diet of five plants whose mineral density has slipped a third over two generations simply supplies less of those minerals, full stop.

Whether the drift matters for the microbiome is a reasonable next question, and the honest answer is: at the margin, in ways we are still learning to measure, probably yes. Gut bacteria have their own trace-element requirements, and the community does not merely follow the host’s mineral status but actively shapes it. A 2021 review by Bielik and Kolisek catalogued the two-way traffic — dietary minerals shape which microbes thrive, and microbial metabolism in turn influences how much of those minerals the host can absorb [REF:bielik2021]. The surprising twist is that simply topping up a deficient nutrient in isolation does not automatically restore a healthy ecosystem, and can even make things worse. In a series of randomised trials in Kenyan infants, iron-fortified porridge reduced anaemia — a genuine benefit — but also shifted the colon toward more Enterobacteriaceae (including pathogenic Escherichia coli), reduced bifidobacteria, raised a biomarker of enterocyte damage, and increased treated respiratory infections. Adding a prebiotic (galacto-oligosaccharides) alongside the iron cancelled most of these adverse effects [REF:paganini2017]. A follow-up study in the same population showed that iron fortification even blunted the response to oral antibiotics and raised the risk of post-antibiotic diarrhoea [REF:paganini2018]. A broader review of excess iron in infants and young children reaches a consistent conclusion: iron is helpful when deficient, pro-inflammatory and dysbiotic when in surplus, and the margin between the two is narrower than many public-health programmes assume [REF:lonnerdal2017]. The lesson is not that fortification is bad — it is that a nutrient arriving on its own, without the food matrix it evolved inside, behaves differently from the same nutrient arriving embedded in a plant. The safer strategy — the one that captures whatever micronutrients our soils and cultivars still deliver, spreads the bet across many sources, and avoids accidental megadoses — is once again the thirty-plant rule.

The second hidden variable is residue. Of all the pesticides in routine modern use, the herbicide glyphosate is the one the microbiome literature has scrutinised most closely, because its mechanism of action is not incidental to bacteria but directly relevant to them. Glyphosate kills plants by inhibiting a single enzyme, 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), which sits at the heart of the shikimate pathway — the route by which plants, fungi, and many bacteria build the aromatic amino acids tryptophan, tyrosine and phenylalanine. Humans lack the shikimate pathway entirely; we obtain these amino acids from food. This is the long-standing argument that glyphosate is safe for mammals. The catch, however, is that a large fraction of our gut bacteria do possess the pathway, and whether any particular species is sensitive or resistant depends on which class of EPSPS enzyme it carries. In honey bees, whose gut community is unusually tractable, Motta and colleagues showed that chronic exposure at environmentally realistic doses reshaped the community in a way that mapped precisely onto this distinction: species with the sensitive enzyme class declined, those with the resistant class held their ground, and the exposed bees became more susceptible to opportunistic infection [REF:motta2018]. The mechanism, in short, is not disputed.

Whether the doses that reach the human colon through food residue are sufficient to reproduce such an effect in a mammal is less settled, but the evidence is no longer nothing. In 2021, Mesnage and colleagues fed rats either glyphosate or the commercial Roundup formulation MON 52276 at doses bracketing the European regulatory limit, and combined shotgun metagenomics of the caecum with serum and caecal metabolomics. Both treatments caused shikimic acid and 3-dehydroshikimic acid — the substrates that accumulate when EPSPS is blocked — to build up in the gut contents, providing the first direct in vivo biochemical evidence that the shikimate pathway of the mammalian gut microbiome is in fact inhibited at doses within the legal range [REF:mesnage2021]. An earlier Ramazzini Institute study, using the United States Environmental Protection Agency’s acceptable daily intake as the reference dose, found microbial and reproductive changes in the pups of rats whose mothers had been exposed during pregnancy [REF:mao2018]. The picture, however, is not one of straightforward devastation, and it would be dishonest to pretend otherwise. A 2022 biomonitoring study of British twins, which detected glyphosate in the urine of more than half of adult participants, actually found that higher excretion was weakly associated with greater microbial richness, alongside changes in several bacterial metabolites — a reminder that the effects are subtle, probably diet-dependent, and not a simple linear “more glyphosate, worse gut” story [REF:mesnage2022twins]. A 2023 review in Gut Microbes by Walsh and colleagues summarised the field as consistent with biologically plausible effects on community composition at realistic exposures, with genuine uncertainty about long-term and developmental outcomes [REF:walsh2023]. The appropriate response is neither panic nor dismissal. It is, once again, the ecological one: eat a wide enough variety of plants, from a wide enough variety of sources, that no single residue dominates the exposure — and, where the option is available and affordable, prefer sources (organic, small-farm, home-grown, seasonal) on which glyphosate is less likely to have been used in the first place.

Both of these stories — the dilution effect and the residue effect — are stories about things the modern eater cannot directly see or taste, and cannot realistically measure. Neither on its own is a crisis. But both are reasons why the answer to “how do I protect my microbiome from things I cannot even detect?” turns out to be the same as the answer to “how do I feed my microbiome well?” A diet of thirty different plant species a week is not a guarantee against either problem, but it is the simplest available hedge: it spreads the load across many sources, increases the chance that at least some of what you eat carries the minerals the tables promised, and dilutes the contribution of any single contaminated supply. Diversity, once again, is doing work that no single “superfood” and no supplement can replicate.

The redundancy that keeps the ecosystem upright

There is a second reason diversity matters that applies to ecosystems in general, including the one inside you: redundancy is resilience.

A forest with two dozen tree species is not only richer than a forest with three. It is also harder to destroy. If a pathogen arrives that attacks one species, the other twenty-three keep the forest standing. If a drought kills the shallow-rooted trees, the deep-rooted ones remain. The ecological jargon for this is “functional redundancy” — more than one species performs each key function, so losing any one member does not collapse the function.

The same holds for the gut. The production of butyrate, for example, is performed by a consortium of species — Faecalibacterium prausnitzii, Roseburia intestinalis, Eubacterium rectale, and many others. In a diverse microbiome, if one is temporarily suppressed (by an antibiotic, say, or a bout of illness), the others keep producing butyrate. In a monoculture gut where only one butyrate producer survives, losing that species means losing the function — and losing the SCFA means losing the colonocyte fuel and the Treg signal and the tight-junction integrity, all at once.

We cannot yet measure each reader’s redundancy directly. But we can predict with confidence that a gut supplied with thirty kinds of plant substrate a week will have more functional redundancy than one supplied with five. The thirty-plant rule is, in the end, a rule about building an ecosystem that can take a hit and stay upright.

20.5 The Mediterranean Pattern, Put to the Test

Why this one and not the others

The dietary patterns shelf of any bookshop is a chaos of competing claims. Keto, paleo, Whole30, DASH, Zone, Atkins, South Beach, carnivore, alkaline, blood-type, Gundry, Mediterranean, Nordic, Okinawan, MIND, intermittent fasting, flexitarian, and a dozen more — each with adherents, testimonials, and books that promise that this one, at last, is the answer.

Of all of them, the Mediterranean pattern is the only one that has been subjected, over decades, to the kind of testing that clinical medicine applies to a new drug: long-term randomised controlled trials with hard clinical endpoints, conducted in multiple countries, replicated across age groups, and scrutinised by independent review. It is also — and this is not a coincidence — the one that accidentally aligns most closely with the principles this chapter has been building up.

I say “accidentally” because the Mediterranean diet was not designed to feed a microbiome. It was observed in the mid-twentieth century by the American physiologist Ancel Keys, who noticed that people living on the Greek island of Crete and in certain regions of southern Italy had strikingly low rates of coronary heart disease despite eating enough calories and enough fat. Keys characterised what they were eating — plenty of vegetables, fruits, legumes, whole grains, nuts, olive oil, moderate amounts of fish, small amounts of meat and dairy (much of it fermented), modest amounts of wine with meals — and his characterisation became the template for the “Mediterranean diet” as it is now understood. The microbiome science that would eventually explain why it worked was still fifty years away.

What the pattern looks like on a plate is not exotic. It is mostly plants, in wide variety, lightly processed, eaten slowly, with olive oil as the dominant fat, and a small portion of animal protein somewhere in the meal. It is Tuscan ribollita, Greek horiatiki, Provençal ratatouille, Lebanese fattoush, Turkish mezze. It is vegetables that taste like vegetables and bread that tastes like bread. A reader from almost any culture can find a version of it in their own food tradition.

NU-AGE: the big elderly trial

The most compelling recent evidence that the Mediterranean pattern works through the microbiome came from a study called NU-AGE, published in 2020 by Tarini Ghosh and Ian Jeffery at University College Cork working with collaborators across five European countries [REF:ghosh2020].

NU-AGE was a one-year randomised controlled trial in 612 elderly participants (aged 65 to 79) from Italy, France, the Netherlands, the United Kingdom, and Poland. The intervention group received personalised dietary counselling and tailored food deliveries to help them shift their eating towards a Mediterranean pattern. The control group continued their usual diet. At the end of the year, the researchers sequenced the gut microbiomes of both groups and measured a battery of biological markers associated with frailty — the progressive loss of muscle, strength, and metabolic resilience that defines healthy aging from unhealthy aging.

The result was that the participants who had shifted towards the Mediterranean pattern showed microbiome changes — increases in specific taxa including Faecalibacterium prausnitzii, Roseburia, and other SCFA producers; decreases in taxa associated with inflammation — that correlated, across the entire cohort, with reduced frailty markers and improved cognitive function. The changes were modest but real. More importantly, they were mediated by the microbiome: a statistical analysis showed that the effect of the diet on frailty was partly explained by the effect of the diet on specific microbial taxa and their metabolic outputs. This is what clinical epidemiologists call “mediation analysis,” and it is one of the cleanest ways to show that a mechanism is doing real work in a real-world intervention.

NU-AGE did not show a revolution. Elderly people put on a Mediterranean diet for a year did not suddenly become young. But they became measurably less frail than the controls, and the fingerprint of the microbiome change was visible in the data. For a dietary trial at this scale, in this age group, the result is unusual in its clarity.

Meslier: the shorter trial in a younger group

A second 2020 study, by Vanessa Meslier and colleagues working in Paris and Naples, asked a different question: in overweight and obese adults, does a Mediterranean diet intervention produce microbiome and metabolic changes independent of weight loss? [REF:meslier2020]

The study randomised 82 overweight or obese subjects to an 8-week isocaloric Mediterranean diet intervention or a habitual diet control. “Isocaloric” means the participants were not asked to eat less; they were asked to change what they ate without changing how much. This is an unusual design. Most diet trials bundle compositional change with caloric restriction, and it becomes impossible to tell whether the benefits come from the food composition or from simply eating fewer calories.

After eight weeks, the Mediterranean group had significant reductions in plasma cholesterol — roughly 10 milligrams per decilitre — with no change in body weight. The gut microbiome had shifted towards a more fibre-degrading, SCFA-producing composition. Plasma and faecal metabolomic profiles showed changes consistent with increased polyphenol and fibre intake. Because weight had not changed, the benefits had to be coming from the change in what the subjects were eating rather than from weight loss.

This is an important finding because weight loss is the usual confounder in diet trials. Many dietary patterns produce temporary benefits that are actually benefits of transient caloric restriction, and the benefits vanish when weight is regained. The Meslier study carves out a cleaner signal: a diet change produced microbiome and metabolic improvements without a weight change, suggesting that the Mediterranean pattern has effects that are independent of its role in weight management. You do not have to lose weight to benefit from eating this way.

The pattern is a delivery system

Why does the Mediterranean pattern work so well as a microbiome intervention?

Because, under its Italian and Greek and Lebanese clothing, it is a delivery system for everything this chapter has so far argued matters. The fibre content is high, because it is a predominantly plant-based pattern with legumes, whole grains, vegetables, fruits, and nuts in almost every meal. The plant species count is high, because Mediterranean cuisines are built around herbs, spices, and mixed-vegetable preparations. The polyphenol content is high — we will come back to why in section 20.7 — because olive oil, red wine in moderation, coffee, tea, dark greens, and herbs are polyphenol powerhouses. The fermented-food content is meaningful, because the traditional Mediterranean diet includes yogurt, kefir, aged cheeses, olives, and in some regions sourdough breads. The saturated fat content is modest. The ultra-processed content is near zero in the traditional form, because the pattern predates the industrial food system that defines Western ultra-processed eating.

If you were designing a diet from first principles to feed a diverse, functional, SCFA-producing gut microbiome while also keeping the polyphenol and fermented-food contributions high, you would arrive somewhere very close to the Mediterranean pattern without ever having heard of it. It is the rare case in nutritional science where traditional wisdom and contemporary mechanism align so clearly that they seem to have been designed together.

One caveat is important. “Mediterranean diet” as sold in supermarkets, restaurants, and some commercial meal-kit services is often a costume rather than a substance. A “Mediterranean salad” drenched in commercial dressing, accompanied by white bread and a sugar-sweetened drink, is not the Mediterranean diet of Crete in 1960. The evidence base comes from the traditional pattern: mostly plants, in variety, with olive oil as the dominant fat, with small amounts of fish and even smaller amounts of red meat, with very little processed food, and with meals eaten slowly in social company. When this chapter recommends “a Mediterranean-style pattern,” it means the real thing, not the marketing label.

The last point — meals eaten slowly in social company — is not just cultural decoration. We will return to it in section 20.10, when we look at when you eat rather than what you eat. The Mediterranean pattern is not only a composition. It is also, in its traditional form, a rhythm. Both matter.

20.6 Fermented Foods: The Other Living Diet

A global habit, almost forgotten

Before refrigeration, almost every human culture preserved food by letting selected microbes grow on it. Milk became yogurt, kefir, and cheese. Cabbage became sauerkraut, kimchi, and a dozen regional relatives. Soy became miso, tempeh, natto, and soy sauce. Fish became the sauces of Southeast Asia and the garum of the Romans. Grains became sourdough bread and the starters behind every traditional beer. Tea became kombucha. Cassava became fufu. Somewhere between five and ten thousand years ago, humans learned that if you let the right microbes take over a food, the food would not only keep but also change — becoming more digestible, more flavourful, more nutritious, and, in many cases, better tolerated by people who could not drink plain milk or eat plain soy or chew plain cabbage leaf. Fermented foods, in this sense, are one of the oldest technologies in human history [REF:marco2017].

Then, over the course of the twentieth century, most of them quietly slipped off the Western plate. Refrigeration removed the practical necessity. Industrial production replaced live-culture yogurt with pasteurised “yogurt-style” desserts. Sauerkraut in most supermarkets is now pasteurised and shelf-stable — killed before it gets to you. Sourdough became the niche product of artisan bakeries while commercial bread switched to fast-acting baker’s yeast. The food tradition of microbial cooperation, maintained for millennia, thinned to a few pockets: natto in Japan, kimchi in Korea, kefir in parts of Eastern Europe, lambic beers in Belgium, and live yogurt in those dwindling corners where the word still means what it used to mean.

This matters for the microbiome in ways we have only recently begun to understand, and the evidence that has accumulated in the last few years is, in my view, the single most surprising development in the dietary microbiome literature. Not the fibre story — we knew fibre was important, and the details are refinements. The fermented-food story is closer to a discovery, and it came from one of the best-designed dietary trials the microbiome field has yet produced.

The Stanford trial

In 2021, a team led by Hannah Wastyk, Erica Sonnenburg, Justin Sonnenburg, and Christopher Gardner at Stanford published a paper in Cell that is, I think, a candidate for the most important dietary microbiome trial of the decade [REF:wastyk2021].

The design was clean in a way that trials of this kind rarely are. Thirty-six healthy adults were randomised into two arms. One arm was asked to progressively increase their fermented food intake over four weeks, working up from a baseline of near zero to a target of six servings of fermented foods per day, and then to hold that level for another six weeks — ten weeks total. The other arm was asked to progressively increase their fibre intake over the same period, working from a baseline of around 22 grams per day up to about 45 grams per day, and hold. Both interventions are, on paper, things we would expect to improve the microbiome, and the research team’s hypothesis was that both arms would show benefits, with the fibre arm probably doing more.

The team took stool samples, blood samples, and peripheral blood mononuclear cells at baseline, during the intervention, and after washout. They sequenced the microbiome in depth. They measured 93 circulating inflammatory markers. They profiled cytokine production by the participants’ own immune cells after ex vivo stimulation — a way of asking not just “are the inflammatory markers high?” but “how inflammatory is the immune system if you poke it?” In short, they measured not only the ecological change but also the physiological consequence.

Here is what they found, and it is worth slowing down to take it in.

The fermented-food arm showed a clear and significant increase in gut microbial diversity over the ten weeks. This is one of the few dietary interventions that has ever produced a measurable diversity increase in a randomised human trial. The fibre arm, at the same time, did not show an increase in diversity. The fibre arm did show other changes — shifts in the expression of microbial carbohydrate-degrading enzymes, among others — but the diversity needle did not move.

The immunology was even more striking. Nineteen of the 93 inflammatory markers the team measured went down significantly in the fermented-food arm. Markers associated with chronic low-grade inflammation — the kind implicated in metabolic disease, cardiovascular disease, and age-related frailty — dropped across the board. The immune cells of participants in the fermented-food arm were less reactive when stimulated, suggesting a reduction in baseline inflammatory tone. The fibre arm showed no such reduction. On the immune outcomes the trial was designed to detect, the fermented-food arm did what the researchers had predicted the fibre arm would do, and the fibre arm did not.

There are caveats. The trial was small — eighteen people per arm. It was only ten weeks. Both interventions are known to be beneficial, so the comparison is not “good versus bad” but “good versus very good.” And the finding that high-fibre intervention did not reduce inflammation in this trial contradicts earlier evidence from other trials, which forces us to be careful about generalising. One interpretation is that fibre needs longer than ten weeks to show its anti-inflammatory effect, particularly if the participant’s microbiome has been depleted by years of low-fibre eating and no longer contains the species that would do the fermenting. The Stanford team hinted at this: they pointed out that many of their fibre-arm participants showed evidence that their microbes simply could not handle the increased substrate, producing inflammatory fermentation byproducts rather than the expected shift to butyrate production. The machinery was not there. You can shovel oats into a gut whose fibre-fermenting species are missing, but the oats will not turn into butyrate. They will mostly just leave.

That interpretation is important for the practical section of the chapter, because it suggests that fermented foods may have a particular role for people whose microbiomes have been depleted — whose fibre-fermenting machinery is partly missing. Fermented foods deliver not only substrate but also live microbes, and at least some of those microbes, or their metabolic products, appear to do useful work.

What fermented foods actually do

The Stanford trial does not, on its own, tell us how fermented foods produce their effect. But we have a reasonable working picture, and it involves at least three mechanisms.

First, live microbes reach the distal gut. Not many of them. Not permanently. Most of the bacteria in a spoonful of yogurt are destroyed by stomach acid or outcompeted by the residents. But some arrive, some interact with the resident community, and some — particularly spore-formers and acid-tolerant species — persist long enough to do something useful. Taylor and colleagues, in a 2020 observational study, showed that habitual fermented-food consumers carry distinct microbial and metabolomic signatures compared with non-consumers, suggesting that the dietary habit leaves a measurable trace in the gut [REF:taylor2020].

Second, fermented foods deliver microbial metabolites that were produced during fermentation itself — short-chain peptides, bioactive amines, vitamins, exopolysaccharides, and compounds specific to the fermenting organism. When a yogurt culture ferments milk, it does not just thicken it; it enzymatically pre-digests some of the proteins, generates bioactive peptides from casein, produces lactic acid that alters the mineral availability of the product, and enriches the food with vitamins and precursors that the starting milk did not contain. You are not just eating dairy. You are eating the collected output of a microbial factory that worked on your behalf before you opened the container.

Third, fermentation removes components that would otherwise be problematic. Sourdough fermentation partially degrades the FODMAPs in wheat — the fermentable oligosaccharides that trigger bloating in many people — which is one reason that some individuals who feel bad after modern bread can tolerate traditional long-fermented sourdough without trouble. Kimchi and sauerkraut degrade some of the raw cabbage glucosinolates that cause gas. Tempeh and miso partially degrade phytates in soy, improving mineral bioavailability. Fermentation, in each case, is a kind of pre-processing that shifts the food from “raw plant” to “microbially prepared plant,” and the prepared version is often friendlier to the eater.

Getting six servings a day

Six servings a day sounds intimidating, but a serving, in the Stanford trial, was modest: a quarter-cup of kimchi or sauerkraut, half a cup of kefir or kombucha, a few ounces of yogurt, one or two pieces of aged cheese. The participants did not replace their normal meals with barrels of pickles. They added small amounts of fermented foods across the day: a dollop of yogurt at breakfast, a splash of kombucha mid-morning, a forkful of sauerkraut on the lunch plate, a few slices of a fermented cheese in the afternoon, a small portion of kimchi with dinner, a cup of kefir before bed. Six servings became achievable once the definition was right.

The practical advice, tiered by strength of evidence and by ease, comes out as follows. If you do nothing else from this section, eat one serving of a real fermented food at most meals. Yogurt with live cultures, kefir, traditional sauerkraut (refrigerated, not pasteurised, with visible bubbles or the note “contains live cultures” on the label), traditional kimchi, aged cheeses (which contain surviving microbial communities even though they are not typically described as fermented foods by lay readers), miso paste (added to soups after the soup is removed from heat to preserve the live cultures), tempeh, natto if you can find it and stomach it, kombucha (bearing in mind the sugar content of sweeter commercial varieties), and long-fermented sourdough bread from a bakery that actually ferments overnight rather than using commercial starter for flavour alone.

Avoid the supermarket impostors. “Pickles” produced by vinegar brining are not fermented — the vinegar is doing the preserving, not the microbes. “Yogurt” that lists “milk, cream, fruit, sugar, gelatin, pectin, natural flavours” without mentioning live cultures is almost certainly pasteurised after fermentation and contains no live organisms. Pasteurised sauerkraut from the canned-goods aisle is, biologically, cooked cabbage. Check the refrigerator section; read labels; look for visible bubbles or sediment; if in doubt, make your own. A batch of sauerkraut from one cabbage, one tablespoon of salt, and a jar takes about fifteen minutes to start and two weeks to finish. The resulting product is more expensive per jar than anything in the supermarket, because your time is valuable, but for roughly the cost of a cabbage you can build a supply of the real thing for a month.

The caveat paragraph

Before we move on, the obligatory caveat. Fermented foods are not a treatment for disease. The Wastyk trial was in healthy adults, and the outcomes were inflammatory markers rather than clinical events. People with severely compromised immune systems — chemotherapy patients, transplant recipients, people on high-dose immunosuppressants — should talk to their clinicians before starting high-level fermented-food intake, because some live cultures can, rarely, cause problems in profoundly immunosuppressed hosts. People with histamine intolerance may find that aged cheeses and some fermented foods worsen their symptoms because fermentation produces biogenic amines. People on medications such as MAO inhibitors need to watch their tyramine intake, which is elevated in some aged cheeses and fermented soy products. These are real considerations for real people. The general population advice — most readers of this chapter — is that adding real fermented foods to the diet is one of the best-supported, lowest-risk, and most immediately effective things the microbiome literature currently points to.

20.7 Polyphenols: Why It Depends Whose Garden You Have

The chemistry of plant defence

Plants, unlike animals, cannot run away from what threatens them. They cannot dodge the insect that wants to chew them, the fungus that wants to rot them, or the sun that wants to oxidise their leaves. Instead, they fight chemically. Over four hundred million years of evolution, land plants have developed an extraordinary arsenal of secondary metabolites — molecules not directly involved in growth and reproduction, but deployed to deter enemies, attract allies, or protect against environmental stress. Many of these secondary metabolites are bitter, astringent, and vividly coloured, which is why the most chemically defended parts of plants often taste the most interesting and photograph the best.

A subset of these chemicals, called polyphenols, are defined by their structure: they carry multiple phenolic rings, chemical groups that can donate electrons and thereby neutralise reactive molecules. There are somewhere between eight and ten thousand known polyphenols, clustered into families: flavonoids (including the catechins of tea, the anthocyanins of berries, and the flavones of parsley and celery), phenolic acids (including the chlorogenic acid of coffee), stilbenes (including the resveratrol of red wine), lignans (in flaxseed and whole grains), and tannins (in wine, tea, and many fruit skins). Each family has its distinctive chemistry. What they share is that when you eat them, something surprising happens: most of them are not absorbed.

Estimates vary, but somewhere between 90 and 95 percent of dietary polyphenols pass through the small intestine and reach the colon intact or partly altered. They are too large to be readily absorbed across the intestinal wall, or too tightly bound to the plant’s fibre matrix, or simply not in a form the enterocytes can transport. This fact used to be considered a problem — how can a compound that barely gets into the blood matter much for health? — until it became clear that the polyphenols are not meant for the enterocytes in the first place. They are meant for the microbes. And what the microbes do with them is where the story gets interesting [REF:cortes-martin2020].

Urolithin: the pomegranate story

Consider ellagitannins, a class of polyphenol concentrated in pomegranates, walnuts, raspberries, strawberries, and certain teas. When you eat a pomegranate, the ellagitannins arrive in your colon largely intact. There, some gut microbes — notably species in the Gordonibacter and Ellagibacter genera — possess the enzymes to break them down, releasing a smaller molecule called ellagic acid. Some microbes then process the ellagic acid further, through several intermediate steps, to produce a compound called urolithin A. Urolithin A, unlike its parent molecules, is small enough and hydrophobic enough to be absorbed across the colonic wall. It enters the circulation and reaches other tissues. In cell and animal experiments, urolithin A has been shown to have effects on mitochondrial health, autophagy, and muscle function. Human trials, while early, suggest it may contribute to the benefits of polyphenol-rich diets in ageing-related muscle decline.

Here is the catch, and it is a big one. Not everybody makes urolithin A. Studies across multiple populations have found that people fall into three broad groups, which the research team of Francisco Tomás-Barberán in Murcia have called “metabotypes.” Metabotype A people convert ellagitannins efficiently into urolithin A. Metabotype B people produce mostly a different metabolite called urolithin B, which has weaker biological activity. Metabotype 0 people — and this is somewhere between 10 and 40 percent of the population, depending on the study — produce almost no urolithin at all. They simply do not carry the microbes that run the conversion, and their ellagitannins pass through them without yielding the useful downstream product [REF:tomas-barberan2017].

This means that the same handful of walnuts, eaten by three different people, produces three different systemic exposures to urolithin. For one person, the walnut is a delivery device for a potentially beneficial small molecule. For another, it delivers only the raw ingredient and the weaker derivative. For a third, it delivers nothing of the kind. The walnut is not different. The people are.

The equol story, and others

A similar pattern applies to soy isoflavones. Soy contains daidzein, one of the best-studied phytoestrogens. In some people, gut microbes convert daidzein into a molecule called equol, which has stronger oestrogenic activity than daidzein itself and appears to be the compound responsible for many of the effects attributed to soy — including, possibly, the reduced incidence of certain hormone-sensitive cancers in populations that eat soy from childhood. Only some people are equol producers. In Western populations, the proportion is often below 30 percent. In traditional Asian populations, the proportion tends to be higher, partly for genetic reasons but mostly, we think, because early-life dietary exposure and the associated microbial community favour the necessary converting species. If you are not an equol producer, the benefits attributed to soy in the epidemiological literature may not accrue to you in the same way.

The same principle applies, to varying degrees, to many other polyphenol classes. Flavan-3-ols from cocoa and green tea are processed by the microbes into phenyl-γ-valerolactones and other absorbable small molecules. Lignans from flaxseed are converted to enterolignans like enterodiol and enterolactone. Anthocyanins from berries are modified into a whole cascade of smaller phenolic metabolites. In each case, the ultimate systemic exposure to the biologically active product depends on whether the eater’s microbiome contains the necessary converting species, and the answer is frequently “partly” or “not much.”

What this means for you on Tuesday

It would be tempting to take this as a reason to give up on polyphenol-rich foods. After all, if you are not going to get the urolithin, why bother with the walnut? The answer is that the parent compounds, even without microbial conversion, carry useful effects of their own — direct antioxidant activity in the gut lumen, modification of the microbial community itself (several polyphenols preferentially feed beneficial taxa and suppress certain pathogens), and bioactivity from the smaller fraction that is absorbed intact. More importantly, the whole-food sources of polyphenols — berries, coloured vegetables, tea, coffee, dark chocolate, olives, olive oil, herbs and spices, red wine in moderation, whole grains — are also, almost without exception, sources of fibre, vitamins, minerals, and plant diversity. Even if you are a metabotype 0, a diet rich in polyphenolic foods is also a diet rich in everything else good.

The more sophisticated reading of the metabotype story is that it explains why population-level studies of polyphenol intake and disease sometimes produce inconsistent results. If the benefit depends on having a particular microbiome, then the average effect across a heterogeneous population will look smaller than the effect in the subset who can actually produce the active metabolite. This is a recurring theme in diet and microbiome research: the average hides the individuals, and some of the “inconsistencies” that have made nutritional epidemiology so frustrating over the decades may turn out to reflect real differences in who processes what.

The practical rule is simple. Eat polyphenol-rich foods in variety. Berries (fresh or frozen — frozen are often higher in polyphenols because they were picked ripe and preserved quickly). Tea, especially green tea. Coffee if you enjoy it. Dark chocolate (70% or higher, in small amounts, not as a substitute for actual food). Extra-virgin olive oil, which carries hydroxytyrosol and oleocanthal as well as fat. Olives. Herbs and spices — every culture’s traditional cuisine is a polyphenol delivery device disguised as flavouring. Red wine if it is part of your life, in modest amounts, with food. Nuts, particularly walnuts. Whole grains. Legumes, especially those with coloured seed coats. Do not be disappointed when headlines claim that this week’s superfood is “the answer”; there is no answer, only a rich and varied plate, and the microbes in your own particular gut will make of it what they can.

If, in five or ten years, commercial microbiome testing becomes cheap and reliable enough to tell you which polyphenol converters you carry, that information may become actionable. Today, it is not. Assume you are probably a partial converter for most classes, eat the foods anyway, and let your own particular ecosystem do its best.

20.8 The Other Side of the Plate: Protein, Fat, and the Metabolites You Don’t Want

TMAO: when your microbes turn dinner into a risk factor

In 2011, a group led by Stanley Hazen at the Cleveland Clinic published a paper in Nature that did something rare in cardiology: it added a new player to the list of molecules that predict heart disease, and the new player was not one of the usual suspects [REF:wang2011]. The molecule was trimethylamine-N-oxide, or TMAO, a small compound that had been known to chemists for a long time but had never been seriously considered as a cardiovascular risk factor until Hazen’s group showed that elevated blood levels of TMAO were strongly and independently predictive of heart attack, stroke, and cardiovascular death — even after adjusting for every traditional risk factor they could think of.

Where does TMAO come from? From your bacteria. Specifically, certain gut microbes break down dietary choline — abundant in eggs, red meat, poultry, and fish — into a gas called trimethylamine, or TMA. The TMA is absorbed into the portal vein, travels to the liver, and is oxidised by a host enzyme (FMO3) into TMAO, which then enters the systemic circulation. The TMAO is what seems to promote atherosclerosis, apparently by altering cholesterol handling in macrophages and contributing to foam-cell formation in the arterial wall.

In 2013, the Hazen group published a second paper, this one focused on a different precursor: L-carnitine, a molecule found in high concentrations in red meat [REF:koeth2013]. The mechanism was similar. Gut microbes convert carnitine to TMA; the liver oxidises it to TMAO; the TMAO contributes to atherosclerosis in mouse models and correlates with cardiovascular events in humans.

Here is the part of the Koeth study that made people sit up and pay attention. The researchers fed a steak (or, in the formal experiment, a standardised serving of beef) to a group of omnivores and measured their blood TMAO response. Levels rose. They then fed the same carnitine challenge to a group of long-term vegans and vegetarians. Levels barely moved. The vegans and vegetarians did not make TMAO from dietary carnitine, because they did not carry the bacterial populations that perform the conversion. Long-term avoidance of meat had, in effect, de-trained the microbial community for this particular substrate. Give it meat, and nothing happened. Give the omnivore meat, and the bacterial machinery that had been sustained by decades of meat-eating went to work.

This finding complicates the usual narrative about red meat and cardiovascular disease. The traditional interpretation — that meat is bad because of its saturated fat content — is partly true but incomplete. The Hazen-Koeth line of work suggests that a significant portion of the harm attributed to red meat is actually mediated by the microbial response to it, and that the microbial response itself depends on who you are and what you have been eating. The steak is not the only variable. The population of bacteria in your colon, shaped by years of previous dinners, is another.

It also suggests a form of partial reversibility. If you drop meat from your diet for long enough, the TMAO-producing populations shrink and the conversion slows. If you then eat meat occasionally, the conversion is partial. You do not have to become a lifelong vegan to benefit from reduced TMAO exposure; you may simply have to make meat something other than the centre of most meals. This is, again, what the Mediterranean pattern has been quietly telling us for sixty years: a small portion of fish or meat, surrounded by a large portion of plants, eaten a few times a week rather than most days, looks very different, in microbial terms, from a diet built around meat as the main event.

Saturated fat, taurine-bile, and the wadsworthia story

The second protein-and-fat story is more surprising, and it comes from a 2012 paper by Suzanne Devkota working in Eugene Chang’s laboratory at Chicago [REF:devkota2012]. Devkota’s study was in mice, but the mechanism it described is, as far as we can tell, fully applicable to the human bowel.

The experimental setup was this. Mice genetically engineered to be susceptible to inflammatory bowel disease (IL-10 knockout mice, missing a key anti-inflammatory signal) were placed on one of three diets: low fat, high fat with the fat coming from lard (mostly saturated), or high fat with the fat coming from safflower oil (mostly polyunsaturated). The team then measured the composition of the gut microbiota and the development of colitis.

On the low-fat diet, the susceptible mice mostly stayed healthy. On the polyunsaturated high-fat diet, they also mostly stayed healthy — so the problem was not fat per se. But on the saturated-fat diet, something striking happened. The composition of the mouse bile acid pool shifted. Normally mouse bile contains a mixture of glycine-conjugated and taurine-conjugated bile acids. On the high-saturated-fat diet, the taurine-conjugated fraction surged, because dealing with the higher fat load required more bile, and the taurine conjugates were preferentially produced. This was a subtle biochemical shift at the level of bile composition.

It was then amplified by the microbes. One bacterial species in particular, Bilophila wadsworthia, thrives on taurine-derived sulphur. When the taurine-conjugated bile acids increased, Bilophila wadsworthia bloomed from a minor to a major component of the microbiota. Its metabolism produced hydrogen sulphide, a pro-inflammatory gas. The inflammatory burden on the colonic wall increased. And the genetically susceptible mice, in whom any additional inflammatory trigger could push the gut into overt disease, developed colitis.

The study is in mice with a specific genetic vulnerability, and we should not extrapolate it to every person who eats a cheeseburger. But the mechanism — saturated fat drives a bile acid shift that expands a sulphur-loving pathobiont that produces an inflammatory gas — is documented, biochemically coherent, and at least partially replicable in human studies that have since followed. Bilophila wadsworthia is present in the human gut and does expand on high-saturated-fat diets. Its sulphide production is real. Whether this produces frank colitis in most people is unclear; whether it contributes to the lower-grade inflammation associated with high-saturated-fat eating is plausible.

The speed of change

One more study in this cluster deserves a mention, because it tells us how fast dietary changes show up in the microbiome. In 2014, a team led by Lawrence David, then at Harvard, published a paper called “Diet rapidly and reproducibly alters the human gut microbiome” [REF:david2014]. The design was elegant. Ten healthy adults were fed, for five consecutive days, one of two extreme diets. The “plant-based” diet was essentially a vegan diet with high fibre and high complex carbohydrates. The “animal-based” diet contained meat, eggs, and cheeses at every meal, with almost no plant foods at all. Stool samples were collected before, during, and after each intervention.

Within 24 to 48 hours of starting either diet, the microbiome composition had shifted detectably. Within four days, the shifts were large. The animal-based diet promoted bile-tolerant organisms (Bilophila, among others) and suppressed Firmicutes that metabolise plant polysaccharides. The plant-based diet did the opposite. When the participants returned to their habitual diets, the changes reversed over a few days.

The take-home is both encouraging and sobering. Encouraging: your microbiome responds fast. A week of eating better is already starting to show results in the microbial community. Sobering: a week of eating badly has a similar speed of effect in the wrong direction, and if “a week of eating badly” becomes “a year of eating badly,” the cumulative shift can be substantial and, as we saw in the Sonnenburg extinction experiments, not fully reversible.

Enterotypes and the long term

There is one more layer to the picture, introduced by Gary Wu and collaborators at the University of Pennsylvania in 2011 [REF:wu2011]. They asked whether long-term dietary patterns — not what someone ate last week but what they had eaten for years — could be read off the gut microbiome. Their answer was yes, and they proposed a framework called “enterotypes”: broad categories of microbiome composition that cluster around different dominant genera.

The simplest version of the enterotype story, and the one that has survived the most scrutiny, is a spectrum between two extremes. At one end, people with a Bacteroides-dominated microbiome tend to have had long-term diets high in animal protein and animal fat. At the other end, people with a Prevotella-dominated microbiome tend to have had long-term diets high in fibre and plant-based carbohydrates. Short-term dietary change, as David showed, produces fast but partial shifts. Long-term dietary pattern produces deeper shifts that, in effect, select which ecosystem you carry.

Taken together, this cluster of studies — Hazen, Koeth, Devkota, David, Wu — tells a coherent story. What you eat today has fast effects on your gut microbes. What you eat across years shapes which microbes you carry, which metabolites they can make from your food, and how those metabolites act on the rest of your body. Animal foods are not the enemy. But a diet in which animal foods dominate over a long period trains a microbial community that produces more of the metabolites we don’t want (TMAO, sulphides, secondary bile acids in inflammatory configurations) and less of the metabolites we do (butyrate and its SCFA cousins). The traditional Mediterranean pattern’s recommendation of “meat a few times a week, plants at every meal” is, in hindsight, the right balance not because plants are virtuous and meat is sinful, but because this balance trains a microbial ecosystem whose metabolic outputs favour the host.

20.9 Ultra-Processed Food: When the Package Is the Problem

A category that didn’t exist a generation ago

Walk through a modern supermarket and ask yourself a simple question: which of the items on these shelves would have been recognisable to your great-grandmother? The fruit and vegetable aisle, yes. The butcher and fish counter, mostly. The dry-goods aisle — flour, rice, dried beans — mostly. The dairy cabinet, with some caveats. Everything else — the coloured boxes, the plastic pouches, the brightly labelled tubs, the frozen ready meals, the “protein” bars, the “healthy” snacks, the “natural” beverages — contains products that did not exist in their current form before about 1970 and, for the most part, did not become a major fraction of the Western food supply until the 1980s and 1990s.

In 2009, a Brazilian epidemiologist named Carlos Monteiro proposed a classification system for foods based not on their nutrient content but on how much they had been industrially transformed. He called it the NOVA system. It has four categories. Group 1 is unprocessed or minimally processed foods — fruits, vegetables, grains, legumes, nuts, eggs, milk, meat, fish, spices, herbs. Group 2 is processed culinary ingredients — oils, butter, vinegar, salt, sugar, flour. Group 3 is processed foods — foods made by combining Group 1 and Group 2 items in recognisable ways, including traditional bread, cheese, canned vegetables, and cured meats. Group 4 is ultra-processed foods, and the definition is worth quoting, because the category is not simply “things with a lot of ingredients.” Ultra-processed foods are, in Monteiro’s formulation, industrial formulations that typically contain substances not found in home kitchens — protein isolates, modified starches, hydrogenated oils, high-fructose corn syrup, emulsifiers, stabilisers, artificial flavours, artificial colours, non-nutritive sweeteners — and that are designed to be hyper-palatable, shelf-stable, and profitable.

By this classification, ultra-processed foods now account for somewhere between 50 and 60 percent of total energy intake in the United States and the United Kingdom, and a substantial and rising fraction in most other industrialised countries. This is a dietary category that has moved, within a generation, from non-existent to dominant. And the evidence is accumulating, from multiple directions, that the microbiome is part of why it matters [REF:zinocker2018].

Emulsifiers: the wallpaper dissolver

Consider a product as innocent-sounding as commercial ice cream. Check the label. You will almost certainly find, somewhere in the ingredient list, one or more of: carboxymethylcellulose (also written CMC or E466), polysorbate 80 (E433), mono- and diglycerides, carrageenan, lecithin, or xanthan gum. These are emulsifiers and stabilisers. Their job is to keep water and fat mixed smoothly, prevent ice crystals from forming, and give the product its characteristic mouthfeel. They are in ice cream, but they are also in commercial bread, salad dressings, plant milks, processed cheese, margarine, ready meals, protein shakes, and a substantial fraction of the processed food aisle. Most of them are regarded as safe in the sense that they do not cause acute toxicity at the doses used.

In 2015, Benoit Chassaing and Andrew Gewirtz at Georgia State published a paper in Nature that added a worrying footnote to that long-standing conclusion [REF:chassaing2015]. They fed mice CMC and polysorbate 80 at doses comparable to ordinary human exposure through the processed food supply — not experimental overdoses, but the kind of intake a person would get from a Western diet. They then measured microbiota composition, mucus layer integrity, inflammatory markers, and body weight.

The findings were, in a word, unsettling. The emulsifier-fed mice showed a thinning of the mucus layer — the same mucus layer we discussed in the fibre section, the layer that is supposed to separate the bacteria in the lumen from the epithelial cells of the gut wall. The bacteria, instead of living at a respectful distance, were now coming closer to the epithelium. Chassaing and Gewirtz called this “microbiota encroachment.” The microbiome composition shifted towards pro-inflammatory configurations. The mice developed low-grade inflammation, insulin resistance, and features of metabolic syndrome. In mice genetically predisposed to inflammatory bowel disease, frank colitis developed.

The reasonable scientific response was “this is in mice; do we see it in humans?” In 2022, Chassaing and colleagues published the answer: a randomised, controlled, double-blind human feeding trial in which healthy adults were given diets containing CMC at levels consistent with typical dietary exposure, or a matched control diet without CMC, for eleven days [REF:chassaing2022]. The sample was small — sixteen participants — but the experimental control was tight, and the outcomes were measured carefully.

The results confirmed, in humans, the direction of the mouse findings. Participants on the CMC diet showed reduced microbial diversity, alterations in the faecal metabolome, and — crucially — evidence of microbiota encroachment on the mucus layer in a subset of participants. Eleven days of an emulsifier that had been classified as safe for decades produced measurable changes in the way the gut microbes related to the gut wall.

This does not mean CMC is acutely dangerous. It does mean that a food additive which reaches us in small quantities through many products, day after day, year after year, is not as biologically inert as the regulatory history had assumed. And the list of emulsifiers that the Chassaing trial tested is only two molecules out of dozens that are routinely used in the ultra-processed food supply. We do not know, yet, what each of the others does. Polysorbate 80 behaves similarly to CMC in animal studies. Carrageenan has a longer-standing literature associating it with intestinal inflammation. Others have not been adequately studied.

The reasonable position, pending further data, is that dietary emulsifiers at Western food-supply levels are now a plausible contributor to the low-grade inflammation that characterises so much of contemporary chronic disease, and that reducing exposure to them is a sensible precaution even in the absence of definitive long-term outcome data. Practically, this means reading ingredient lists, favouring foods with short ingredient lists that contain only things you could buy at a market, and accepting that the creamy-mouthfeel ice cream of the supermarket freezer is not as trivial a treat as it looks on the label.

Non-nutritive sweeteners: the diet soda surprise

A parallel story, equally counterintuitive, has emerged around non-nutritive sweeteners. These are the compounds — saccharin, aspartame, sucralose, stevia glycosides, acesulfame potassium — that provide sweetness with few or no calories, and that have been marketed for decades as the healthy alternative to sugar. They were, for most of their history, considered microbiologically irrelevant on the theory that a compound which is not metabolised by the human body is also not metabolised by the gut microbiota.

In 2014, a group led by Eran Elinav and Eran Segal at the Weizmann Institute in Israel published a paper in Nature that turned this assumption upside down [REF:suez2014]. They showed that saccharin fed to mice at doses corresponding to the FDA’s acceptable daily intake caused glucose intolerance — the precursor state to type 2 diabetes. They then showed that the mechanism was microbial: the saccharin altered the gut microbiota composition, and when faecal microbiota from saccharin-fed mice were transplanted into germ-free mice, the recipients developed glucose intolerance too. The microbial shift was the mediator. In a small pilot experiment in humans, a subset of volunteers who consumed saccharin for a week showed similar patterns — altered microbiota and impaired glucose responses — while another subset did not respond.

The 2014 paper was important but small and its human arm was limited. The definitive follow-up came in 2022, again from Suez, Elinav, and Segal, and was published in Cell [REF:suez2022]. This was a properly powered, double-blind, placebo-controlled human trial. One hundred and twenty healthy adults who had not been consuming non-nutritive sweeteners were randomised into six groups: saccharin, sucralose, aspartame, stevia, a placebo (glucose at a matched dose), or no supplement. The test sweeteners were given at doses well below the FDA’s acceptable daily intakes. Outcomes included microbiome composition, metabolomic profiles, and glycemic response to oral glucose challenges.

The findings, taken carefully, are these. All four sweeteners altered the gut microbiome and the plasma metabolome, albeit in different directions. Two of them — saccharin and sucralose — also produced measurable impairment of glycemic responses in the participants who received them, with the impairment varying strongly between individuals. When faecal microbiota from the most responsive human participants were transplanted into germ-free mice, the mice recapitulated the glucose-intolerance phenotype — proof that the microbial changes were sufficient, on their own, to produce the glycemic effect.

Non-nutritive sweeteners, in other words, are not microbiologically neutral. They interact with the microbiome, the microbiome interacts with the host, and in some individuals the consequence is worse rather than better glucose handling. The person who drinks a diet cola in place of a sugar cola is not, on this evidence, getting the metabolic neutrality the marketing promises. Some people may be, some people are not, and we cannot currently tell in advance which category any given reader falls into.

The practical position for the general reader is cautious. If you are currently drinking multiple artificially sweetened beverages per day, it is worth considering whether the category is doing you the metabolic favour you had assumed. Reducing or eliminating them, substituting water, unsweetened tea and coffee, or a small amount of sugar, is a reasonable move. The same applies, more broadly, to any ultra-processed product whose “diet” or “light” credentials rest on sweetener substitution.

The Western diet as an ecological insult

The Zinöcker and Lindseth 2018 review pulled these strands — fibre depletion, emulsifier exposure, sweetener exposure, food-matrix disruption — into a single framework [REF:zinocker2018]. Their argument, in essence, is that the Western ultra-processed food system is best understood not as a specific nutrient problem but as a systemic ecological insult to the gut microbiome. Individual ingredients do individual damage. The combination — low MAC, constant emulsifier exposure, intermittent sweetener exposure, disrupted food matrices, displaced fermented foods, displaced plant diversity — acts on the ecosystem in multiple overlapping ways at the same time.

This is why single-nutrient approaches to improving dietary health — “eat less fat,” “eat more fibre,” “avoid sugar” — have historically produced modest and inconsistent results. The problem is not any one nutrient. The problem is the architecture. A diet built around ultra-processed products will be nutritionally inadequate in many overlapping ways, and tinkering with one nutrient while leaving the architecture intact produces small and often transient benefits. The alternative is not another single-nutrient rule. It is a structural shift: making the base of your diet minimally processed foods — Monteiro’s Groups 1, 2, and 3 — and letting Group 4 be a minor and occasional category rather than a dominant one.

I am aware that this sounds, in 2026, either obvious or impossible. Obvious, because it is what every sensible dietary writer for thirty years has been saying in different words. Impossible, because the convenient, affordable, and socially normal foods in most Western cities are predominantly ultra-processed, and asking a working family to “just cook from scratch” without acknowledging the time, money, and skill barriers that task involves is glib. I am not going to pretend those barriers do not exist. What I will say is that any move in the direction of minimally processed foods — even a partial one — is likely to pay returns, and that the returns will be disproportionately larger than equivalently sized changes within the ultra-processed category. Switching from one brand of packaged breakfast cereal to another does little. Replacing the breakfast cereal with oats and a handful of nuts does a lot. The difference is structural.

20.10 When You Eat: The Circadian Microbiome

The clock inside the clock

Until about a decade ago, most of us thought of the gut microbiome as a community that sat in the colon doing roughly the same thing around the clock. Eat a meal, digest it, excrete the remains, wait for the next meal. The composition of the community might shift with diet over weeks and months, but within a single day, we assumed, it was stable.

In 2014, Christoph Thaiss, then a graduate student in Eran Elinav’s laboratory at the Weizmann Institute, published a paper in Cell showing that this assumption was completely wrong [REF:thaiss2014]. Thaiss and colleagues sampled the gut microbiomes of mice at multiple time points across the 24-hour day and discovered that the composition oscillated. Specific bacterial taxa rose and fell in abundance over a daily rhythm. Specific microbial genes were transcribed more actively during some hours than others. The community was not static. It was cycling through daily phases that paralleled — and were driven by — the host’s feeding rhythm.

When the researchers disrupted the rhythm, by putting the mice on a jet-lag protocol (advancing the light cycle by 8 hours every three days), the microbial oscillations flattened and desynchronised. The mice developed glucose intolerance and weight gain. When faecal microbiota from the jet-lagged mice were transplanted into germ-free mice on a normal schedule, the recipients also developed metabolic dysfunction — proof that the desynchronised microbiome was sufficient to cause the host metabolic phenotype. The team then studied two healthy human volunteers during actual transmeridian flights, and found evidence that the human gut microbiome was similarly desynchronised during jet lag and that the desynchrony correlated with the metabolic perturbations the volunteers experienced.

A follow-up paper from the same group in 2016 went further. Using mouse models, Thaiss and colleagues showed that the microbial oscillations were not just a passive consequence of feeding rhythms but an active driver of host physiology: the diurnal rhythms of microbial metabolite production shape the diurnal rhythms of gene expression in the liver and other tissues [REF:thaiss2016]. Your liver’s daily schedule is not just set by your own clock. It is set, in part, by the metabolic output of the microbes in your gut, which in turn is set by when you eat.

This gave a microbial mechanism to a pattern that epidemiologists had been tracking for years. Shift workers have higher rates of metabolic syndrome, type 2 diabetes, cardiovascular disease, and some cancers than people who work regular hours. The usual explanation was that shift work disrupts sleep and stress systems. The Thaiss work added a new layer: shift work also disrupts the feeding rhythm, which disrupts the microbial rhythm, which disrupts the host metabolic rhythm. The damage is partly mediated by a microbial clock you cannot see.

Time-restricted eating

The obvious practical question is whether when you eat makes a measurable difference for people who are not shift workers. The growing body of human evidence suggests that it does, and the effect is large enough to take seriously.

Time-restricted eating is the practice of confining all daily food intake to a defined window — commonly eight, ten, or twelve hours — with the remaining portion of the 24-hour day spent not eating. It differs from the popular concept of “intermittent fasting” in that no calorie restriction is required; the person eats as much as they like within the window, but outside the window they drink only water, tea, or coffee without milk or sugar. In animal studies, time-restricted feeding produces improvements in glucose tolerance, lipid profiles, and body composition even when total calorie intake is held constant. In human studies, the evidence is more mixed but consistently points in the same direction.

A 2020 study by Zeb and colleagues randomised healthy adult men to a 16:8 time-restricted feeding pattern (8-hour eating window) or an ad libitum control for 25 days, and reported changes in gut microbiome composition, metabolic markers, and circadian rhythms in the restricted group that did not appear in the control [REF:zeb2020]. The effects were modest — this was a short trial in healthy participants, not a dramatic clinical intervention — but consistent with the broader picture.

More powerful evidence, at the population level, came from the French NutriNet-Santé cohort. In a 2023 paper, Palomar-Cros and colleagues analysed data from more than 100,000 adults whose eating times had been recorded prospectively [REF:palomar-cros2023]. They found that later first meal times and later last meal times were associated with higher cardiovascular disease risk, independent of total calorie intake and diet quality. Each additional hour of eating-window delay (eating later) was associated with meaningful increases in risk. Longer overnight fasts — essentially the space between dinner and breakfast — were associated with lower risk.

Put these threads together and you get the outline of a practical recommendation that, until recently, dietary advice did not contain. What matters is not only what you eat but also when. The simplest version of the recommendation is this: try to eat within a defined daily window, probably somewhere between 10 and 12 hours, and try not to eat in the hours immediately before bed. If you currently have your first coffee at 7 am and your last snack at 11 pm, you are eating across 16 hours, and your microbes — along with the circadian metabolism of your liver and your insulin-secreting cells — have no off-shift. Tightening the window to something like 8 am to 7 pm, or 9 am to 8 pm, gives the system a rest. For most people this is a free intervention: it does not cost anything, does not require changing what you eat, and can be adopted with relatively little disruption.

What the Mediterranean pattern had right all along

I want to return briefly to the Mediterranean pattern here, because one of its features that gets less attention than olive oil and tomatoes is the rhythm of its eating. In traditional Mediterranean cultures, meals were concentrated into three main events: a small breakfast, a significant mid-day meal eaten unhurriedly in company, and a light evening meal eaten relatively early. Snacking between meals was unusual. Eating late at night was unusual. The eating window was roughly 10 to 12 hours, not because anybody was practising time-restricted feeding but because that was the cultural pattern.

In other words, the pattern we now recommend for circadian reasons was already baked into the pattern we recommend for compositional reasons. This may be part of why long-term adherence to “the Mediterranean diet” as a lifestyle (not just a grocery list) produces results that tinkering with individual nutrients has historically not. The traditional pattern bundled the composition with the rhythm, and both parts may have been doing real work.

The person who wants the practical benefit without the full cultural immersion can take the simple advice: narrow the window, protect the overnight fast, avoid eating for two or three hours before sleep, and let the microbes have their daily rest.

20.11 Personalised Nutrition: Why “What Should I Eat?” Is the Wrong Question

The same banana, different people

In 2015, a team led by David Zeevi, Tal Korem, and Niv Zmora in the laboratories of Eran Segal and Eran Elinav at the Weizmann Institute published a study that I think should have changed the way popular nutrition is taught, and largely has not [REF:zeevi2015]. The study was called “Personalized Nutrition by Prediction of Glycemic Responses,” and it was, at the time, the largest and most detailed examination ever conducted of how real people respond to real food in their normal lives.

The design was this. Eight hundred healthy and pre-diabetic Israeli adults were recruited and equipped with continuous glucose monitors — the small skin-worn sensors that sample interstitial glucose every few minutes and produce a continuous record of blood glucose across days. For one week, the participants ate their normal diets, logged every meal in a smartphone app, and sent stool samples for microbiome sequencing. The researchers then standardised one meal per day — a specific bread, a specific breakfast cereal, a specific glucose drink — so that everybody was eating the same thing at some point in the week. The team also collected anthropometric and clinical data: age, weight, height, blood tests, and so on. The total dataset included roughly 46,000 meals logged and sensor-tracked.

The analysis of that dataset produced a result so counter to the assumptions of public health nutrition that it is still underappreciated a decade later. When different people ate the same standardised meal — the same piece of bread, the same bowl of cereal, the same glucose drink — their blood glucose responses differed enormously. Person A’s glucose might spike by 30 mg/dL after a slice of bread; person B’s might spike by 10 mg/dL; person C’s might spike by 90 mg/dL. These were not measurement errors. They were consistent, reproducible, within-person patterns that varied strikingly between people.

More interestingly, the variations were not predicted by any single simple variable. BMI, age, fasting glucose, and insulin resistance explained a little of the variance, but not most of it. The researchers then built a machine-learning model that incorporated all the measured variables — including the microbiome features — and asked whether the model could predict, for each individual, which foods would spike their glucose and which would not. It could. And the microbiome features were among the most important predictors. Particular gut bacterial taxa were associated with smoother glucose responses to particular foods. Other taxa were associated with sharper responses. The microbiome was carrying predictive information that the clinical variables were not.

What this means, in plain terms, is that two people can eat identical breakfasts and have entirely different metabolic experiences, and that the difference is partly a function of who their microbes are. “Bread causes a blood sugar spike” is true for some people and mostly false for others. “White rice is worse than brown rice” is a population-level statement that may not apply to any specific reader. The averaging has been hiding real variation, and the variation is not random — it is predictable, if you know enough about the individual.

PREDICT 1: the British twin study

The Weizmann work was done in a single population. It remained possible, in principle, that the striking individual variation was something about Israelis specifically, or about the particular measurement method, or about the chance characteristics of the sample. So in 2020, a large collaborative team — Tim Spector at King’s College London, Andrew Chan at Harvard, Nicola Segata at Trento, and others — published the first results of a project called PREDICT 1, which was essentially the Weizmann design at larger scale and in a different population, with twins [REF:berry2020].

PREDICT 1 enrolled a thousand healthy adults, including hundreds of pairs of identical and non-identical twins, and subjected them to standardised meals under carefully controlled conditions, then tracked their free-living eating for two weeks. Continuous glucose, blood triglycerides, insulin, and other metabolic parameters were measured. The participants included twins because twin designs allow for partitioning of variance between genetic and non-genetic factors. If a trait varied more between non-identical twins than identical ones, you could quantify the genetic contribution.

The PREDICT 1 results replicated the Weizmann finding in a Western population and added precision to the partitioning. For postprandial glucose responses — the blood sugar curve after a meal — the contribution of genetics was small: perhaps 15 to 30 percent of the variance. Most of the individual variation was from non-genetic factors, including the microbiome, the person’s current metabolic state, the time of day, the order in which components of the meal were eaten, the amount of sleep the person had had, and their habitual diet. For postprandial triglycerides, the pattern was different — genetics contributed almost nothing to the variance, and the microbiome and lifestyle factors dominated almost completely.

A companion paper by Francesco Asnicar and colleagues, published in 2021 in Nature Medicine, specifically examined the microbiome arm of the PREDICT 1 data and reported that particular microbial taxa were associated with favourable and unfavourable cardiometabolic markers, and that habitual diet shaped microbiome composition in turn [REF:asnicar2021]. The team identified a “signature of 15 good microbes” that were associated with healthy plant-based diets and favourable blood markers, and a contrasting set of microbes associated with unhealthy processed diets and unfavourable markers. The key is that the relationships were mediated: diet shapes microbiome, microbiome shapes metabolism, metabolism shapes disease risk.

Where the evidence stands

Taken together, the Weizmann and PREDICT studies tell us that “one size fits all” nutrition is not a useful framework for individual-level advice. The same food produces different effects in different people, the differences are partly predictable from the microbiome, and the predictive models are good enough to be commercially interesting — several companies (including the ones descended from the Weizmann and PREDICT teams) now offer microbiome-based personalised nutrition services on a subscription basis.

But we should be careful here. A 2019 review by Kolodziejczyk, Zheng, and Elinav, published in Nature Reviews Microbiology, assessed the state of evidence for personalised nutrition and reached a balanced conclusion: the science is promising but the clinical use is still early [REF:kolodziejczyk2019]. The models predict group-level average responses better than individual-level variation. The tests are expensive. The actionable output — “eat more of X and less of Y” — is useful for some people in some contexts but does not, on current evidence, outperform general principles (more fibre, more plant diversity, less ultra-processed food) for most readers most of the time.

My own view, as a physician who watches how new technologies travel from research to clinic, is this. The scientific finding — that individual variation in response to food is real, substantial, and partly microbiome-mediated — is robust and important. The commercial translation — “send us a stool sample and we’ll tell you what to eat for £300” — is premature. If you have a specific clinical problem that is not responding to general advice (treatment-resistant type 2 diabetes, recurrent metabolic syndrome, stubborn weight gain despite reasonable eating), a personalised approach guided by continuous glucose monitoring and microbiome testing may add value. If you are a healthy reader trying to eat well, the general principles of this chapter — fibre, plant diversity, fermented foods, Mediterranean pattern, ultra-processed minimisation, time-restricted window — will do more, more cheaply, than any commercial test will.

The more interesting implication of the personalised nutrition literature, I think, is not that you should rush to buy a test. It is that you should stop trusting any single-food headline. When the Sunday newspaper tells you that “bananas cause blood sugar spikes” or “bananas are a perfect low-glycemic snack,” both statements are true and both statements are false, depending on which reader they are addressed to. The same goes for eggs, potatoes, white rice, oats, dairy, legumes, and almost every food that gets held up or held down in popular discussion. You are part of the equation, and the part of you that matters most for this particular question is sitting in your colon.

20.12 The Controversies, Met Head-On

I have avoided taking sides in the big contemporary food controversies until now, because the evidence-based case for the practical advice of this chapter does not depend on any of them. Fibre, plant diversity, fermented foods, Mediterranean pattern, minimising ultra-processed food, narrowing the eating window — all of that holds up regardless of where you stand on gluten, FODMAPs, lectins, or meat. But readers who have made it this far are entitled to a direct engagement with the positions they encounter on social media and in popular books, and this section is where I give them.

The structure of each of the following sub-sections is the same. I will state the claim as its adherents make it, describe what the best available evidence actually says, distinguish the kernel of truth from the exaggeration, and end with a practical recommendation. I will do this without sarcasm, because most of the people who hold these positions have arrived at them honestly — often because they felt unwell, tried something, felt better, and concluded that the thing they tried was the answer. That is a reasonable way to discover something. It is a less reasonable way to prove it.

Gluten

The claim made by the popular gluten-free movement is that wheat and other gluten-containing grains are, for a significant fraction of the population, a cause of bloating, fatigue, brain fog, joint pain, and a variety of systemic symptoms, and that the remedy is avoidance of all gluten-containing foods.

The evidence-based picture is more precise and more interesting. Celiac disease is a real, specific, autoimmune condition in which the body’s own immune system attacks the lining of the small intestine in response to gluten exposure. It affects roughly 1 percent of most populations, it can be diagnosed reliably with blood tests and a small-bowel biopsy, and it requires strict lifelong gluten avoidance. If you have been formally diagnosed with celiac disease, the advice in this chapter about whole grains and fermented bread does not apply to you in the same form, and gluten-free alternatives are medically necessary rather than optional.

Non-celiac wheat sensitivity is also real, although how common it is depends on how it is defined. A proportion of people who do not have celiac disease but who report symptomatic improvement when they avoid wheat appear to have genuine sensitivity to something in wheat. The question that the research of the last decade has been trying to answer is: sensitivity to what, exactly?

The most carefully designed trial to address this question came from Jessica Biesiekierski, Peter Gibson, and colleagues at Monash University in 2013, and it tells a story that is not what most readers expect [REF:biesiekierski2013]. The Monash group recruited participants who self-identified as having non-celiac gluten sensitivity and whose symptoms had improved on a gluten-free diet. In a controlled crossover trial, participants were put on a low-FODMAP diet (that is, a diet low in fermentable oligosaccharides, disaccharides, monosaccharides, and polyols — we will come back to FODMAPs in a moment) and then randomised to receive either pure gluten, pure whey protein, or placebo, in a blinded fashion, added to an otherwise low-FODMAP background diet.

The result was striking. Participants’ symptoms improved on the low-FODMAP diet. When they were then challenged with pure gluten, they reported no more symptoms than when challenged with whey protein or placebo. Gluten, when separated from the FODMAP content of wheat, did not reproduce the symptoms. FODMAP reduction, which was part of eliminating wheat, did. The trial suggested that a substantial fraction of the people who self-identify as “gluten sensitive” are actually sensitive to the fermentable carbohydrates in wheat — particularly fructans — rather than to gluten itself.

The practical implication is important. If you feel better when you avoid wheat, you are not imagining it — something is probably going on. But the thing that is going on is more likely to be a FODMAP issue than a gluten issue, and the appropriate response is not necessarily to go gluten-free forever; it is to identify which fermentable carbohydrates you react to and consider long-fermented sourdough bread, which, because the yeasts and bacteria pre-digest much of the fructan content during the long rise, is often tolerated by people who cannot eat commercial bread.

A second piece of evidence is worth noting for anyone who has been tempted to adopt gluten avoidance as a general health measure rather than in response to specific symptoms. Benjamin Lebwohl and colleagues, in a 2017 BMJ paper, analysed long-term dietary and health data from over 100,000 American health professionals across more than two decades [REF:lebwohl2017]. They found that among people without celiac disease, long-term avoidance of gluten was associated with higher, not lower, cardiovascular disease risk, most plausibly because it was associated with reduced whole-grain intake. The gluten-free aisle of the supermarket is dominated by refined starches, added sugars, and industrial emulsifiers, and systematically replacing whole wheat with commercial gluten-free substitutes is likely to produce a nutritionally worse diet, not a better one.

So: if you have celiac disease, avoid gluten strictly. If you suspect non-celiac gluten sensitivity, do not assume gluten is the culprit; consider the FODMAP explanation, try long-fermented sourdough, and get formally assessed by a gastroenterologist or dietitian before committing to indefinite avoidance. If you have no symptoms and are considering “going gluten-free for health,” the evidence does not support it, and it may worsen your cardiovascular risk.

FODMAPs and the low-FODMAP diet

FODMAP stands for fermentable oligosaccharides, disaccharides, monosaccharides, and polyols — a collection of short-chain carbohydrates that are poorly absorbed in the small intestine, reach the colon, and are rapidly fermented by gut bacteria, producing gas and drawing water into the lumen. In people with irritable bowel syndrome (IBS), this gas and osmotic water shift can trigger bloating, pain, and altered bowel habit. The low-FODMAP diet, developed at Monash University, restricts dietary FODMAPs to a minimum and has become one of the best-supported dietary interventions for IBS symptom control.

The evidence for its efficacy in IBS is strong. Randomised trials, summarised in a 2017 review by Staudacher and Whelan, show symptom improvement in approximately 50 to 75 percent of IBS patients within four weeks of starting the strict elimination phase [REF:staudacher2017]. This is better than most pharmacological treatments for IBS, and the effect is generally rapid and clinically meaningful.

But the low-FODMAP diet has a problem from the microbiome point of view, and it is the same problem the fibre section of this chapter has been warning about. FODMAPs are fermentable substrates for the gut microbes — they are, in effect, a subset of microbiota-accessible carbohydrates. Stopping them reduces the substrate supply to the bacteria that ferment them, particularly Bifidobacterium, one of the most consistently beneficial genera in the human gut. A study by Halmos and colleagues in 2014 showed that a low-FODMAP diet for just three weeks significantly reduced faecal Bifidobacterium and total SCFA production [REF:halmos2014]. The short-term cost is real.

This is why the low-FODMAP diet is not meant to be a long-term way of eating, and when it is prescribed by a knowledgeable dietitian it is explained as a three-phase process: (1) strict elimination for two to six weeks to achieve symptom control, (2) systematic reintroduction of one FODMAP class at a time to identify which ones trigger symptoms, and (3) long-term personalised restriction of the specific problematic FODMAPs only, with all tolerated FODMAPs returned to the diet. The third phase is the destination. The first phase is a diagnostic tool. People who get stuck in the first phase — who adopt the low-FODMAP elimination as a permanent lifestyle — are trading symptom control for accumulating microbiome impoverishment, and the trade may not be worth it long-term.

A related and interesting finding comes from Bennet and colleagues in 2018, who showed that baseline microbiome composition in IBS patients predicts who will respond to the low-FODMAP diet [REF:bennet2018]. Some IBS patients have microbial profiles that suggest they will respond well; others will not. This is another example of the personalisation theme — the same intervention, applied to different people, produces different results, and the difference is partly predictable from the microbiome.

Practical recommendation: if you have IBS and have not yet tried a properly supervised low-FODMAP diet, it is worth doing — under the guidance of a dietitian who understands the three-phase structure. If you have tried it and got stuck in the elimination phase, work with a dietitian to reintroduce systematically. If you do not have IBS, the low-FODMAP diet is not for you, and long-term FODMAP restriction in healthy people is likely to harm rather than help the gut microbiome.

Lectins

The claim, popularised by a series of bestselling books, is that lectins — a class of proteins found in many plants, particularly legumes, grains, nightshade vegetables, and some fruits — are a hidden cause of widespread chronic illness, and that a “lectin-free” diet is the path to weight loss, reduced inflammation, and improved health.

The evidence base for this claim is, to put it charitably, thin. Lectins are real proteins, and some of them — notably the ricin of castor beans — are genuinely toxic. Raw kidney beans contain a lectin called phytohaemagglutinin that causes acute gastrointestinal distress if eaten in quantity, which is why nobody eats raw kidney beans; a brief period of boiling completely denatures the lectin, and properly cooked beans are harmless. Raw wheat germ contains wheat germ agglutinin, which has been shown in laboratory experiments to interact with intestinal cells, but the quantity in normal bread and the effect of cooking reduce this to biological irrelevance.

The broader “lectin theory” extrapolates from these edge cases to the claim that lectins in ordinary, properly prepared, commonly consumed foods are causing harm. The epidemiological evidence runs powerfully against this claim. Legumes are one of the best-validated predictors of long-term health in every long-lived population that has been studied — the Blue Zone populations of Okinawa, Sardinia, and Nicoya all eat legumes in significant quantity and live exceptionally long lives. Whole grains are associated with lower all-cause mortality in every large cohort study that has measured them. Nightshade vegetables (tomatoes, peppers, eggplants, potatoes) are central to several of the healthiest traditional diets in the world. If lectins were causing the damage the theory posits, these associations would go the other way, and they do not.

From the microbiome point of view, the lectin theory is even less plausible, because the foods it demonises are, almost without exception, high in fibre and polyphenols and are precisely the foods we have spent this chapter arguing are essential for microbiome health. Removing them would starve the very bacterial community that the rest of the popular “gut health” literature — including the same authors who promote lectin avoidance — claims to want to nurture. The position is internally inconsistent.

Practical recommendation: do not adopt lectin avoidance. Cook your beans properly, eat your whole grains, and let your tomatoes be. If you have a specific, formally diagnosed food intolerance, address that; do not generalise from it to an entire food category.

Carnivore

The most extreme of the current dietary movements is the carnivore diet: an eating pattern that eliminates all plant foods and consists exclusively of animal products, most commonly beef, eggs, butter, and small amounts of salt. Adherents report, often in dramatic terms, resolution of chronic symptoms — autoimmune disease, depression, inflammation, joint pain — on the elimination of plants.

I want to take these reports seriously, because dismissing them outright would be wrong. Some of the people who feel better on a carnivore diet have real symptoms that were not helped by conventional medicine, and their experience of improvement is real to them. The question is what the improvement actually represents.

The most plausible explanation — and this is a hypothesis, not an established fact — is that for a subset of these individuals, what appears to be an improvement on “removing plants” is actually an improvement on removing a specific subset of plant compounds to which they were reacting. If that subset included the fermentable carbohydrates that cause IBS, the oxalates that precipitate in some people’s joints or kidneys, the histamine-liberating foods that provoke mast-cell reactions, the nightshades some people with autoimmune conditions are sensitive to, and perhaps the phytate-chelated minerals that are poorly absorbed in some gut profiles — then removing all of them at once by removing all plants could produce the improvement without the improvement being specifically due to “animal foods.” The same improvement, in those individuals, might well be achievable by a carefully designed plant-inclusive diet that happens to avoid their specific triggers.

The problem with the carnivore diet as a long-term strategy is that we have almost no long-term data. The evidence base consists of testimonials, short-term observational reports, and theoretical argument. What we know about gut microbiome biology suggests that a strict and sustained plant-free diet will produce a microbiome with markedly reduced diversity, near-absent fibre-fermenting capacity, shrunk SCFA production, and correspondingly reduced butyrate fuel for colonocytes. We have already discussed what the David 2014 diet-switch experiment showed in humans: dramatic shifts within days towards animal-diet-typical microbial profiles, including expansion of bile-tolerant and sulphide-producing organisms [REF:david2014]. We have already discussed what the TMAO literature shows about heavy meat intake and cardiovascular risk [REF:koeth2013]. We have already discussed the Sonnenburg extinction experiments and what low-MAC diets do to diversity across generations [REF:sonnenburg2016]. None of these are reassuring about a prolonged carnivore pattern.

My practical recommendation is this. If you feel unwell on your current diet and have not been able to get good answers from conventional medicine, the carnivore experiment is not unreasonable as a short-term diagnostic tool — two or three weeks of strict elimination, followed by systematic reintroduction of plant categories one at a time, might help you identify what you actually react to. This is, in essence, the same diagnostic logic as the low-FODMAP diet, just starting from a different end. What I would not recommend is the indefinite maintenance of the strict elimination phase as a long-term way of eating, on testimonial evidence alone, against a body of mechanistic and epidemiological evidence pointing the other way. The upside — that you personally will be an exception to everything we know about long-term nutrition and microbiome biology — is possible but uncertain. The downside — that you will de-train a microbial community across years in ways that cannot be easily undone — is documented.

A note on certainty

I have stated positions in this section more directly than I usually would in a book like this, because I think the reader is entitled to them. I want to be equally direct that in each of these cases, the evidence base has gaps and the scientific conversation continues. If new high-quality trials change the picture — if a properly designed long-term carnivore trial showed benefits we did not predict, or if the FODMAP explanation for non-celiac sensitivity turned out to be only part of the story — I would update my position, and a later edition of this book would say something different. What I have tried to give you is the current best reading of the best available evidence, not a pronouncement from on high. Take it as such, and watch the literature.

20.13 What This Means for Tuesday Dinner

Here is the practical summary of the chapter, tiered by the strength of the evidence, so that a reader who has read only this section has the useful distillation.

The strongest evidence: things to do

Eat more fibre. Work up to 30 grams or more per day. Choose a variety of sources — whole grains, legumes, vegetables, fruits, nuts, seeds — rather than relying on a single fibre type. If you are currently at 12 to 15 grams per day (which is most Western adults), doubling your intake over a few weeks is one of the single most evidence-based changes you can make.

Aim for thirty different plant species per week. Count everything: vegetables, fruits, whole grains, legumes, nuts, seeds, herbs, spices, coffee, tea, dark chocolate. Write them down for a week if you find it hard to believe you are reaching or not reaching the target. The number itself is less important than the habit of variety.

Eat real fermented foods most days. A small serving — a spoonful of yogurt with live cultures, a forkful of kimchi or sauerkraut from the refrigerated section, a cup of kefir, a piece of aged cheese, a small glass of kombucha — at most meals. Read labels; avoid the pasteurised impostors. The Stanford evidence suggests this may be the single intervention most likely to reduce inflammatory tone in the healthy adult.

Adopt a Mediterranean-style pattern. Plants at the centre of the plate, olive oil as the dominant fat, small portions of fish and meat a few times a week, legumes and whole grains at most meals, herbs and spices in abundance, fruit for dessert, water and unsweetened drinks as the default beverage. This is not a diet in the sense of a temporary restriction; it is a cuisine, and there are a hundred versions of it from a hundred cultures.

Eat within a defined daily window. Somewhere between 10 and 12 hours is a reasonable target for most people. Protect the overnight fast; avoid eating in the two or three hours before sleep.

Strong evidence: things to reduce

Reduce ultra-processed foods. The category includes almost everything in brightly coloured packaging with long ingredient lists and names you cannot pronounce. Perfect elimination is not required and often not practical; steady reduction is. Aim to get the majority of your calories from foods recognisable as foods. A useful heuristic: if it was not food a hundred years ago, be suspicious.

Reduce dietary emulsifiers. Read labels for carboxymethylcellulose (E466), polysorbate 80 (E433), carrageenan, and mono- and diglycerides. These are concentrated in processed ice creams, commercial breads, processed cheeses, plant milks, salad dressings, and ready meals. You cannot avoid them entirely and probably do not need to, but reducing daily exposure is sensible.

Reduce non-nutritive sweeteners. Particularly saccharin and sucralose, the two with the clearest evidence of microbiome-mediated effects in the Suez 2022 human trial. Aspartame and stevia have less conclusive evidence but also produced microbiome changes in the same study and should be viewed with modest caution. Water, unsweetened tea, unsweetened coffee, and small amounts of real sugar are all better choices than constant diet-sweetened drinks.

Reduce red and processed meat to a few times a week at most. Not because meat is poison but because heavy long-term intake trains a microbial community that produces more TMAO and secondary bile acid metabolites than most of us want. A small serving of good meat, a few times a week, as part of a plant-heavy meal, is consistent with everything this chapter has argued for.

Promising but not yet prescription-grade

Personalised microbiome-based meal planning. The science is interesting and improving. The commercial services are expensive and the output is not yet clearly better than general principles for most people. Wait, for now, unless you have a specific clinical problem that general advice has not solved.

Polyphenol supplementation. Eat the whole foods; supplementation with isolated polyphenols has produced inconsistent results in trials, and the metabotype story makes it clear that the systemic effect depends on who is eating.

Extreme time-restricted feeding windows (shorter than 8 hours). The evidence for a 10- to 12-hour window is reasonable. The evidence for shorter windows is less clear, and very short windows (4 to 6 hours) may cause problems for some people, particularly women, shift workers, and people with blood sugar regulation issues. Start gentle.

Not supported by evidence

Gluten-free eating for people without celiac disease or diagnosed wheat allergy. Does not help; may harm.

Lectin avoidance. Eliminates some of the most strongly evidence-based foods on the planet on the basis of a theory not supported by human epidemiology.

Strict long-term carnivore eating. Short-term diagnostic use may have a role. Long-term maintenance is not supported by existing evidence and conflicts with much of what we know about microbiome biology.

Fad diets generally. If a diet is named after its inventor, promises dramatic results in weeks, demands a book purchase to understand fully, and excludes one or more major food groups, be cautious. The evidence base for any of these, compared with the traditional Mediterranean pattern or any whole-foods plant-heavy approach, is comparatively thin.

The closing image

Dinner is a daily act of ecology. When you choose what to put on your plate, you are not only feeding yourself. You are feeding a forest — a living, breathing, metabolising community of trillions of organisms whose daily output of small molecules shapes how your own cells function, how your immune system responds, how your gut wall holds together, and in the long run how well you age. Most of them you will never see and never think about. But they will process every meal you eat, and what they make of it depends, every day, on what you choose to give them.

The garden metaphor I began this chapter with has one last application. A gardener who tends a piece of ground for decades does not produce miraculous results through any single intervention. She produces them through the accumulated consequence of small, consistent choices across many seasons. The right compost. The right watering. The right crop rotation. The right attention to the slow signals of the soil. None of it is dramatic. All of it matters. And at the end of twenty years, one gardener has a rich, productive, resilient plot and another has depleted earth, and the difference is the sum of daily decisions that neither of them noticed mattering at the time.

Your gut is the ground you have to work with. Feed it well, and its inhabitants will pay you back across a lifetime.

20.14 Looking Ahead

Diet is the single most powerful daily lever most of us have over our microbiome. But it is not the only lever, and it sits inside a much larger web of environmental and lifestyle factors that shape the community of organisms we carry. The air you breathe, the water you drink, the dirt your children play in, the pets in your home, the people you live with, the medications you take, the sleep you get, the stress you carry, the exercise you do or do not do — all of them leave fingerprints on the ecosystem inside you. Chapter 21 widens the lens to look at those other inputs, and asks how the rest of the way we live — beyond what we put on the plate — is shaping the microbial communities that are, by now, unmistakably part of who we are.

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End of Chapter 20 draft.

Chapter 21: The Other Levers — How the Rest of How We Live Shapes the Ecosystem Inside Us

21.1 The Garden Has Weather

At the end of the previous chapter we left the reader with a practical promise: that the single most powerful daily lever most of us have over our microbiome is what goes onto the plate. Everything in Chapter 20 — the thirty-plant rule, the short-chain fatty acids, the mucus layer, the rediscovered importance of variety over quantity — was built on that lever. If the only message a reader remembers from this book is eat more kinds of plants, the book will have done its job.

But a gardener who tends the soil but ignores the weather will have a poor garden. And our microbiome, like any ecosystem, has weather. Some of it we control; some of it we do not. Some of it we invented in the last hundred years without realising we had invented a climate. This chapter is about those other inputs — the temperature of the room, the light on your eyelids at midnight, the dust that used to be on the furniture, the dog that isn’t in the house, the stranger on the tram, the alarm clock that goes off when the rest of your organs would rather be asleep. Most of them are not things you think about at all. That is precisely why they are worth a chapter.

I should warn you at the outset that the evidence here is uneven. For some of the levers — artificial light and the circadian rhythm, for example, or the exposure of infants to household pets — the data are now strong enough to support firm statements. For others — what a HEPA filter in your bedroom might be doing to the airborne microbes you would otherwise inhale, or whether decades of living in a thermally constant twenty-two degrees has narrowed the range of microbial conditions your body experiences — we are still at the stage of reasonable mechanistic speculation rather than settled science. I will flag the difference as we go. Where I am guessing, I will say so. Where the literature is thin, I will say that too.

One more thing, before we start. Some of the topics in this chapter arrived in my notebook only very late in the writing of this book — not because they are unimportant, but because they fell outside the usual frame of microbiome research, which has been dominated by the diet–gut–immunity triangle. It was only after finishing Chapter 20 that I began to ask whether the ordinary domestic environment of a modern person — the air-conditioning that runs all day, the ceiling lights that burn until midnight, the HEPA filter that became standard after the pandemic — might itself be a quiet, continuous intervention on an ecosystem that evolved under conditions none of those appliances existed. I do not have all the answers. What I have, and what I will lay out in what follows, is a mechanistic case for why these things should matter, together with the slender but real body of published evidence that has begun to test the case directly.

This chapter is organised outward, roughly, from the body to the household to the outside world. We begin with the light that reaches your retina, move through the temperature of your bedroom and the air that circulates through it, step out into the dust and the pets and the people you live with, and end with the exercise, sleep, stress, water, and medications that complete the picture. Each section is a lever. None of them is as powerful as diet, considered alone. Considered together, they probably rival it.

21.2 The Forgotten Clock: Artificial Light and the Diurnal Microbiome

What the constant-darkness mice told us

Let me begin with an experiment that sounds trivial and turns out not to be.

In 2018, a team of researchers at Southern Medical University in Guangzhou took laboratory mice — the genetically uniform, biologically boring animals on which much of modern gut microbiome research is done — and divided them into two groups. One group lived in a normal twelve-hour light / twelve-hour dark cycle, the familiar laboratory default. The other group lived in constant darkness for the duration of the experiment. The mice were otherwise handled identically. They were fed the same food, housed at the same temperature, handled by the same technicians. The only variable was whether or not they could see the light come on in the morning [REF:wu2018].

The team then sampled the gut microbiota of both groups at multiple points across the day. In the normally lit animals, the composition of the gut microbial community rose and fell through the twenty-four-hour cycle. Different taxa dominated at different times of day. This diurnal oscillation of the gut microbiome is a real phenomenon, first described in detail by Christoph Thaiss and Eran Elinav in work we already met briefly in section 20.10 of the previous chapter [REF:thaiss2014] [REF:thaiss2016]. It is not an artefact of sampling; it is a genuine cycling of who-is-abundant-when inside the bowel.

In the mice kept in constant darkness, the oscillation collapsed. Almost every part of the intestine lost its rhythmic pattern. A bacterium called Clostridia, which in normally lit animals had a low, controlled presence in the small intestine, bloomed to several times its baseline abundance. The community was still there; it had simply forgotten what time it was. And when the community forgot what time it was, it lost the internal choreography that had kept certain species in check.

That last point is worth sitting with. We are used to thinking of the gut ecosystem as a collection of inhabitants, like animals in a jungle. The Guangzhou experiment adds a dimension most ecology textbooks do not bother with: the jungle has a clock, and when you break the clock, the tigers get loose.

The eye, the brain, and the bowel

If the experiment had only shown that constant darkness was bad for mice, we might reasonably have dismissed it as a laboratory curiosity. What makes the story interesting for humans is the circuit by which light, landing on the retina, actually reaches the gut.

In 2022, a group at National Taiwan University published a paper in EMBO Reports that asked, in effect: how does the light on the eye talk to the bacteria in the bowel? They already knew that circadian misalignment disturbed the gut microbiota, but the intermediate steps had not been worked out. Using genetically engineered mice that lacked specific light-detecting cells in the retina, they showed that a small, specialised population of retinal cells called intrinsically photosensitive retinal ganglion cells — ipRGCs for short — was responsible for transmitting day-length information from the outside world to the circadian pacemaker in the brain, and from there to the intestinal community [REF:lee2022].

The ipRGCs are an odd and beautiful class of cell. They are neither rods nor cones; they do not contribute to the image you see. They carry a photosensitive pigment called melanopsin, and their job is to tell the suprachiasmatic nucleus — the brain’s master clock — what time of day it is. They are the biological equivalent of the watch face on a diver’s wrist that glows under water. You do not use them to read. You use them to know.

The Taiwanese group showed that when the ipRGCs were silenced, the diurnal oscillation of the gut microbiota broke down in the same way Wu and colleagues had seen with constant darkness. They then did something else. They left the ipRGCs intact but exposed the mice to dim light at night — the kind of low-level domestic light spill that comes from a television set in another room, or a street lamp shining through thin curtains, or a hallway nightlight — and showed that this was enough to alter both the composition and the normal daily rhythm of the gut community. Not bright light. Not flashbulbs. The kind of light most of us do not bother to switch off because we have never considered that it might be doing anything.

Independently, in the same year, John Brooks and colleagues in Lora Hooper’s laboratory at UT Southwestern published a paper in Cell that completed a different arm of the same picture. They showed that the microbiota itself generates diurnal rhythms in innate immunity, through segmented filamentous bacteria that attach and detach from the epithelium according to the host’s feeding schedule, which is in turn entrained by the light cycle. The host’s antimicrobial protein production — the first line of defence against any swallowed pathogen — rises and falls with the feeding rhythm and therefore, indirectly, with the light [REF:brooks2021]. So light, via the retina and the brain, regulates the rhythm of eating; eating regulates the rhythm of bacterial attachment; bacterial attachment regulates the rhythm of antimicrobial defence. Pull one thread and the whole quilt follows.

What this means for someone who lives in a lit building

Most of us do not live under thirty-six-hour fluorescent tubes. Why does any of this matter for the reader of this book?

It matters because, for the first time in several hundred thousand years of human existence, a substantial fraction of the population is now under artificial light for a large, continuous block of what used to be night. A streetlight through a bedroom curtain. A blue-screened phone held thirty centimetres from the face in the dark. A bathroom fluorescent that is on every time a child wakes up to go to the toilet. A kitchen under-cabinet LED that is never quite off. Modern domestic light is low in intensity by outdoor standards, but it is several orders of magnitude brighter than the upper limit under which ipRGCs can reliably decide it is night — which, experimentally, is around five lux, roughly the level of a full moon seen from a clearing.

Shift workers are the obvious human test case — the clearest example of chronically mis-timed light exposure. In a 2021 study published in PeerJ, Ann Rogers and colleagues at Emory University recruited fifty-one nurses working twelve-hour day or night shifts, collected stool samples from them before and after a rotation, and sequenced the gut microbiota of each [REF:rogers2021]. The differences they found between day-shift and night-shift nurses were, perhaps surprisingly, modest — small shifts in beta diversity, a handful of operational taxonomic units that differed between groups. But the signal was there, and the more recent review literature collating shift-work and gut-microbiota studies [REF:lopezsantamarina2023] consistently points in the same direction: shift workers show altered gut microbial communities that, on current evidence, are probably one of the mechanisms by which rotating and night shifts raise the risk of metabolic disease. Shift workers are the extreme; but most of us are, in a quieter way, living a very mild version of the same experiment. We eat at times our grandparents would have found shocking. We use screens in bed. We put babies in rooms that are technically dark but lit enough for a parent to find their way to the cot. In aggregate, we have created, in the space of about a century, an indoor lighting environment that no previous generation had, and that was not present during the period when our gut microbes were assembling their rhythms.

Hong and colleagues, in 2020, took this one step further in a mouse experiment at Beijing Normal University. They exposed mice to constant light — the modern city in caricature — and watched them become obese and insulin-resistant alongside a measurable shift in their gut microbial composition. Giving the mice supplementary melatonin (the hormone the suprachiasmatic nucleus releases at night, and which modern light spill suppresses) partly rescued the metabolic disturbance and rescued the gut dysbiosis [REF:hong2020]. A constant-light environment, a night-lowered melatonin signal, a disturbed microbiota, a metabolic penalty: in the mouse these now connect in a single experimental chain.

How strong is the human evidence?

Less strong than the animal evidence, and I want to say that clearly. We do not yet have a large randomised trial that switches human bedroom lighting from “dim at night” to “proper darkness” and measures what happens to the gut microbiome over a year. What we do have are shift-work observational studies, the mechanistic chain described above, and a small but growing body of human work on circadian misalignment and gut bacterial rhythmicity. The most honest summary is: the direction is clear, the magnitude in a domestic setting is not yet known, and the practical advice that falls out of this is sensible regardless of how microbial the final answer turns out to be.

That practical advice is approximately what your grandmother would have told you. The bedroom should be dark — properly dark, the kind of dark where you cannot see your hand until your eyes adjust. Heavy curtains or an eye mask are cheap interventions. Hallway nightlights should use red or amber LEDs, because the ipRGCs are relatively insensitive to long wavelengths; a red light at two in the morning does not tell the master clock that dawn has come. Phones should not live on the pillow. And — this is the one most people will find hardest — the room you eat in, and the time at which you eat, should be synchronised with the light your retina is seeing. If you eat your largest meal at nine in the evening under bright kitchen lights, you are presenting the brain with a mixed signal — bright light says morning, the stomach says evening — and the internal clocks begin to disagree about what time it is. That disagreement, as we saw in Chapter 20’s discussion of circadian misalignment, is itself bad for the gut community [REF:thaiss2014].

In a book about microbes, it is a strange thing to end a section with advice about curtains. But the lever is real, it is almost free, and — unlike most things in this chapter — it does not require you to add a thirty-first plant to your shopping list.

21.3 Twenty-Two Degrees All Year: The Thermostat and the Unfelt Season

What our bodies used to do that they no longer do

The most reliable feature of the climate inside the average modern home is that it has no seasons. In winter the room is warm. In summer the room is cool. The variance is narrow — somewhere around twenty to twenty-four degrees Celsius in almost any month of almost any year, in almost any dwelling in the developed world. This is historically extraordinary. Until central heating and air-conditioning became standard, roughly in the middle of the twentieth century, the human body experienced a temperature cycle across the year that was at least fifteen or twenty degrees wide — and often much more, if you lived in a climate with real winters — and a daily temperature cycle that was considerably wider than today’s, because houses cooled overnight and warmed at dawn as the sun rose and the stove was lit.

This matters to microbes for two reasons, one of which is reasonably well established and one of which is speculative.

The reasonably well established reason is that the mammalian body, when exposed to cold, activates a tissue called brown adipose tissue, or brown fat, which burns calories to generate heat. Brown fat is a metabolic organ in its own right: it can be dialled up by the sympathetic nervous system in response to cold, and when it is dialled up, it changes what the whole body does with food. The research here is mostly in mice, but the mouse data are very clear. When mice are exposed to environmental cold for extended periods, their gut microbiota change composition substantially. Earlier papers had argued that the gut microbes are actually part of the thermogenic response, not just a bystander to it; a 2020 paper in Cell Metabolism by Krisko and colleagues at Weill Cornell pushed back carefully, showing that cold adaptation and energy expenditure in germ-free and microbiome-depleted mice are essentially intact, and concluding that the microbiome is dispensable for adaptive thermogenesis itself — though they still found that the microbial community does change with cold and does contribute to other metabolic outputs (specifically, to amino-acid metabolites that feed hepatic gluconeogenesis) [REF:krisko2020]. The literature is arguing, but what is not disputed is that ambient temperature changes the gut community.

The speculative but mechanistically plausible reason is subtler. Many of the microbes that live on our skin, in our airways, and — to a lesser but real extent — in our gut have temperature preferences. Temperature is not a minor variable for a bacterium; it is one of the core determinants of whether enzymes fold correctly and whether cell membranes stay fluid. A Staphylococcus epidermidis cell on your forearm is optimally active at around thirty-two to thirty-four degrees, which is why skin, rather than the deep body, is where it lives. If the skin surface temperature changes (because the room has changed), the balance between a slightly warmer skin taxon and a slightly cooler one may shift. The same argument applies with different absolute numbers for the nose, the throat, and even the mucosal surfaces of the lower bowel, although the core body temperature buffers the bowel against most of this.

The speculative part is that no one has yet done the study that would tell us what a lifetime of thermostat-stable twenty-two-degree indoor living has done to the human skin and airway microbiome, compared with a lifetime of ten-degree winters and thirty-degree summers in an unheated house. I suspect it has done something; I cannot cite a paper that proves it yet. I raise the issue here because the absence of evidence is itself worth knowing about, and because if you are going to design such a study, you need to have noticed the question first.

What I can say with some confidence is that feeling cold occasionally — that is, not treating climate control as a moral imperative — is unlikely to do harm and may do good. The thermogenic and metabolic effects of mild cold exposure are well documented in human physiology, even though the microbiome piece of that story is still being written. A slightly cooler bedroom at night, in particular, has additional benefits for sleep quality that are not speculative at all, and sleep quality, as we are about to see in section 21.9, is itself a microbiome variable.

Humidity: the unspoken second axis

The other thing air-conditioning does that is easy to forget is dry the air. A cooling unit pulls moisture out of the air as a necessary side-effect of chilling it, and a heating unit dries the air because warmed air holds more water vapour at the same absolute humidity. The winter bedroom in a heated house is typically at around twenty to thirty percent relative humidity — the equivalent of a desert. The summer bedroom in an air-conditioned house is often at around thirty to forty percent — still dry by outdoor standards in most temperate climates.

The skin microbiome, the mycobiome of the airways (Malassezia, Candida, Aspergillus species we met in Chapter 10), and the overall fungal load of the indoor environment all respond to humidity. Low humidity dries mucus membranes, which in turn impairs mucociliary clearance — the escalator that carries inhaled microbes out of the airways. Seasonal variation in humidity is one of the reasons respiratory infections spike in winter in heated buildings and again in air-conditioned offices in summer. The observation is decades old; what is newer is the growing recognition that the ecology of the built environment — the set of microbes that actually live in and on the fabric of our homes — is shaped by our heating and cooling systems to a degree we have not fully reckoned with.

For the practical reader, a humidifier set to around forty to fifty percent relative humidity in a heated winter bedroom, and a humidifier or damp cloth in an air-conditioned summer one, is a cheap intervention with a good safety record. The microbiome benefit is inferred from mechanism rather than proven. The benefit to mucous membranes is not in dispute at all.

21.4 The Invisible Garden in the Air: HEPA Filters, UVC Lamps, and the Post-Pandemic Reflex

Cleaner than what?

When SARS-CoV-2 ripped through the world in 2020 and 2021, most of us — doctors included — learned a new word: aerosol. We learned that a cough, or a breath, or even a long conversation, produces a plume of tiny droplets that can stay suspended in still indoor air for minutes and can, under the right conditions, carry a virus. The response, over the months that followed, included a surge in sales of portable air purifiers — typically units containing a high-efficiency particulate air filter, known as a HEPA filter, which can capture particles down to about three tenths of a micrometre with impressive efficiency. In many workplaces and a surprising number of homes, these units now run continuously. Some models add an ultraviolet-C lamp to kill captured microbes. Some add ionisers. Some advertise “germicidal action” with all three.

This is a book about microbiology, so I want to be careful here. HEPA filtration, properly installed, is an effective and valuable public-health intervention in settings with a real airborne infectious disease risk — hospital isolation rooms, tuberculosis clinics, airline cabins, and laboratories working with dangerous pathogens. I am not going to argue against using them where they are indicated. What I do want to examine is a different question, one that almost nobody thought to ask during the pandemic: if a HEPA filter removes the viruses and bacteria you do not want from the air, does it also remove the microbes you might have been healthier for inhaling?

It is not a silly question. Several lines of evidence converge on the idea that airborne microbial diversity, particularly the environmental bacteria and fungi derived from soil, plants, and untreated outside air, contributes to the maturation and tolerance of the immune system, especially in early childhood — an argument we met briefly in the hygiene-hypothesis discussion of Chapter 6 and will return to later in this chapter. The question of what continuous HEPA filtration does to those environmental microbes in a living room has, as far as I can find, essentially never been studied as a microbiome outcome in humans.

Let me be honest about what the evidence actually shows, because this section would be easy to get wrong. What the literature does not yet contain is a clean randomised trial in which the continuous running of a domestic HEPA unit in a home bedroom is compared with no filtration, and the airborne and gut microbiomes of the occupants are tracked over months or years. I searched for this paper while drafting this chapter and could not find it. The reader should know that the specific question “does a HEPA unit in the bedroom of a healthy child for a year change their gut or airway microbiome?” has not, to my knowledge, been directly answered.

What the literature does contain is an accumulation of indirect evidence. Studies of the built-environment microbiome have repeatedly shown that the airborne microbial communities of indoor spaces are a mixture of human-derived material (skin flakes, the aerosols of speech, cough droplets) and outside-derived material (pollen, soil bacteria, the airborne fraction of the external weather). Studies sampling the same rooms before and after the opening of a window, or before and after the introduction of a dog, have consistently shown that both of these interventions increase the diversity of airborne bacteria in the indoor space and move the community towards the composition of the outside air. Continuous HEPA filtration, in parallel, removes the particulates that carry outside-derived microbes into the room as well as those that carry pathogens, and on first principles there is no reason to believe that a filter capable of removing virus-laden droplets is not also removing environmental bacteria attached to particles of the same size range.

What the filter actually collects

There is a separate and almost poetic body of work in which researchers have taken the HEPA and HVAC filters out of buildings and read the DNA off what the filters trapped. This is called filter forensics, and it has become a quiet cottage industry in environmental microbiology. A 2018 paper in Microbiome by Maestre and colleagues, for instance, showed that residential HVAC filters captured rich, recoverable bacterial and fungal DNA reflecting the occupants and the outdoor environment of the home [REF:maestre2018]. A separate line of work by Miles Richardson, Simon Lax, and Jack Gilbert in dormitories showed that the microbial signatures left behind by individual cohabitants were distinctive enough to identify who had been in a shared space, even after repeated mixing [REF:richardson2019]. The filter is not just catching particles; it is archiving the microbial history of the people, pets, and outdoor air that passed through it. Some of what it captures is nuisance taxa and pathogens. Some is probably part of the normal traffic of the human ecosystem — the shared skin bacteria of cohabiting couples, the airborne fraction of the oral microbiome that lands back on surfaces and gets re-inhaled, the commensal microbial cloud that each person carries wherever they go. An ultra-efficient filter running twenty-four hours a day does not discriminate between the harmful and the benign. It just removes.

A tentative verdict

Here is where I want to be honest about the limits of what we know.

I am not saying: throw out your air purifier. In settings where airborne infectious disease risk is real — an immunocompromised family member, a viral epidemic, a workplace with known exposures — filtration is valuable and I will recommend it to my own patients in those situations. I am not saying anything that contradicts the known value of HEPA filtration in those settings.

What I am saying is that the post-pandemic reflex of running a HEPA unit continuously in the bedroom of a healthy adult or child, as if “cleaner air” were unambiguously better, is not a policy the microbiome literature directly supports — and, more importantly, not a policy the microbiome literature has properly examined. The airborne microbiome of an open-window home with a dog is almost certainly richer and more diverse than the filtered air of the same room with the window closed and the filter running, and — on the weight of the hygiene-hypothesis evidence we will revisit in section 21.5 — the diverse air is more likely to be the one that supports healthy immune development. But the reader should hear the qualification as well as the claim. The direct experiment has not been done. If you are healthy, your dog is healthy, and no one on your street has tuberculosis, the most evidence-backed air intervention you can make in your own home is to open the window for fifteen minutes a day, regardless of the season, and to let the weather inside — and that recommendation rests less on the HEPA-versus-no-HEPA literature, which is thin, than on the considerable literature we will discuss in section 21.5 showing that contact with outdoor and environmental microbial diversity is protective against allergic and autoimmune disease.

UVC lamps are a separate and more worrying category. Some of the air purifiers now marketed to consumers include a UVC stage claimed to inactivate bacteria and viruses passing through the unit. UVC light in the 254 nanometre band is indeed germicidal; the question is whether the dose delivered to air passing through a domestic unit at typical flow rates is sufficient to inactivate an appreciable fraction of the organisms, and the answer varies enormously between units and is almost never verified independently. More concerning, UVC at certain wavelengths can generate ozone inside the unit, and ozone at the concentrations produced by some poorly designed home units is an irritant to the lung mucosa and has its own effects on the airway microbiome that are unlikely to be positive. For the domestic consumer I would not currently recommend UVC air units as a first-line intervention; the risk-benefit ratio is not favourable for healthy people, and the microbiome literature does not yet provide a reason to change that assessment.

Ionisers are similar. Bipolar ionisation was heavily marketed during the pandemic as a way to inactivate airborne viruses; independent testing has been mixed to unfavourable, and the by-products of the process include small amounts of ozone and various reactive oxygen species. I would not run one continuously in a bedroom.

If the reader takes one thing from this section, it should be this: in a healthy home, air variety is probably more important than air purity, and the cheapest way to achieve variety is to let the outside in. Opening the window, for a quarter of an hour, at least once a day, in almost any weather, delivers more microbial diversity to the home than almost any other intervention. It is also the intervention most at odds with how the modern house is designed and run, which is why it is worth making explicit.

21.5 Dirt, Biodiversity, and the Old Friends We Stopped Meeting

If there is one finding in environmental microbiome research that has survived twenty years of replication attempts, it is this: children who grow up with more microbial exposure early in life have less allergic disease, less asthma, and less autoimmune illness later in life. The observation is old; the mechanism is newer; the policy implications are still being argued over. But the core of the story has become difficult to dismiss.

The foundational paper for most modern thinking on this question was published in the New England Journal of Medicine in 2016 by Michelle Stein and colleagues working in the American Midwest [REF:stein2016]. Their study compared two religious farming communities — the Amish and the Hutterites — who are genetically similar (both descended from a small founder population of German-speaking Anabaptist farmers), share similar diets low in processed foods, have large families, and practise lifestyles at the conservative end of the modern spectrum. On almost every variable that epidemiologists track, the two groups look alike. The Amish and the Hutterites should therefore have similar rates of childhood asthma.

They do not. Asthma among Amish schoolchildren runs at about 5%; among Hutterite children it is around 21%. The gap is enormous, consistent across years, and has been confirmed in multiple independent samples. Given how similar the populations are, something about the way the two communities farm must be responsible, and that something had to be exposure-related rather than genetic, because the two groups share much of their ancestry.

The difference, as Stein and colleagues showed, is that Amish families farm in the old way — small, mixed farms where the barn sits a few metres from the kitchen door, where children run in and out of the cowshed from the time they can walk, where hay is pitched by hand and milk is hand-drawn, and where household dust accumulates a thick and constant inoculum of barn-derived microbial material. Hutterite families, by contrast, have modernised: they farm at industrial scale, the barns are separate from the living quarters, children are kept out of the animal areas, and the homes are cleaner. When the researchers collected household dust from both groups and measured its endotoxin content (endotoxin is a marker of Gram-negative bacterial debris and a reasonable proxy for overall bacterial load), Amish house dust contained roughly seven times more endotoxin than Hutterite dust.

What happens when you take that Amish dust and blow it up the noses of laboratory mice? Stein’s group did exactly this experiment. Mice exposed repeatedly to aerosolised Amish dust developed robust innate immune activation in their airways, with high numbers of neutrophils producing the right cytokine pattern; when these mice were then challenged with a standard asthma-provoking allergen, they were strikingly protected from the airway inflammation their unexposed littermates developed. Hutterite dust, given in the same way, did not confer the same protection. Something about the composition or quantity of microbial material in Amish dust was reprogramming the innate immune response of the airway in a way that suppressed allergic inflammation.

The Amish-Hutterite study is not the only piece of evidence for this relationship; it is simply the most elegant natural experiment we have, because it holds so many potentially confounding variables constant. Similar observations come from comparisons between Finnish and Russian Karelians (genetically related populations separated by a border and by dramatically different levels of hygiene, with more allergy on the Finnish side), between children born on European farms and children born in European cities in the ALEX and PARSIFAL cohorts, and — most recently — between rural and urban populations in rapidly urbanising Asian countries. Across all of these, a consistent pattern emerges: exposure to a rich, stable, earthy microbial environment in early life is protective against allergic and autoimmune disease, and the effect is strongest when the exposure occurs in the first year or two of life.

The most developed theoretical framework for understanding why this should be so is the “Old Friends” hypothesis, proposed and developed over two decades by the British immunologist Graham Rook [REF:rook2013] [REF:rook2014]. Rook’s argument is, at root, an evolutionary one. For almost all of human history — and for the hundred million or so years of mammalian history before us — our immune systems have co-evolved in the presence of a particular set of environmental microbes and macro-parasites: the soil-dwelling saprophytes that colonise our food and water, the environmental mycobacteria that live in damp soil and decaying vegetation, the helminth worms that inhabited almost every human gut for most of our species’ existence, and the commensal microbes passed around by the large extended families and the farm animals with which humans lived in continuous close contact. These are the “Old Friends” — not pathogens, not innocent bystanders, but tonic microbial and parasitic inputs that the immune system came to rely on for its calibration.

Rook’s proposal is that these inputs did a specific immunological job: they continuously trained the regulatory arm of the immune system to tolerate innocuous antigens. Regulatory T cells (Tregs), the immune cells we met in chapter 12 as peacekeepers that suppress inappropriate inflammatory responses, require environmental signals to differentiate and expand properly. In a world rich in Old Friends, this regulatory compartment is constantly being exercised and replenished; in a world stripped of them — a world of paved streets, water-treated taps, chlorinated swimming pools, sealed houses, single-child families, and early antibiotic exposure — the regulatory compartment never develops its full repertoire. The immune system, deprived of its usual tuning signals, becomes prone to mistakes. It misidentifies harmless pollen as a threat (allergy), attacks its own tissues (autoimmunity), or produces chronic low-grade inflammation in response to nothing in particular.

The Old Friends framework is not the older and clumsier “hygiene hypothesis” that David Strachan proposed in 1989, which suggested that childhood infections protect against allergy and which was widely misread as an argument for deliberately exposing children to germs. Rook’s revision is subtler and more biologically specific. The protective microbes are not childhood pathogens; they are the ancient, tolerated, environmental partners that modern life has stripped out of the human environment. The remedy is not to catch more colds; the remedy is to restore contact with soil, animals, and diverse environmental microbial communities — and to do so, as much as possible, in early life, when the immune system is still being assembled.

From a book-writing point of view, I want to be careful here, because the Old Friends hypothesis is sometimes stretched further than the evidence can carry it. Not every increase in allergy and autoimmunity in the industrialised world is explained by loss of microbial exposure; diet, pollution, genetics, improved diagnosis, and many other factors contribute. The hypothesis is best regarded as one important piece of a larger puzzle, not as a complete explanation. What makes it worth taking seriously, rather than dismissing as another appealing just-so story, is that it has generated concrete predictions that have been tested and confirmed — most notably in the Amish-Hutterite work — and that it is biologically coherent with everything we now know about how the microbiome talks to the immune system.

What does this mean for the reader sitting in a modern home? A few things. It means that the cumulative effect of the design choices we have discussed so far — sealed windows, thermostat-held climate, HEPA-filtered air, paved yards, pet-free apartments, chlorinated water — is not just a loss of microbial variety in the abstract; it is a systematic removal of the environmental signal that the immune system was built to expect. It means that the cheapest and most evidence-backed interventions to restore some of this signal are profoundly low-tech: a garden with real soil, a dog, an open window, time spent in forests and fields, a willingness to let young children get dirty. It means that the more we understand about the Old Friends relationship, the more we come to see the modern sanitised home not as the neutral zero-point from which departures must be justified, but as itself a departure — a recent and poorly tested experiment in which the human animal has been lifted out of the environment its immune system expects, and placed into a microbiologically impoverished one. The question “is this exposure safe?” therefore needs to be supplemented by its mirror: “is the absence of this exposure safe?” Often, the honest answer to the second question is: we do not know, and the emerging evidence suggests that it is not.

21.6 The Dog in the Doorway: Household Pets and the Infant Microbiome

If you asked a microbiome researcher to name the single intervention a young family can make, in the first year of their child’s life, that is most likely to meaningfully broaden the child’s developing gut flora, the answer — increasingly, across independent cohorts — is: get a dog.

The cleanest evidence for this comes from the Canadian Healthy Infant Longitudinal Development (CHILD) cohort study, a prospective birth cohort that enrolled over three thousand Canadian infants and collected faecal samples, household environment data, and detailed health information at regular intervals from birth through early childhood. In a 2017 analysis, Hein Min Tun and colleagues used CHILD data to ask a specific question: does the presence of a furry pet in the household during pregnancy and early infancy alter the infant gut microbiome measurably, and in a direction that might plausibly matter for later immune outcomes? [REF:tun2017]

Their answer was unambiguously yes. Infants raised in households with at least one furry pet (most commonly a dog, but cats also contributed) had significantly higher relative abundance of two bacterial genera — Ruminococcus and Oscillospira — that in other studies have been linked to reduced risk of childhood allergic disease and reduced risk of obesity. The effect was dose-dependent, in the sense that exposure both during pregnancy and after birth had a larger effect than exposure at only one of those periods, and it persisted after adjustment for breastfeeding, delivery mode, antibiotic exposure, and the usual confounders. Most strikingly, the pet effect was strong enough to partly compensate for the microbiome impoverishment normally seen after caesarean delivery or after antibiotic exposure in the perinatal period: C-section babies raised with a dog in the home had gut microbiomes that looked more similar to vaginally-delivered babies raised without a dog than to their own C-section peers in pet-free households.

Independent cohorts have broadly confirmed the direction of the effect, even if the specific taxa that shift vary between studies. A 2020 paper by Nielsen and colleagues [REF:nielsen2020] used the Danish COPSAC2010 cohort to show that pet exposure in early infancy was associated with increased infant gut microbiome diversity and with altered levels of specific short-chain fatty acid producers, and that these changes mediated some of the already-known protective association between pet exposure and later allergic sensitisation. A German cohort, a Dutch cohort, and multiple smaller studies have produced broadly similar findings. The detail differs; the direction of the effect does not.

Mechanistically, several things are going on, and they overlap. Dogs and cats drag the outside world into the house on their paws and fur — they carry soil, grass, pollen, and the microbial communities of wherever they have been — and they transfer this material to floors, furniture, and the hands and faces of the children who crawl and toddle over that terrain. Dogs in particular share a portion of their own skin and oral microbiome with the humans they live with; studies of household microbial sharing (which we will get to in the next section) have found that dogs behave, microbiologically, like an extra cohabitant whose effect on household flora is larger than any single human’s. And finally, dogs structure the behaviour of the family: owning a dog means walking outside in weather the family might otherwise avoid, which in turn means incidental contact with the full seasonal range of environmental microbes.

The practical implication is interesting, and — importantly — it is one of the very few microbiome interventions that is essentially free of downside for a family that was already disposed to considering a pet. The Old Friends hypothesis predicts that dogs should be beneficial; the CHILD cohort and its siblings have confirmed that they are; the effect is robust across populations; and there is no plausible mechanism by which routine exposure to a healthy pet harms the infant microbiome. Allergic sensitisation to the pet itself is, counter-intuitively, less common in children exposed from birth than in those who meet a dog for the first time at age five. If a family is considering a pet and a young child is in the picture, the microbiome literature now points fairly clearly in the direction of saying yes, bringing the pet in early, and letting the child and the dog share a considerable amount of physical contact.

A word of caution is in order, because nothing about this recommendation should be read as license to expose vulnerable people to genuinely sick animals, or to overlook zoonotic risks. Immunocompromised patients, pregnant women handling cat litter (because of toxoplasmosis), and anyone receiving intensive chemotherapy face real trade-offs that are more complicated than the average healthy family does. For the typical healthy household, however, the risk-benefit calculus favours pet ownership rather clearly, and the microbiome data have now added a specific reason to the older, more general reasons — companionship, exercise, childhood wellbeing — that people keep dogs.

21.7 The Household as a Microbial Unit: What We Share With the People We Live With

When two strangers move into a flat together, they bring two different microbiomes with them. Within a few weeks, if they share meals, bathrooms, couches, and doorknobs, a curious thing starts to happen: their microbiomes begin to converge. Not completely, and not on every body site equally, but enough that investigators sampling the skin, nose, and gut of cohabitants can distinguish “same household” from “different household” with surprising accuracy on the basis of microbial sequences alone.

The most comprehensive early documentation of this phenomenon came from the “Home Microbiome Project” led by Jack Gilbert at Argonne National Laboratory, published in Science in 2014 by Simon Lax and colleagues [REF:lax2014]. The study followed seven families (eighteen people in total, plus three dogs and a cat) over six weeks, swabbing skin surfaces, kitchen and bathroom fixtures, doorknobs, and floors every few days. The results were striking. Each household developed a characteristic microbial “signature” — a pattern of species and their relative abundances — that was far more similar to itself over time than to any other household. Within a household, there was constant microbial exchange: the floor microbiome and the shoe microbiome tracked the outside environment; the countertop microbiome tracked the skin and hand microbiomes of the residents; the doorknob and light-switch microbiomes were essentially human-skin-derived and distinguishable by person. When a family moved house during the study, the new house acquired the old house’s microbiome within a day — the families brought their microbial community with them, and it overwrote whatever had been there before.

A separate line of work — most influentially from Brent Stephens and colleagues at Illinois Institute of Technology, and from the Gilbert lab — has shown that this household microbiome is not simply the sum of the people who live in it. It has its own ecology. Dust in the corners of rooms accumulates over weeks a stable community that includes skin shed from residents, environmental bacteria tracked in on shoes, outdoor air input via the windows and HVAC, and, in humid bathrooms, locally-blooming moisture-loving genera. Vacuuming, mopping, and airing the house reshape but do not eliminate this community; within a few days of a thorough cleaning, the dust flora returns to something close to its previous composition.

More recently, attention has turned from the built microbiome to direct person-to-person sharing of gut microbes within households. In a major 2023 Nature paper, Mireia Valles-Colomer and colleagues from the Segata lab in Trento analysed nearly ten thousand metagenomes from cohabitants in multiple countries to reconstruct strain-level transmission of gut bacteria between people [REF:vallescolomer2023]. Their results show that vertical transmission from mother to infant is the dominant source of early-life gut strains (as expected), but that horizontal transmission between cohabitants continues throughout life at levels much higher than previously appreciated. Spouses who live together for years share a substantial proportion of their gut bacterial strains — far more than random pairs from the same population — and the degree of sharing increases with duration of cohabitation. Children share with their parents; siblings share with each other; in multi-generational households, grandparents share with grandchildren.

This has several important implications, and I want to spend a moment on the two that I think matter most for the general reader.

The first is that the gut microbiome is not a private possession, sealed within an individual body. It is a shared domestic ecology, maintained collectively by the people who live under the same roof. Your spouse’s microbiome is, in a real sense, partly yours. The father who makes a radical dietary change in mid-life is not only remodelling his own gut community; he is, slowly, exchanging strains with everyone else in the household. The child who is put on a course of antibiotics does not suffer the disturbance in isolation; the household reservoir of bacterial strains is part of what the child’s gut will draw on as it recovers. This interconnectedness gives a new meaning to the old public health observation that health outcomes cluster within families. It turns out that one of the mechanisms by which lifestyle diseases and health behaviours run in families is, almost certainly, microbial.

The second implication is more uncomfortable. The widespread assumption that microbiome-focused dietary interventions — more fibre, more fermented foods, the changes we discussed at length in chapter 20 — are an individual choice with individual consequences is not quite right. The household is the unit; the rest of the household both helps and hinders the individual’s efforts. A partner who refuses to eat vegetables and lives almost entirely on ultra-processed food is, in small but measurable ways, reintroducing the strains associated with that way of eating back into the household reservoir, from which they can re-colonise the person who is trying to eat better. Household-level interventions — where a whole family shifts together — would be predicted by this biology to be more effective than individual-level interventions. That is also what the (admittedly limited) interventional evidence tends to show.

The household microbiome framework does not lead to any particularly drastic recommendations. It does, however, re-frame a set of questions that we usually treat as individual questions into collective ones. It suggests that the microbiomes of the people we live with are part of our own microbial environment, and that caring for our own gut flora is, in practice, partly a matter of caring for the domestic ecosystem we share with them.

21.8 Moving the Body: Exercise as a Microbial Intervention

Exercise affects almost every physiological system we know how to measure, and in the past decade the microbiome has been added to that list. The question is no longer whether physical activity changes the gut microbiome — it does — but whether the changes are clinically meaningful, whether they explain any of exercise’s well-known benefits, and whether the effect is independent of the dietary and lifestyle shifts that usually accompany a decision to start exercising.

The early, eye-catching evidence came from a 2014 study of elite Irish international rugby players by Siobhan Clarke and colleagues at University College Cork [REF:clarke2014]. The researchers sequenced the gut microbiomes of forty professional rugby players during a pre-season training camp and compared them with matched sedentary controls. The athletes had markedly more diverse gut microbiomes, with higher relative abundance of Akkermansia muciniphila (a mucus-dwelling bacterium associated with metabolic health), more short-chain fatty acid producers, and a generally “healthier-looking” community profile. The problem with this study, which the authors openly acknowledged, is that elite rugby players are not just more active than the controls; they also ate dramatically more food, consumed more protein, had higher creatine intakes, and differed in dozens of ways that could have driven the microbiome differences independent of the exercise itself. The headline finding was suggestive but confounded.

The interventional evidence needed to address that confound came later. The cleanest trial I am aware of is a 2020 randomised controlled trial by Mika Motiani and colleagues in Finland [REF:motiani2020], which enrolled sedentary adults with insulin resistance — a group likely to benefit from any genuine metabolic effect of exercise — and randomised them to either sprint interval training or moderate-intensity continuous training for two weeks, with dietary intake carefully held constant. Both exercise protocols produced measurable changes in the gut microbiome: decreased Bacteroidetes relative to Firmicutes, increased Akkermansia, and increased plasma short-chain fatty acid levels. The changes correlated with improvements in whole-body insulin sensitivity, and importantly, they occurred without any change in diet. This was, as best we can tell, the direct effect of the exercise itself on the gut ecosystem.

The mechanisms by which exercise moves the microbiome are still being worked out, but several plausible routes have been identified. Exercise raises core body temperature modestly, alters gut blood flow and intestinal transit time, increases bile acid secretion, reduces systemic inflammation, and triggers the release of exercise-responsive signalling molecules (myokines from muscle, and, in longer sessions, stress hormones) that reach the gut lumen and may reshape which bacteria thrive there. In parallel, the act of breathing deeply and being outdoors while exercising brings the airway microbiome into contact with a wider range of environmental microbes than sitting indoors does — which means that, at least for outdoor exercise, some of the microbiome benefit may come through the same “Old Friends” pathway as gardening or dog-walking does.

What dose of exercise moves the needle? The current literature does not allow a precise prescription, but the pattern across studies is that it does not take much. The Motiani trial produced measurable effects in two weeks at moderate intensity. Observational studies comparing physically active adults with sedentary ones find microbiome differences at activity levels well below what an elite athlete does — levels closer to the standard public health recommendation of roughly 150 minutes of moderate exercise a week, or about twenty to thirty minutes most days. The effect is larger with outdoor exercise than with treadmill exercise (for the environmental exposure reason), and it appears to plateau rather than increase linearly: there is no evidence that doing twice as much produces twice the microbiome benefit.

For the purposes of this chapter, exercise is interesting less as a surprise and more as a reinforcement of the central theme: the levers that most reliably move the gut microbiome in a favourable direction are not exotic. They are the same levers that human bodies have always needed for health — movement, contact with the outside world, varied food, sleep, social and family connection. The microbiome literature is not overturning those familiar recommendations; it is providing a new and specific biological reason for why they matter.

21.9 Sleep and Stress: The Nervous System’s Dialogue With the Gut

Few subjects in popular microbiome writing are more vulnerable to overclaim than the effects of sleep and stress on gut flora. The relationship is real; the mechanisms are being worked out; and at the same time, some of the more enthusiastic claims in the self-help literature rest on animal data that have not yet been replicated in humans, or on small observational studies whose direction of causation is impossible to establish. I will try to describe what the evidence actually supports.

Sleep first. The clearest experimental evidence that disrupted sleep reshapes the gut microbiome comes from shift-work studies and from short-term sleep-deprivation protocols. In 2016, Christian Benedict and colleagues in Sweden ran a small crossover trial in which healthy young men spent two nights either sleeping normally or being kept awake through the night, with identical diets provided in both conditions [REF:benedict2016]. Two nights of partial sleep deprivation produced measurable shifts in the ratio of Firmicutes to Bacteroidetes that, in the broader literature, have been associated with metabolic dysfunction. The changes were small, the study was short, and whether the shift would persist or reverse with habitual poor sleep is not known from that design alone. But the signal was real and has been replicated in larger observational studies of chronic short sleepers and shift workers.

A more recent and larger piece of work is a 2023 analysis by Sun and colleagues [REF:sun2023sleep] using data from a population-based cohort that paired detailed sleep metrics (from validated questionnaires and, in a subset, from actigraphy) with gut metagenomic data. Habitually poor sleepers and those with fragmented sleep had measurably different gut community structures from good sleepers, with reduced alpha diversity, lower relative abundance of several butyrate-producing genera, and higher relative abundance of taxa previously linked to metabolic disease. The correlations were not enormous, and the cross-sectional design cannot establish direction of causation — it is possible that poor gut health disturbs sleep rather than the reverse, or that both are downstream of something else — but the pattern is consistent with the more mechanistic animal work, in which circadian-disrupted mice rapidly develop microbiome changes and metabolic impairment, and in which transferring the disrupted-mouse microbiome into a healthy mouse transfers some of the metabolic phenotype.

The link with the diurnal oscillation findings of chapter 20 is not coincidental. The gut microbiome itself oscillates over the day, with different taxa rising and falling in predictable patterns driven by the host’s feeding schedule, host-derived circadian signals, and the local metabolic environment [REF:thaiss2014] [REF:thaiss2016]. Sleep disruption is fundamentally a disruption of the circadian clock, and it is the loss of oscillation — the loss of the orderly alternation between active and quiescent states — rather than any single static change that appears to be the proximate mechanism. The gut bacterium that was supposed to bloom at three in the morning and fade by six, in a well-entrained human, loses that rhythm when the host loses theirs, and the downstream metabolic outputs that depend on the rhythmic alternation are diminished. The analogy to the light section of this chapter is deliberate: the gut responds not to averages but to patterns, and when the patterns are smeared, the ecosystem responds by losing structure.

Stress is, if anything, a harder subject to write about honestly, because the word “stress” covers so many physiologically different states — acute versus chronic, physical versus psychological, controllable versus uncontrollable, novel versus familiar — and because the human trials in this area are mostly small, short, and prone to confounding by the dietary and behavioural changes that stressed people make. The clearest animal evidence comes from the maternal-separation paradigm, in which rat or mouse pups are briefly separated from their mothers during a critical developmental window and develop lifelong alterations in gut microbiome composition and in stress-axis reactivity. The clearest human evidence is bidirectional: people with chronic anxiety and depression have, on average, gut microbiomes that differ from healthy controls, with reductions in several taxa that produce neuroactive metabolites; and, in the other direction, short-term psychological stressors (exam periods, combat deployment, spaceflight) produce measurable short-term shifts in gut community structure.

What is harder to say — and I want to be explicit about this — is what any given person should do about the stress-microbiome link, because the intervention literature is thin. There is some evidence that mindfulness-based interventions and cognitive behavioural therapy shift the gut microbiome in favourable directions in parallel with their mental health effects, but it is currently impossible to say whether the microbiome changes are driving the improvement, responding to it, or simply accompanying it because mood and eating habits shift together. For now, the honest summary is: there is a dialogue between the nervous system and the gut microbiome, the dialogue runs both ways, and interventions that reduce chronic stress also tend to move the microbiome in the direction we associate with better health. Whether any of this warrants calling meditation a “microbiome intervention” is a matter of taste; in strict evidential terms, the case is not yet made.

What I will say confidently is this: the ordinary human things — getting enough sleep, protecting the dark at night, allowing the body to rest and recover, having stable daily rhythms, maintaining social and family connection — are, on current evidence, good for the gut microbiome as well as for everything else we knew they were good for. The microbiome provides yet another reason to treat sleep and stress as first-class health inputs rather than as inconveniences to be worked around.

21.10 The Medicine Cabinet: Non-Antibiotic Drugs and the Microbiome

Antibiotics will get their own full chapter, because their effects on the gut microbiome are so profound and because their story is a story in itself. What I want to address in this section is something subtler and, for most readers, more relevant: the effects of the non-antibiotic medications that millions of people take every day, often for years at a time, without any idea that they are modifying their gut ecosystem in the process.

The paper that forced this question into the mainstream of microbiome research was a 2018 Nature study by Lisa Maier and colleagues at EMBL Heidelberg [REF:maier2018]. The researchers took the library of pharmacological compounds routinely used in human medicine — more than a thousand of them, spanning every major therapeutic category from blood pressure medications to antihistamines to antipsychotics — and screened each of them, one at a time, against a panel of forty representative human gut bacterial strains grown in the laboratory. They asked a simple question: at doses comparable to those achieved in the human gut during normal therapeutic use, how many of these “non-antibiotic” drugs actually suppress the growth of human gut bacteria?

The answer, astonishingly, was nearly a quarter. Of the roughly 835 compounds that reached the gut at plausible concentrations, 24% inhibited the growth of at least one of the tested strains, and many inhibited several. The drugs that came out as strongly antimicrobial included proton-pump inhibitors (used for heartburn and acid reflux), certain antipsychotics (including chlorpromazine), some of the anti-diabetic medications, calcium channel blockers, and several classes of drug that had never been suspected of having any effect on bacteria at all. A proper antibiotic, the researchers noted, typically inhibits gut bacteria at far lower concentrations than these drugs do — but at the concentrations actually present in the gut of a person taking these medications long-term, the effects are real and measurable.

The implication is important. We have been dosing large fractions of the adult population for years or decades with drugs that quietly exert selection pressure on the gut microbiome, without anyone — not the prescribers, not the regulators, not the patients — having any framework for assessing what this cumulative pressure might be doing. The specific example that has become best characterised is proton-pump inhibitors (PPIs), a class of drugs that includes omeprazole, lansoprazole, esomeprazole, and pantoprazole and that is prescribed to hundreds of millions of people worldwide for heartburn, gastric ulcer prevention, and reflux disease.

PPIs suppress acid production in the stomach. One of the oldest functions of gastric acid is to sterilise incoming food and water — to kill off the microbes that enter the mouth and keep them from reaching the small intestine and colon alive. When you turn the acid off, oral and upper-respiratory bacteria that would normally have been killed in transit survive and establish themselves further down. A 2016 Gut paper by Floris Imhann and colleagues [REF:imhann2016] analysed stool microbiomes from over 1800 Dutch volunteers, some of whom were taking PPIs long-term, and found that PPI users had significantly altered gut microbial communities — specifically, increased abundance of oral bacteria (including Streptococcus and related genera) in the gut, increased abundance of Enterococcus, and shifts in several other markers consistent with upper-GI bacteria colonising the lower bowel. The effect size was substantial — comparable in magnitude to the effect of a course of antibiotics — and it was dose-dependent on the duration of PPI use.

Whether this shift causes clinical harm is a separate and still-evolving question. PPI users have been reported to have a modestly increased risk of Clostridium difficile infection, of small intestinal bacterial overgrowth, and possibly of community-acquired pneumonia, all of which are biologically consistent with loss of the acid barrier. The risks are not enormous for any individual, but they are real, and they add up when the drug is taken for years as many people do. I do not want this section to be read as telling every PPI user to stop their medication; PPIs are valuable drugs, and for patients with severe reflux disease or with high ulcer risk, the benefits clearly exceed the microbiome cost. What it should be read as telling is this: the assumption that a drug is “only” acting on its declared target, and that its effects outside that target can be ignored, is an assumption that microbiome research has comprehensively overturned. Whenever a non-antibiotic drug is taken for a prolonged period, it is worth asking — with a prescriber’s help — whether the duration is still necessary, whether the dose is still the minimum that achieves the goal, and whether alternative approaches might reduce the cumulative burden of chronic pharmacological pressure on the gut ecosystem.

Metformin, the first-line drug for type 2 diabetes, provides a particularly interesting counterexample. Metformin also alters the gut microbiome — a 2015 Nature paper by Karin Forslund and colleagues established this definitively, by showing that microbiome differences once attributed to type 2 diabetes itself were in fact largely due to metformin treatment [REF:forslund2015]. What is interesting is that the microbiome changes metformin induces appear to be part of how the drug works: metformin increases the abundance of several short-chain-fatty-acid-producing bacteria and of Akkermansia muciniphila, and these shifts contribute to the drug’s glucose-lowering effect. In germ-free mice, some of metformin’s metabolic benefits are substantially attenuated, and when the “metformin-shifted” human microbiome is transferred to germ-free mice, the mice show improved glucose handling even without the drug. The microbiome-mediated component of metformin’s effect is not a side effect; it is part of the mechanism. This is almost certainly not unique to metformin — other drugs are likely to work, at least partly, through their effects on the gut microbiome, and the field is only beginning to ask systematically which ones.

The general point I want to leave the reader with is that almost every oral medication meets the gut microbiome before it meets its target, and the encounter is rarely without consequence. If you are on a long-term medication and you have never asked how it interacts with your gut flora, the answer is probably “we do not fully know, but it almost certainly does something.” For most drugs most of the time, the net balance is positive; the drug’s benefits outweigh the microbiome costs. But the balance is not zero, and pretending that it is — as we did for most of the last century — is no longer intellectually honest.

21.11 The Water We Drink: Chlorine, Hardness, and the Microbes That Aren’t There

I had not intended, when I started this chapter, to write a section on drinking water. It seemed too narrow a topic. But as I worked through the environmental inputs to the microbiome one after another, it became difficult to leave water out, because the argument about water parallels the argument about air so closely: we have taken a substrate that once carried a rich and seasonally varying microbial community and have processed it, for perfectly understandable historical reasons, into something nearly microbially inert. The question of whether that was a good trade-off on net is, once again, one that the microbiome literature is starting to ask.

Chlorination of public drinking water was one of the most successful public health interventions of the twentieth century. Before chlorination, waterborne cholera, typhoid, and dysentery killed millions every year in the industrialised world; after chlorination, these diseases essentially disappeared from places with treated water supplies. Nothing I write in this section should be read as questioning that history. Chlorine in the municipal water supply has saved more lives than almost any drug we have invented since, and it continues to save lives every day in countries where water treatment has recently been introduced. The microbiome question is not whether chlorination is good — it is, and it remains so — but whether the particular version of “clean water” we deliver to modern homes has microbial consequences beyond the absence of pathogens that deserve acknowledgement.

The honest answer is: probably, but we know less than we should. A handful of studies have examined whether long-term consumption of chlorinated tap water (versus bottled water, or filtered water, or well water) correlates with differences in the gut microbiome. The results are mixed and the studies are small; there is no convincing evidence that drinking chlorinated water at normal municipal concentrations measurably shifts the gut microbiome in a meaningful way. The chlorine is substantially consumed in transit through the digestive tract, is diluted by stomach contents, and is further neutralised by organic matter in food, such that the concentration that reaches the distal small intestine and colon is low. If there is an effect, it is small and has not been convincingly isolated from the many other variables that distinguish tap-water drinkers from bottled-water drinkers.

More interesting, and better documented, is the effect of water hardness — the mineral content, particularly calcium and magnesium — on the gut microbiome. Several observational studies have linked residential water hardness with gut microbiome composition, with harder water (higher mineral content) correlating with modest differences in community structure and, in some studies, with differences in the abundance of specific short-chain fatty acid producers. The mechanistic picture is not yet clear; calcium and magnesium are known to interact with bile acid metabolism in ways that could plausibly feed back into microbial community structure. This is the kind of subtle environmental variable whose effects are probably real but whose magnitude is unlikely to matter much compared with diet or antibiotic exposure.

What does seem worth mentioning — and what actually prompted me to include this section — is the contrast between the water we drink and the water our ancestors drank. For most of human history, drinking water came from streams, rivers, springs, wells, and rain barrels, and it carried with it a living community of environmental microbes. Some of those microbes were dangerous, which is why chlorination was introduced; but many were harmless or tonic, part of the “Old Friends” stream of exposure that Graham Rook describes. When we replaced stream water with treated water, we did not just remove the pathogens; we removed the entire microbial content, pathogen and tonic friend alike. The calorie-free, pathogen-free, essentially sterile water that flows from a modern tap is an evolutionary novelty, and although no one is seriously proposing that we should return to drinking untreated river water, it is worth pausing to notice that the water input to the microbiome has been one more channel by which modern life has removed environmental microbial exposure from the daily experience of the human gut.

I would not make any drinking-water recommendations on the basis of current microbiome evidence. Municipal treated water is safe and should be drunk; bottled water has no advantage and a substantial environmental disadvantage; well water is fine if it has been properly tested. The point of this short section is only to note that water, too, belongs on the list of environmental inputs that modern sanitation has quietly shifted, and that the microbiome question will eventually need to be asked even for a substrate we think of as elementally simple.

21.12 Taking Stock: The Modern Environment as a Microbiome Experiment

If I have done my job in this chapter, the reader will by now be carrying a picture that is subtly but importantly different from the one most of us grew up with. The picture is this: a modern sanitised home, a climatised office, a filtered air supply, a paved neighbourhood, a pet-free apartment, a chlorinated tap, a bright screen at midnight, a mild chronic sleep debt, and a long-term maintenance medication — each of these, taken on its own, is a small environmental departure from the conditions under which the human microbiome evolved. Taken together, they are a substantial and cumulative one. They are not, individually, harmful in any dramatic way; nobody is about to die because their bedroom is too warm at night. But they add up, and the thing they are adding up to is a quiet, multi-decade, uncontrolled experiment on the human microbiome at population scale, an experiment for which none of us ever signed a consent form and whose long-term outcomes we are still learning to measure.

The honest scientific position is that we do not yet know how large the net effect of all these environmental departures is, or how much of the rise in allergic, autoimmune, metabolic, and possibly neuropsychiatric disease in industrialised populations it accounts for. The candidate mechanisms are plausible and increasingly well-documented; the direct causal evidence from controlled human trials is thin, and will remain thin for a long time, because you cannot randomise people to sleep in darkness for twenty years or to grow up with a dog versus without one. What we have instead is converging evidence from animal work, natural experiments (like the Amish-Hutterite comparison), observational cohorts, and short-term interventional trials, all of which point in the same direction without any one of them being definitive. A reasonable person can, and should, look at that evidence base and conclude that the cumulative environmental modifications of the last century are probably making some meaningful contribution to the modern disease pattern, while being careful not to overclaim about the magnitude.

What should a reader take away from this chapter in the form of practical changes they might actually make? I want to be specific, because the biggest risk of a chapter like this is that it leaves the reader feeling simultaneously alarmed and paralysed. In descending order of how well the evidence supports the recommendation, here is what I think the current literature warrants:

Sleep in the dark, and let the morning be bright. The evidence on circadian disruption and the microbiome is among the strongest in this chapter. Black-out curtains in the bedroom, no bright screens in the last hour before sleep, and deliberate morning light exposure together cost almost nothing, are supported by both mechanism and outcome data, and are beneficial for reasons that go well beyond the microbiome.

Open the window every day, in almost any weather. The HEPA and household air evidence suggests that microbial variety, not microbial purity, is what matters for a healthy home, and the simplest and cheapest way to deliver variety is to let the outside in. Fifteen minutes a day is enough.

If you are considering a dog, and especially if you have young children, bring the dog in early. The pet-microbiome evidence is unusually clean for this field, and the effect is substantial enough to partially compensate for the microbiome disruptions caused by caesarean delivery and early antibiotic exposure.

Let young children get dirty. Soil, grass, gardens, small farms, puddles, mud — these are not hazards to be managed but inputs to be welcomed. The Old Friends evidence, while theoretical in places, is the best explanation we currently have for the epidemic of allergy and autoimmune disease in industrialised populations, and the practical response to it is simply to stop preventing the ordinary childhood contact with environmental microbial life that used to be taken for granted.

Move the body, preferably outside. The exercise-microbiome link is real, the outdoor-exercise link is probably real for a second and additional reason (increased environmental exposure), and the dose need not be extreme. Thirty minutes of walking in a park most days delivers much of what the literature currently supports.

Let the house experience some seasons. The thermostat section is the most speculative part of this chapter, and I acknowledged that it was. But the general idea that a body and its microbiomes expect some variation in temperature and humidity over the year, and that holding everything constant is a novel experiment whose consequences are incompletely understood, seems worth taking seriously enough to let the house be a degree or two warmer in summer and cooler in winter than you might otherwise default to.

Be mindful of long-term medication. Not fearful; mindful. If you have been on a proton-pump inhibitor for five years without ever revisiting whether the indication still holds, that is worth a conversation with your doctor — not because the drug is dangerous, but because the principle of minimum necessary duration is a good one when a medication is interacting with the gut microbiome every day.

What this chapter has not covered is the intervention most likely to be the single largest environmental disruptor of the human microbiome in the modern era: antibiotics. I have deferred antibiotics deliberately, because their effects are large enough and their story is complicated enough that they deserve their own chapter, not a subsection in this one. Chapter 22 takes up that story, and it is where much of what we have discussed in this chapter and the previous one will be brought into contact with the most powerful tool modern medicine has for reshaping microbial communities — for good and for ill.

Key References

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[REF:lee2022] Lee CC, Liang F, Lee IC, Lu TH, Shan YY, Jeng CF, Zou YF, Yu HT, Chen Alen SK. External light-dark cycle shapes gut microbiota through intrinsically photosensitive retinal ganglion cells. EMBO Reports. 2022;23(6):e52316. doi:10.15252/embr.202052316

[REF:brooks2021] Brooks JF, Behrendt CL, Ruhn KA, et al. The microbiota coordinates diurnal rhythms in innate immunity with the circadian clock. Cell. 2021;184(16):4154-4167.e12. doi:10.1016/j.cell.2021.07.001

[REF:rogers2021] Rogers AE, Hu YJ, Yue Y, Wissel EF, Petit RA 3rd, Jarrett S, Christie J, Read TD. Shiftwork, functional bowel symptoms, and the microbiome. PeerJ. 2021;9:e11406. doi:10.7717/peerj.11406

[REF:lopezsantamarina2023] Lopez-Santamarina A, Mondragon ADC, Cardelle-Cobas A, Santos EM, Porto-Arias JJ, Cepeda A, Miranda JM. Effects of unconventional work and shift work on the human gut microbiota and the potential of probiotics to restore dysbiosis. Nutrients. 2023;15(13):3070. doi:10.3390/nu15133070

[REF:hong2020] Hong F, Pan S, Xu P, Xue T, Wang J, Guo Y, Jia L, Qiao X, Li L, Zhai Y. Melatonin orchestrates lipid homeostasis through the hepatointestinal circadian clock and microbiota during constant light exposure. Cells. 2020;9(2):489. doi:10.3390/cells9020489

[REF:thaiss2014] Thaiss CA, Zeevi D, Levy M, et al. Transkingdom control of microbiota diurnal oscillations promotes metabolic homeostasis. Cell. 2014;159(3):514-529. doi:10.1016/j.cell.2014.09.048

[REF:thaiss2016] Thaiss CA, Levy M, Korem T, et al. Microbiota diurnal rhythmicity programs host transcriptome oscillations. Cell. 2016;167(6):1495-1510.e12. doi:10.1016/j.cell.2016.11.003

[REF:krisko2020] Krisko TI, Nicholls HT, Bare CJ, et al. Dissociation of adaptive thermogenesis from glucose homeostasis in microbiome-deficient mice. Cell Metabolism. 2020;31(3):592-604.e9. doi:10.1016/j.cmet.2020.01.012

[REF:richardson2019] Richardson M, Gottel N, Gilbert JA, Lax S. Microbial similarity between students in a common dormitory environment reveals the forensic potential of individual microbial signatures. mBio. 2019;10(4):e01054-19. doi:10.1128/mBio.01054-19

[REF:maestre2018] Maestre JP, Jennings W, Wylie D, Horner SD, Siegel J, Kinney KA. Filter forensics: microbiota recovery from residential HVAC filters. Microbiome. 2018;6(1):22. doi:10.1186/s40168-018-0407-6

[REF:stein2016] Stein MM, Hrusch CL, Gozdz J, et al. Innate immunity and asthma risk in Amish and Hutterite farm children. New England Journal of Medicine. 2016;375(5):411-421. doi:10.1056/NEJMoa1508749

[REF:rook2013] Rook GAW. Regulation of the immune system by biodiversity from the natural environment: an ecosystem service essential to health. Proceedings of the National Academy of Sciences. 2013;110(46):18360-18367. doi:10.1073/pnas.1313731110

[REF:rook2014] Rook GAW, Raison CL, Lowry CA. Microbial “Old Friends”, immunoregulation and socioeconomic status. Clinical and Experimental Immunology. 2014;177(1):1-12. doi:10.1111/cei.12269

[REF:tun2017] Tun HM, Konya T, Takaro TK, et al. Exposure to household furry pets influences the gut microbiota of infants at 3-4 months following various birth scenarios. Microbiome. 2017;5(1):40. doi:10.1186/s40168-017-0254-x

[REF:nielsen2020] Nielsen CC, Gascon M, Osornio-Vargas AR, et al. Natural environments in the urban context and gut microbiota in infants. Environment International. 2020;142:105881. doi:10.1016/j.envint.2020.105881

[REF:lax2014] Lax S, Smith DP, Hampton-Marcell J, et al. Longitudinal analysis of microbial interaction between humans and the indoor environment. Science. 2014;345(6200):1048-1052. doi:10.1126/science.1254529

[REF:vallescolomer2023] Valles-Colomer M, Blanco-Míguez A, Manghi P, et al. The person-to-person transmission landscape of the gut and oral microbiomes. Nature. 2023;614(7946):125-135. doi:10.1038/s41586-022-05620-1

[REF:clarke2014] Clarke SF, Murphy EF, O’Sullivan O, et al. Exercise and associated dietary extremes impact on gut microbial diversity. Gut. 2014;63(12):1913-1920. doi:10.1136/gutjnl-2013-306541

[REF:motiani2020] Motiani KK, Collado MC, Eskelinen JJ, et al. Exercise training modulates gut microbiota profile and improves endotoxemia. Medicine and Science in Sports and Exercise. 2020;52(1):94-104. doi:10.1249/MSS.0000000000002112

[REF:benedict2016] Benedict C, Vogel H, Jonas W, et al. Gut microbiota and glucometabolic alterations in response to recurrent partial sleep deprivation in normal-weight young individuals. Molecular Metabolism. 2016;5(12):1175-1186. doi:10.1016/j.molmet.2016.10.003

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End of Chapter 21 draft.

Chapter 22: Drugs Beyond Antibiotics — The Quiet Pharmacology of the Gut

22.1 The Quietest Side Effect

In 2018, a small group of researchers at the European Molecular Biology Laboratory in Heidelberg did something that, in retrospect, ought to have been done thirty years earlier and was not. Lisa Maier, Mihaela Pruteanu, Michael Kuhn, and their colleagues took the routine pharmacological library that any university medicinal-chemistry department keeps in its freezer — more than a thousand drugs in regular human medical use, spanning every major therapeutic category from blood-pressure pills to antihistamines to antipsychotics to anti-cancer agents — and exposed each one, individually, to a panel of forty representative human gut bacteria grown in the laboratory. They asked an embarrassingly simple question: at concentrations that the gut of an actual patient would experience during normal therapeutic use, how many of these supposedly “non-antibiotic” drugs actually inhibit the growth of the human gut microbiota? [REF:maier2018]

The answer, when their paper appeared in Nature, was nearly a quarter. Of the roughly 835 compounds that reached the gut at plausible concentrations, 24 percent inhibited the growth of at least one of the tested strains, and many inhibited several. The list of culprits made for uneasy reading. Proton pump inhibitors — the drugs hundreds of millions of people swallow every day for reflux. Several major antipsychotics, including chlorpromazine. Calcium-channel blockers used for blood pressure. Anti-diabetic agents. A handful of common painkillers. None of these had ever been designed to interfere with bacteria. None had ever been licensed with that intention. None were called “antibiotics” by any regulatory authority on Earth. And yet in the dish, at human-relevant concentrations, they behaved as though they were.

I want to start the chapter with this paper because it overturned a comfortable assumption that had quietly governed medicine for most of the twentieth century. The assumption was this: a drug acts on its declared target. A blood-pressure pill acts on blood pressure. An antihistamine acts on the H1 receptor in the lining of your nose. An antipsychotic acts on dopamine receptors in the brain. Whatever else the drug is doing — whatever it incidentally bumps into on its way through the gut — is either negligible, or shows up as a side effect noticed by patients and tabulated in the package insert. The Heidelberg paper made this assumption untenable. Most oral medications meet the gut microbiome before they meet anything else, and the encounter, the data showed, is rarely without consequence for the bacteria. The bacteria simply have not been complaining loudly enough for us to notice.

In Chapter 21, I introduced this idea briefly, in section 21.10, as one of several “other levers” that shape the modern microbiome. I told you about Maier’s screen, said a few words about proton pump inhibitors and metformin, and promised that the topic deserved its own chapter. This is that chapter. What I want to do here is take the question seriously: across the full range of drugs that ordinary people take across an ordinary lifetime, which ones substantially shift the gut ecosystem, by how much, and for how long, and — crucially — does it matter?

I will tell you in advance what kind of chapter this is and what kind of chapter it is not. It is not a list of drugs to avoid. I am an emergency physician; I have spent most of my professional life seeing patients whose lives have been saved or made bearable by exactly the medications discussed in the pages that follow. Telling people to throw away their proton pump inhibitor because it shifts their Lactobacillus count would be irresponsible to the point of dangerous, and I will not be doing it. The chapter is also not a guide to “alternatives”. The question of whether an existing medication should be changed, paused, deprescribed, or replaced is a question for the patient and the doctor who knows the patient — not for a popular-science book that knows neither. What this chapter is, and what I hope it manages to be, is an honest map of where the evidence currently stands, so that readers who are taking long-term medication know what their drug is doing inside them, can have an informed conversation with their treating doctor about it, and can make sensible adjustments — to diet, to fibre intake, to fermented foods — that are within their own power and that the evidence reasonably supports.

There is one more thing I want to set out at the beginning. Most of the drugs in this chapter have positive reasons to exist. They prevent strokes, they prevent heart attacks, they keep people alive who would otherwise be dead. Their effect on the microbiome is, in almost every case, a cost set against a much larger benefit. The arithmetic, on the available evidence, comes out in favour of the benefit. What the microbiome story changes is not whether to take the drug; it is the hidden ledger on which the taking is recorded. Knowing the cost is on the ledger allows the cost to be discussed, monitored, and — where necessary, and only in conversation with a clinician who knows the case — minimised. That is the only practical use to which this chapter should be put.

22.2 Reading the Evidence: A Note Before We Start

Before I start describing individual drug classes, I want to spend a paragraph or two on the evidence itself, because the evidence in this field is uneven in a way the reader should know about.

For some drugs the evidence is, by the modest standards of microbiome research, very strong. For metformin we have multiple large human cohorts, a meta-analysis pooling more than a thousand individuals, mechanistic experiments in germ-free mice, and a clear molecular story about how the drug talks to specific bacterial taxa. For proton pump inhibitors we have several large observational cohorts and a coherent biological mechanism — remove the gastric acid barrier, and microbes that would normally have been killed in the stomach reach the small bowel in living form. For these drug classes, the science has reached the point where I can write declarative sentences without needing to hedge every other word.

For other drugs the evidence is much thinner. For ordinary H1 antihistamines — the cetirizines and loratadines that millions of hay-fever sufferers swallow every spring — the published microbiome literature is, as far as I can find, almost nothing. For chronic laxative use we have one really good landmark paper from 2018 [REF:tropini2018] and very little since. For opioids we have rich and convincing evidence from mouse experiments and almost no proper human cohort data. In each of these cases I am going to tell you what the mechanistic story looks like, but I am also going to tell you that the human evidence has not been done, or has been done badly, or has been done in groups too small to draw conclusions from. Where I am extrapolating from animals or from theory, I will say so. Where I am guessing, I will say that too.

Finally, the field has a recurring problem with what statisticians call confounding by indication. People who take a drug for years are different from people who do not, in many ways other than the drug. Someone on long-term proton pump inhibitors usually has reflux or has been on aspirin or both, and reflux and aspirin are themselves things that can shift the gut community. Someone on long-term antipsychotics usually has schizophrenia or bipolar disorder, and severe mental illness is itself associated with a different gut microbiome — it is not clear which way the arrow points. The cleanest evidence comes from studies that follow the same person before and after starting a drug, or from randomised trials, or from animal experiments in which the only variable is the drug. Where I am drawing on those, the evidence is more trustworthy than where I am drawing on a one-time snapshot of a clinic population. I will not stop the narrative every paragraph to drill this in, but the reader should keep it in the back of their mind throughout.

With those caveats acknowledged, let me begin where the gut begins — with the acid in the stomach.

22.3 Closing the Acid Gate: PPIs and Their Cousins

A wall that is almost always there

The human stomach is, for most of every day, an acid bath. Its pH sits between 1.5 and 3 — about the acidity of lemon juice, sometimes more — and it is held there by the parietal cells of the stomach lining, which actively pump hydrogen ions into the lumen via an enzyme called the gastric proton pump. The acid is there for three reasons. It denatures dietary proteins so digestive enzymes can attack them. It activates pepsin, the stomach’s main protein-cleaving enzyme. And — most relevant for this chapter — it kills almost everything that comes in through the mouth.

Drink a glass of water, swallow a mouthful of food, take a deep breath of air with a few aerosolised bacteria in it, and the great majority of the live microbial cells you ingest will be dead within minutes. The acid wall is the front line of what immunologists once called colonisation resistance and what an ecologist would simply call a hostile climate at the door of the ecosystem. It is the reason you can eat a slightly off sandwich and not get violently ill. It is the reason cholera has to evolve specific acid-resistance machinery to survive long enough to reach the gut and infect you. It is the reason the small bowel of a healthy adult contains a few hundred bacteria per millilitre of its contents, while the colon a metre further down contains a hundred billion. The acid does most of the sorting.

Now imagine what happens if you switch the wall off.

A proton pump inhibitor, or PPI, is a drug that turns the wall down — though, importantly, not permanently. The molecule binds tightly and covalently to an individual proton pump and disables that particular pump molecule for good. But the pump is not a permanent fixture of the parietal cell. It is a protein, and like almost all proteins it is constantly being replaced. The stomach is a harsh environment even for the cells that make the acid, and the pump itself wears out: its own half-life in the membrane is on the order of two days, and the parietal cell replaces it on a rolling basis regardless of whether the drug is present or not. When the next dose of the PPI does not arrive, new pumps keep being made, old (drug-bound) pumps keep being degraded, and within a day or two the stomach is back to its normal acid-secreting self. Recovery of acid secretion after a dose of omeprazole happens with a half-time of roughly twenty-eight hours in humans, and a little quicker for lansoprazole [REF:shin2002ppi]. For most PPIs, an additional mechanism helps along the return to baseline: the cell’s own glutathione — the same antioxidant that handles day-to-day molecular housekeeping — can partly reverse the drug’s binding to the pump, releasing the inhibitor again. So although each individual pump–drug interaction is covalent and irreversible at the molecular level, the effect on the patient’s stomach is very much reversible, and the acid wall is rebuilt from scratch every couple of days. This matters because it tells us something reassuring: stopping a PPI does not leave a permanently disabled stomach behind. The machinery comes back. The first PPI, omeprazole, was approved in the late 1980s; the class now includes lansoprazole, esomeprazole, pantoprazole, and rabeprazole, and is one of the most-prescribed drug classes in the world. Estimates vary by country, but in most of the developed world somewhere between five and ten percent of the adult population is on a PPI on any given day, often for years at a stretch, sometimes for decades. They are excellent drugs for what they were designed to do: they heal peptic ulcers, they treat severe reflux disease, and they prevent stomach bleeding in people taking long-term aspirin or anti-inflammatories. For a patient with bad oesophagitis or a recent gastric bleed, a PPI is, without exaggeration, the difference between misery and a normal life. Nothing in this section should be read as suggesting otherwise.

What the PPI does that no one really thought about for the first twenty years of the drug’s existence is open the front door.

What gets through

Frank Imhann and colleagues, working in the Netherlands, published the first really convincing human-cohort paper on this question in Gut in 2016 [REF:imhann2016]. They sequenced the gut bacteria of nearly two thousand people in the LifeLines DEEP cohort, a prospective study of the population of the northern Netherlands, and asked whether PPI users had different microbiomes from non-users. They did. The differences were not subtle. Bacteria that normally live in the mouth and upper throat — Streptococcus, Veillonella, Rothia, Actinomyces, and various other oral commensals — were substantially more abundant in the lower gut of PPI users. The reason was straightforward. With the acid wall down, mouth bacteria that are continuously swallowed in saliva (we swallow about one to two litres a day) were no longer being killed in the stomach. They survived the trip. They reached the small bowel and the colon, where, in many cases, they began to colonise tissue they had never reached before.

The Imhann team also noted that the magnitude of these shifts, when adjusted for confounders, was larger than the shifts observed in the same dataset between people with and without irritable bowel syndrome, or with and without inflammatory bowel disease. That is a striking comparison. Taking a daily pill of omeprazole was, by their analysis, a bigger statistical perturbation of the gut community than having an inflammatory bowel disease.

Independent work has consistently found the same thing. Subsequent studies in larger cohorts and in different populations have reproduced the core finding — PPI users have an oralised gut microbiome — and have begun to add detail to the mechanism. One recent line of work has shown that PPIs also increase the permeability of the intestinal lining itself, by reducing the expression of tight-junction proteins (occludin in particular) that hold gut epithelial cells together [REF:zhang2025ppi]. A leakier gut is a more inflamed gut, and a more inflamed gut is one in which previously commensal microbes can begin to cause trouble.

The clinical consequences

What does any of this mean for an actual patient on a PPI?

The honest answer is: in most cases, very little that they would notice in any given week. The microbiome shifts are real and measurable, but the corresponding clinical risks — although they exist — are individually small in absolute terms, even if they are statistically significant in large populations. Long-term PPI users have a modestly increased risk of Clostridioides difficile infection (the bug that causes the most feared form of post-antibiotic diarrhoea), of small intestinal bacterial overgrowth (SIBO, in which the small bowel ends up containing a colon-like density of bacteria it is not equipped to handle), and probably of community-acquired pneumonia from inhaled oral microbes that are no longer being cleared from the throat by adequate gastric backstop. For most healthy adults who happen to be taking a PPI for two months for a peptic ulcer, none of these risks ever materialises. For an eighty-year-old in a nursing home who has been on a PPI for fifteen years and who takes it because somebody put her on it for vague indigestion in 2010 and never reviewed the prescription, the picture is different. The cumulative risk has had time to accumulate. The benefit, in many such cases, was never very large to begin with.

A 2025 meta-analysis of cancer immunotherapy outcomes added another wrinkle [REF:xu2025ppi]. Pooling sixty-nine studies and more than twenty-two thousand patients, the authors showed that taking a PPI within three months of starting an immune checkpoint inhibitor — the class of cancer drugs that includes pembrolizumab and nivolumab and that has revolutionised the treatment of melanoma, lung cancer, and several other malignancies — was associated with significantly worse overall survival, progression-free survival, and response rate. The mechanism is not certain, but the leading hypothesis involves the gut microbiome: the PPI shifts the community in a way that interferes with the systemic immune responses on which checkpoint inhibitor therapy depends. This is a striking result and one that, if it holds up, has direct implications for prescribing in oncology.

I want to be careful here. The Xu meta-analysis is an observational pooling, not a randomised trial; people who happen to be on PPIs while starting cancer immunotherapy are not a random subset of cancer patients, and the confounding-by-indication problem is alive and well. But the signal has appeared in enough independent cohorts that the major cancer centres have begun to discuss minimising PPI use around the start of checkpoint inhibitor therapy when this can be done safely. It is a good example of how the microbiome conversation, once it gets specific enough, starts to change real prescribing.

What “rational PPI use” looks like

The professional literature on PPI prescribing has slowly converged on a position that is worth sharing in plain language. PPIs are excellent drugs for the conditions they were designed to treat: severe oesophagitis, peptic ulcers, prevention of bleeding in patients on chronic NSAIDs or anti-platelet therapy, and a few other specific indications. For these conditions, the benefits clearly outweigh the microbiome cost. PPIs are not excellent drugs for vague indigestion, occasional heartburn, or “just in case” prescription continued long after the original reason for starting it has gone away. For these uses, the benefits are smaller and the cost — to the gut ecosystem, and very probably to bone density and to magnesium status as well, although those mechanisms are different — adds up over years.

If you have been on a PPI for more than a year and you have not had a conversation with your doctor about whether the indication still holds, that conversation is worth having [REF:sharma2025ppi]. It is not a conversation about stopping the drug. It is a conversation about whether the dose can be the lowest effective one, whether step-down to an as-needed pattern is reasonable, and whether the original reason still applies. For some patients the answer will be “yes, keep going at full dose” — that is fine, and the microbiome cost is an accepted part of the package. For others the answer will be “we can taper”, and the gut ecosystem will quietly thank them for it.

I should mention briefly the cousin class of acid-reducing drugs, the H2 receptor antagonists — ranitidine, famotidine, cimetidine — that do the same job as PPIs by a different molecular route (blocking the histamine receptor on the parietal cell rather than the proton pump itself). These drugs raise gastric pH less profoundly than PPIs do, and on theoretical grounds should have a smaller effect on the gut microbiome through the same acid-gate mechanism. The published evidence on this is, frankly, sparse — I could find very little serious recent work on H2 blockers and the gut microbiome at the time of writing — but the mechanistic prediction is that any microbiome effects should be in the same direction as PPIs but smaller in magnitude. For a patient who needs acid suppression and for whom a PPI feels excessive, an H2 blocker is a reasonable option, and the absence of dedicated microbiome data should not be read as evidence that the class is harmful in a way the class above is not.

22.4 The Drug That Works Through Microbes: Metformin

The puzzle of the missing mechanism

Metformin is the most-prescribed drug in the world for type 2 diabetes. It has been on the market since 1957 in France, since 1995 in the United States, and is taken by tens of millions of people on every continent. It is cheap, it is effective at lowering blood sugar, it modestly reduces cardiovascular events and possibly all-cause mortality, and — this is the embarrassing part — for most of its history nobody could explain exactly how it worked. Textbooks dutifully cited “activation of AMP-activated protein kinase” and “inhibition of hepatic gluconeogenesis”, but the molecular details kept refusing to add up. The drug clearly worked. The question of why it worked was strangely unsettled for a medicine taken by hundreds of millions of people.

In 2015, Karin Forslund and colleagues at the European Molecular Biology Laboratory in Heidelberg published a paper in Nature that began to provide an answer in a place no one had been seriously looking [REF:forslund2015]. The Forslund team did something subtle and important. They took a large dataset of 784 gut metagenomes from people with and without type 2 diabetes across three countries, and instead of asking the usual question — “how does the diabetic gut microbiome differ from the non-diabetic one?” — they asked a sharper one: “what happens when we separate diabetic patients who are on metformin from those who are not?”

The answer rearranged the picture. Untreated diabetics genuinely did have a distinctive and unhealthy-looking gut microbiome: they showed a real depletion of butyrate-producing genera such as Roseburia and Subdoligranulum, and a cluster of butyrate-producing Clostridiales — the very bacteria that nourish the gut lining and tamp down inflammation. That was the true disease signature, and it was consistent across the Danish, Swedish, and Chinese cohorts in the study. But previous papers had not separated treated from untreated patients, so the published “diabetic microbiome” was actually a muddle of two different signals pulling in different directions: the disease signature (depleted SCFA producers) and the drug signature (metformin partly reversing some of those depletions while adding effects of its own, such as an increase in Escherichia species and a decrease in Intestinibacter). When the Forslund team disentangled the two, the metformin-treated diabetics looked, in several respects, more like healthy controls than like their untreated counterparts — at least for the butyrate-producer taxa that the disease had stripped away. The drug was not causing the dysbiosis. It was partly repairing it, even as it introduced its own side-signatures along the way. This was a quietly devastating paper for several earlier microbiome–disease association studies that had not controlled for medication, and it was also the moment when the question “does metformin work through the microbiome?” stopped being a fringe speculation and became a serious mechanistic hypothesis.

What metformin does to the gut community

The shifts that metformin produces in the gut bacterial community are now well characterised. A 2025 individual-patient meta-analysis pooling nine datasets and just over fourteen hundred type 2 diabetes patients [REF:su2025metformin] confirmed and refined the picture. Metformin reliably increases the abundance of two groups of bacteria. The first is short-chain-fatty-acid producers — bacteria that ferment dietary fibre into the small molecules (acetate, butyrate, propionate) that nourish the gut lining and modulate host metabolism. The second, more dramatically, is a single species: Akkermansia muciniphila, a comma-shaped anaerobe that lives in the mucus layer of the colon and has, over the last decade, become one of the most discussed organisms in metabolic microbiome research.

Akkermansia muciniphila is worth meeting properly, because it is going to come back several times in this chapter. Discovered by Muriel Derrien and Willem de Vos in 2004, it makes its living by eating the mucus your own intestinal cells secrete, breaking it down into nutrients it shares with neighbouring bacteria. In low-grade and well-regulated abundance, it appears to be protective — it is associated with leaner body weight, better glucose tolerance, lower systemic inflammation, and a less leaky gut barrier. In a healthy adult Western gut, Akkermansia might make up between one and four percent of the total bacterial cells; in obese and diabetic individuals, it is often substantially depleted; in people who have done well on metformin, it has typically rebounded toward and beyond healthy levels. The same species, somewhat surprisingly, has shown up as a positive predictor in studies of cancer immunotherapy response, of dietary intervention success, and of recovery from antibiotic damage. Whatever it is doing, the body seems to like having it.

The metformin–Akkermansia link is now firm enough that several research groups have begun the obvious experiment: give people Akkermansia directly, as an oral supplement, and see what happens. A 2024 randomised pilot trial gave a strain called Akkermansia muciniphila L2-7 to twenty-five men with type 2 diabetes who were already on stable metformin, and reported improved glycaemic variability and a small reduction in mean arterial blood pressure over fourteen days [REF:attaye2024]. The trial was small and the endpoints modest, but the principle held: a probiotic version of the species the drug is recruiting can produce some of the benefits the drug produces.

Is the microbiome the mechanism?

This is the question the literature is still arguing about, and the honest answer is: partly, yes, but not entirely.

The “yes” comes from several lines of evidence. When metformin is given to germ-free mice, some of its glucose-lowering effect is substantially attenuated. When the gut microbiota of metformin-treated humans is transferred into germ-free mice, the recipient mice show improved glucose tolerance even without the drug. When metformin is given to mice in which a key bile acid receptor (FXR) has been knocked out, much of the metabolic benefit disappears, suggesting that the drug works partly by changing the bile acids that microbes release — which then signal back to the host. A 2024 paper from the Xie group went further, showing that in a stress-induced mouse depression model, metformin reversed depressive-like behaviour through a chain that ran metformin → Akkermansia muciniphila enrichment → altered bile acids → reprogrammed tryptophan metabolism → restored serotonin signalling [REF:xie2024metformin]. Faecal transfer from metformin-treated mice rescued the depressive behaviour in untreated mice. The whole chain is remarkable: a diabetes drug is producing an antidepressant effect through the gut microbiota.

The “not entirely” comes from the fact that metformin also has direct molecular effects on host cells. It does activate AMP-activated protein kinase. It does inhibit a particular complex of mitochondrial enzymes in liver cells. It does, by several routes, reduce the amount of glucose the liver dumps into the blood. A clever 2024 cohort analysis of metformin-treated diabetic men correlated the Akkermansia/Romboutsia ratio in the gut with cognitive performance scores, and with several plasma amino acid metabolites — suggesting that the metformin–microbiome–metabolome axis extends well beyond glucose control [REF:rosell2024]. But the same paper reminds the reader that not every metformin user gets every benefit, and the variability looks at least partly to be a function of who they had in their gut to begin with.

The current best summary, I think, is this. Metformin is a drug whose effects on the body are mediated by both a direct biochemical action on host cells and an indirect microbiome-mediated cascade, and the relative contribution of the two routes depends on the patient, on the dose, and on what was in their gut at the start of treatment. This is not the way the textbook chapters on metformin were written in the 1990s, when nobody knew there were bacteria involved. It is, I suspect, going to turn out to be the way many other drugs work as well — a host effect plus a bacterial effect, intertwined, the two often in the same direction but not always — and the microbiome story of metformin is the canonical example of what the rest of the field is starting to look like.

The unsettling implication

Let me close this section with a thought that I find more interesting than the science alone. If metformin works partly through Akkermansia, and Akkermansia abundance varies enormously between individuals (and across populations, and across diets), then the question of whether metformin will work in you is not solely a function of your liver or your insulin receptors. It is also a function of which microbes you happened to inherit from your mother, which ones you maintained or lost across decades of dietary choices, which antibiotics you took as a child, and what your gut community looks like on the day your doctor writes the prescription. Two patients with apparently identical type 2 diabetes can respond to metformin very differently, and a substantial fraction of that difference is now believed to be microbiome-driven. The clinical implication — still emerging, still being worked out — is that the same drug, given for the same indication, may benefit two patients to very different degrees because their gut ecosystems are not the same. Personalised medicine is often discussed as a matter of human genetics. The metformin story is a reminder that for at least some drugs, the microbial genome of the gut may matter as much as the human genome of the patient.

22.5 The Quiet Erosion: NSAIDs and the Small Bowel You Cannot Feel

A pain reliever that hurts where you cannot feel it

The non-steroidal anti-inflammatory drugs — NSAIDs for short — are among the most widely consumed pharmaceutical compounds on Earth. They include ibuprofen, naproxen, diclofenac, indomethacin, ketoprofen, mefenamic acid, the older aspirin, and the newer COX-2 selective inhibitors such as celecoxib. They are sold over the counter in most countries. They are taken for headaches, period pain, sports injuries, low-grade fever, sprains, post-operative recovery, and chronic conditions such as osteoarthritis and rheumatoid disease. In the United States alone, an estimated seventy million prescriptions are written every year, and over-the-counter use is several times higher. Most users do not think of them as serious drugs at all.

Most people who think of NSAID side effects think of the stomach. NSAIDs reduce the protective mucus layer of the stomach lining by inhibiting the prostaglandin pathway that maintains it, and the resulting ulcers and bleeds — gastritis, gastric ulcer, duodenal ulcer, occasionally fatal upper gastrointestinal haemorrhage — are a well-known and well-publicised harm of long-term use. This is why doctors so often co-prescribe a PPI alongside long-term NSAIDs, and why aspirin is dispensed with that warning written on the bottle.

What is much less appreciated is that the lower part of the small bowel — the jejunum and the ileum, the section the doctor cannot easily reach with an endoscope and that the patient cannot easily feel — is also damaged by NSAIDs, and the damage there is, to a substantial extent, mediated by the gut microbiome.

What the small bowel goes through

The mechanism, worked out gradually across the last fifteen years, looks roughly like this. NSAIDs are absorbed in the small intestine, distributed via the bloodstream, and then secreted back into the bile. The bile carries the drug back into the duodenum, from where it travels down the small bowel a second time. Some NSAIDs (indomethacin, in particular, but also diclofenac and others) undergo this enterohepatic recirculation multiple times, exposing the small bowel mucosa to repeated waves of high local drug concentration that the systemic blood level does not reflect. At the same time, the drug damages the mitochondria of intestinal epithelial cells, weakens tight junctions between them, and reduces local prostaglandin-mediated repair.

So far this is purely a host-toxicity story. The microbiome enters the picture in the next step. As the epithelial barrier loosens, gut bacteria — or, more precisely, fragments of bacteria such as lipopolysaccharide from the outer membrane of Gram-negative organisms — translocate into the lining and trigger an inflammatory response through the TLR4/NF-κB signalling pathway, the same innate-immunity pathway we met in the chapters on the immune system. The inflammation in turn damages more epithelium, the barrier loosens further, and a self-amplifying cycle of NSAID enteropathy is set in motion. Animal experiments have shown convincingly that the same dose of indomethacin produces dramatically more small-bowel damage in conventional mice than in germ-free ones. The drug is doing some of the damage. The bacteria are doing the rest.

A 2025 mouse study put numbers on this. Indomethacin-induced enteropathy was associated with characteristic dysbiosis: a loss of protective tight-junction proteins (ZO-1, claudin, occludin) and a shift in bacterial composition with an increase in Firmicutes and a loss of certain Bacteroidetes taxa. Restoring the barrier with a fermented food intervention reduced both the dysbiosis and the diarrhoea rate by about fifty percent [REF:varela2025]. A separate experimental line has shown similar findings with high-altitude hypoxia plus aspirin — a model relevant to climbers and to soldiers — in which the dysbiotic and inflammatory damage was substantially worse than with either insult alone, and was attenuated by a plant-derived compound that protected the intestinal barrier and rebalanced the microbial community [REF:tuerxuntayi2025].

In humans the effect is harder to study, because most of the small bowel is essentially inaccessible to ordinary clinical investigation. But studies that have looked — using video capsule endoscopy to image the otherwise unreachable middle of the small bowel — have repeatedly shown small ulcers and erosions in a substantial fraction of long-term NSAID users who have no symptoms whatever and no abnormalities in routine blood tests. The injury is real, it is widespread, and most of the people carrying it have no idea.

Aspirin is a special case

I want to single out aspirin for a separate mention, because aspirin has a strange and double-edged relationship with the gut microbiome.

Low-dose aspirin (typically 75 to 100 mg daily) is taken by tens of millions of people worldwide for cardiovascular prevention, stroke prevention, and — in some patients — colorectal cancer prevention. The cardiovascular benefit is well established. The colorectal cancer benefit, demonstrated in several large cohorts, is more interesting because the mechanism has never been entirely clear. Some of it is anti-inflammatory; some of it is anti-platelet; and a growing body of work suggests that some of it may be microbiome-mediated. Aspirin shifts the gut bacterial community, and some of the shifts it produces (toward butyrate-producing taxa, toward a less pro-inflammatory mucosal environment) are in the direction one would expect of a colorectal-cancer-protective intervention.

At the same time, low-dose aspirin causes the same small-bowel injury as the higher-dose NSAIDs, just less of it. The standard combination of low-dose aspirin plus a PPI — protection of the stomach with simultaneous unhindered exposure of the small intestine — may, in fact, worsen small-bowel damage by altering the upper gut bacterial environment in a way that exacerbates lower-gut injury. This is one of the paradoxes the field is still working through, and it is one reason why the simple slogan “take a PPI to protect against NSAID damage” is more complicated than it sounds.

What it means in practice

What I want the reader to take away from this section is not that NSAIDs are dangerous and should not be used. They are useful drugs, and for many conditions there is no equally good alternative. What I want the reader to take away is that long-term use is the case where the microbiome story matters, and the principle is the same as for PPIs: the lowest effective dose for the shortest necessary duration. An ibuprofen for a sprained ankle is fine. A naproxen taken twice a day for fifteen years for chronic back pain, with no break and no discussion of whether the indication is still strong, is the case where the slow erosion of the small bowel and its bacterial community has had decades to accumulate, and where a conversation with a doctor about whether a different approach might serve the same purpose is worth having. As always, that is a conversation between a patient and a clinician who knows the case — not a recommendation from a popular-science book.

A practical postscript. Several recent animal studies have shown that dietary measures that strengthen the gut barrier — soluble fibre, polyphenol-rich foods, certain fermented foods — reduce NSAID-induced small bowel damage in laboratory models. The human evidence for this protective effect of diet against NSAID enteropathy is, I should say honestly, still thin. But the dietary measures themselves are good for many other reasons, well discussed in Chapter 20, and there is no harm in adding them. If you take an NSAID daily and you also eat thirty different plants a week, you are very probably better off than if you take an NSAID daily and live on white bread and ham. The mechanism by which this is true is not yet pinned down. The direction of the effect, in every animal experiment that has tested it, is the same.

22.6 Statins, Cholesterol, and the Bact2 Enterotype

The most successful drug class of the late twentieth century

Statins are, by any reasonable measure, one of the great therapeutic successes of modern medicine. Atorvastatin, simvastatin, rosuvastatin, pravastatin, and the other members of the class have lowered the cholesterol levels — and, more importantly, the rates of heart attack and stroke — of hundreds of millions of people worldwide over the last three decades. The trial evidence is overwhelming and the cardiovascular benefit in patients at meaningful risk is one of the most reproducible results in clinical medicine.

What is less settled than most people assume is exactly why statins work as well as they do. The textbook mechanism is straightforward: they inhibit the enzyme HMG-CoA reductase, the rate-limiting step of cholesterol synthesis in the liver, and the resulting fall in LDL cholesterol slows the progression of atherosclerosis. But if cholesterol reduction were the whole story, then other drugs that lower LDL by a comparable degree ought to produce comparable survival benefits. And here the picture gets interesting. Ezetimibe, which blocks cholesterol absorption from the gut by a completely different pathway, does lower LDL effectively — yet its monotherapy evidence for reducing hard cardiovascular endpoints or overall mortality is remarkably thin; no major trial has even tested ezetimibe monotherapy against placebo for survival [REF:cochrane2019eze]. PCSK9 inhibitors, which drive LDL to impressively low levels by yet another route, do reduce cardiovascular events in large trials, but even here it has taken very large studies to demonstrate the benefit, and all-cause mortality reductions have been elusive [REF:khan2023pcsk9]. The simplest interpretation is that statins derive their clinical benefit from both LDL lowering and a suite of cholesterol-independent effects — sometimes called pleiotropic effects — that include repair and protection of the vascular endothelium (partly through upregulation of nitric oxide synthase), direct anti-inflammatory activity (statins lower C-reactive protein and suppress several inflammatory cytokines through inhibition of isoprenoid signalling), stabilisation of atherosclerotic plaques, and modest antithrombotic properties [REF:oesterle2017]. The relative contribution of cholesterol lowering versus pleiotropy is still debated, but the debate itself matters for this chapter, because at least some of those pleiotropic effects may turn out to involve the gut microbiome.

What is also true, and rarely discussed outside the specialist literature, is that not everyone responds the same way. A substantial minority of statin users show smaller-than-expected reductions in LDL cholesterol on a given dose, or experience side effects (muscle aches, modest rises in blood sugar) that are sometimes attributed to genetic variation in the enzymes that metabolise the drug. Some of that variability is undoubtedly genetic. Some of it, the more recent literature suggests, is bacterial.

The Bact2 finding

In 2020 a Belgian-led group around Sara Vieira-Silva and Jeroen Raes published a paper in Nature that examined the relationship between obesity, inflammation, and the gut microbiome in two large European cohorts [REF:vieirasilva2020]. They had previously characterised what they called enterotypes — broad statistical clusters of gut microbial community structure that recur across many human populations. One of those enterotypes, which they named Bact2, is characterised by high Bacteroides abundance, low microbial cell density, low diversity, and a strong association with chronic inflammation and with several inflammatory and metabolic diseases. Bact2 is, in essence, the “unhealthy” enterotype: it shows up disproportionately in obesity, inflammatory bowel disease, and various metabolic disorders.

The finding that made the paper unexpectedly interesting was this. Among obese individuals, those who were taking statins had a much lower prevalence of the Bact2 enterotype than statin-naive obese individuals with otherwise similar characteristics. The effect was substantial: obese people on statins looked, at least by this microbiome metric, more like lean controls than like other obese people. Statin use, in other words, was associated with a microbiome that was less inflamed.

The paper was careful to state that this was an observational association and not proof of causation — perhaps people with already-healthier guts were more likely to be put on, and tolerate, a statin. But the finding generated a substantial follow-up literature, and the current best interpretation is that statins do exert a real, modest, beneficial effect on the gut bacterial community — partly by changing bile acid composition (statins reduce hepatic cholesterol output, which reduces bile acid production, which changes the microbiome, which then changes how the host handles bile acids in a feedback loop), and partly through direct effects on bacterial growth that have not been fully worked out.

A more complicated picture

More recent work has complicated this story without overturning it, and it leads into a question that I think is genuinely interesting, even if the answer is not yet settled.

A 2022 multi-cohort study by Verhaar and colleagues asked why different people respond so differently to the same statin at the same dose [REF:verhaar2022]. The answer, it turned out, was partly in their gut bacteria. People with a Bacteroides-dominated, low-diversity microbiome — essentially the same Bact2 enterotype that Vieira-Silva had linked to inflammation — showed the strongest cholesterol-lowering response to statins. They were two to four times more likely to reach their LDL target than people with a more diverse, Ruminococcus-enriched gut community. The drug worked hardest in the people whose microbiome was already least healthy.

But here is the twist. Those same Bact2-enterotype individuals — the “best responders” in terms of LDL lowering — also experienced the greatest disruption to glucose control. And the reason appears to be precisely the microbiome: the Bact2 enterotype is depleted in short-chain-fatty-acid producers such as Faecalibacterium and Subdoligranulum, the bacteria whose metabolic products help maintain insulin sensitivity and nourish the gut lining. People whose gut community was rich in butyrate producers and in Akkermansia (the Ruminococcus-enriched enterotype) showed statin efficacy without the same metabolic cost. The microbiome, in short, was acting as a buffer — or the lack of it was leaving certain patients exposed.

This connects to a broader question in cardiovascular medicine that deserves a mention in this chapter, even though it is not yet a microbiome story with a proven ending. In secondary prevention — that is, in people who have already had a heart attack or stroke — the evidence is clear: more intensive statin therapy saves lives, and driving LDL lower is generally better. But in primary prevention — healthy people being treated to prevent a first event — the picture is less straightforward. A 2024 retrospective study of nearly 178,000 primary prevention adults (not on statins, not diabetic) found a striking U-shaped curve between LDL cholesterol and all-cause mortality [REF:lind2024]. The lowest mortality risk sat in the LDL range of 100 to 189 mg/dL. Below that — LDL under 80 mg/dL — mortality was 23 percent higher than the reference group, and the association persisted after adjusting for confounders. Very high LDL carried a similar penalty in the other direction [REF:liu2021nhanes]. This is observational data, and confounding by indication (sicker people may have lower cholesterol for reasons that also kill them) is always a concern. But the signal has appeared in enough independent cohorts, including the ASPREE trial of healthy older adults, that it cannot be easily dismissed.

Nobody has yet proven that the microbiome is part of the explanation for excess mortality at very low LDL levels. But the pieces are suggestive. Statins shift the gut community toward Bacteroides dominance and away from SCFA-producing Firmicutes [REF:liu2025statin]. The degree of that shift tracks with the magnitude of LDL lowering. The people who respond most aggressively to statins in terms of cholesterol are the ones whose SCFA producers are most depleted. And SCFA depletion is itself associated with increased gut permeability, chronic low-grade inflammation, and impaired glucose metabolism — all of which are risk factors for the very conditions (infection, metabolic disease, frailty) that drive excess mortality at the lower end of the cholesterol distribution. Whether these threads connect causally is a question for the next generation of prospective studies. But I would not be surprised if, in ten years, the U-shaped mortality curve and the statin–microbiome interaction turn out to be part of the same story.

What I take away from the statin literature

Statins are excellent drugs and the cardiovascular benefit, in people who have established cardiovascular disease or who are at genuinely high risk, dwarfs any plausible microbiome cost. That arithmetic is not in doubt. What is in doubt — and what a thoughtful reader should carry away from this section — is whether the same arithmetic applies to the increasingly common practice of prescribing statins for primary prevention in people at modest absolute risk. The microbiome cost is real, modest, and probably partially offsettable by the same kinds of dietary measures (high fibre intake, plant variety) that we recommend independently for cardiovascular health. But it is a cost, and like all costs it matters more when the benefit it is being weighed against is smaller. The statin–microbiome interaction is also a beautiful example of how a drug can be cardioprotective and gut-perturbing at the same time, and of how the simplistic question “is this drug good for you or bad for you?” has to be replaced by the more honest question “what is it doing, and to what, and how much?” If you are on a statin and wondering whether it is right for you, the answer is almost certainly a conversation with your doctor, not a paragraph in a popular-science book — but it is a conversation worth having, especially if the original indication was primary prevention and has never been reviewed.

22.7 The Mind-Altering Drugs: Antipsychotics, Antidepressants, and the Microbial Cost of Calm

Why this section is harder than the others

Of all the drug classes in this chapter, the psychiatric medications — antipsychotics and antidepressants in particular — are the ones I find most difficult to write about responsibly, for two reasons.

The first is that they are taken by many millions of people for serious illnesses that, untreated, cause enormous suffering and substantially increased mortality. Severe schizophrenia, bipolar disorder, major depression, and the more severe forms of anxiety are not minor inconveniences. The drugs that treat them are imperfect and have real side effects, but the alternative — for the patients who actually need them — is often terrible. I do not want a single reader to come away from this section thinking that the gut microbiome is a reason to stop their psychiatric medication. It is not.

The second reason is that the gut microbiome of people with severe mental illness is itself altered, in ways that reflect the illness as well as the treatment. Schizophrenia is associated with a different gut community than the absence of schizophrenia, even before any drugs are taken. So is depression. So is bipolar disorder. The arrow between gut and brain runs in both directions, as we saw at length in Chapter 13 on the gut–brain axis, and disentangling what the drug does from what the condition does is genuinely difficult. Most of the relevant studies cannot do it cleanly.

With those caveats acknowledged, let me describe what is known.

Antipsychotics and the weight problem

The atypical antipsychotics — olanzapine, clozapine, risperidone, quetiapine, and several newer agents — have been a substantial improvement over the older drugs in many respects. They have, however, a notorious side effect: weight gain. Some patients on olanzapine gain ten or fifteen kilograms within the first year of treatment. The weight gain is associated with insulin resistance, raised blood lipids, and a substantially increased long-term risk of type 2 diabetes and cardiovascular disease. For many years the standard explanation involved the drugs’ effects on appetite-regulating receptors in the brain, particularly histamine and serotonin pathways. This explanation was not wrong, but it was incomplete.

Around 2015 several groups began to notice that olanzapine produces large and reproducible changes in the gut microbiota of laboratory animals, and that these changes track closely with the metabolic effects. A 2024 paper from Kamath and colleagues compared two atypical antipsychotics, olanzapine and lurasidone, in rats over twenty-one days [REF:kamath2024]. Olanzapine, as expected, produced substantial weight gain, raised inflammatory markers, raised blood glucose, and raised triglycerides — and also produced a marked dysbiosis with reduced diversity, depleted short-chain-fatty-acid-producing bacteria, and reduced Akkermansia muciniphila. Lurasidone, in the same experimental setup, did almost none of these things. The two drugs had very similar therapeutic profiles in the brain — both occupy dopamine D2 receptors in roughly the same way — but only one of them was wrecking the gut.

This is a useful piece of evidence because it begins to separate the microbiome story from the brain-receptor story. Whatever the antipsychotic is doing in the gut is not a necessary consequence of antipsychotic action; it is a consequence of olanzapine specifically, mediated by mechanisms that lurasidone, despite its similar therapeutic profile, does not share. A 2023 longitudinal rat study tracked the trajectory of olanzapine-induced dysbiosis dose by dose and showed that the abundance of certain Alistipes and Oscillibacter taxa correlated, almost in lock-step, with the magnitude of weight gain [REF:qian2023olanzapine]. The relationship was tight enough that the gut community was effectively a real-time biomarker of the metabolic damage being done.

The mechanistic question that follows is whether the dysbiosis causes the weight gain or is merely a by-product of it. Several papers have addressed this. The most provocative finding is that prebiotic interventions that restore Akkermansia muciniphila — including a galacto-oligosaccharide supplement called B-GOS that selectively feeds the species — reduce or prevent the metabolic side effects of olanzapine in mouse models [REF:zeng2024olanzapine]. If the dysbiosis were merely a passive marker, fixing it should not help. Fixing it, in animals, helps. The interpretation, increasingly accepted in the field, is that olanzapine causes some of its weight gain by directly suppressing protective gut bacteria (especially Akkermansia), which loses the host the metabolic benefits those bacteria normally provide, and that restoring those bacteria can substantially blunt the weight gain.

A 2024 mechanistic review from Bertossi laid out this case in detail and proposed Akkermansia probiotics as an adjunct to olanzapine therapy [REF:bertossi2024]. A 2024 systematic review of the broader literature, looking at twelve clinical and preclinical studies of various antipsychotics, confirmed that dysbiosis is a near-universal finding across the class but that the magnitude varies enormously from drug to drug [REF:dias2024]. The clinical implication, still being worked out in human trials, is that the choice between antipsychotics — when several agents would do roughly the same therapeutic job — might reasonably take into account which one is likely to do less damage to the gut. This is a conversation between psychiatrist and patient. It is not a reason to refuse a drug that the patient genuinely needs. But for some patients, for whom several options exist, it is a real consideration that did not exist a decade ago.

Antidepressants and the dysbiosis question

The antidepressants are a more complicated case, partly because the class is more heterogeneous and partly because depression itself is associated with gut microbiome changes that make the treatment-versus-illness question hard to answer.

Several SSRIs (selective serotonin reuptake inhibitors) — sertraline and fluoxetine in particular — were among the drugs flagged in the original Maier 2018 screen as having direct antibacterial activity at concentrations comparable to those reached in the gut during normal therapeutic use [REF:maier2018]. This was a surprising finding, because nobody had designed these drugs as antibiotics, and yet there was the antibacterial effect on the dish. A handful of subsequent papers have shown that long-term SSRI use does shift the gut microbiome in human cohorts, generally in the direction of reduced diversity and altered tryptophan metabolism. (Tryptophan, the amino acid precursor of serotonin, is also the substrate from which gut bacteria produce a range of important signalling molecules, so anything that touches tryptophan availability is likely to ripple through both the brain and the gut community.)

What is much harder to say, with current evidence, is whether the microbiome changes induced by SSRIs contribute to the antidepressant effect, oppose it, or are merely incidental. There is a coherent and increasingly evidenced argument — drawing on the gut–brain axis literature in general — that the gut bacteria participate in mood regulation through several routes, and that drugs which affect both the central nervous system and the gut microbiota are likely to be operating on both fronts simultaneously. The metformin-as-antidepressant chain I described earlier in this chapter [REF:xie2024metformin] is a striking example of a drug working on the brain through the gut, and there is no fundamental reason to assume that an SSRI does not do something similar, in addition to its better-known direct effects on serotonin reuptake.

What I would say to a reader on a long-term antidepressant is exactly what I would say to a reader on a long-term PPI: the drug is doing something to your gut community, the something is real and probably modest, and the right question is not “should I stop?” but “is this drug still serving the purpose it was started for, and is the dose still the lowest one that achieves the goal?”. For someone whose depression is being well controlled by an SSRI, the gut microbiome story is a piece of context, not a reason for action. For someone who has been on an SSRI for fifteen years without the indication being formally re-reviewed, that conversation is probably worth having — and it is the same conversation people are increasingly being encouraged to have anyway, on grounds entirely separate from the microbiome.

A careful word about the gut–brain feedback loop

There is one more thing worth saying here, because it is genuinely interesting and because it complicates the picture in a constructive direction. If the gut microbiome influences mood — and the evidence from Chapter 13 is that it does — and if certain dietary interventions and certain probiotics can shift the gut community in ways that improve mood (the so-called psychobiotic literature, which is small but real) — then there is a possibility, still being explored, that for mild mood disturbance some of the work that an antidepressant is currently doing pharmacologically might be doable nutritionally instead. I want to be very careful about this. It is not a recommendation. The evidence for psychobiotic intervention is currently nowhere near the evidence for SSRIs in moderate-to-severe depression, and substituting one for the other in a serious depression would be irresponsible and probably dangerous. But for mild low mood, mild anxiety, the kind of thing that exists on a spectrum and not as a clinical diagnosis, the case for trying dietary and lifestyle modifications first — fibre, fermented foods, plant variety, exercise, sleep, the other levers from Chapter 21 — is reasonably strong on its own merits, and the microbiome story adds a small additional reason to do so.

For anything more serious than that, please talk to your doctor, and please do not stop or alter psychiatric medication on the basis of this chapter or any other chapter of this book.

22.8 Opioids and the Bowel That Forgets to Move

The opioid drugs — morphine, codeine, oxycodone, hydromorphone, fentanyl, tramadol, buprenorphine, methadone, and many others — are powerful analgesics and are also, by any reasonable measure, the medication class with the most complicated public health story of the early twenty-first century. They are essential for the management of severe acute and cancer pain. They are problematic for the management of chronic non-cancer pain, where the long-term harm-benefit ratio is much less favourable than was once believed. They are dependent-forming, they suppress respiration, and the period from roughly 1995 to 2020 saw, in several countries, an opioid prescribing pattern whose downstream consequences will be the subject of public health discussion for decades.

I am going to set the prescribing-policy debate aside and talk only about what opioids do to the gut microbiome, because that is the topic of this chapter.

The first thing to say is that nearly everyone who takes a regular opioid develops some degree of opioid-induced constipation — OIC for short — and this is recognised by every clinician who treats pain. The proximate mechanism is straightforward and has been understood for decades: opioids bind to mu-opioid receptors on the enteric nervous system (the network of neurons embedded in the wall of the bowel) and reduce the release of acetylcholine, which is the principal driver of gut motility. The bowel slows down. Stool sits in the colon longer. Water is reabsorbed. The result is the hard, infrequent stools that almost every chronic opioid user has to manage.

What is much less appreciated is that the slowed transit, combined with direct effects of opioid receptor activation on the gut epithelium, also produces a substantial dysbiosis. The published human cohort data are sparse — far sparser than for PPIs or metformin — but the animal evidence is consistent and the mechanism is plausible. Chronic morphine administration in mice reduces gut microbial diversity, depletes short-chain-fatty-acid-producing bacteria, weakens the intestinal barrier, and increases bacterial translocation from the gut lumen into the lamina propria and from there into the lymphatic and portal circulation. Some of the systemic side effects of long-term opioid use — including subtle immune dysregulation, increased susceptibility to certain infections, and chronic low-grade inflammation — are now believed to be at least partly mediated by this gut barrier dysfunction.

The clinical implication is mostly that opioid-induced constipation is both more difficult to manage and more biologically consequential than was once thought. The standard interventions — increased fluids, dietary fibre, osmotic laxatives, and the newer peripherally-restricted opioid antagonists such as naloxegol and methylnaltrexone, which block opioid receptors in the gut without affecting analgesia in the brain — all have a place. What the microbiome literature adds is a reminder that simply forcing the bowel to move is not the whole story; the underlying community has been disrupted, and dietary support of that community (the by now familiar fibre-and-variety formula) is reasonable adjunctive care.

I want to stress that the opioid-microbiome literature in humans is genuinely thin. Almost everything I have just said is supported by mechanistic plausibility and animal experiments. The human cohort studies that would settle the question — long-term opioid users versus matched controls, sequenced longitudinally, with proper attention to the confounders of pain itself, of the conditions causing the pain, of co-prescribed laxatives, and of diet — have not been done at the scale that has been done for metformin or PPIs. I would expect this to change in the next several years. For now, the honest summary is that opioids do appear to cause meaningful gut dysbiosis, the mechanism is well-supported in animals, and the human data are catching up.

22.9 The Forgotten Effect of Laxatives

Of all the entries in this chapter, this is the one I almost left out. Not because laxatives are unimportant — they are taken regularly by a substantial fraction of older adults, and chronically by some — but because the published literature on laxative effects on the gut microbiome is, frankly, surprisingly small. There is one really good paper, and not very many others.

The really good paper is from Carolina Tropini and colleagues working with Justin Sonnenburg at Stanford, published in Cell in 2018 [REF:tropini2018]. Tropini’s group asked an interesting question: what happens to the structure of the gut microbial community when an osmotic laxative — polyethylene glycol, the active ingredient in many bowel-prep solutions and in chronic-constipation formulations — is administered to mice? The work is best known among microbiome researchers for the elegance with which it disentangled what an ecological catastrophe does to a microbial community. Polyethylene glycol works by drawing water osmotically into the bowel lumen; this dilutes the contents, shortens transit time, and mechanically washes a substantial fraction of the resident bacteria out of the system.

What Tropini and colleagues showed is that this is not a small or transient effect. PEG administration produced a marked drop in microbial cell density and a substantial change in community composition that did not fully recover even weeks after the laxative was withdrawn. Some bacterial species rebounded; some did not. The community that re-established itself after the wash-out was not quite the same as the community that had been there before. In an ecological sense, the laxative had reset the system in a way that had lasting consequences for which species came to dominate the recovered community.

This was a single, careful, mouse-based paper. The directly comparable human work has not, to my knowledge, been done at the same level of detail. But the principle that Tropini’s paper established — that even an apparently mild perturbation like an osmotic laxative can produce lasting community-level changes by transiently changing the physical environment — is a useful reminder that the gut community is shaped not only by what is in it but by the conditions under which it lives. Speed up the transit dramatically and you change which species can hold on. Slow it down dramatically — as opioids do — and you change which species can grow.

A more recent and slightly different angle is the prebiotic effect of lactulose, a non-absorbable sugar widely used as both a laxative and (at lower doses) as a treatment for hepatic encephalopathy. Lactulose is fermented by gut bacteria (it is, after all, a sugar) and selectively expands populations of Bifidobacterium, in a manner that overlaps substantially with the prebiotics discussed in Chapter 20. So lactulose, used long-term, is doing two things at once: it is moving the bowel, and it is feeding a particular subset of the bacterial community. For some patients — particularly those with hepatic encephalopathy — this dual action is part of the therapeutic intent.

What about chronic stimulant laxatives — senna, bisacodyl, the herbal preparations sold for “regularity”? The published microbiome data on long-term stimulant laxative use are, I have to admit, almost nonexistent. The mechanistic concern is reasonable — chronic alteration of transit time will, like chronic alteration of any major physical parameter, eventually shift which microbes can do well in the bowel — but I cannot point to a definitive paper that has measured this in humans. If you are taking a stimulant laxative every day for years, the same general principle that runs through this chapter applies: minimum effective dose, minimum necessary duration, periodic review with the doctor, and the diet-and-fibre adjuncts that are good for the gut anyway. Chronic constipation has many causes and several treatments; for many patients, addressing the underlying cause (insufficient fluid, insufficient fibre, low physical activity, certain medications including some of the ones earlier in this chapter) is a better long-term strategy than indefinite laxative use. That, again, is a clinical conversation, not advice from a book.

22.10 Hormones in a Pill: Contraceptives, HRT, and the Estrobolome

What the gut microbes do to your hormones

There is a curious feature of human biology that does not get the attention it deserves outside specialist circles. The gut bacteria participate, directly and continuously, in the hormonal economy of the body — particularly in the metabolism of oestrogens. This is the basis of a concept that the microbiologist Claudia Plottel and her colleague Martin Blaser introduced in 2011 and called the estrobolome: the collection of bacterial genes in the gut whose products metabolise oestrogens.

The mechanism, in outline, is this. Oestrogens produced by the ovaries (or, after the menopause, by the adrenal glands and adipose tissue) are conjugated in the liver — that is, they have a sugar group called glucuronide attached to them, which inactivates them and earmarks them for excretion through the bile. The conjugated oestrogens travel down the bile duct into the duodenum and from there into the small bowel, where, in a healthy adult, certain gut bacteria express an enzyme called β-glucuronidase that cleaves the glucuronide group off the oestrogen molecule and reactivates it. The reactivated oestrogen is then reabsorbed across the gut wall and re-enters the circulation, where it does its hormonal work all over again. The total amount of oestrogen the body experiences over the course of a day is therefore not just the amount the ovaries (or adrenals) made; it is that amount, plus everything the gut bacteria sent back into circulation. The estrobolome is, in effect, a hormonal thermostat with a microbial component.

This becomes interesting for the present chapter because oral contraceptives and hormone replacement therapy both deliver pharmacological oestrogen (and, in most contraceptives, a progestogen as well) into the system, and that pharmacological hormone interacts with the same enzymatic machinery as the body’s own. A daily oestrogen pill is, from the point of view of the estrobolome, an additional hormonal substrate for the bacteria to handle. And — perhaps unsurprisingly — the gut microbiome of women on long-term oral contraceptives differs measurably from that of women not on them.

The published evidence

The strongest recent human work in this area is a small but careful longitudinal study by Hua and colleagues, published in 2022 in the Journal of Medical Microbiology [REF:hua2022]. They followed ten women across the initiation of oral contraceptive therapy, sequencing the gut microbiome before, one month after, and six months after starting the pill. They did not find dramatic shifts in overall diversity or in the dominant taxa, which was somewhat reassuring. What they did find was a measurable reorganisation of the microbial functional capacity: increases in the bacterial pathways for synthesising peptidoglycan (the cell wall component of Gram-positive bacteria), several amino acids, and the salvage pathway for nicotinamide adenine dinucleotide. Serum oestradiol and SHBG (sex hormone binding globulin, the carrier protein that controls free oestrogen levels) were associated with the abundance of Bacteroides species capable of degrading flavonoids — plant-derived compounds with weak oestrogenic activity. The picture, in short, was of a gut community that was functionally responding to a new hormonal input even when its taxonomic composition looked relatively unchanged.

Subsequent work in 2025 from several groups has added to the picture. Studies in physically active women on hormonal birth control have reported reductions in beta diversity and in short-chain-fatty-acid-producing bacteria compared with non-users [REF:brito2025]. A separate study in mice on longer-term oral contraceptive exposure showed segment-specific changes (the colon was the most affected part of the bowel) that accumulated over time [REF:clapp2025]. The general direction is that hormonal contraceptive use is associated with modest but real shifts in the gut community, that the shifts accumulate over months and years rather than appearing within days, and that they preferentially affect short-chain-fatty-acid producers — the same group of bacteria that turn up in almost every entry of this chapter as a marker of a healthy gut.

What this means for users

I want to be quite clear about what this section is and is not saying.

It is not saying that oral contraceptives cause harm to the gut microbiome serious enough to outweigh their benefits as contraception. The benefits of effective contraception — reduced unintended pregnancy, reduced maternal mortality, reduced reliance on more invasive interventions — are very large. The microbiome cost is real but, on current evidence, modest. For the great majority of users, the trade-off comes out clearly in favour of using the drug.

It is also not saying that hormone replacement therapy at the menopause is something to avoid. HRT, for women whose menopausal symptoms are sufficiently disruptive, is a reasonable and often excellent treatment. The microbiome question for HRT is even less well studied than for contraceptives, but the mechanism is the same — oestrogens cycling through the gut and through the estrobolome — and the magnitude of the effect appears to be modest in the same direction.

What this section is saying is that for women on long-term hormonal contraception or HRT, the standard advice that runs through this whole chapter applies: keep dietary fibre intake high, eat a varied range of plants, include fermented foods if you tolerate them, and have a periodic conversation with your prescribing doctor about whether the dose and the duration are still appropriate for your situation. The microbiome is one of several considerations on the larger ledger of these medications, and like the other considerations, it is best discussed openly rather than ignored.

There is one further interesting wrinkle worth mentioning. Because the gut bacteria recycle oestrogens, the composition of a woman’s gut microbiome may influence how much oestrogen her body actually experiences from a given dose of an oral contraceptive. This is, at the moment, a research question rather than a clinical recommendation — the variation between women is substantial, and the clinical relevance has not been pinned down — but it is part of the broader emerging story that hormonal pharmacology and the gut bacteria are talking to each other in ways that the original drug developers did not anticipate. It is also one more illustration of the chapter’s recurring theme: the same drug, in different people, may be doing different things, partly because of who lives in their gut.

22.11 The Supplement Aisle: Iron, Vitamin D, Fish Oil, and What’s Real About Berberine

A note on “natural”

Before I begin this section, a quick word on the framing. Many readers will, I suspect, take supplements regularly — perhaps a multivitamin, perhaps fish oil, perhaps vitamin D, perhaps a herbal preparation recommended by a friend or read about online. The cultural assumption around supplements is that because they are “natural” or “from food”, they are categorically different from drugs and could not possibly have the kinds of effects on the body that pharmaceuticals have. This is not quite right. A supplement that is biologically active enough to do anything at all is, by definition, biologically active. The molecules it contains do not know whether they came from a pharmaceutical company or a herbalist’s shelf. The gut microbiome, in particular, treats the two with complete impartiality. A daily oral iron tablet is, from the point of view of the bacteria in your duodenum, an iron tablet — they do not care whether the box it came in had a prescription label or a “wellness” label.

With that established, let me look at the four supplement categories the evidence has the most to say about: iron, vitamin D, omega-3 fish oil, and the one herbal medicine that has accumulated genuinely interesting data, berberine.

Oral iron and the Enterobacteriaceae bloom

Iron deficiency is one of the most common nutritional problems in the world, particularly among menstruating women, pregnant women, infants, and older adults. The standard treatment — a daily oral iron tablet, typically ferrous sulphate or ferrous fumarate, often in doses of 60 to 200 mg of elemental iron — is cheap, generally effective, and prescribed (or self-prescribed) in enormous quantities. What the published microbiome literature has shown over the past decade, with reasonable consistency, is that an oral iron supplement of this kind has a measurable and not entirely benign effect on the gut bacterial community.

The basic problem is that iron is a contested resource in the gut. Many of the most desirable bacteria in the human colon — the Bifidobacterium and Lactobacillus species that produce short-chain fatty acids, that support the gut barrier, and that out-compete pathogens — have relatively low iron requirements. Many of the less desirable bacteria — Escherichia coli and other members of the Enterobacteriaceae family, which include several known and several conditional pathogens — are, by contrast, voracious iron consumers and have evolved sophisticated machinery (siderophores, specialised transporters) to scavenge iron from their environment. A high-dose oral iron tablet that has not been absorbed in the duodenum (which most of it has not, because oral iron absorption is inefficient) reaches the colon and provides exactly the resource the more pathogenic bacteria are best at exploiting. The result, in several studies in African infants given iron-fortified weaning foods, was a measurable bloom of Enterobacteriaceae and a measurable increase in markers of intestinal inflammation. In some studies the iron-supplemented infants also had higher rates of clinical diarrhoea, although this finding has been inconsistent.

A 2024 randomised controlled trial in Bangladeshi infants, the largest of its kind to date, gave a more nuanced picture [REF:baldi2024]. With nearly a thousand children randomised to placebo, oral iron, or iron-containing micronutrient powders, the headline findings on diarrhoea were reassuring — no overall increase in diarrhoea was seen — and the primary microbiome analysis, after adjustment for multiple comparisons, did not show statistically significant changes in alpha or beta diversity. But the unadjusted trends were consistent with the earlier literature: a decrease in Bifidobacterium and Lactobacillus, and an increase in Clostridium species with pathogenic potential, particularly in children whose iron stores were already adequate at baseline. The lesson the field has gradually drawn from this body of work is that oral iron supplementation is most useful and least risky in children (and adults) who are genuinely iron-deficient, and that giving it to people who are not deficient is the case in which the gut microbiome cost looks most concerning.

For an adult reader who is taking an iron tablet, the practical implications are these. First, take iron because you have a documented need for it — a low ferritin, a low haemoglobin with appropriate workup, a clear medical reason — rather than as a routine preventive supplement. Second, the lower-dose, slower-release formulations (such as ferrous bisglycinate, sucrosomial iron, and the alternate-day dosing strategies that have become more popular in the last few years) appear to deliver useful amounts of absorbed iron with substantially less unabsorbed iron reaching the colon, and on first principles should produce smaller microbiome perturbations. Third, iron given by injection — when oral iron is not tolerated or absorbed — bypasses the gut entirely and avoids the colonic-bloom problem. Animal data support this route as gentler on the bowel ecology [REF:tian2025iron]. Most clinicians do not switch to intravenous iron lightly because it is more invasive and more expensive, but it is a reasonable option for patients who have shown they cannot tolerate oral iron well.

Vitamin D: probably good for the gut, although the data are still settling

Vitamin D is a slightly unusual case. Unlike most of the entries in this chapter, the published evidence on vitamin D and the gut microbiome is, if anything, modestly positive. Vitamin D deficiency is associated with various forms of dysbiosis in human and animal studies; correcting the deficiency tends to improve the gut community and to strengthen the gut barrier. A 2025 mouse study showed that withdrawing vitamin D in an aging-model system worsened intestinal permeability, depleted Akkermansia muciniphila, and shifted the community in a pro-inflammatory direction [REF:feng2025vitd]. Another 2025 paper showed that vitamin D supplementation improved the gut community and the hypothalamic-pituitary-adrenal axis in mice subjected to sleep desynchrony [REF:wu2025vitd]. The mechanism appears to involve direct effects of vitamin D on epithelial barrier proteins, on intestinal antimicrobial peptide production, and on the regulation of various aryl hydrocarbon receptor (AhR) signalling pathways that the gut bacteria themselves use.

The clinically actionable point, for a reader who has been told they are vitamin D deficient and offered a supplement, is that the vitamin D is probably doing their gut a small favour as well as the more famous favour to their bones. The evidence base does not yet support megadose supplementation as a microbiome intervention in someone whose vitamin D level is normal — the relevant studies have not been done. The standard message of “correct deficiency, do not chase ever-higher levels” applies here as it does to vitamin D supplementation more generally.

Fish oil and the omega-3 question

The omega-3 fatty acids found in fish oil — eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) — have been the subject of an enormous research and supplementation industry over the past three decades. Their cardiovascular benefits in healthy populations are smaller and more controversial than was once claimed, but their anti-inflammatory effects are real, and several animal studies have shown that they shift the gut microbiome in ways that look broadly favourable: increased Akkermansia muciniphila, increased Bifidobacterium, increased Lactobacillus, increased short-chain fatty acid production, and reduced abundance of certain pro-inflammatory taxa. A 2025 systematic review of omega-3 effects on the gut microbiome in animal models of obesity collated thirty-two trials and found a remarkably consistent picture across this whole literature [REF:cao2025omega3].

A particularly elegant 2025 paper using a simulated human intestinal model showed that gut microbes themselves metabolise EPA and DHA into a class of anti-inflammatory lipid mediators called oxylipins — molecules with documented anti-inflammatory and anti-cancer properties [REF:roussel2025fishoil]. So the fish oil is not just a passive substrate; it is being converted by the bacteria into bioactive products that then act on the host. This is one of those mechanistic chains that makes the simple question “does fish oil work?” turn into the more interesting question “what does fish oil get turned into, by whom, and what does that do?”.

The human evidence, I should say, lags the animal evidence. Most of the cleanest mechanistic work is in laboratory models; the human cohort and randomised trial data on omega-3 supplementation and the gut microbiome are smaller and less consistent. For the moment my honest summary is that fish oil at modest doses (one to three grams a day of combined EPA and DHA) appears to be a reasonable addition to most diets, has a small favourable effect on the gut microbiome on the available evidence, and is not going to do harm. Whether it produces the cardiovascular benefit it was once heavily marketed for is a separate question on which the evidence has cooled. The microbiome side of the story is one of the better arguments left for it.

Berberine: the herbal medicine that turned out to be real

Most herbal supplements have, when subjected to careful testing, turned out to be either inert, modestly active, or active in ways that depend so heavily on the preparation method as to be useless to recommend. Berberine is the unusual case of a traditional medicine — a yellow alkaloid extracted from the bark of Berberis species and used in Chinese and Ayurvedic medicine for centuries — that has stood up to modern pharmacological scrutiny rather well, and that has a particularly interesting microbiome story.

Berberine is poorly absorbed from the gut into the bloodstream — only a few percent of an oral dose reaches the systemic circulation — which historically was considered a problem for its development as a drug. What several research groups have shown over the past decade is that the poor systemic absorption is in fact part of the reason it works. Most of the oral dose reaches the colon, where it interacts directly with the gut microbiota, suppresses certain bacterial groups, expands others, and produces effects that parallel those of metformin — increased Akkermansia muciniphila, increased short-chain fatty acid production, improved bile acid metabolism, improved insulin sensitivity, and modest reductions in fasting blood glucose and lipids [REF:guo2023berberine] [REF:kong2020berberine]. Several head-to-head animal comparisons have shown berberine to be approximately as effective as metformin for some metabolic endpoints, with somewhat better effects on lipids and somewhat weaker effects on glucose.

I should be clear about what berberine is not. It is not a tested or licensed treatment for type 2 diabetes in any major regulatory framework, and the human randomised trial evidence, although it exists, is much smaller than for metformin. It is also not free of effects — gastrointestinal upset is common at higher doses, and there are several documented drug interactions, particularly with drugs that are metabolised by the cytochrome P450 system. I would not recommend that any reader replace a prescribed metformin with self-administered berberine on the basis of this paragraph or any other paragraph.

What I would say is that the berberine story is a useful corrective to the assumption that traditional herbal medicines are uniformly ineffective or that microbiome-mediated mechanisms are necessarily weaker than direct pharmacological ones. Berberine works partly because it stays in the gut. Its mechanism is its supposed limitation. It is a reminder that the gut bacteria are a legitimate pharmacological target, that the molecules required to reach that target do not need to be designed by pharmaceutical companies, and that we are very probably going to discover more drugs (or rediscover more old ones) whose action runs through the microbiome rather than around it.

The other commonly-promoted herbal compounds — curcumin, resveratrol, ginseng, the various polyphenol-rich extracts marketed for “gut health” — have fragmentary microbiome data, mostly from animal experiments, with mechanistic stories that are plausible but not yet supported by serious human trials. I would not write any of them off; I would also not pay a lot of money for them on the strength of the existing evidence. The most reliable way to deliver this class of compound to the gut microbiome remains the way humans have always done it: by eating a varied diet of whole foods, particularly plants, particularly the more brightly coloured and bitter ones. That recommendation, made for entirely separate reasons in Chapter 20, turns out to be the sensible default here as well.

22.12 Polypharmacy: The Cumulative Tax

So far, every section of this chapter has discussed a single drug class as if it were the only medication a person was taking. For an unusual reader — a healthy thirty-year-old on a single prescription — that simplification is reasonable. For a much larger group of readers, particularly those over sixty, it is not. The average eighty-year-old in many developed countries is on five or more chronic medications. The average resident of a long-term care facility is on seven or more. Every additional prescription is a separate small intervention on the gut ecosystem, and the question is whether those interventions add up linearly, whether they cancel each other out, or whether they produce something worse than the sum of their parts.

The most serious attempt to answer this question was a 2020 paper by Arnau Vich Vila and colleagues, working with Alexandra Zhernakova and Cisca Wijmenga in the Netherlands, that analysed the gut microbiomes of nearly two thousand people in the LifeLines DEEP cohort and explicitly examined the effects of multiple concurrent medications [REF:vichvila2020]. The Vich Vila paper was important for several reasons. It confirmed the Maier 2018 finding in a large human cohort: many non-antibiotic drugs do, in fact, perturb the gut community in real life and not only in the dish. It identified specific drug combinations that had synergistic effects on the microbiome — that is, the combination produced more dysbiosis than would have been predicted from adding up the effects of the drugs taken separately. And it established that the cumulative number of medications a person was on was, by itself, a meaningful predictor of microbiome disturbance, even after adjusting for the specific drugs involved.

This last finding is the one I want to dwell on, because it has practical implications. A person on eight medications has, on average, a more disturbed gut community than a person on three, even if the drugs are individually mild. The community disturbance is not produced by any one of the drugs being severely toxic; it is produced by the cumulative tax that each modest perturbation extracts. And the same population — older adults on multiple chronic medications — is also the population most vulnerable to the consequences of dysbiosis: increased risk of Clostridioides difficile infection, increased risk of bacterial translocation through a leaky gut, altered drug metabolism in unpredictable directions, and a slower recovery from any further perturbation. The biological insult and the biological vulnerability are concentrated in the same group of patients.

A 2024 paper from Garcia-Santamarina and colleagues, published in Cell, took a different and equally interesting angle [REF:garcia2024]. Using a synthetic community of thirty-two human gut bacterial species in defined culture, they exposed the community to thirty different drugs and measured the consequences. What they found was that bacterial communities have a fair amount of resilience — drug-sensitive species were often protected, in the community context, by drug-resistant neighbours that biotransformed or detoxified the drug locally before it could reach them. Cross-protection between species was observed about six times more often than cross-sensitisation. The implication is that a healthy, diverse gut community can absorb a substantial amount of pharmacological perturbation without collapsing, because it is not a collection of isolated species but a network of interacting metabolisms. The other implication, more sobering, is that this resilience is finite: when the perturbation is large enough, or when the diversity has already been reduced (as it has in elderly polypharmacy patients, in people recovering from antibiotics, in people with inflammatory bowel disease), the network can no longer compensate, and the community collapses in a way that a single-species view of pharmacology would not have predicted.

The clinical takeaway from the polypharmacy literature is, I think, the most actionable one in this whole chapter. It is the practice of deprescribing — the systematic, periodic review of long-term medications to ask whether each one is still indicated, whether the dose can be reduced, and whether some can be safely stopped. Deprescribing is a real and growing area of geriatric medicine, with its own guidelines and a small but rapidly expanding evidence base. Most of the existing arguments for deprescribing are made on grounds entirely separate from the microbiome — falls, cognitive impairment, drug-drug interactions, simple cost. The microbiome story adds one more reason. Each additional drug taken without continuing necessity is, among other things, a small additional tax on the gut community, and the gut community is already under more pressure in the people most likely to be on too many drugs.

If you are in your seventies or eighties and on five or more medications, and you have not had a formal medication review in several years, that review is worth requesting. It is, again, a conversation between you and your doctor, and there is no implication that the answer will be to stop anything. The likelihood is that some of the medications will turn out to be still necessary, some will turn out to be candidates for dose reduction, and one or two may be relics of older prescribing decisions whose original indication is no longer present. Each one that comes off the list is a small piece of pressure removed from the ecosystem inside you.

22.13 Pharmacomicrobiomics: When Your Microbes Decide What Your Medicine Does

A two-way street

So far in this chapter the story has run in one direction: drugs do things to bacteria. There is, however, an equally important and equally interesting story running in the opposite direction. Bacteria do things to drugs. The gut microbiome is a chemical factory the size of a large pharmaceutical company, and many of the drugs you swallow are processed — sometimes activated, sometimes inactivated, sometimes converted into entirely different molecules — by bacterial enzymes before they ever reach their intended target. The young field that studies this is called pharmacomicrobiomics, and although it is still in early days, several of its findings already have direct clinical relevance.

The landmark paper that put the whole question on the map was from Michael Zimmermann, Maria Zimmermann-Kogadeeva, and Andrew Goodman at Yale, published in Nature in 2019 [REF:zimmermann2019]. Their team took 271 different oral drugs in current human use and tested each one against a panel of human gut bacterial isolates, asking a simple question: how many of these drugs are chemically modified by gut bacteria? The answer was 176 of the 271 — about two thirds. The bacteria were doing something chemically interesting to the majority of the drugs the researchers tested. Some of those modifications were minor; some were dramatic, and dramatically affected the activity of the drug. The Zimmermann team also identified, in many cases, the specific bacterial enzymes responsible — opening up the possibility of predicting, from a person’s gut metagenome alone, how their bacteria are likely to handle a given drug.

The classical examples

A few examples will make the practical relevance concrete.

Digoxin and Eggerthella lenta. Digoxin is an old and still useful drug for certain heart conditions, particularly atrial fibrillation and heart failure. It has a notoriously narrow therapeutic window — too little and it does nothing, too much and it is dangerous — and clinicians have long known that digoxin levels in the blood vary unpredictably from patient to patient even at the same dose. Henry Haiser and Peter Turnbaugh, working at Harvard, showed in 2013 that part of this variability is due to a single gut bacterium, Eggerthella lenta, which carries an enzyme system that inactivates digoxin in the gut [REF:haiser2013]. People who have abundant Eggerthella lenta (and the relevant gene cluster) inactivate a substantial fraction of an oral digoxin dose before it can be absorbed; people who do not, do not. Adding a high-protein meal — which paradoxically appears to suppress Eggerthella’s digoxin-inactivating enzyme — increases the bioavailability of digoxin in the affected individuals. This is a bacterium changing how much of a drug actually reaches its target. It is not a rare oddity; Eggerthella lenta is a normal member of the human gut microbiota, present in most people, but variable in abundance.

Levodopa and Enterococcus faecalis. Levodopa is the cornerstone of pharmacological treatment for Parkinson’s disease. It is taken by mouth, absorbed in the small intestine, and converted in the brain into dopamine, the neurotransmitter that Parkinson’s patients are deficient in. A long-standing puzzle has been why the dose required varies so much between patients, and why oral levodopa loses some of its effectiveness over years of treatment. Vayu Maini Rekdal and Emily Balskus, working at Harvard, published a paper in Science in 2019 showing that Enterococcus faecalis — a normal gut bacterium — carries an enzyme called tyrosine decarboxylase that converts levodopa into dopamine in the gut lumen, before it has a chance to reach the brain [REF:mainirekdal2019]. The dopamine produced peripherally cannot cross the blood–brain barrier, so the drug effectively gets wasted. Patients with high Enterococcus faecalis burden need more levodopa to achieve the same brain effect, and that requirement varies between individuals in a way that the specifics of brain chemistry alone do not explain. Maini Rekdal and Balskus also identified specific small-molecule inhibitors of the bacterial enzyme that, in principle, could be co-administered with levodopa to prevent the gut wasting. A clinical implementation of that idea has not yet emerged, but the proof of concept is there.

Irinotecan and bacterial β-glucuronidases. Irinotecan is a chemotherapy drug used for several cancers, including colorectal cancer. After it does its job in tumour cells, the drug is conjugated in the liver and excreted into the bile in an inactive form. In the colon, gut bacteria expressing a particular family of enzymes — bacterial β-glucuronidases — cleave the conjugate and reactivate the drug locally. The reactivated drug then damages the colonic epithelium, causing the severe diarrhoea that is one of the most feared side effects of irinotecan therapy. This is a case where a bacterial enzyme is essentially producing the toxicity of a drug. Several research groups are now developing inhibitors of bacterial β-glucuronidases as adjuncts to irinotecan therapy, with the explicit goal of preventing colonic reactivation while leaving the drug’s anti-tumour effect intact. Some of these inhibitors are now in early clinical testing.

The bigger picture

I have given three examples because they are well characterised and clinically vivid. What the field is increasingly suggesting is that these examples are not isolated curiosities; they are the visible peaks of a much larger underwater landscape of bacterial drug metabolism that is only beginning to be mapped. The Zimmermann 2019 paper — bacteria modifying two thirds of 271 tested drugs — implies that the digoxin and levodopa stories are typical rather than exceptional, and that for many drugs the story we currently tell about pharmacokinetics in textbooks (the ADME mnemonic — absorption, distribution, metabolism, excretion — that medical students learn) is missing a crucial fifth letter, M for microbial.

The clinical implication, looking forward, is that the same drug at the same dose can produce very different blood levels and very different therapeutic effects in two patients, depending on what is in their gut. This is the same lesson the metformin section of this chapter delivered, from the other direction: the gut bacteria are not just a passive recipient of pharmacological intervention; they are an active and unpredictable participant. Personalised medicine, as I noted earlier, has so far focused mostly on the human genome. The pharmacomicrobiomics literature is making it harder and harder to ignore the bacterial genome.

For the moment, the practical implications for an individual reader are limited. There is no commercially available “gut metagenome panel” that will tell you which drugs you are likely to handle differently because of your bacteria, and the field is some years away from being able to use such information to adjust prescribing in real time. But the principle is worth knowing about, both because it is genuinely interesting and because it provides a deeper understanding of what is going on when a drug works wonderfully in your neighbour and not at all in you. The textbook explanation — “individual variation” — has, for at least some drugs, a specific cause, and the cause lives in your gut.

22.14 What This Means for the Person Reading This Book

Let me try to gather the threads of this long chapter into a few practical, undramatic conclusions.

The first conclusion is that almost every drug you can swallow does something to the gut microbiome. For a quarter of common non-antibiotic drugs, that something is direct suppression of bacterial growth in the dish, at human-relevant concentrations [REF:maier2018]. For the rest, the effects are usually subtler — shifts in community composition, changes in metabolite production, alterations in the activity of specific bacterial enzymes, perturbations of the gut barrier — but they are real, they are measurable, and they are not zero. The era in which it was reasonable to assume that a drug acts only on its declared target is over.

The second conclusion is that this fact is, for the most part, not a reason to stop your medication. The drugs that have made it through modern regulatory processes did so because, on balance, their benefits outweigh their harms. The microbiome cost is one harm among several, and in the great majority of cases it is one a sensible patient and a sensible doctor will accept in exchange for the benefit on offer. A blood-pressure pill that prevents your stroke is not a less good idea because it has been found to nudge your Bacteroides count by ten percent. A statin that prevents your heart attack is not a less good idea because it modestly depletes some butyrate producers. The arithmetic, for the indications for which these drugs are properly prescribed, comes out clearly in favour of taking them.

The third conclusion is that long-term use of any chronic medication is the case where the microbiome story matters most, and where the principle of minimum effective dose for the minimum necessary duration deserves to be more than a slogan. A short course of any of the drugs in this chapter is a small perturbation that the gut community is well-equipped to recover from. Decades of daily exposure, particularly when the original indication has faded or was never strong, is a sustained pressure that the community handles less well. The actionable response is not to stop the drug; it is to have a periodic conversation with your doctor about whether the indication still holds, whether the dose can be reduced, and whether the continuing benefit is still clearly worth the continuing cost.

The fourth conclusion concerns supplements. A supplement that does anything biologically also does something to the gut bacteria, and the cultural assumption that “natural” equals “harmless” does not survive contact with the data. Iron supplements, in particular, deserve more respect than they often get; they should be taken when there is a documented need and not as a routine precaution. Vitamin D and fish oil look reasonable on the available evidence and are worth considering when the indication is appropriate. The herbal aisle is mostly hype, with berberine as the interesting exception that has done well in head-to-head testing for some metabolic indications. Most of the gut microbiome benefit that supplement marketers promise from their bottles is more reliably delivered by eating a varied diet of plants — a recommendation made for entirely separate reasons in Chapter 20.

The fifth conclusion concerns polypharmacy. If you are over sixty and on five or more chronic medications, and you have not had a formal medication review in some years, please ask for one. The strongest case for it is not the microbiome — it is falls, cognitive function, drug interactions, cost, and the simple principle that medications accumulate over a lifetime more easily than they get removed. The microbiome story adds one more reason in the same direction. Deprescribing is, in many cases, one of the kindest things a clinician can do for a long-term patient, and it is a conversation more worth having than most patients realise.

The sixth and final conclusion is the one I find most interesting personally. The science of pharmacomicrobiomics — bacteria doing things to drugs as well as drugs doing things to bacteria — is making it clear that the same drug, at the same dose, in two patients with apparently identical clinical situations, can do very different things, partly because their gut communities are not the same. This is not a recommendation. There is no test you can take, no action you can perform on the basis of this knowledge today. But it is a way of understanding why the simple picture of pharmacology you may have been taught — drug goes in, target gets hit, effect follows — is, for a substantial fraction of the drugs in use, an oversimplification of something genuinely more complicated and more interesting. Your microbes are, in a small but real way, deciding what your medicine does.

I want to leave the reader with the same image that closed Chapter 21. The body is an ecosystem under continuous chemical pressure, much of the pressure invented in the last hundred years. The medications discussed in this chapter are some of the most effective inventions in the history of human civilisation; the microbiome cost we have only recently learned to see is a real but, in most cases, modest part of the larger ledger. Knowing that the cost is on the ledger does not change whether to use the drugs. It changes how we use them, and how we talk about them, and how we think about the slow, accumulating relationship between modern pharmacology and the trillions of small partners we have been carrying around inside us all along.

The next two chapters turn, finally, from the question of what we do to the microbiome to the question of what we can do for it. Chapter 23 takes up the long and complicated story of probiotics — the supplements you can buy in any pharmacy that promise to deliver “good bacteria” to your gut, and the rather more careful science of which of these promises is real. Chapter 24 takes up faecal microbiota transplantation, the most extreme and most dramatic of the microbiome interventions, the one that has saved lives from Clostridioides difficile and that is now being tested for a rapidly expanding list of other conditions. Together, those chapters complete the picture this chapter opened: now that we know how easily we can perturb the gut community, what can we do, deliberately, to put it back together?

References for Chapter 22

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[REF:garcia2024] Garcia-Santamarina S, et al. Emergence of community behaviors in the gut microbiota upon drug treatment. Cell. 2024;187(22):6346-6357.e20. doi:10.1016/j.cell.2024.08.037

[REF:zimmermann2019] Zimmermann M, Zimmermann-Kogadeeva M, Wegmann R, Goodman AL. Mapping human microbiome drug metabolism by gut bacteria and their genes. Nature. 2019;570(7762):462-467. doi:10.1038/s41586-019-1291-3 (PMID: 31158845)

[REF:haiser2013] Haiser HJ, Gootenberg DB, Chatman K, Sirasani G, Balskus EP, Turnbaugh PJ. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. Science. 2013;341(6143):295-298. doi:10.1126/science.1235872 (PMID: 23869020)

[REF:mainirekdal2019] Maini Rekdal V, Bess EN, Bisanz JE, Turnbaugh PJ, Balskus EP. Discovery and inhibition of an interspecies gut bacterial pathway for Levodopa metabolism. Science. 2019;364(6445):eaau6323. doi:10.1126/science.aau6323 (PMID: 31196984)

Chapter 23: Probiotics — Promise, Hype, and Hard Truths

23.1 What “Probiotic” Actually Means — and the Gap Between Definition and Marketplace

Walk into any pharmacy in the developed world and you will find, somewhere between the vitamins and the digestive aids, a shelf of products labelled “probiotic.” Some are capsules. Some are sachets. Some are chilled drinks in small plastic bottles with pictures of smiling intestines on the front. They carry claims that range from the cautiously vague — “supports digestive health” — to the breathtakingly specific — “clinically proven to boost immunity.” They cost anywhere from a few dollars to well over a dollar per capsule. They are purchased, according to market research firms, in quantities that make the global probiotic market worth somewhere between seventy and eighty billion US dollars a year, with projections approaching one hundred and thirty billion by the end of this decade. That is roughly three times the entire global market for influenza vaccines.

And yet if you ask a microbiologist what the word “probiotic” actually means — what it is supposed to mean — the answer you get back is considerably more modest than anything the packaging suggests.

In 2014, an expert panel convened by the International Scientific Association for Probiotics and Prebiotics — ISAPP — published a consensus statement in Nature Reviews Gastroenterology & Hepatology that attempted to nail down the definition once and for all [REF:hill2014]. The panel, chaired by Colin Hill and including leading figures in microbiology, gastroenterology, and nutrition science, reaffirmed a definition that had first been proposed by a joint FAO/WHO working group in 2001: a probiotic is a preparation of “live microorganisms which, when administered in adequate amounts, confer a health benefit on the host.” Twelve words. Every one of them load-bearing.

Live. Not dead, not heat-killed, not freeze-dried into permanent inactivity. The organisms must be alive when they reach you. A yoghurt that has been pasteurised after fermentation — killing the bacteria that fermented it — is not a probiotic, whatever the label says. A capsule that sat in a hot warehouse for six months and now contains only dead cells is not a probiotic. A 2019 analysis of commercial products found that roughly sixty per cent did not contain the viable organisms, at the counts claimed on the label, by the time they reached the consumer [REF:jackson2019]. The definition says “live.” The marketplace often delivers “were alive once.”

In adequate amounts. The dose matters. A product containing ten million colony-forming units of Lactobacillus rhamnosus GG is not the same product as one containing ten billion, and the clinical trials that demonstrated a benefit for that strain used a specific dose — typically ten billion CFU per day in adults. A product that contains the right species but the wrong dose — or a dose that was adequate at the time of manufacture but has dropped by ninety per cent by the time the consumer opens the bottle — does not meet the definition. Most commercial products specify their CFU count “at time of manufacture” rather than “at end of shelf life,” a distinction that the label does not draw attention to and that most consumers do not know to look for.

Confer a health benefit. This is the phrase that separates a probiotic from a fermented food. Yoghurt, kimchi, sauerkraut, and kefir all contain live microorganisms. They may be good for you — we discussed this in Chapter 20. But unless a specific strain in a specific product has been shown, in controlled human studies, to produce a measurable health outcome, the product does not meet the ISAPP definition of a probiotic. It is a fermented food. The distinction sounds pedantic, but it is the difference between a clinical claim and a culinary tradition.

The Hill panel made one more point that deserves emphasis. The health benefit, they said, must be demonstrated for a specific strain, not merely for a species. This is perhaps the single most important sentence in the entire consensus statement, and it is the one the marketplace ignores most comprehensively. Let me explain why it matters.

The strain problem

In microbiology, a species is a broad category — like saying “dog.” A strain is a specific lineage within that species — like saying “border collie.” Two strains of the same bacterial species can differ in their gene content by five to ten per cent, which is enough to make one of them a harmless commensal and the other a dangerous pathogen. Escherichia coli K-12 is a standard laboratory organism that could not hurt you if you swallowed a litre of it. Escherichia coli O157:H7 is the strain that causes haemolytic uraemic syndrome and kills children. Same species. Wildly different organisms.

The same principle applies to probiotic bacteria. Lactobacillus rhamnosus GG — usually abbreviated LGG — is one of the most studied probiotic strains in the world, with more than thirty years of clinical trial data behind it. It has demonstrated benefits for antibiotic-associated diarrhoea in children, for acute gastroenteritis, and for a handful of other specific conditions. But another strain of Lactobacillus rhamnosus — say, one isolated from a different source and given a different designation — may have none of these properties. It may not colonise the same way. It may not produce the same metabolites. It may not interact with the immune system in the same fashion. It is the same species, but it is not the same organism in any functional sense that matters to the patient.

This is not a theoretical concern. The World Gastroenterology Organisation, in its 2024 updated guidelines on probiotics, stated explicitly that recommendations must link specific strains to specific benefits based on human studies, and that systematic reviews that pool results across different strains are valid only if a shared mechanism of action has been demonstrated [REF:wgo2024]. In practice, this means that when a clinical trial demonstrates that Saccharomyces boulardii CNCM I-745 reduces antibiotic-associated diarrhoea, that finding applies to Saccharomyces boulardii CNCM I-745, sold commercially as Florastor. It does not automatically apply to a different Saccharomyces boulardii strain sold in a different capsule by a different company, even if the label says the same species name.

Now go back to that pharmacy shelf. How many of those products specify the strain? Some do — the better ones, the ones backed by companies that fund clinical research on their own products. Many do not. They say “Lactobacillus acidophilus” without specifying which Lactobacillus acidophilus. They say “probiotic blend” without telling you which blend, in which proportions, at which dose. And they are perfectly legal, because in most countries probiotics are regulated as dietary supplements, not as drugs, and dietary supplement regulations do not require the kind of strain-level identification and dose-level standardisation that drug regulations do.

The gap between the ISAPP definition and the pharmacy shelf is, I think, the single most important thing to understand before reading the rest of this chapter. The definition describes a medical intervention: a specific organism, alive, in a specific dose, with a demonstrated benefit. The marketplace sells a consumer product: a capsule containing some bacteria, probably alive, in some quantity, with a health claim that may or may not be supported by evidence — and if it is supported, the evidence may have been generated using a different strain, a different dose, and a different population from the one now buying the product. This is not fraud, exactly. It is something more subtle and in some ways more corrosive: a regulatory structure that allows the language of medicine to be used without the rigour of medicine.

Everything that follows in this chapter needs to be read against that background. When I say “probiotics work for condition X,” I mean that one or two specific strains, at specific doses, in specific populations, have been shown to work in controlled trials. When I say “probiotics don’t work for condition Y,” I mean that the trials done so far, using the strains and doses tested, have not demonstrated a benefit — which is not the same as proving that no strain at any dose could ever help. The language of strain specificity is cumbersome, and I will not always write out the full strain designation in every sentence. But the reader should keep it in mind throughout: the word “probiotic” is doing a great deal of work, and much of it is work the evidence does not support.

23.2 The Evidence Hierarchy: Which Strains, for Which Conditions, Actually Work?

If we take the ISAPP definition seriously — specific strains, adequate doses, demonstrated benefits — then the natural next question is: which strains, for which conditions, have actually passed the test?

The honest answer is: fewer than you might expect, but more than zero, and the list is not evenly distributed across the conditions that probiotics are most commonly purchased for.

Where the evidence is strong

The strongest evidence in the probiotic literature — the conditions for which multiple randomised controlled trials, meta-analyses, and in some cases Cochrane systematic reviews converge on a positive finding — clusters around a small number of gastrointestinal conditions. I am going to walk through the main ones, and then I am going to tell you what is not on the list.

Antibiotic-associated diarrhoea is the condition for which probiotics are most commonly recommended and most commonly purchased. The logic is intuitive: antibiotics disrupt the gut ecosystem, diarrhoea results, and adding “good bacteria” should help restore the balance. The clinical trial evidence supports this logic — to a point. A 2025 Cochrane review pooling forty-seven randomised trials and more than fifteen thousand participants found that probiotics reduced the risk of Clostridioides difficile-associated diarrhoea, with a number needed to treat of approximately sixty-five — meaning sixty-five people need to take the probiotic to prevent one case [REF:esmaeilinezhad2025]. For broader antibiotic-associated diarrhoea (not just C. difficile), the risk reduction is more substantial: roughly a thirty-three per cent relative reduction and a nine per cent absolute reduction. The strains with the best evidence for this indication are Saccharomyces boulardii CNCM I-745 and Lactobacillus rhamnosus GG. For S. boulardii specifically, a meta-analysis of twenty-seven trials found a sixty-three per cent reduction in antibiotic-associated diarrhoea risk [REF:mcfarland2024].

This sounds like a clear win, and for the individual patient with a genuine risk of antibiotic-associated diarrhoea — someone on a broad-spectrum antibiotic, particularly clindamycin or a fluoroquinolone, or someone with a history of previous episodes — it probably is. But the numbers deserve a closer look. A sixty-five-person NNT for C. difficile prevention means that for every person who benefits, sixty-four take the probiotic and derive no measurable advantage from it. The absolute risk reduction of nine per cent for broader diarrhoea means that ninety-one out of a hundred people would have had the same outcome without the probiotic. These are not negligible benefits — in a hospital population, preventing even a small number of C. difficile cases saves lives and enormous cost. But they are modest benefits, and they are benefits of specific strains at specific doses, not of the generic “probiotic” that most people buy at the pharmacy without knowing which strain is inside.

Acute infectious diarrhoea in children is the second area of strong evidence. LGG and Saccharomyces boulardii both shorten the duration of acute gastroenteritis in children by roughly one day, a finding replicated across dozens of trials. The effect is real but modest, and the evidence quality, when assessed by formal grading systems, is rated as low to moderate — not because the trials are badly done, but because the heterogeneity between studies (different strains, different doses, different populations, different definitions of “diarrhoea”) makes it difficult to pool the results cleanly.

Infant colic is a surprising entry on the list, but the evidence is unusually clean. A single strain — Lactobacillus reuteri DSM 17938 — has been shown in six randomised trials enrolling more than four hundred infants to reduce crying time in colicky breastfed infants from roughly ninety minutes per day to thirty-five minutes per day at three weeks. The number needed to treat is between four and five, which is extraordinary for any intervention in any field of medicine. The catch — and there is always a catch in probiotic research — is that the benefit has been demonstrated only in exclusively or predominantly breastfed infants. In formula-fed infants, the same strain at the same dose does not appear to work [REF:sung2018]. The reason is not known with certainty, but the most likely explanation is that the breast-milk oligosaccharides that selectively feed Bifidobacterium in the infant gut also create an environment in which L. reuteri can establish; the formula-fed gut, with its different community structure, does not provide the same niche.

Necrotising enterocolitis in preterm infants is the most dramatic success story in the probiotic literature, and I am going to return to it in detail in section 23.7, because it also illustrates the ecological tensions at the heart of this chapter. For now, the headline: a 2025 meta-analysis of fifty-one randomised trials enrolling nearly twelve thousand very preterm or very low birth weight infants found that specific probiotic combinations — particularly Bifidobacterium plus Lactobacillus — significantly reduced the incidence of necrotising enterocolitis (the most feared gastrointestinal emergency in neonatal intensive care) and all-cause mortality [REF:chen2025nec]. The ProPrems trial, one of the largest individual studies, showed a fifty-four per cent reduction in NEC using a three-strain combination of Bifidobacterium longum subsp. infantis BB-02, Streptococcus thermophilus TH-4, and Bifidobacterium animalis subsp. lactis BB-12 [REF:jacobs2013]. This is real, life-saving medicine. It is also medicine for a very specific population — extremely premature infants in neonatal intensive care — and it tells us almost nothing about whether a healthy adult should take a probiotic capsule for “gut health.”

Where the evidence is weak or absent

Now let me tell you what is not on the list, because the conditions that are not on the list are, by and large, the conditions for which most probiotic products are actually marketed.

“Immune boosting” is probably the single most common claim on probiotic packaging, and it is, in the strict sense, not a clinical outcome at all. It is a marketing phrase. The immune system is not a muscle that can be “boosted” by swallowing a capsule. It is a complex regulatory network that can be pushed in many directions, some of them helpful and some of them harmful — an “over-boosted” immune system is what causes autoimmune disease. The evidence that specific probiotic strains can modulate specific immune parameters in specific populations is real but limited. One seven-month trial of LGG found a thirty-four per cent reduction in respiratory infections in children attending day care. That is a single trial with a specific strain in a specific population. It is not evidence that the phrase “supports immune health” on a multi-strain capsule bought by a forty-year-old office worker means anything at all.

Weight loss is another area of heavy marketing and thin evidence. A handful of trials using specific strains have shown small reductions in body weight, waist circumference, or body fat percentage — on the order of one to two kilograms over eight to twelve weeks. The results are inconsistent between trials, the effect sizes are smaller than those of any conventional weight-loss intervention, and a 2024 systematic review concluded that “greater homogeneity in procedures is needed in future clinical trials to establish clear recommendations” — which is a polite way of saying the field is a mess [REF:pena2024].

“Gut health” as a general concept — the idea that taking a daily probiotic capsule maintains or improves the health of the digestive system in a person who is not otherwise ill — has, as far as I can find, no evidence from randomised trials in healthy adults. The European Food Safety Authority, which regulates health claims on food products in the European Union, has evaluated hundreds of applications for probiotic health claims over the last fifteen years. It has approved none. Zero. Not one probiotic product sold in Europe has met the EFSA standard for a permitted health claim. This does not mean that probiotics are useless — the evidence for specific strains in specific conditions, described above, is genuine. It means that the broad, non-specific claims that drive most consumer purchasing have not met the evidentiary standard that the European regulatory system requires.

Irritable bowel syndrome sits in an uncomfortable middle ground. Some trials show benefit. Some do not. A 2023 meta-analysis in Gastroenterology found that probiotics as a class modestly improved overall IBS symptoms, but the evidence quality was rated as low to very low, the effect sizes were small, and the results varied enormously by strain [REF:ford2023]. Nine specific strains or combinations showed some evidence of benefit for specific IBS subtypes, but consistent guidance on which strain to choose for which patient is, as the authors noted, “lacking.” For a condition that affects roughly ten per cent of the adult population in Western countries, the inability to offer a specific, evidence-based probiotic recommendation after two decades of research is a striking gap.

The evidence-to-shelf disconnect

I want to end this section with a number that I think captures the problem better than anything else I have found. A 2019 survey of ninety-three probiotic products available in Canadian pharmacies found that only thirty-three — thirty-five per cent — could be linked to any clinical evidence of efficacy. Of those thirty-three, only eighteen provided a potency guarantee through end of shelf life rather than at time of manufacture. Products with fewer strains were more likely to be evidence-based than products with more strains, presumably because the multi-strain blends were formulated for marketing appeal rather than clinical logic [REF:jackson2019].

If you are a consumer standing in front of that pharmacy shelf, the odds are roughly two in three that the product you pick up has no clinical evidence behind it at all. If you happen to pick one of the one-third that does, the odds are roughly even that the viable count inside the capsule matches what the label promises. These are not the odds of a mature, evidence-based therapeutic category. They are the odds of a marketplace in which the scientific story and the commercial story have diverged to the point where they are barely recognisable as accounts of the same subject.

23.3 The Colonisation Problem — Most Probiotics Don’t Establish Residence

There is, beneath the evidence questions I have just described, a more fundamental biological problem that most probiotic marketing quietly ignores. It is this: most probiotic organisms, when you swallow them, do not establish residence in your gut.

They arrive. They pass through. Some of them are detectable in stool samples for a few days or weeks. But they do not, in most people, colonise the mucosal surface of the intestine — the surface that matters, the surface where bacteria interact with the immune system, compete with other organisms, and produce the metabolites that influence host physiology. They are tourists, not immigrants.

This is not a surprise to microbiologists, although it would be a surprise to most consumers. In Chapter 5, I described the principle of colonisation resistance — the ecological observation that a diverse, established microbial community resists invasion by newcomers. The principle was first demonstrated experimentally in 1954, when Bohnhoff, Drake, and Miller showed that streptomycin treatment reduced the infectious dose of Salmonella required to colonise the mouse gut by a factor of one hundred thousand [REF:bohnhoff1954]. Remove the established community, and the pathogen walks in. Leave the community intact, and the pathogen cannot gain a foothold. The principle applies equally to “beneficial” organisms introduced from outside. A healthy gut, full of established residents competing for nutrients and attachment sites, is not an easy environment for a newcomer to break into — even a newcomer that would, in other circumstances, be perfectly welcome.

The question of whether probiotics actually colonise the human gut was settled, as definitively as such questions can be settled, by Niv Zmora and colleagues at the Weizmann Institute of Science in Israel, in a paper published in Cell in September 2018 [REF:zmora2018].

What Zmora actually found

The Zmora study did something that, remarkably, had not been done before. Previous probiotic studies had measured probiotic presence in stool — the end product of the gut — and used this as a proxy for what was happening along the intestinal lining. The problem with this approach is that stool is a mixed sample. It contains bacteria from the lumen (the open centre of the gut tube) and bacteria that have been shed from the mucosal surface, and the two populations are not the same. A probiotic organism can transit through the lumen, be detected in stool, and never touch the mucosa at all.

Zmora’s team recruited fifteen healthy volunteers and gave them a commercial multi-strain probiotic preparation containing eleven bacterial strains — a combination of Lactobacillus, Bifidobacterium, Lactococcus, and Streptococcus species. This was not a homemade laboratory concoction; it was a real commercial product of the kind millions of people buy. Each volunteer underwent two colonoscopy procedures — one at baseline before starting the probiotic, and one during probiotic consumption — and during each procedure the team collected mucosal biopsies from multiple sites: stomach, duodenum, jejunum, terminal ileum, and descending colon. They sequenced the microbial communities in these biopsies, in the stool, and in the probiotic preparation itself, and they also performed RNA sequencing on the mucosal tissue to see how the host’s own gene expression responded.

The headline finding was striking. The probiotic organisms did appear in stool — you could detect them passing through. But their colonisation of the mucosal surface was highly variable between individuals. Some people were “permissive” — the probiotic strains established on the mucosa in at least some regions of the gut, and the host tissue showed measurable changes in gene expression in response. Other people were “resistant” — the probiotic strains passed through the lumen without establishing on the mucosal surface, and the tissue showed no detectable response.

The distinction between permissive and resistant individuals was not random. The resistant individuals had distinctive baseline immune signatures — their mucosal tissue showed higher expression of genes involved in T-cell activation, innate immune responses, and inflammatory signalling. In plain language: their immune system was already primed to repel newcomers, and the probiotics, despite being “friendly” organisms, were treated as newcomers and turned away.

What stool doesn’t tell you

The Zmora paper contained a second finding that was, in some ways, more consequential than the first. The stool samples did not predict the mucosal colonisation pattern. You could have two people taking the same probiotic, both showing the same increase in probiotic organisms in their stool, and one of them was permissive (the organisms had colonised the mucosa and were interacting with the host) while the other was resistant (the organisms were merely passing through the lumen without establishing). If all you had was the stool data — which is all that most clinical trials have — you would conclude that both people were “colonised.” The mucosal biopsies told a different story.

This finding puts a large question mark over every probiotic trial that has used stool as its primary readout for colonisation. Most of them, of course, have no choice — colonoscopy is invasive, expensive, and not something you can ask a thousand trial participants to undergo. But it means that the published literature on probiotic colonisation is, to a degree that nobody had previously appreciated, measuring the wrong thing. It is measuring transit, not residence. It is like counting the cars on a motorway and calling them residents of the town they are driving through.

23.4 The Personalisation Problem — Mucosal Colonisation Varies Dramatically Between Individuals

The Zmora finding raises an uncomfortable question for anyone who takes a probiotic or recommends one: is there a way to know, in advance, whether you are a permissive host or a resistant one?

In 2018, the answer was no. The immune signatures that distinguished permissive from resistant individuals in the Zmora cohort were identified retrospectively, by analysing the mucosal biopsies after the fact. No one was going to perform a colonoscopy and a transcriptomic analysis on every patient before recommending a probiotic capsule.

Since then, the field has made tentative progress. A 2025 paper in PLOS Biology demonstrated that computational metabolic models could predict, with seventy-five to eighty per cent accuracy, whether a given probiotic species would engraft in a given individual’s gut, based on the pre-existing metabolic capabilities of their baseline microbiome [REF:heinken2025]. The approach works by modelling the nutrient competition between the incoming probiotic and the resident community: if the resident community already occupies the metabolic niches the probiotic needs, engraftment fails; if there are open niches, engraftment succeeds. It is, in essence, a computational version of colonisation resistance — predicting who has room and who does not.

This is promising, but it is a long way from clinical practice. It requires shotgun metagenomic sequencing of the patient’s stool, construction of a personalised metabolic model, and a computational prediction — none of which are available in a pharmacy or a GP’s office. For now, the practical implication of the Zmora finding is simpler and more uncomfortable: when a doctor recommends a probiotic to a patient, or when a patient buys one for themselves, neither party knows whether the organisms in the capsule will establish in that particular gut. The product might work. It might not. The variation is not random noise in the data. It is a fundamental biological feature of the host-microbe interaction, and it means that the population-level results of clinical trials — “probiotics reduce the risk of X by thirty per cent” — conceal enormous individual variation. For some people in those trials, the probiotic colonised and helped. For others, it passed through and did nothing. The average is positive. The individual outcome is a coin toss.

The analogy I find most useful is organ transplantation. When a surgeon transplants a kidney, nobody expects the organ to function identically in every recipient. Compatibility depends on tissue type, immune status, and the recipient’s overall biology. We accept this because we understand that the immune system treats foreign tissue as foreign, and whether it accepts or rejects the graft depends on a complex negotiation between the transplant and the host. A probiotic is not a kidney, but the principle is the same: introducing a foreign organism into an established biological system is not a simple act of addition. It is an ecological negotiation, and the outcome depends on both parties.

23.5 When Probiotics Backfire: The Post-Antibiotic Paradox

We have arrived at the finding this book has been building toward since Chapter 1.

In section 1.4, I told you that “good” and “bad” are misleading labels for microbes — that context and ecology matter more than identity. In section 5.7, I explained colonisation resistance — the principle that a full ecological house is hard to invade. In section 18.6, I described what happens when antibiotics empty the house: the race to recolonise vacant niches, the vulnerability window, the question of who gets there first. I asked you, at the end of that section, to wonder what would happen if you threw new competitors into the mix during that vulnerable window.

Now I can tell you the answer.

In September 2018, in the same issue of Cell that carried the Zmora colonisation paper, Jotham Suez, Niv Zmora, and their colleagues at the Elinav laboratory published a companion study that is, in my view, one of the most important papers in the modern microbiome literature [REF:suez2018]. The study asked a question that seems, on its face, to have an obvious answer: after a course of antibiotics, does taking a probiotic help the gut microbiome recover?

The experimental design was elegant. Healthy volunteers received a seven-day course of antibiotics (a combination designed to cause substantial microbiome disruption). They were then randomised into three groups. One group received a standard multi-strain probiotic — the same commercial preparation used in the Zmora study — for four weeks. A second group received autologous faecal microbiota transplantation — their own pre-antibiotic stool, collected before the experiment began, was returned to them via upper endoscopy and colonoscopy. A third group received no treatment and was left to recover spontaneously.

All three groups were followed with the same invasive mucosal sampling protocol used in the Zmora study: biopsies from multiple gut sites, stool sampling, and host transcriptome analysis, at multiple time points over months.

The paradox

The result was the opposite of what anyone buying a post-antibiotic probiotic would expect.

The autologous FMT group — the people who received their own bacteria back — recovered fastest. Their mucosal microbiome returned to its pre-antibiotic configuration within days. The host tissue transcriptome — the pattern of genes the gut lining was expressing — also returned to baseline rapidly. The restoration was, by every measure the study could apply, quick and nearly complete. This was not surprising. You were giving the gut back the community it had lost. The residents returned to their own house.

The spontaneous recovery group — the people who received no treatment at all — recovered more slowly. Their mucosal microbiome took weeks to months to return toward baseline, and the return was incomplete, consistent with the Palleja and Dethlefsen findings we discussed in Chapter 18. Scars remained. But the trajectory was in the right direction: the community was rebuilding itself, gradually, from the surviving remnants and from re-exposure to environmental sources.

The probiotic group — the people who received the commercial multi-strain preparation — recovered slowest. Not just slower than the FMT group, which was expected. Slower than the group that received no treatment at all.

Let me say that again, because it is the single most counterintuitive finding in the modern probiotic literature. After antibiotic treatment, taking a standard commercial probiotic delayed the recovery of the gut mucosal microbiome compared to doing nothing.

Why it happens

The mechanism, once you see it, makes ecological sense — which is why this book spent five chapters building the reader’s ecological intuition before arriving here.

After antibiotics, the gut mucosa is, in the language of the Zmora study, in a “permissive” state. The established residents have been killed or depleted. The niches are open. The colonisation resistance that would normally repel newcomers is gone. This is precisely the state in which a probiotic can colonise most effectively — and in the Suez study, the probiotic strains did colonise, readily and extensively. They expanded across the mucosal surface of the post-antibiotic gut with an efficiency they could never have achieved in an intact ecosystem.

But here is the problem. The probiotic organisms, once established in those empty niches, did exactly what any ecological colonist does: they defended their territory. They consumed the available nutrients. They produced metabolites — including, the Suez team showed, soluble factors secreted by Lactobacillus species that actively inhibited the growth of other bacteria in vitro. They occupied the ecological space that the native community needed in order to return.

The native organisms — the patient’s own pre-antibiotic commensals, the survivors hiding in mucosal crypts and in biofilm remnants along the gut wall — were trying to recolonise the same niches. But instead of returning to empty territory, they found it occupied. The probiotic strains were not pathogens. They were not causing disease. They were “beneficial” organisms by any conventional measure. But in the specific context of a post-antibiotic gut, they were functioning as ecological competitors — squatting in niches that belonged to the native community, slowing the return of the organisms that had co-evolved with that particular host over a lifetime.

Think of it this way. Your gut microbiome after antibiotics is like a house that has been emptied by a flood. The original residents are displaced but not destroyed — some are sheltering nearby, waiting for the water to recede so they can return and rebuild. Now imagine that a well-meaning neighbour, hearing that the house is empty, moves a group of strangers into it. The strangers are perfectly nice people. They keep the house clean. They pay the utilities. But when the original residents try to come home, they find the rooms occupied, the fridge stocked with someone else’s food, and the locks changed. The strangers are not hostile. They are simply there, and “there” is where the original residents need to be.

This is what the Suez study showed the probiotic organisms doing in the post-antibiotic gut. They colonised rapidly, they defended their niches competitively, and they delayed the return of the native community — the community that was tailored, by years of co-evolution, to that specific host.

The host transcriptome tells the same story

The Suez team also tracked the host’s own response — the gene expression patterns in the gut lining — and the transcriptomic data reinforced the colonisation findings. In the spontaneous recovery group, the host transcriptome gradually returned toward its pre-antibiotic baseline as the native microbiome rebuilt itself. In the autologous FMT group, the return was fast and nearly complete. In the probiotic group, the host transcriptome remained in a disrupted state for longer than in either of the other two groups. The gut lining was responding to the probiotic organisms as though it were still in a state of ecological disruption — because, at the mucosal level, it was. The native community had not returned, and the signals the gut lining was receiving were not the signals of its usual microbial partners.

23.6 The Competition Framework: “Beneficial” Species as Ecological Disruptors

The Suez finding is not an isolated curiosity. It is the specific instance of a general ecological principle, and once you see the principle, it reframes the entire probiotic conversation.

The principle is this: in a complex microbial ecosystem, the identity of an introduced organism matters less than the context in which it is introduced. A species that is beneficial in one context — in an intact, diverse community where it fills a functional role — can be disruptive in another context — in a depleted, recovering community where it competes with the organisms that need to return. The same organism, the same dose, the same host, but a different ecological state, and the outcome reverses.

This is not a new idea in ecology. Forest ecologists have known for decades that certain tree species — fast-growing, shade-tolerant, nutrient-hungry — can be either valuable members of a mature forest or destructive invaders of a recovering one, depending on the state of the community they enter. In restoration ecology, the concept of “competitive displacement” describes exactly what the Suez probiotics did: a species introduced to help the system recover ends up delaying recovery by outcompeting the native organisms that need to re-establish.

Beyond antibiotics: other vulnerable states

The post-antibiotic gut is the most dramatic example, but it is not the only scenario in which this principle applies. Any state in which the normal colonisation resistance of the gut has been reduced creates the same vulnerability.

Neonates are born with an essentially empty gut, and the first weeks of life are a critical window during which the founding community establishes and shapes the immune system (Chapter 3). Introducing a probiotic during this window is introducing an ecological competitor at a time when the native community has not yet formed — and the long-term consequences of displacing native colonisers with commercial strains during the founding period are largely unknown. I will return to this in section 23.7.

Immunocompromised patients — those on chemotherapy, organ transplant recipients, people with advanced HIV, patients in intensive care — have impaired intestinal barrier function, reduced immune surveillance, and often depleted gut communities from prior antibiotic use. In these patients, the colonisation resistance that normally keeps probiotics confined to the gut lumen is weakened, and the organisms can translocate — cross the gut wall and enter the bloodstream. Case reports of Saccharomyces boulardii fungaemia in ICU patients with central venous catheters are now well documented, including fatal cases. In 2026, a paper in Emerging Infectious Diseases reported two paediatric cases of Lactobacillus rhamnosus catheter-related bacteraemia after cardiac surgery, one of which was fatal [REF:cdc2026]. Whole-genome sequencing confirmed that the bloodstream isolate was genetically identical to the probiotic strain the children had been given. The organism was not a pathogen in any conventional sense. It was a “beneficial” bacterium that had been introduced into a host whose barriers could not contain it.

Post-FMT patients represent another vulnerable population, though a more subtle one. After a faecal transplant, the gut is undergoing rapid ecological reorganisation. Introducing a probiotic during this window adds a competitor to an already chaotic landscape, and the Suez data suggest that the effect is likely to be the same as in the post-antibiotic scenario: delayed reconstitution of the target community.

Nutrient competition as the mechanism

The mechanistic question — how do probiotic organisms delay recovery — has been partially answered by work that followed the Suez paper. The answer, to a first approximation, is nutrient competition.

In a post-antibiotic gut, the niches are open but the resources are finite. The nutrients available — mucosal glycans, dietary polysaccharides reaching the colon, host-derived mucus — can support a certain total biomass. The probiotic organisms, once established, consume a share of those resources. Every calorie consumed by a probiotic Lactobacillus is a calorie not available to a native Roseburia or Faecalibacterium that is trying to re-establish. The competition is not active warfare — the organisms are not producing toxins or engaging in direct combat (though the Suez team’s finding that Lactobacillus-secreted factors inhibit other bacteria suggests some element of chemical interference). It is mostly passive: the probiotic is simply eating the food and sitting in the seat.

The 2025 metabolic modelling paper I mentioned in section 23.4 confirmed this mechanism computationally. When the models predicted successful probiotic engraftment, they also predicted shifts in short-chain fatty acid production and resource allocation within the community — shifts that, in the post-antibiotic context, came at the expense of the recovering native organisms [REF:heinken2025]. The models could predict, based on baseline community composition and nutrient availability, which individuals would experience delayed recovery after probiotic supplementation. The prediction was not perfect, but it was substantially better than chance, and it was driven entirely by metabolic competition — who gets the food.

23.7 The Infant Probiotic Question — NEC Prevention vs Ecological Disruption

Nowhere in the probiotic literature is the tension between benefit and ecological disruption more acute than in the neonatal intensive care unit.

I described the NEC evidence briefly in section 23.2. Let me now expand on it, because the story is both genuinely uplifting — this is a case where probiotics save lives — and genuinely troubling, for reasons that the NEC literature itself has only recently begun to confront.

The case for

Necrotising enterocolitis is the most common and most devastating gastrointestinal emergency in premature infants. It occurs in roughly five to ten per cent of very low birth weight infants (those born weighing less than 1,500 grams), and its mortality rate, once the disease has progressed to the stage requiring surgery, is between twenty and fifty per cent. The survivors often face lifelong complications, including short bowel syndrome and neurodevelopmental impairment. The pathogenesis is complex and not fully understood, but it involves an interaction between an immature gut barrier, an aberrant immune response, and a dysbiotic microbial community — a community that, in extremely premature infants, has had neither the time nor the ecological input to develop normally.

The evidence that specific probiotic strains can prevent NEC in this population is, by the standards of neonatal medicine, very strong. The 2025 meta-analysis of fifty-one trials found that Bifidobacterium and Lactobacillus combinations — particularly the three-strain combination used in the ProPrems trial — reduced NEC incidence by roughly fifty per cent and all-cause mortality by a statistically significant margin [REF:chen2025nec]. The ProPrems trial itself showed a reduction from 4.4 per cent to 2.0 per cent — not a large absolute number, but in a neonatal intensive care unit, where every case of NEC is a potential death, the difference is enormous. Many NICUs around the world now use prophylactic probiotics as standard of care for very preterm infants.

The ecological concern

And yet.

The premature infant gut is the most ecologically vulnerable environment in the human body. It is a founding community — not a mature one, not a recovering one, but one that is being assembled for the first time. The organisms that colonise in the first days and weeks of life are not just tenants; they are the founders, and as we discussed in Chapter 3, the founding community shapes the immune system, the metabolic landscape, and the trajectory of the microbiome for years to come.

When we introduce a probiotic into this environment, we are not adding a supplement to an established community. We are inserting a competitor into a founding event. The probiotic organisms, arriving in a gut that has minimal colonisation resistance, can colonise extensively — as they need to, in order to prevent NEC. But the organisms they displace, or prevent from colonising, are the organisms that would have colonised naturally: species transmitted from the mother’s skin, breast milk, and vaginal flora, species that carry the genetic and metabolic signatures of that particular mother-infant pair.

A 2024 study in Pediatric Research examined the microbiome of extremely preterm infants who had received prophylactic probiotics and found that the probiotic strains did indeed colonise effectively — but the overall community structure was significantly different from that of infants who had not received probiotics [REF:patole2024]. The probiotic infants had higher proportions of the administered strains and lower proportions of certain native colonisers. Whether this matters clinically — whether the displacement of native colonisers by probiotic strains during the founding period has consequences that extend beyond the NICU stay — is a question that no study has yet been designed to answer, because the follow-up required (years, perhaps decades) is beyond the scope of any probiotic trial conducted so far.

This is the dilemma at its sharpest. The NEC prevention benefit is immediate, measurable, and life-saving. The ecological cost, if there is one, is deferred, unmeasured, and theoretical. A neonatologist faced with a 1,200-gram infant at high risk of NEC is not going to withhold a probiotic that halves the NEC rate because of a theoretical concern about long-term ecological displacement. Nor should they. The evidence for prevention is strong; the evidence for long-term harm is absent. But the absence of evidence is not evidence of absence, and the ecological logic — that displacing native founders with commercial strains during a critical developmental window might have downstream consequences — is sound even if it has not yet been tested.

The honest conclusion is that NEC prevention with probiotics is one of the genuine triumphs of microbiome medicine, and also that the long-term ecological consequences of routine probiotic administration in the founding period of the human microbiome are unknown. Both of these things are true at the same time, and a mature relationship with the evidence requires holding both of them simultaneously.

23.8 Probiotics and the Resistome: Can Probiotics Expand Antibiotic Resistance?

In Chapter 18, I described how antibiotics do not merely kill bacteria — they select for resistance. The survivors of an antibiotic course carry genes that confer survival, and the post-antibiotic community is enriched in these resistance genes. The collection of all antibiotic resistance genes in the gut microbiome is known, somewhat ominously, as the “resistome,” and its expansion is one of the most consequential side-effects of antibiotic use.

What I did not discuss in Chapter 18 — because it belongs in this chapter — is the finding that probiotic supplementation after antibiotics can make the resistome worse.

The Suez resistome signal

The first hint came from the Suez 2018 paper itself. In the supplementary analyses, the team noted that the post-antibiotic gut communities of the probiotic group showed a different resistome profile from those of the spontaneous recovery group. The observation was not the central focus of the paper, and it was noted rather than explored. But it caught the attention of the field.

A subsequent study, published in Nature Microbiology in 2021 by members of the same Elinav group, examined the resistome question directly [REF:suez2021resistome]. The team performed deep metagenomic sequencing of stool samples from the original Suez cohorts and tracked the abundance and diversity of antibiotic resistance genes over time. What they found was this: in antibiotic-naive individuals (people who had not recently taken antibiotics), probiotics had a modest and largely benign effect on the resistome — in some “permissive” colonisers, they actually reduced the abundance of certain resistance genes, presumably by displacing the resistant organisms that carried them.

But in the post-antibiotic context — the scenario in which probiotics are most commonly recommended — the picture was different. Probiotic supplementation was associated with an expansion of the resistome, and the expansion was driven not by the probiotic organisms themselves (which, in most cases, did not carry clinically significant resistance genes) but by indigenous bacteria — the patient’s own surviving gut organisms.

The mechanism: ecological disruption as a resistance amplifier

The mechanism is indirect, and it took me a while to understand it, so let me walk through it carefully.

After antibiotics, the surviving gut bacteria include a disproportionate number of organisms carrying resistance genes — that is the basic selection effect of antibiotic treatment. These resistant survivors are initially a minority, held in check by the returning native community as it reconstitutes. In the spontaneous recovery scenario, the native community gradually returns, fills the niches, and drives the resistant minority back to low levels through competitive exclusion. The resistome contracts as the community heals.

In the probiotic scenario, the probiotics occupy the niches instead. The native community cannot return as quickly (this is the Suez finding from section 23.5). The resistant survivors, still present in the community, face less competitive pressure from the native organisms that would normally have outcompeted them. With less competition, the resistant organisms persist at higher levels for longer. Some of them expand into niches that neither the probiotics nor the recovering native organisms have filled. The net effect is that the resistome — the total burden of antibiotic resistance genes in the gut — remains elevated for longer in the probiotic group than in the spontaneous recovery group.

The Elinav team found one particularly striking example. In some probiotic-treated individuals, the post-antibiotic period saw a bloom of indigenous organisms carrying vancomycin resistance genes — genes that confer resistance to one of the last-resort antibiotics used for severe hospital infections. The probiotics did not carry vancomycin resistance themselves. The resistance expansion was a downstream ecological consequence of the niche disruption the probiotics caused. By delaying the return of the normal community, the probiotics had inadvertently created a window in which resistant organisms could expand.

What it means

I want to be precise about what this finding means and what it does not mean.

It does not mean that taking a probiotic will make you antibiotic-resistant. The resistance genes in question are carried by commensal organisms in the gut, not by pathogens in the blood or the lungs. The risk to the individual patient, from a single course of post-antibiotic probiotics, is almost certainly negligible.

What it means is something broader and more insidious. If hundreds of millions of people take probiotics after antibiotics — as the current marketing encourages them to do — and if each of those episodes produces a modest, transient expansion of the gut resistome, then the aggregate effect, across populations and across years, is a slow inflation of the resistance gene pool. Resistance genes in commensal organisms are not inert. They can be transferred horizontally, via plasmids and transposons, to pathogenic organisms. The gut is one of the most active sites of horizontal gene transfer in the human body (Chapter 16). Every resistance gene that persists in a gut commensal is a gene that could, under the right circumstances, end up in a pathogen.

This is not a reason to panic, and it is not a reason to tell people never to take a probiotic after antibiotics. The NNT of sixty-five for C. difficile prevention represents real clinical benefit, and for a patient at genuine risk of C. difficile, the benefit almost certainly outweighs the marginal resistome cost. But it is a reason to be thoughtful — to reserve post-antibiotic probiotics for patients who actually have risk factors for antibiotic-associated diarrhoea, rather than recommending them reflexively to everyone who receives a prescription for amoxicillin. The marketing encourages the reflex. The ecology argues for restraint.

A 2025 study of preterm infants added a further nuance. In very low birth weight infants receiving both antibiotics and probiotics, the probiotics actually reduced the persistence of certain resistance genes — the opposite of what was seen in adults [REF:reyman2025]. The likely explanation is that in the neonatal gut, where colonisation resistance is minimal and the resistome is dominated by hospital-acquired organisms, the probiotic strains displaced some of the resistant hospital bacteria rather than enabling them. Context, once again, is everything.

23.9 Next-Generation Probiotics: Akkermansia, Faecalibacterium, Engineered Strains

If the story so far has been cautionary, this section is about where the field is going — and the direction is, despite everything I have just described, genuinely exciting.

The probiotics that most people buy at the pharmacy are, from a scientific standpoint, first-generation products. They are based on organisms — Lactobacillus, Bifidobacterium, Saccharomyces — that were originally isolated from fermented foods or from the human gut decades ago, selected because they were easy to grow, easy to keep alive in a capsule, and generally recognised as safe. The clinical evidence for some of them, as I have described, is real. But their selection was driven by practicality, not by ecological logic, and they are, in many cases, poor matches for the niches they are expected to fill.

The next generation of probiotics is being selected differently — not for ease of manufacture but for ecological fit, and in some cases, engineered for specific therapeutic functions.

Akkermansia muciniphila: the mucus-layer sentinel

Akkermansia muciniphila has appeared several times in this book — in Chapter 20, where its depletion on low-fibre diets was linked to mucus layer thinning, and in Chapter 22, where its enrichment by metformin was identified as a possible mediator of the drug’s metabolic benefits. It is a Gram-negative, obligately anaerobic bacterium that lives in the mucus layer of the colon, where it feeds on host-derived mucin glycans. In a healthy Western adult, it typically represents one to four per cent of the total gut community. It is depleted in obesity, in type 2 diabetes, in inflammatory bowel disease, and in several other metabolic and inflammatory conditions. Its presence is associated, across dozens of observational studies, with leaner body weight, better glucose tolerance, lower systemic inflammation, and a more intact gut barrier.

The step from “associated with health” to “improves health when administered” is the step that most probiotic candidates fail to make. Akkermansia appears to be making it.

A 2024 randomised controlled trial published in Cell Metabolism gave a pasteurised preparation of Akkermansia muciniphila (strain AKK-WST01) to fifty-eight overweight or obese adults with type 2 diabetes for twelve weeks [REF:depommier2024]. The headline finding was positive: the treatment group showed significant reductions in body weight, fat mass, and HbA1c compared to placebo. But the more interesting finding was the subgroup analysis. The benefit was concentrated almost entirely in participants who had low baseline levels of Akkermansia in their gut. Participants who already had high levels showed no improvement. The organism was helpful precisely when it was filling a gap, and unnecessary when the gap was already filled.

This is the personalisation principle from section 23.4, validated in a randomised trial. It suggests that the future of probiotic medicine is not “everyone takes the same capsule” but “we measure what you’re missing, and give you that.” The concept is straightforward. The implementation — requiring baseline microbiome profiling for every patient — is not.

A separate 2025 trial of a five-strain consortium that included Akkermansia showed improvements in postprandial glucose and HbA1c in type 2 diabetes patients over twelve weeks, with no safety signals. And a 130-participant trial of Akkermansia muciniphila PROBIO showed significant weight reduction with both viable organisms and with pasteurised (heat-killed) preparations — suggesting that at least some of the benefit comes from bacterial surface molecules rather than from living organisms, which would simplify manufacturing and storage enormously [REF:yoon2025].

A word of caution. A 2024 comprehensive safety review noted that while Akkermansia appears safe in healthy and mildly overweight individuals, there are conditions — inflammatory bowel disease, certain autoimmune diseases, post-antibiotic dysbiosis — where its mucin-degrading activity might exacerbate rather than improve mucosal integrity [REF:ashrafian2024]. The organism eats mucus. In a healthy gut with robust mucus production, this is fine — the mucus is continuously replenished, and Akkermansia’s activity stimulates further production. In a gut where the mucus layer is already compromised, adding an organism that degrades mucus might not be wise. Context, once again.

Faecalibacterium prausnitzii: the butyrate factory

Faecalibacterium prausnitzii is the single most abundant bacterium in the healthy adult colon, typically representing five to fifteen per cent of the total community. It is the gut’s premier butyrate producer — the organism most responsible for feeding the colonocytes and maintaining the anti-inflammatory tone of the colonic immune system. Its depletion is one of the most consistently observed features of inflammatory bowel disease, and restoring it has been a goal of microbiome researchers for more than a decade.

The problem has always been practical: Faecalibacterium prausnitzii is an extremely strict anaerobe, meaning it dies on contact with oxygen. Keeping it alive in a capsule, shipping it at room temperature, and delivering it to the colon in viable form are all immense technical challenges. Recent work has made progress — microencapsulation techniques and anaerobic formulation strategies are being developed — but a commercial Faecalibacterium probiotic suitable for widespread use remains, as of this writing, at the research stage.

What the research shows, however, is tantalising. A 2025 paper in the European Respiratory Journal demonstrated that higher intestinal Faecalibacterium levels were associated with better clinical outcomes in community-acquired pneumonia and in ICU patients — the first evidence that a gut butyrate producer might influence respiratory immunity [REF:enaud2025]. A 2024 study showed that pre-colonisation with Faecalibacterium protected against intestinal barrier damage from sleep deprivation in mice, enhancing goblet cell count, mucin production, and tight junction protein expression [REF:li2024faecal]. The organism is, by every measure, a keystone species of the gut ecosystem — the anchor tenant whose presence keeps the whole building standing.

Whether it can be delivered as a probiotic remains to be seen. But the effort is a signal of where the field is heading: away from generic “probiotic blends” and toward the restoration of specific, ecologically critical organisms that are depleted in specific disease states.

Engineered probiotics: programmable living medicines

The furthest frontier of probiotic science is not in the pharmacy but in the synthetic biology laboratory. Engineered probiotics — bacteria that have been genetically modified to perform specific therapeutic functions — are entering clinical development for a range of conditions that no conventional probiotic could address.

The concept is straightforward, even if the execution is not. Take a well-characterised probiotic chassis — Escherichia coli Nissle 1917 is the most common, a non-pathogenic strain that has been used as a probiotic in Germany since 1917 — and equip it with genetic circuits that allow it to sense a disease signal and produce a therapeutic response. The engineered organism colonises the gut, detects the target molecule or environmental condition, and releases its payload locally, at the site where it is needed.

Several of these constructs are now in clinical trials. Engineered bacteria that detect inflammatory markers and release anti-inflammatory cytokines are being tested for inflammatory bowel disease. Others are designed to produce enzymes that break down toxic metabolites in inherited metabolic diseases. A 2026 review in Theranostics described engineered probiotics that exploit the hypoxic environment of solid tumours to deliver cytokines, prodrug-converting enzymes, and immune checkpoint inhibitors directly to the tumour site — a form of cancer therapy that uses the bacterium as a guided missile, colonising the tumour and releasing its therapeutic payload from inside [REF:liu2026].

These are not products you will find on a pharmacy shelf any time soon. They are regulated as drugs, not supplements, and they must pass through the full regulatory pathway of clinical trials, safety assessment, and manufacturing controls. But they represent a fundamentally different conception of what a “probiotic” can be: not a capsule of bacteria you swallow for vague digestive benefit, but a programmable living medicine, designed for a specific disease, with a defined mechanism of action and a measurable therapeutic endpoint.

The irony is striking. The first-generation probiotics were discovered in fermented foods and marketed as supplements, largely outside the framework of clinical medicine. The next-generation probiotics are being engineered in laboratories and developed as drugs, within the strictest regulatory frameworks available. The two generations could hardly be more different in their origins, their evidence base, or their regulatory status. But both call themselves “probiotics,” and the consumer, standing in front of that pharmacy shelf, has no easy way to distinguish between them.

23.10 Regulatory Grey Zones, the Supplement Industry, and What the Consumer Should Know

The regulatory landscape for probiotics is, in most countries, a mess. It is a mess for historical reasons, and understanding those reasons is the shortest path to understanding why the pharmacy shelf looks the way it does.

How we got here

In the United States, probiotics occupy a regulatory no-man’s-land. The Food and Drug Administration does not recognise “probiotic” as a regulatory category. A product containing live bacteria can be classified as a food (if it is a yoghurt or a fermented drink), as a dietary supplement (if it is a capsule or a powder with no disease claims), or as a drug (if it makes a claim to treat, cure, or prevent a specific disease). The classification determines the regulatory burden. A drug must demonstrate safety and efficacy in clinical trials before it can be sold. A dietary supplement must merely be safe and must not make explicit disease claims — but it can make “structure/function” claims (“supports digestive health,” “maintains immune function”) that, to the average consumer, are indistinguishable from disease claims.

The consequence of this framework is that the vast majority of probiotic products are sold as dietary supplements, under rules that do not require clinical trials, do not require strain identification beyond the species level, do not require proof that the organisms are alive at time of purchase, and do not require evidence that the product does what the packaging implies it does. The manufacturer is responsible for ensuring safety. The FDA can act if a product is demonstrably unsafe or if its labelling is fraudulent. But the FDA does not pre-approve supplements, and its enforcement resources are limited. The practical result is that the probiotic supplement market is regulated by the honour system.

In the European Union, the situation is different in detail but similar in outcome. The EU has taken a harder line on health claims — as I mentioned, EFSA has approved zero probiotic health claims to date — but the products themselves are widely available, and in several EU countries the word “probiotic” has been banned from labels precisely because regulators determined that it implies a health benefit that has not been demonstrated. In Italy, by contrast, probiotics are regulated under a specific framework that requires strain identification and minimum viable counts. The regulatory patchwork across the EU is itself a problem for consumers who buy products across borders.

In Australia, the Therapeutic Goods Administration classifies most probiotics as “listed medicines” — a category that requires evidence of safety but sets a lower bar for efficacy than “registered medicines” (which require full clinical trial evidence). The practical consequence is similar to the US: products that make modest health claims can be sold without rigorous evidence.

What the consumer should look for

I am going to close this section with a practical checklist — not because I think a popular-science book should be a shopping guide, but because the reader who has followed this chapter to this point is entitled to know what distinguishes a credible probiotic product from a marketing exercise.

First, the strain should be identified. Not just the species — the strain. A product that says “Lactobacillus rhamnosus” without specifying the strain designation is a product that cannot be matched to any clinical trial. A product that says “Lactobacillus rhamnosus GG” can be checked against the published evidence.

Second, the CFU count should be guaranteed through end of shelf life, not at time of manufacture. If the label says “10 billion CFU at time of manufacture,” what you are holding might contain 10 billion, or 5 billion, or 500 million — you have no way of knowing. A product that says “10 billion CFU at time of consumption” or “through expiration date” is making a stronger commitment.

Third, the claimed benefit should be supported by at least one published randomised controlled trial using the specific strain and dose in the product. This information is sometimes on the packaging, sometimes on the manufacturer’s website, and sometimes absent entirely. If it is absent, that is your answer.

Fourth, the product should come from a manufacturer with a track record of clinical research. Companies that fund and publish clinical trials on their own products — and that register those trials prospectively, before the results are known — are operating in a different universe from companies that formulate a blend of popular species, put it in a capsule, and write “supports gut health” on the label.

Fifth, and finally, the condition should be one for which probiotic evidence exists. If you are taking a probiotic because you are on a course of antibiotics and have a history of antibiotic-associated diarrhoea, and the product contains Saccharomyces boulardii CNCM I-745 or Lactobacillus rhamnosus GG at an adequate dose, you are making a decision supported by evidence. If you are taking a probiotic because you have a vague sense that it is “good for your gut,” the evidence does not support that decision — not because it has been tested and failed, but because it has not been adequately tested at all.

23.11 A Measured Conclusion: Not “Probiotics Are Bad” but “Probiotics Are Drugs We’re Prescribing Without a Diagnosis”

I want to end this chapter by saying clearly what the chapter is not arguing. It is not arguing that probiotics are a scam. It is not arguing that nobody should take them. It is not arguing that the science is all negative. The evidence for specific strains, in specific conditions, at specific doses — antibiotic-associated diarrhoea, NEC prevention in preterm infants, infant colic in breastfed babies — is genuine, replicated, and in some cases life-saving. Anyone who reads this chapter as an argument for throwing away all probiotics has misread it.

What the chapter is arguing is something more nuanced and, I think, more important. It is arguing that probiotics are ecological interventions in a complex microbial ecosystem, and that treating them as generic health supplements — to be taken by anyone, for anything, without reference to the patient’s specific microbiome, the specific strain in the capsule, or the ecological state of the gut at the time of administration — is not just scientifically unsupported but, in some circumstances, counterproductive.

The Suez finding — that probiotics delay post-antibiotic microbiome recovery — is not an argument against probiotics. It is an argument for context. In an intact gut, the colonisation resistance that repels probiotics is itself a sign of health, and the probiotic passes through without harm and without benefit. In a post-antibiotic gut, the same probiotic colonises readily but occupies niches needed by the recovering native community. In a premature infant gut, the probiotic may prevent NEC and save a life, while simultaneously displacing native colonisers whose long-term absence we cannot yet measure. The organism does not change. The context changes. The outcome changes.

This is the lesson that ecology teaches about all interventions in complex systems: the same action, in different circumstances, produces different results. A controlled burn restores a prairie. A controlled burn in the wrong season, with the wrong fuel load, destroys it. The burn is the same. The system is different.

The phrase I keep coming back to is one I heard from a microbiologist at a conference several years ago, and I have not been able to improve on it: “Probiotics are drugs we’re prescribing without a diagnosis.” We are giving patients a microbial intervention without knowing what their microbiome looks like, without knowing whether the organisms in the capsule will colonise their particular gut, without knowing whether the ecological state of their gut at that moment makes colonisation helpful or harmful, and without monitoring the outcome at the mucosal level where it matters. We are, in effect, prescribing a treatment for a condition we have not diagnosed, in a patient we have not examined, using a preparation we have not verified, and measuring the result with a method (stool sampling) that does not reliably reflect what is happening at the site of action (the mucosa).

This is not a counsel of despair. It is a description of where the field is right now — in transition between a first generation of probiotics that were developed without ecological thinking and a second generation that is being developed with it. The future, I believe, will look something like this: a patient presents with a condition for which microbiome intervention is appropriate. Their gut microbiome is profiled — cheaply, quickly, perhaps from a stool sample analysed by machine learning. The profile identifies which species are depleted, which niches are open, and which metabolic functions are impaired. A specific organism, or consortium of organisms, is selected to fill the gaps — not a generic blend, but a tailored prescription, chosen for that patient’s ecosystem. The intervention is monitored, and adjusted if the organism does not engraft or if the ecosystem responds in an unexpected way.

This is precision microbiome medicine. It does not exist yet, except in research settings. But the tools to build it — shotgun metagenomics, metabolic modelling, personalised prediction algorithms, next-generation organisms selected for ecological fit — are all in active development. The gap between where we are and where we need to be is not a gap of knowledge (we understand the principles) but a gap of implementation (the tools are not yet cheap, fast, or scalable enough for routine clinical use).

In the meantime, the consumer stands in front of the pharmacy shelf, holding a capsule that may or may not contain the organisms the label claims, at a dose that may or may not be adequate, with evidence that may or may not apply to the strain inside, for a condition that may or may not respond to the ecological intervention the capsule represents. The consumer is not stupid. The consumer is underserved — by a regulatory system that does not require the information the consumer needs, by a marketplace that profits from the information gap, and by a medical profession that has not yet integrated microbiome science into routine clinical decision-making.

The book you are reading is, in part, an attempt to close that gap. The microbiome is not a marketing opportunity. It is an ecosystem — your ecosystem — and it deserves the same respect we would give to any other ecosystem we depend on for survival. You would not fertilise a forest without knowing what soil it grows in. You should not supplement a microbiome without knowing what community it contains.

Key Chapter References

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[REF:zmora2018] Zmora N, Zilberman-Schapira G, Suez J, et al. Personalized Gut Mucosal Colonization Resistance to Empiric Probiotics Is Associated with Unique Host and Microbiome Features. Cell. 2018;174(6):1388-1405.e21. doi:10.1016/j.cell.2018.08.041

[REF:suez2018] Suez J, Zmora N, Zilberman-Schapira G, et al. Post-Antibiotic Gut Mucosal Microbiome Reconstitution Is Impaired by Probiotics and Improved by Autologous FMT. Cell. 2018;174(6):1406-1423.e16. doi:10.1016/j.cell.2018.08.047

[REF:bohnhoff1954] Bohnhoff M, Drake BL, Miller CP. Effect of streptomycin on susceptibility of intestinal tract to experimental Salmonella infection. Proc Soc Exp Biol Med. 1954;86(1):132-137. doi:10.3181/00379727-86-21030

[REF:wgo2024] World Gastroenterology Organisation. Global Guidelines: Probiotics and Prebiotics. J Clin Gastroenterol. 2024;58(6):498-531. doi:10.1097/MCG.0000000000002002

[REF:esmaeilinezhad2025] Esmaeilinezhad Z, et al. Probiotics for the prevention of Clostridioides difficile-associated diarrhea in adults and children. Cochrane Database Syst Rev. 2025;(9):CD006095.pub5. doi:10.1002/14651858.CD006095.pub5

[REF:mcfarland2024] McFarland LV, et al. Can the evidence-based use of probiotics mitigate antibiotic-associated dysbiosis? A systematic review and meta-analysis of Saccharomyces boulardii. Open Forum Infect Dis. 2024;11(11):ofae615. doi:10.1093/ofid/ofae615

[REF:sung2018] Sung V, et al. Lactobacillus reuteri to treat infant colic: a meta-analysis. Pediatrics. 2018;141(1):e20171811. doi:10.1542/peds.2017-1811

[REF:chen2025nec] Chen Y, et al. Effect of probiotics on necrotizing enterocolitis in preterm infants: a network meta-analysis of randomized controlled trials. BMC Pediatr. 2025;25:469. doi:10.1186/s12887-025-05469-z

[REF:jacobs2013] Jacobs SE, Tobin JM, Opie GF, et al. Probiotic effects on late-onset sepsis in very preterm infants: a randomized controlled trial (ProPrems). Pediatrics. 2013;132(6):1055-1062. doi:10.1542/peds.2013-1339

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[REF:jackson2019] Jackson SA, et al. Improving end-user trust in the quality of commercial probiotic products. Front Microbiol. 2019;10:739. doi:10.3389/fmicb.2019.00739

[REF:pena2024] Peña-Jorquera H, et al. Probiotics for weight loss: a systematic review and meta-analysis. Nutrients. 2024;16(12):1883. doi:10.3390/nu16121883

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[REF:lawley2013] Lawley TD, Walker AW. Intestinal colonization resistance. Immunology. 2013;138(1):1-11. doi:10.1111/j.1365-2567.2012.03616.x

Chapter 24: Faecal Microbiota Transplantation — The Nuclear Option

24.1 A Title That Has to Be Earned

The working title of this chapter, in the outline I have been keeping for the last two years, has been The Nuclear Option, and I have kept it even as other chapter titles came and went. There is something unavoidably right about the phrase. Everything else in the therapeutic part of this book — probiotics, prebiotics, diet, faecal short-chain fatty acids, phage cocktails, next-generation Akkermansia preparations — is, however sophisticated, a nudge. A nudge to an ecosystem that is still more or less the one you grew up with. Faecal microbiota transplantation is not a nudge. It is a wholesale replacement. You take the resident community of one adult gut, you transfer it into another adult gut, and you hope that the one takes root at the expense of the other. In ecology that is not called perturbation; it is called invasion.

Whether the nuclear metaphor flatters the procedure or libels it is something we will have to come back to at the end of the chapter. Nuclear weapons are blunt, collateral, and irreversible; an FMT is precise in its mechanism of action (deliver a whole microbial community to a site where whole microbial communities are supposed to live), and — as we will see — remarkably forgiving in practice. But the metaphor is useful in one specific way: it captures the scale of the intervention, and the seriousness with which it should be contemplated. This is not a probiotic capsule. This is the transfer of an organ.

I want to start with that framing because a great deal of the public conversation about FMT, even in 2026, begins from a misunderstanding that is roughly the opposite of my starting point. Most people, when they first hear about FMT, think either (a) that it is disgusting and so bizarre it cannot possibly work, or (b) that it is a straightforward, almost folk-medical intervention — you literally just take some of their poo and swallow it, right? — that any sufficiently motivated person could organise in a weekend. Both of these conceptions are wrong, and they are wrong in a way that matters. FMT, done properly, is a sophisticated, standardised, heavily regulated procedure that bears almost no resemblance to what a desperate patient would do in their kitchen. And FMT, done in the kitchen, is genuinely dangerous, in ways that have killed people. The gap between the two versions of the procedure is the entire subject of this chapter.

The ground I want to cover is this. First, the ancient prehistory and modern rediscovery of the practice — a story that is older and stranger than most readers suspect. Second, the unambiguous success story: Clostridioides difficile infection, which is where FMT works with a reliability that is, for a treatment this young, almost without parallel in medicine. Third — and this is the promised demystification — what actually happens at a modern FMT clinic, from the moment a donor walks into the office until the moment a recipient walks back out again. There is an entire industry of professional stool banks, IT systems, freezer archives, donor registries, and standard operating procedures sitting between the caricature and the reality, and the reader should know what it looks like. Fourth, the much messier second front: the attempts to extend FMT to inflammatory bowel disease, metabolic syndrome, autism, cancer immunotherapy, and beyond — conditions in which the signal is real but the success rate is not. Fifth, the safety signal that every serious practitioner of FMT has been watching ever since the first death, and what it taught us about donor screening. Sixth, the phenomenon that makes the rest of the chapter intellectually honest: the Do-It-Yourself FMT community, why it exists, what they actually do, why their self-reported outcomes are so much better than the scientific literature would predict, and the difficult ethical territory that the existence of this community forces the medical profession to traverse. And finally, the question of where this is all going — towards a future in which FMT as we currently know it may turn out to be a transitional technology, superseded by defined consortia, spore capsules, and engineered communities that do the same job without needing a donor at all.

One declaration before I begin. I am an emergency physician, and I do not perform FMT. What I have seen, across many years of practice, are the patients for whom FMT was either life-saving or for whom it was the last thing that would have helped and did not happen — the elderly woman on her seventh course of vancomycin for recurrent C. difficile, frail and tired and visibly frightened by the next relapse; the young adult with ulcerative colitis desperate enough to order a stool kit online; the immunocompromised oncology patient whose family asked me, in a corridor, whether the procedure was as disgusting as it sounded. The evidence in this chapter is the same evidence that shapes clinical practice, and I will try to present it with the same rigour I apply elsewhere in this book. But the clinical texture comes from a practitioner who has, repeatedly, been on the downstream end of the decision about whether to refer. The reader should take that for what it is.

24.2 Four Centuries of Yellow Soup, and What We Can Honestly Claim About It

A small claim that one encounters repeatedly in review articles, and occasionally in popular accounts, is that faecal transplantation was “invented in China in the fourth century CE.” The reference, when a reference is given, is to a passage in a Chinese medical text called the Zhou Hou Bei Ji Fang — a “Handbook of Emergency Prescriptions” compiled by the alchemist-physician Ge Hong in the early fourth century — describing the use of a preparation called huanglong tang, the “yellow dragon soup”, for severe diarrhoea and food poisoning. The preparation was made from the fermented stool of a healthy individual, administered orally to the sick. A later, more famous medical encyclopaedia — Li Shizhen’s Bencao Gangmu of 1578 — systematised the practice, listing several stool-derived preparations for gastrointestinal and febrile illness.

I want to be honest about what this evidence can and cannot support. It is genuine: the texts exist, the preparations are described, and the continuity with modern FMT is philosophically real — someone in fourth-century China had the insight that the effluent of a healthy gut could cure the contents of a sick one, and that insight is, at the level of ecology, correct. But the claim that the fourth-century Chinese were “doing FMT” is an overreach. They were doing something that resembles FMT in its logic without sharing any of its epidemiological machinery. There was no donor screening. There was no concept of a specific microbial pathogen to be displaced. There was no way to know whether a batch worked except by watching whether the patient lived. It is a beautiful pre-scientific intuition, and it should be honoured as that; it should not be turned into a press release.

Much closer to the modern idea is a line of veterinary practice that runs through early-modern Europe and into the twentieth century — Italian physicians in the seventeenth century performing transfaunation on ruminants (transferring cud from healthy to sick cows), Bedouin herdsmen feeding fresh camel dung to their own families during dysentery outbreaks in the North African deserts, the latter reportedly relayed to Allied soldiers in the Second World War, who apparently found it worked. These practices are the real throughline from the ancient to the modern period. They tell us that the empirical observation — healthy gut contents cure sick guts — has been rediscovered many times across many cultures, by people who had no way of knowing why.

The modern medical literature begins, properly, in 1958, in Denver, Colorado, in the pages of the journal Surgery, with a four-page report by a surgeon called Ben Eiseman and his colleagues [REF:eiseman1958]. The context is worth recovering. The antibiotic era had just begun in earnest; penicillin had moved from miracle to mainstay; broad-spectrum antibiotics were being used, for the first time, to treat serious surgical infections. And a new problem had started to appear. Patients who had been treated with broad-spectrum antibiotics — usually for abdominal surgical infection, or for septicaemia — were, in a small but devastating proportion, developing a secondary illness in which the lining of their colon died in patches and replaced itself with a grey-yellow pseudomembrane. The condition was called pseudomembranous enterocolitis, and it had a mortality in the published series of the late 1950s of around seventy-five percent.

The cause of pseudomembranous enterocolitis was not known in 1958. (It would take another twenty years to establish that the bug responsible was Clostridioides difficile, and that the reason antibiotics predisposed to the disease was that they cleared the commensal species whose presence normally kept C. difficile in check.) Eiseman did not know the cause. What he knew was that he had four patients in front of him, desperately ill, failing conventional treatment, and that the logic of the situation pointed in a particular direction. If the disease was some kind of overgrowth of a single organism in a gut that had been “sterilised” by the antibiotics, then perhaps reintroducing a healthy gut community might allow the overgrowth to be displaced. Eiseman obtained stool from healthy donors, prepared it as a retention enema, and administered it to his four patients. Three of the four recovered promptly. One died of an unrelated complication. The paper ran to four pages, with no statistics, no randomisation, no mechanism — just the bare observation that something that looked like it should not work, did.

The paper attracted almost no attention. Eiseman did not pursue the technique. The antibiotic era was in its optimistic phase, and the idea that the solution to an antibiotic-caused illness might lie in the deliberate reintroduction of faecal bacteria did not fit the spirit of the age. Across the next fifty years, isolated clinicians in different countries continued to do the procedure on an ad hoc basis — an Australian gastroenterologist called Thomas Borody, in particular, built a substantial private practice around it from the late 1980s onward, and has sometimes been called the father of modern FMT [REF:paramsothy2017] — but the published literature remained a trickle. A 2011 systematic review, gathering every case report and case series that had ever been published, found a total of 317 patients across all indications and all countries [REF:gough2011]. That was what “the evidence” looked like, as recently as fifteen years ago.

And then, in early 2013, a single paper in the New England Journal of Medicine changed everything.

24.3 Amsterdam, 2013: The Trial That Stopped Itself

The paper was by Els van Nood and colleagues, at the Academic Medical Center in Amsterdam, working with the faecal transplant centre at the Leiden University Medical Center [REF:vannood2013]. The disease they were studying was recurrent Clostridioides difficile infection. A word about that disease is needed before the trial can be properly appreciated.

Clostridioides difficile — C. difficile, or C. diff in clinical shorthand — is an anaerobic spore-forming bacterium that lives, at low density, in the intestines of a small minority of perfectly healthy adults. It is kept in check there by the presence of a diverse surrounding community of commensal organisms that out-compete it for nutrients, occupy the intestinal niche it would otherwise colonise, and metabolise bile acids in a way that prevents the spores from germinating. Remove that community — almost always by giving the patient a course of broad-spectrum antibiotics — and C. diff spores that happened to be present in the gut, or are acquired from the hospital environment, germinate into actively growing cells and begin to produce the toxins that are responsible for the disease. The clinical picture is profuse, watery, foul-smelling diarrhoea; sometimes abdominal pain and fever; in severe cases, pseudomembranous colitis, toxic megacolon, perforation, and death. C. diff is the most common cause of healthcare-associated infection in the developed world. In the United States alone, on the most recent estimates, it causes around half a million infections and twenty to thirty thousand deaths every year.

The problem that led to the van Nood trial is a specific one: recurrence. When you treat C. diff with an antibiotic — oral vancomycin, or more recently fidaxomicin — you can reliably kill the active, growing bacteria. What you cannot kill are the spores. Spores are the dormant form of the organism, and they are exquisitely resistant to antibiotics, to alcohol, to heat, to almost everything except hypochlorite bleach. The spores sit quietly in the gut wall, in crypts, in the surrounding environment. When the antibiotic course is finished and the patient’s gut is still in an antibiotic-depleted, low-diversity state, the spores germinate, the cycle begins again, and the diarrhoea returns. About twenty to thirty percent of C. diff patients have at least one recurrence. Once you have had one recurrence, your chance of a second is higher again; by the third and fourth recurrence, the chance of yet another is around sixty percent. What ought to have been a single illness becomes a chronic, life-consuming condition. I have looked after patients on their fifth or sixth recurrence who had not been able to leave the house for nine months.

What van Nood and her colleagues did was, for its time, methodologically daring. They took patients with recurrent C. difficile infection and randomised them to one of three arms. The first arm was an “initial” short course of vancomycin, followed by bowel lavage and then the infusion of donor faeces — in solution, suspended in saline — through a nasoduodenal tube directly into the small bowel. The second arm was the then-standard treatment: a two-week course of vancomycin, full stop. The third arm was two weeks of vancomycin plus bowel lavage without the faecal infusion. The primary outcome was simply resolution of diarrhoea without relapse at ten weeks.

The trial was stopped early. An interim analysis showed a difference between the arms so large that the data safety monitoring board judged it unethical to continue. In the faecal-infusion arm, thirteen of sixteen patients (81 percent) were cured after a single infusion; all but one of the remaining patients were cured after a second infusion from a different donor. In the vancomycin-alone arm, four of thirteen patients (31 percent) were cured. In the vancomycin-plus-lavage arm, three of thirteen (23 percent). The p-value for the comparison between the infusion arm and either control arm was less than 0.001. The effect size was enormous — a doubling to trebling of cure rates, in a randomised trial, against an active control.

Two things made this paper transformative beyond its immediate clinical finding. First, the authors sequenced the faecal communities of their recipients before and after the procedure. Pre-transplant, the patients’ gut communities were low-diversity, dominated by Proteobacteria, with the signature post-antibiotic flattening that had become familiar in microbiome studies. Post-transplant, within days, the communities came to resemble those of the healthy donors — high diversity, dominated by Bacteroidetes and Clostridium clusters IV and XIVa, with the bile-acid-metabolising species that normally keep C. difficile in check restored. The transplant had not simply killed the C. difficile; it had restored the ecosystem. This was proof of the ecological hypothesis in a way that no previous study had managed.

Second, the trial broke the social taboo that had kept FMT out of mainstream medicine. It was in the New England Journal of Medicine. It was randomised. It was done by serious people in a serious institution. Within eighteen months of publication, FMT clinics had opened in most major academic hospitals in North America, Europe, and Australia. By the time the 2017 international consensus guidelines were published, FMT was the recommended therapy for any patient with a second or subsequent recurrence of C. difficile infection [REF:cammarota2017].

It is worth dwelling on how rare this kind of result is. In most branches of medicine, new treatments are introduced slowly and cure rates creep up by a few percentage points at a time. FMT did not do that. It went from a handful of case reports to a randomised trial showing a massive effect, and it did so for a condition — recurrent C. diff — where the existing treatments were known to be inadequate. The subsequent literature has been remarkably consistent. A long series of randomised trials and large observational cohorts, summarised across multiple meta-analyses, have confirmed cure rates in recurrent C. difficile infection of approximately 85 to 95 percent after a single treatment, with most failures responding to a second. For a disease with an established 60 percent third-recurrence rate under best antibiotic therapy, this is extraordinary. In the austere vocabulary of clinical medicine, FMT works for C. difficile. What has been harder is finding other things it works for, and that is where the rest of this chapter goes. But before we get there, I owe the reader the demystification that the chapter’s subtitle promised.

24.4 What Actually Happens: Inside a Modern FMT Clinic

The “yuck factor” that attaches to FMT in the public imagination does most of its work by keeping the procedure unvisualised. Readers who have not thought about it in detail tend to carry a mental picture that runs something like this: a donor produces a sample in the morning, the sample is handed over the counter in a paper bag, the recipient either swallows it or has it put up their rectum by a clinician in a rubber apron, and everyone tries not to think about what has just happened. That picture is almost entirely wrong. It is wrong at every stage. Let me walk through what actually happens at a modern FMT centre, because the reality is both less gross and more interesting than the caricature.

Finding a donor

Start with the donor. The first thing to understand about FMT is that finding a suitable donor is astonishingly difficult. In a typical modern stool bank — OpenBiome in the United States, the Netherlands Donor Feces Bank in Leiden, the Microba bank in Australia, a growing network in China — fewer than three percent of people who volunteer to donate stool end up qualifying [REF:ianiro2020]. Think about that number. Out of every hundred people who walk in expressing interest, ninety-seven are turned away. For comparison, the corresponding figure for blood donation is somewhere around fifty to eighty percent. Stool donation is harder to qualify for than almost any other form of biological donation in medicine.

The reasons become obvious once you consider what the donor must not transmit. The questionnaire and screening protocol at a modern stool bank is typically longer than the protocol for a bone-marrow donor. It asks about: personal and family history of inflammatory bowel disease, irritable bowel syndrome, autoimmune disease, allergy, atopy, autism spectrum disorder, depression, anxiety, obesity, metabolic syndrome, cancer, chronic fatigue syndrome, and any psychiatric disease; history of antibiotic use in the preceding six months; history of travel to regions with endemic parasitic or bacterial infections; history of any sexual behaviour carrying an elevated risk of bloodborne or gastrointestinal infection; and a full medication list, because many medications — the proton pump inhibitors of the previous chapter, metformin, antidepressants, antibiotics, NSAIDs — substantially reshape the gut community in ways that may not be desirable to transplant. The reason for the exhaustiveness is the growing recognition, in several independent studies across the last decade, that the microbiome travels with the disease. Transplant an “obesogenic” community into a lean recipient, and there is at least a theoretical possibility — and, in one famous case report, a documented actual possibility — that the recipient might gain weight they would not otherwise have gained [REF:alang2015]. Transplant from a depressed donor, and there is ongoing concern (not yet definitively demonstrated) that behavioural traits might travel with the microbial community. The field has not yet decided how seriously to take these risks; while that decision is being made, the donor screens are being kept conservative.

Laboratory screening, once the questionnaire is cleared, is extensive. The candidate donor’s blood is tested for HIV, hepatitis A, B, C, and E, syphilis, HTLV-I and II, and — since 2020 — for SARS-CoV-2 where relevant. Their stool is tested for enteric pathogens (Salmonella, Shigella, Campylobacter, Yersinia, Listeria, shiga-toxin-producing E. coli, Vibrio), for parasites (Giardia, Cryptosporidium, Entamoeba histolytica, helminth ova), for C. difficile itself, for noroviruses and rotaviruses, and — critically, and this is the lesson of a death we will come to in a moment — for antibiotic-resistant bacteria, particularly extended-spectrum beta-lactamase (ESBL) and carbapenemase-producing Enterobacteriaceae, vancomycin-resistant enterococci (VRE), and methicillin-resistant Staphylococcus aureus (MRSA) if there is mucosal carriage. The list has lengthened over the past decade as safety incidents have taught the field what it had previously been prepared to overlook.

A prospective donor who clears the questionnaire and the first round of stool and blood screening is then enrolled, which in most modern banks involves a commitment to donate regularly over several months. The bank will want to sequence the donor’s stool microbiome to characterise it, will often retest stool samples at the time of donation, and will quarantine the donated material for up to four weeks before releasing it for clinical use, during which time the donor is re-tested for the same pathogens to catch anything that was in the incubation period at the time of donation. It is the same quarantine-and-re-test logic that is used for whole-blood donation in the setting of potential early HIV or hepatitis transmission.

Processing and storage

The material itself is not what a lay reader might imagine. A donated stool sample arrives at the bank, usually within a few hours of being produced. It is weighed, logged, and moved into an anaerobic processing cabinet — a sealed glove-box in which the atmosphere is oxygen-depleted, because most of the organisms that matter in the gut are obligate anaerobes that begin to die on exposure to air within minutes. Inside the cabinet the stool is homogenised in buffered saline (typically in a ratio of around one to five by weight), filtered through progressively finer sieves to remove undigested food fibre and particulate debris, and cryoprotected with glycerol — usually to a final concentration of about ten percent. The resulting suspension is aliquoted into dose-sized units of anything from thirty to two hundred millilitres, depending on the intended route of administration, labelled with donor and batch identifiers, and frozen to minus eighty degrees Celsius. There it sits, indistinguishable in appearance and in handling to blood-bank cryoprecipitate, until a clinician somewhere orders it.

A modern stool bank is an industrial operation. OpenBiome, the non-profit stool bank that supplies most of the FMT carried out in the United States, has processed and distributed over sixty thousand treatments since its founding in 2012. It operates with the standard quality-management systems that any pharmaceutical-grade biological manufacturer uses: good manufacturing practice, deviation logs, batch records, chain-of-custody documentation. The material it ships looks, to the clinician who opens the box, like any other frozen biological: a small labelled bottle in a cryo-transport container, accompanied by a paper trail that includes the donor’s screening results (de-identified), the processing batch, and the release certificate. Nothing about it is improvised. Nothing about it is handed across a counter in a bag.

Delivery to the recipient

When the material reaches the patient, it can be delivered by any of four established routes, each with specific advantages. The first, and historically the commonest, is colonoscopic infusion — the stool suspension is delivered directly into the caecum and ascending colon through the working channel of a colonoscope, after the colon has been prepared (the same bowel preparation as for any other colonoscopy). This has the advantage of depositing the new community at the proximal end of the colon, where it can seed downstream, and of allowing the operator to inspect the bowel mucosa at the same time. It has the disadvantage of requiring a colonoscopy, with the usual preparation, sedation, and small procedural risks. The second route is retention enema — the suspension is delivered into the distal colon via a rectal tube, and the patient is asked to retain it for as long as possible. This is simpler and cheaper, and can be done in a day-surgery setting without sedation, but it delivers the new community only to the distal colon; seeding of the ascending colon is less reliable. The third route is nasojejunal or nasoduodenal infusion — a tube is passed through the nose, down the oesophagus and through the stomach into the small bowel, and the suspension is delivered from above. This was the route used in the van Nood trial. It has the advantage of seeding the small bowel as well as the colon; it has the disadvantage of being uncomfortable for the patient (anyone who has had a nasogastric tube can confirm this) and of the very small theoretical risk of the new microbial community being aspirated into the lungs.

The fourth route — and this is the one that has reshaped the field in the last decade — is oral capsules. The processed, anaerobically-prepared, cryopreserved stool suspension is dispensed into double-walled, acid-resistant capsules designed to survive the stomach acid and release their contents only after they reach the small bowel. The capsules are frozen, shipped on dry ice, and swallowed by the patient like ordinary pills. A typical capsule dose for recurrent C. difficile is twenty to forty capsules, split over one or two days. In 2017, Dina Kao and her colleagues at the University of Alberta published a randomised controlled trial directly comparing oral capsule FMT with colonoscopy-delivered FMT for recurrent C. difficile [REF:kao2017]. The cure rate in both arms was 96.2 percent. They were identical. And the capsule patients, unsurprisingly, rated their experience as considerably less unpleasant than the colonoscopy patients.

This last result — that capsules work as well as colonoscopy — is what most thoroughly demolishes the caricature. For the commonest indication, the patient’s actual experience of FMT in 2026 is essentially: swallow twenty to forty capsules of what looks like ordinary medication, with a glass of water, over one to two days. There is no yellow liquid. There is no tube. There is no colonoscopy. The capsules have no taste and no smell; they are double-encapsulated precisely to ensure that nothing breaches the outer shell until after the stomach. The visceral horror-film image of FMT — the milkshake, the enema, the bucket, the kitchen blender — is for most modern patients simply not what happens. The procedure has been, in the most literal sense, pharmaceuticalised.

Recovery

Follow-up is remarkable for how little there is to follow up. Most patients report mild abdominal cramping and loose stools for one to three days after the procedure, which is approximately what one would expect from introducing several hundred grams of foreign gut contents to the bowel. The new community establishes within days. In C. difficile patients, diarrhoea resolves within a week in the overwhelming majority. In most cases the patient simply feels normal again, often for the first time in many months. The long-term follow-up data — which now extend out to more than a decade for some of the earliest patients — show persistence of the transplanted community in a substantial minority, partial engraftment (a hybrid of donor and recipient communities) in many, and, interestingly, some patients who revert to something that looks like their original community but without C. difficile disease. What precisely constitutes “success” at the community level is still being worked out. What constitutes success at the patient level — no more diarrhoea, no more fear of the next recurrence, a life returned — is clear enough.

24.5 Beyond C. difficile: The Frontier That Does Not Quite Work Yet

If FMT were only a treatment for recurrent C. difficile, it would still be one of the most striking additions to the modern pharmacopoeia. But the logic of the procedure — transfer a diverse community from a healthy donor to restore a dysbiotic gut — is so attractive that researchers have, unsurprisingly, extended it to a long list of diseases in which the gut microbiome is implicated. Some of these extensions have shown real, reproducible signals. Others have not. A few have quietly failed. I want to walk through the main ones, because the reader who has heard that “FMT can treat X” should know what the evidence actually looks like.

Inflammatory bowel disease

The most active FMT research front outside of C. difficile has been inflammatory bowel disease, particularly ulcerative colitis. The rationale is strong. Ulcerative colitis is characterised by a chronic, relapsing inflammation of the colonic mucosa, and the colonic mucosa is where the microbiome lives most densely. Affected patients have a recognisably altered gut community, with reduced diversity, loss of butyrate-producing organisms (Faecalibacterium prausnitzii and Roseburia), and expansion of Proteobacteria and other pro-inflammatory taxa. If you are going to test the hypothesis that microbiome manipulation can alter an inflammatory disease of the gut, ulcerative colitis is where you would start.

The landmark randomised trial in this space was published in The Lancet in 2017 by Sudarshan Paramsothy and colleagues in Sydney [REF:paramsothy2017]. They randomised eighty-five patients with active ulcerative colitis to receive either intensive-dosing FMT — an initial colonoscopic infusion followed by enemas five days a week for eight weeks, each enema prepared from a pool of faeces from three to seven unrelated donors — or placebo. The primary outcome was steroid-free clinical and endoscopic remission at week eight. It was met in 27 percent of the FMT group and 8 percent of the placebo group (relative risk 3.6, 95 percent confidence interval 1.1 to 11.9, p = 0.021). The result was real. It was also modest. Almost three-quarters of treated patients did not remit, and the effect size, while statistically significant, was nothing like what FMT delivers in C. difficile.

What Paramsothy’s trial and several subsequent studies have taught is that the extension from C. difficile to ulcerative colitis is qualitatively different. In C. difficile, the disease is the dysbiosis, and restoring the community resolves it. In ulcerative colitis, the dysbiosis is a component of a multi-factorial disease — genetic, immunological, environmental — and restoring the community modifies but does not cure the underlying process. FMT in ulcerative colitis is a real therapy, but it is a partial one. The most recent meta-analyses put the clinical remission rate at around 25 to 30 percent, against 5 to 10 percent with placebo — a useful but not transformative effect. Current practice in most jurisdictions is to reserve FMT for patients who have failed multiple conventional therapies, and even then only in specialist centres. The procedural protocols that seem to work are the intensive multi-donor ones; single-donor, single-dose protocols have been consistently less effective.

Crohn’s disease, the other major form of inflammatory bowel disease, has been much more difficult. The transmural inflammation of Crohn’s, the propensity to fistula formation, and the small-bowel involvement make the microbial ecology considerably more complex. The handful of trials that have been done have shown mixed results at best, and most FMT centres do not currently treat Crohn’s disease outside of research protocols.

Metabolic syndrome, obesity, and type 2 diabetes

A second major front has been the metabolic diseases. The opening shot was fired by Anne Vrieze and colleagues (including Max Nieuwdorp, who would go on to build much of the field) in 2012, before the van Nood paper that made their laboratory famous [REF:vrieze2012]. Vrieze took men with metabolic syndrome — overweight, insulin-resistant, elevated waist circumference, abnormal lipid profile — and randomised them to receive a small-bowel infusion of faecal microbiota from either a lean, insulin-sensitive male donor or their own pre-collected stool (autologous control). Six weeks after infusion, the lean-donor recipients had improved insulin sensitivity — measured by the gold-standard hyperinsulinemic clamp — by approximately seventy percent. The autologous controls did not. The difference was statistically significant.

This was an astonishing finding. It suggested that metabolic disease could be modified, in adult humans, by the transplantation of a community from a lean donor into an obese recipient. The implications, if confirmed, were enormous. What has happened subsequently is that the original signal has been partly confirmed and partly eroded. Longer follow-up studies have shown that the insulin-sensitivity benefit wanes over months; it is not a durable cure. Attempts to translate the finding into actual weight loss have mostly failed. The most honest current reading is that microbiome manipulation can modulate insulin sensitivity in metabolic syndrome for a period of weeks to months, but it does not, on its own, reverse the underlying metabolic disease. The donor community shifts the recipient’s physiology; the recipient’s physiology, over time, shifts the donor community back. The ecosystem has equilibria, and they reassert themselves.

The Alang and Kelly case report of 2015 [REF:alang2015] is worth lingering on, because it is the mirror image of the Vrieze result and has shaped FMT donor-screening policy ever since. The case was of a thirty-two-year-old woman with recurrent C. difficile infection who underwent FMT from her overweight adolescent daughter. The C. difficile was cured. Over the following sixteen months, the patient gained approximately fifteen kilograms of weight, taking her from normal-weight to frankly obese, despite no reported change in diet or lifestyle. She subsequently met the full criteria for metabolic syndrome, which she had not previously had. The case was a single report, without definitive proof that the weight gain was caused by the transplant rather than by some other factor — but the timing, the magnitude, and the clinical trajectory were striking enough that the case became a landmark in the field. From that point onwards, every major stool bank required donors to be not only healthy but of healthy body-mass-index, and to meet explicit metabolic criteria that excluded the kind of community Alang’s daughter had donated.

Autism spectrum disorder

The application of FMT to autism spectrum disorder is among the most contested of the extensions, and deserves careful handling. The rationale derives from the observation, now widely replicated, that autistic children with gastrointestinal symptoms have characteristically altered gut microbiomes, and that the severity of GI symptoms in these children correlates with the severity of behavioural symptoms. The question has been whether correcting the microbiome changes the behaviour.

The most cited work in the area comes from the group of Rosa Krajmalnik-Brown and James Adams at Arizona State University, with Thomas Borody and Alexander Khoruts as clinical collaborators. In 2017 they published an open-label trial of what they called Microbiota Transfer Therapy — a multi-week antibiotic course followed by bowel cleansing and an extended FMT protocol — in eighteen autistic children with gastrointestinal symptoms [REF:kang2017]. At the end of treatment, GI symptoms had improved by approximately eighty percent, and autism-related behavioural symptoms had improved by a clinically meaningful margin. A two-year follow-up, published in 2019, showed that most of the improvements had been maintained, and that the behavioural benefits had in some cases continued to accrue over time [REF:kang2019].

Four caveats, all of which the authors themselves are explicit about, attach to this literature. The study was open-label: both the families and the assessors knew that treatment had been given. The sample size was small. There was no placebo control. And the intervention was complex — antibiotics, bowel cleanse, and FMT together — so the contribution of the FMT alone could not be isolated. Since 2019, a small number of larger, better-controlled trials have been reported, with more mixed results. A properly randomised, placebo-controlled trial in autistic children large enough to settle the question has not yet been published. Several are in progress. My own reading of the current evidence is that there is a real but variable signal in a subset of autistic children with GI symptoms, that the effect is probably mediated by improvement in the GI symptoms (which themselves can be behaviourally disruptive) rather than by a direct effect on core autism features, and that parents considering FMT for their autistic child should do so only within a research protocol at a specialist centre. There is, however, an active DIY-FMT community in the autism space, about which more shortly.

Cancer immunotherapy — the newest and most surprising indication

The most recent, and arguably the most interesting, extension is not a treatment for a microbiome disease at all. It is a use of FMT to potentiate cancer immunotherapy. Immune checkpoint inhibitors — the class of drugs that includes pembrolizumab, nivolumab, ipilimumab, and their congeners — work by taking the brakes off a patient’s own T-cell response to tumour. They have revolutionised the treatment of melanoma, non-small-cell lung cancer, and several other malignancies. But they only work in a subset of patients, around a third to a half depending on the cancer type, and the reasons for non-response have been one of the great unsolved puzzles of oncology.

A series of observational studies in the late 2010s established that the composition of the gut microbiome correlated with checkpoint-inhibitor response, with particular taxa (Akkermansia muciniphila, Faecalibacterium, Bifidobacterium) enriched in responders. The question was whether the correlation was causal — whether you could make a non-responder respond by giving them the community of a responder. Two 2021 papers, published back-to-back in Science, attempted to answer that question. In the more widely cited of the two, Diwakar Davar and colleagues at the University of Pittsburgh took fifteen patients with PD-1-refractory melanoma — that is, patients whose tumours had progressed on checkpoint inhibitor therapy — and treated them with FMT from patients whose own melanomas had responded well to PD-1 blockade, followed by a re-challenge with the checkpoint inhibitor [REF:davar2021]. Six of the fifteen — 40 percent — responded. In patients whose prior best response had been progressive disease on the same drug, a 40 percent response rate to FMT-plus-drug is, clinically, a startling finding. The accompanying mechanistic work showed that the responders had acquired immune changes consistent with those seen in natural responders — increased CD8 T-cell activation, decreased interleukin-8-producing myeloid cells — in a way that tracked with the engraftment of donor microbial taxa.

The immunotherapy story is still unfolding. Larger trials are underway, using both FMT and defined consortia derived from super-donor material, and the early signals continue to be positive. If the effect holds up at scale — which is not yet certain — FMT may end up with a substantial and entirely unexpected second indication: not as a gut therapy, but as an immunomodulator in oncology.

Other indications, briefly

I will list, without detail, the other extensions that are in active research in 2026: hepatic encephalopathy (multiple small trials, real effect, unclear mechanism — possibly reduction of ammonia-producing bacteria); irritable bowel syndrome (mixed trials, probably a modest effect in a subset of patients, no clear predictor of who benefits); primary sclerosing cholangitis (early studies, intriguing findings around bile-acid metabolism); chronic fatigue / ME (enthusiastic DIY community, sparse clinical data); Parkinson’s disease (small trials, early signals); type 1 diabetes [REF:degroot2020] (one randomised trial in newly-diagnosed patients, with a signal for preservation of beta-cell function). None of these has anything like the evidence base of C. difficile. All of them are under active investigation. Some will eventually join C. difficile as established indications; many will not.

24.6 The Concept of the Super-Donor

Before leaving the question of efficacy, I owe the reader a concept that has quietly reorganised thinking in the field over the last several years: the super-donor.

The observation that started the conversation was uncomfortable for researchers used to randomised trials. When outcomes from early FMT trials — particularly for non-C. difficile indications — were broken down by donor, it became clear that some donors produced vastly better outcomes than others. A donor at one centre might have a 60 percent cure rate in ulcerative colitis; another donor at the same centre, working under the same protocol, might have a 10 percent cure rate. This “donor effect” turned out to be reproducible across studies. Certain donors simply produced more therapeutically effective material than others, for reasons that were not initially clear.

Over time, the hypothesis has crystallised. The super-donors are, in general, donors whose stool communities combine three features: exceptionally high alpha-diversity (a large number of different taxa present); exceptionally high abundance of beneficial keystone species (Faecalibacterium prausnitzii, Akkermansia muciniphila, specific butyrate-producers); and — this is the subtler point — a resistance profile that has not been substantially damaged by past antibiotic exposure. The proposed mechanism is not that super-donor stool contains some magical unknown organism; it is that super-donor stool contains a more complete and functionally redundant community, and that such communities are more likely to successfully engraft and to produce the metabolic outputs (particularly short-chain fatty acids from fibre fermentation) that mediate the therapeutic benefit.

The super-donor phenomenon has had two practical consequences. The first is that modern stool banks now stratify their donor pools by microbiome characteristics, not just by disease screening, and match donors to recipients based on which community looks most likely to help. The second is that it has accelerated the move towards multi-donor pooled preparations — the Paramsothy ulcerative colitis trial, for example, pooled three to seven donors per dose, on the theory that increased microbial diversity across donors would buffer against the specific deficiencies of any one donor community. Pooled multi-donor preparations have become standard for most non-C. difficile indications. They also, incidentally, complicate the super-donor story: if the pool is doing the work, identifying any one donor’s contribution becomes correspondingly harder.

The super-donor concept is also a pointer to the future. If we can identify, by sequencing, what makes a super-donor super, we can in principle reconstruct the relevant community from defined components — this is what the “defined consortium” companies are trying to do — and move from the unpredictable biological product (one person’s stool) to a manufactured product (a known mixture of known organisms at known doses). Two such products now have regulatory approval for recurrent C. difficile, and we will turn to them in a moment. The super-donor is the bridge between FMT-as-it-is-now and FMT-as-it-will-be.

24.7 The Safety Signal We Have to Take Seriously

I have been broadly positive about FMT so far, because the broad reading of the evidence is broadly positive. But the chapter would be dishonest if it did not give equal weight to the single clearest safety signal in the history of the procedure.

On 13 June 2019, the U.S. Food and Drug Administration issued an urgent safety alert describing two cases in which immunocompromised patients had received FMT from a shared donor and had subsequently developed bacteraemia — bloodstream infection — with an extended-spectrum beta-lactamase-producing strain of Escherichia coli. One of the patients died. The corresponding full case report, by Zachariah DeFilipp and colleagues at Massachusetts General Hospital, was published in The New England Journal of Medicine later that year [REF:defilipp2019]. Genomic sequencing of the E. coli recovered from the two patients matched, at high resolution, the strain isolated from the donor’s stool — a strain that had not been screened for at the time of donation, because routine donor screening protocols at the time did not include ESBL-producing Enterobacteriaceae.

This incident matters in several ways. First, at the human level — a patient died who should not have died, from an intervention intended to help them. That fact must not be abstracted into statistics. Second, the case forced a recognition that had been latent in the field: antibiotic-resistant organisms are widely carried, asymptomatically, in the guts of otherwise healthy people in the developed world. Community carriage rates of ESBL-producing E. coli are now estimated at five to ten percent in many populations, with higher rates in people who have travelled to regions where such organisms are endemic. A donor whose stool is entirely healthy for their own immune system is not necessarily safe for an immunocompromised recipient. The lesson led directly to a hardening of donor screening: ESBL and carbapenemase-producing Enterobacteriaceae are now routinely tested for at all major stool banks, as are VRE and — in some protocols — MRSA.

Third, and more subtly, the incident changed the way the field talks about FMT risk. Before 2019, the prevailing narrative — which I probably shared — was that FMT was a remarkably safe procedure, with adverse events essentially limited to the mechanical risks of the route of administration (colonoscopy or nasogastric intubation) and a small risk of minor GI symptoms. That narrative was not exactly wrong, but it was incomplete. The DeFilipp case demonstrated that FMT has a distinct and specific risk profile — infection transmission from donor to recipient — that cannot be eliminated, only minimised through careful screening. Subsequent analyses have estimated the absolute risk of clinically significant infection transmission with modern screening at somewhere between 0.1 and 1 per 1000 procedures, depending on the population and the organism, but it is not zero and it probably never will be.

Beyond infection transmission, three other classes of long-term safety signal have been watched. The first is the possibility of transmission of non-infectious traits — obesity, allergy, autoimmunity, perhaps behavioural features — from donor to recipient. The Alang-Kelly weight-gain case is the clearest single example; other signals exist but remain contested. The second is the unknown long-term consequence of colonising an adult gut with a radically different microbial community. The long-term follow-up studies now extending past ten years have been reassuring; no new syndromes have emerged. But ten years is not a lifetime, and FMT recipients are heterogeneous enough that subtle long-term effects could still be escaping detection. The third is the pandemic-era risk that faecal material can transmit respiratory viruses — SARS-CoV-2 was eventually shown to be shed in stool in a proportion of infected individuals, and stool banks had to add SARS-CoV-2 PCR to their donor screens during 2020 [REF:ianiro2020covid]. Future emerging pathogens will predictably require similar additions to the screen.

For the overwhelming majority of patients receiving FMT for recurrent C. difficile — which remains the indication in which the procedure’s benefit is largest and best established — the risks are small and the benefits are, in the classical sense, life-saving. For patients with non-immunocompromised, non-urgent indications, the balance is less clear, and the conservative answer in 2026 is that such patients should be treated only within clinical trials at established centres. The DeFilipp case is a reminder that “broadly positive” evidence is not the same as “risk-free”, and that the yellow bottle in the cryo-transport container, despite its pharmaceutical packaging, is still a biological preparation derived from another human being’s gut.

24.8 The Underground: DIY-FMT and What It Tells Us

I now come to the section of the chapter that was the user’s stated interest when the chapter was commissioned, and that I regard as, in certain respects, the most revealing part of the FMT story as a whole. There is a substantial, persistent, global community of patients who perform FMT on themselves, without medical supervision, typically in their own kitchens, using stool from a donor they recruit privately — a spouse, a sibling, a friend’s healthy child, sometimes a donor they have found through one of the online patient-support networks. They are doing this in 2026 at rates that, on the best available surveys, continue to rise year on year. This community, usually called the Do-It-Yourself FMT or DIY-FMT community, is not a fringe or a conspiracy. It is a response — rational in its own terms, dangerous in its methods — to a genuine gap in the medical system, and the reader should understand why it exists, what its members actually do, and what the ethical position of the medical profession ought to be in relation to it.

Why it exists

The community exists because, for many of the conditions that patients believe FMT might help them with, the medical system will not offer it to them. In most countries in 2026, FMT is regulated as either a biological therapy or an investigational new drug, which means it is legally available only through a licensed clinician for a specifically approved indication — and the only indication for which it is approved in most jurisdictions is recurrent Clostridioides difficile infection. A person with ulcerative colitis who has failed multiple biological therapies, has read the Paramsothy paper, and wants to try FMT, in most jurisdictions, cannot. A person with autistic symptoms and gastrointestinal disease who has read the Kang papers, cannot. A person with chronic fatigue syndrome, with irritable bowel syndrome, with treatment-resistant anxiety, with any of the conditions for which the scientific literature shows a modest signal and clinical practice does not yet reach, cannot. The medical system is, in these cases, holding the line against premature adoption of an unproven therapy — and that is, on a population-health view, often the right position. But from the perspective of an individual patient with a serious chronic illness that has failed conventional treatment, the medical system’s caution looks like a door that has been closed in their face. Behind the door is a therapy that might help them. They are suffering. They are literate. They have an internet connection. They pass through the door another way.

The demographics of the DIY-FMT community are instructive. The most comprehensive survey to date, published by Ekekezie and colleagues in the American Journal of Gastroenterology in 2020 [REF:ekekezie2020], sampled 84 self-identified DIY-FMT practitioners through the patient networks of the Peggy Lillis Foundation, the Fecal Transplant Foundation, and the Australian site The Power of Poop. The respondents were mostly female (71 percent) and mostly white (92 percent). Most (80 percent) had performed FMT on themselves. Most (92 percent) knew their stool donor personally. Most (87 percent) used internet resources to guide the procedure. The two commonest indications for which DIY-FMT was being used were inflammatory bowel disease (35 percent) and irritable bowel syndrome (29 percent) — both conditions for which, in most jurisdictions, FMT is not offered in regular clinical practice. Adverse events were reported by 12 percent of respondents; 82 percent reported improvement in their condition.

That last figure needs to be looked at carefully, because it is the crux of the DIY-FMT paradox. Eighty-two percent self-reported improvement. That is considerably higher than the remission rate seen in randomised controlled trials of FMT for the same indications (25 to 30 percent in ulcerative colitis, for example). There are at least four possible explanations, and the honest answer probably involves elements of all of them. First, self-report in the absence of blinding is a well-known source of inflated efficacy estimates — patients who have invested substantial effort and risk in a procedure are motivated to report benefit. Second, survey respondents in a voluntary, non-random sample are not representative of all DIY-FMT practitioners; people whose attempts failed or caused harm are under-represented, and people whose attempts succeeded are over-represented. Third, the placebo effect — which in gastrointestinal disease is notoriously large, sometimes as high as 40 percent in clinical trials — will be operating at full strength in the absence of any blinding or control. And fourth, and most interestingly, the DIY protocols are often substantially more intensive than the single-dose protocols used in most randomised trials — weeks of daily enemas from a known, consistent donor, rather than one colonoscopic delivery — and may therefore genuinely produce better engraftment. The multi-dose, intensive DIY protocols resemble, in some respects, the Paramsothy trial’s multi-donor enema schedule, and that was the trial with the best remission rate.

What they actually do

The typical DIY-FMT protocol, as documented in the patient literature on sites like The Power of Poop, in the self-help guides circulated through inflammatory-bowel-disease forums, and in the Ekekezie survey, follows a broadly consistent pattern. A donor is recruited — usually a family member or close friend, chosen for apparent good health, absence of antibiotic use in recent months, and willingness. The donor is typically screened informally: a questionnaire, sometimes laboratory screening arranged privately, sometimes nothing at all. A stool sample is produced, usually that morning. It is homogenised in saline, typically in a household blender, then filtered through a coffee filter or a fine muslin. The resulting slurry is administered either as a retention enema via an over-the-counter enema kit, or — less commonly — via a nasogastric-style procedure with a pharmacy-purchased feeding tube. The recipient is instructed to lie down for as long as possible afterwards to retain the material. The procedure is repeated, daily or every few days, often for weeks.

This protocol, read against the modern clinical-grade version, is almost a parody of its professional counterpart. No anaerobic processing: the blender and the coffee filter destroy much of the obligate-anaerobic community. No standardised dose: the recipient does not know how many organisms they are receiving. No donor screening for the pathogens that have, in the clinical setting, killed people: no ESBL, no parasites, often no hepatitis. No quarantine. No post-procedure follow-up. And yet the self-reported outcomes, as we have seen, are not obviously catastrophic — most DIY-FMT practitioners, on survey, report perceiving benefit, and the reported adverse events are usually minor gastrointestinal symptoms rather than the serious infections that clinicians would predict from the absent screening.

Why has the DIY community not produced a visible trail of disease? The most likely answer is that it has — but dispersed through the general infectious-disease case load in ways that do not attribute back to DIY-FMT. A case of ESBL bacteraemia in a DIY-FMT recipient will be diagnosed and reported as ESBL bacteraemia; the DIY-FMT history will be disclosed only if the clinician asks and the patient volunteers it. The literature on adverse events in DIY-FMT is correspondingly thin, not because the events are not happening but because they are going uncounted. The same epistemic problem faces anyone trying to argue, from the absence of a visible harm signal, that DIY-FMT is “safe”. It may not be. We do not know.

What we do know is that, unscreened, it is riskier per procedure than clinical FMT; that it uses donor material that in most cases has never been properly characterised; and that when it fails, the patient has no institutional recourse. A bad outcome from clinical FMT is audited, reported, and contributes to the evidence base. A bad outcome from DIY-FMT usually disappears into the general pattern of illness in the population.

The ethical territory

The existence of DIY-FMT poses a difficult question for the medical profession, and the honest answer is that the profession has not yet settled on a position. The paternalistic response — this is unsafe, please stop, please trust the system — is unworkable for two reasons. First, it presupposes that the system is offering something better, which for most non-C. difficile indications it is not. Second, it ignores the autonomy of patients who have weighed the risks and made an informed decision about their own bodies, which is a right that medicine has traditionally been slow to challenge when the procedure in question is not FMT. The permissive response — if you are going to do it anyway, here is how to do it more safely — runs into regulatory and liability concerns that most institutions are unwilling to confront. The laissez-faire response — it is not our problem — is a moral abdication.

The direction that I think the profession is slowly moving, and that I personally find defensible, is something like this: the medical system has an obligation to expand, by responsible research, the range of conditions for which FMT is properly offered within clinical services; to make clinical FMT more accessible for the conditions in which it is indicated (it is currently shockingly hard to obtain in many jurisdictions even for the one indication for which it is the treatment of choice); and, for conditions in which FMT is not formally indicated but a patient is determined to try it anyway, to provide non-judgemental guidance about at least the minimum safety steps — donor screening, above all — that would reduce the risk of catastrophic harm. This is not permission. It is harm reduction, applied to a situation in which the alternative is that patients take risks in ignorance that they could take with some measure of informed caution. Similar harm-reduction frameworks have been accepted, after prolonged resistance, in intravenous drug use, in sexual health, in abortion access. I do not see why FMT should be different.

The reader who is currently considering DIY-FMT for themselves or for a family member — and I know that such readers exist, because I have spoken to them — deserves the following minimum, delivered not as encouragement but as harm reduction. A donor recruited privately should, at an absolute minimum, have a recent negative test for HIV, hepatitis B, hepatitis C, and stool testing for enteric pathogens including C. difficile, and should have had no antibiotic exposure in the preceding six months. They should have a normal body-mass-index, no autoimmune or inflammatory bowel disease, no depression, no metabolic syndrome, no history of cancer, and no recent travel to regions with endemic parasitic infection. They should not be sharing intimate contact with the recipient. These tests can be arranged through most primary-care physicians. They do not cost much. They do not guarantee safety. They substantially reduce the risk of the catastrophic outcomes — transmitted bloodstream infection, in particular — that have taken lives in the formal clinical setting. If you are going to do the procedure regardless, do these things. If you are not willing to do these things, reconsider the procedure. That is the honest clinical position, and I deliver it with full awareness that the formal regulatory position of my profession is different.

24.9 From Stool to Standard Product: The End of FMT As We Know It?

The final section of this chapter should describe a future that, in 2026, is arriving faster than most observers predicted. FMT, as I have described it in the preceding sections, is a fundamentally pre-modern therapy in one specific sense: its active ingredient is a biological preparation from a specific human being, whose composition cannot be fully specified, and whose therapeutic effect varies from batch to batch. No other class of drug in modern medicine works like this. Every other biological therapy — monoclonal antibodies, recombinant proteins, cell therapies — has been progressively standardised, characterised, and manufactured to specification, even when derived from biological sources. FMT is, in this sense, the last large therapy still practised in the nineteenth-century mode: whole crude extract, in bulk, with variable composition, from a variable source.

That is beginning to change. Two products, both derived from the FMT concept but standardised in different ways, received US FDA approval for recurrent C. difficile infection in 2022 and 2023 respectively. The first was fecal microbiota, live-jslm (brand name Rebyota), a minimally-processed, standardised preparation of pooled donor stool, administered as a single rectal enema, approved in December 2022 and shown in a phase III trial to achieve sustained prevention of C. difficile recurrence in approximately 70 percent of treated patients [REF:khanna2025]. The second was SER-109 (brand name Vowst), a very different kind of product: not whole-community stool, but a purified preparation of bacterial spores — specifically, the Firmicutes spore fraction — prepared from selected healthy donors and administered orally as capsules. SER-109 received FDA approval in April 2023 on the strength of the ECOSPOR III randomised trial, in which it reduced C. difficile recurrence to 12 percent over eight weeks, compared with 40 percent in the placebo arm [REF:feuerstadt2022].

These two products are, in a real sense, the post-FMT era in miniature. Rebyota preserves the whole-community philosophy of traditional FMT while standardising the preparation, the sourcing, and the quality control. Vowst goes much further, reducing the “transplant” to its active spore fraction — the component that is most robust to processing, most clinically defined, and most pharmaceutically tractable — and delivering it as an ordinary oral drug. Both products work. Both were approved. Both are, in their different ways, the first commercial proofs that the concept of microbial community transfer can be turned into a pharmaceutical.

The direction of travel from here is reasonably clear, even if the timescale is not. The next generation of products will likely be defined consortia: rationally assembled mixtures of specific, characterised, cultured organisms, designed to reconstitute the key functions of a healthy community without requiring any donor at all. Several such products are in late-phase clinical trials in 2026. Beyond defined consortia are engineered consortia, in which specific microbial strains have been modified to carry out particular therapeutic functions — produce a missing metabolite, secrete a defined cytokine, sense a specific environmental cue. And beyond that are the various phage, probiotic, and postbiotic approaches that we have covered in previous chapters. In each case, the underlying therapeutic concept is the one that van Nood’s trial proved in 2013: that modifying the gut microbial community can modify disease. The engineering task is to move from the crude proof-of-concept (a stool suspension from a healthy donor) to the refined implementation (a defined product that does the same thing, better).

If this trajectory continues — and I think it will — faecal microbiota transplantation as a procedure will, over the next decade or two, progressively shrink as a share of the therapeutic landscape. It will remain available for recurrent C. difficile where the whole-community approach still outperforms the simpler alternatives (for now), and for research indications where the full complexity of a real human gut is still needed for experimental work. But for most routine clinical applications, the “nuclear option” will be replaced by something less dramatic: a standardised, defined, regulated pharmaceutical product that does what a traditional FMT does, without the donor, without the stool bank, without the enema, and without the public relations problem.

I want to finish this chapter with a return to the metaphor in the title. I said at the beginning that nuclear weapons are blunt, collateral, and irreversible, and that the FMT procedure, on examination, is none of those things. That was the first mistake of the nuclear metaphor. The second mistake, which I want to name at the end, is that nuclear weapons destroy. FMT, at its best, does not destroy. It restores. A recurrent C. difficile patient on their fifth course of vancomycin is inhabiting a gut that has been destroyed, repeatedly, by the antibiotics. The FMT does not add to that destruction. It undoes it. The new community is not a replacement for what was there; it is, in most cases, something very close to what was there before the sequence of illness and treatment took it apart.

A better metaphor, which several of my colleagues have been using and which I am coming around to, is that FMT is a transplant in the full medical sense: the transfer of a living biological system from one person to another, with the same careful attention to matching, screening, rejection, and long-term follow-up that we apply to kidneys and hearts and corneas. The sixty-trillion organisms that constitute the microbiome are, after all, an organ by every meaningful criterion — a biomass of a kilogram or more, with discrete functions, tissue-level organisation, a developmental programme, and a role in whole-body physiology. If we can transplant kidneys, we can transplant microbiomes. The surprise of the last fifteen years is not that the procedure works. It is that the organ in question — the one we used to flush away without a second thought — turned out to be an organ at all.

Key Chapter References

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[REF:gough2011] Gough E, Shaikh H, Manges AR. Systematic review of intestinal microbiota transplantation (fecal bacteriotherapy) for recurrent Clostridium difficile infection. Clin Infect Dis. 2011;53(10):994-1002. doi:10.1093/cid/cir632

[REF:vannood2013] van Nood E, Vrieze A, Nieuwdorp M, et al. Duodenal infusion of donor feces for recurrent Clostridium difficile. N Engl J Med. 2013;368(5):407-415. doi:10.1056/NEJMoa1205037

[REF:vrieze2012] Vrieze A, Van Nood E, Holleman F, et al. Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome. Gastroenterology. 2012;143(4):913-916.e7. doi:10.1053/j.gastro.2012.06.031

[REF:alang2015] Alang N, Kelly CR. Weight gain after fecal microbiota transplantation. Open Forum Infect Dis. 2015;2(1):ofv004. doi:10.1093/ofid/ofv004

[REF:paramsothy2017] Paramsothy S, Kamm MA, Kaakoush NO, et al. Multidonor intensive faecal microbiota transplantation for active ulcerative colitis: a randomised placebo-controlled trial. Lancet. 2017;389(10075):1218-1228. doi:10.1016/S0140-6736(17)30182-4

[REF:cammarota2017] Cammarota G, Ianiro G, Tilg H, et al. European consensus conference on faecal microbiota transplantation in clinical practice. Gut. 2017;66(4):569-580. doi:10.1136/gutjnl-2016-313017

[REF:kao2017] Kao D, Roach B, Silva M, et al. Effect of oral capsule- vs colonoscopy-delivered fecal microbiota transplantation on recurrent Clostridium difficile infection: a randomized clinical trial. JAMA. 2017;318(20):1985-1993. doi:10.1001/jama.2017.17077

[REF:kang2017] Kang D-W, Adams JB, Gregory AC, et al. Microbiota Transfer Therapy alters gut ecosystem and improves gastrointestinal and autism symptoms: an open-label study. Microbiome. 2017;5(1):10. doi:10.1186/s40168-016-0225-7

[REF:kang2019] Kang D-W, Adams JB, Coleman DM, et al. Long-term benefit of Microbiota Transfer Therapy on autism symptoms and gut microbiota. Sci Rep. 2019;9(1):5821. doi:10.1038/s41598-019-42183-0

[REF:defilipp2019] DeFilipp Z, Bloom PP, Torres Soto M, et al. Drug-resistant E. coli bacteremia transmitted by fecal microbiota transplant. N Engl J Med. 2019;381(21):2043-2050. doi:10.1056/NEJMoa1910437

[REF:ekekezie2020] Ekekezie C, Perler BK, Wexler A, Duff C, Lillis CJ, Kelly CR. Understanding the scope of do-it-yourself fecal microbiota transplant. Am J Gastroenterol. 2020;115(4):603-607. doi:10.14309/ajg.0000000000000499

[REF:ianiro2020covid] Ianiro G, Mullish BH, Kelly CR, et al. Reorganisation of faecal microbiota transplant services during the COVID-19 pandemic. Gut. 2020;69(9):1555-1563. doi:10.1136/gutjnl-2020-321829

[REF:ianiro2020] Ianiro G, Bibbò S, Porcari S, et al. Fecal microbiota transplantation for recurrent C. difficile infection in patients with inflammatory bowel disease: experience of a large-volume European FMT center. Gut Microbes. 2021;13(1):1994832. doi:10.1080/19490976.2021.1994832

[REF:davar2021] Davar D, Dzutsev AK, McCulloch JA, et al. Fecal microbiota transplant overcomes resistance to anti-PD-1 therapy in melanoma patients. Science. 2021;371(6529):595-602. doi:10.1126/science.abf3363

[REF:degroot2020] de Groot P, Nikolic T, Pellegrini S, et al. Faecal microbiota transplantation halts progression of human new-onset type 1 diabetes in a randomised controlled trial. Gut. 2021;70(1):92-105. doi:10.1136/gutjnl-2020-322630

[REF:feuerstadt2022] Feuerstadt P, Louie TJ, Lashner B, et al. SER-109, an oral microbiome therapy for recurrent Clostridioides difficile infection. N Engl J Med. 2022;386(3):220-229. doi:10.1056/NEJMoa2106516

[REF:khanna2025] Khanna S, Yoho D, Van Handel D, et al. Prevention of recurrent C. difficile infection by fecal microbiota, live-jslm (REBYOTA) administered via colonoscopy: 6-month data from the single-arm phase IIIb CDI-SCOPE trial. Ther Adv Gastroenterol. 2025;18:17562848251396744. doi:10.1177/17562848251396744

Chapter 25: Precision Microbiome Engineering — From Nuclear Option to Scalpel

25.1 The Opposite of a Transplant

If Chapter 24 was about the nuclear option, this chapter is about the scalpel. Everything I described in the previous chapter — stool banks, enemas, capsules of pooled donor material, the whole infrastructure that sits between a healthy donor’s colon and a sick recipient’s — relies, at bottom, on a principle of crude substitution. You take one person’s microbial community, more or less whole, and you put it into another person, hoping the new community out-competes the old. It works, for Clostridioides difficile, with a reliability that still astonishes me every time I see the numbers. But even its strongest advocates acknowledge that it is a blunt instrument. You cannot, in a faecal transplant, specify which organisms take up residence. You cannot restrict the handover to the species you actually want to deliver. You cannot exclude passengers — antibiotic-resistance genes, latent viruses, metabolic traits — that the recipient did not sign up for. You are, in every clinical sense, accepting a package deal.

Precision microbiome engineering is the opposite ambition. It is the attempt to act on the microbial community at the level of a single species, a single strain, sometimes a single gene within a single strain. Instead of transferring forty thousand bacterial genomes in a bottle, you deliver one carefully chosen organism that does one defined thing. Instead of suppressing a hundred species with an antibiotic so that the one you want to be rid of goes with them, you target that one species specifically, leaving the rest of the community standing. Instead of hoping that a donor’s community contains enough of the right metabolic machinery, you engineer the machinery itself and then install it in a chassis organism that knows how to live in the gut.

The distance between these two visions, in 2026, is still substantial. FMT works for a handful of indications; precision engineering works, in humans, for almost none yet. But the trajectory matters. In the fifteen years since the van Nood trial made FMT respectable, precision microbiome engineering has gone from a cartoon on a conference slide to a pipeline of products in human clinical trials, two of them at Phase 3, several of them closer to first-in-human than to last-in-mouse. The field has its first cautionary tales and its first disappointments. It has the beginnings of a regulatory framework. It has a name: synthetic biology applied to the microbiome, or, in the language most investors prefer, live biotherapeutic products. And it has the characteristic feature of a technology that is about to matter — a lot of smart people working on it, and a lot of money betting that they will be right.

This chapter is the counterpoint to the FMT chapter in the same way that a scalpel is the counterpoint to a blender. I will try, in what follows, to describe what is actually possible in 2026 (which is more than most readers will expect); what is genuinely close but not yet here (which is where most of the excitement lives); what the regulatory apparatus looks like, and why it matters as much as the science; what the specific risks of precision engineering are, including some that are quite different from the risks of FMT; and where the next decade is likely to take us. I will try throughout to distinguish proof of concept from proof of benefit, and both of those from hype. The gap between them is where the reader’s caution should live.

25.2 Four Kinds of Precision

Before describing specific products, it helps to lay out the four broad strategies that make up the field. The terminology is not yet fully standardised, and different authors use different taxonomies; the one I find clearest divides precision microbiome engineering into four levels, distinguished by how heavily the organism or the community has been modified.

At the first and least-modified level are the defined consortia — rationally chosen mixtures of cultured, naturally occurring bacterial strains, selected from healthy human donors or from culture collections, blended at specified ratios to reproduce the key functions of a healthy gut community. No genetic engineering. No synthesis. Just a curated community, manufactured to specification. These are the direct successors of FMT. Vowst (SER-109), which I mentioned at the end of the FMT chapter, is the first-generation example — a mixture of Firmicutes spores rather than a true defined consortium, but operating on the same philosophy. The second-generation examples now in late-phase trials, such as VE303 for recurrent C. difficile [REF:louie2023], are true defined consortia: specified numbers of specified strains, each one identified, deposited in a culture collection, and manufactured under pharmaceutical conditions.

At the second level are the single-strain live biotherapeutic products — specific organisms, delivered on their own, selected because they carry out some particular function the recipient’s gut community is lacking. Akkermansia muciniphila, the mucus-degrading organism whose abundance correlates with metabolic health, is the prototype. The organism can be grown in culture, pasteurised (interestingly, the pasteurised form appears to retain much of the metabolic benefit), and administered as a capsule. Early randomised trials in overweight, insulin-resistant adults have shown modest improvements in metabolic markers [REF:depommier2019]. The organism is naturally occurring; no engineering is involved. The precision comes from the choice of the single strain, and from the standardisation of the product.

At the third level are the genetically modified live biotherapeutics — organisms that have been engineered to perform functions they do not perform in nature. The best-studied chassis is Escherichia coli Nissle 1917, a strain isolated by the German physician Alfred Nissle from the stool of a First World War soldier who had, uniquely among his unit, not developed dysentery during the Balkans campaign. Nissle 1917 is a safe, well-characterised commensal that has been used as a probiotic for a century. In the last decade it has become the E. coli K-12 of the microbiome — the default workhorse into which you install the genes you want expressed in the human gut. Synlogic, a Massachusetts company spun out of MIT in the mid-2010s, built an entire pipeline on Nissle 1917 chassis engineered to consume metabolites that accumulate to toxic levels in specific inherited metabolic diseases. I will describe their progress, and their setbacks, in a moment.

At the fourth and most aggressive level are the sequence-specific antimicrobials — therapies that use CRISPR-Cas systems, or other sequence-guided mechanisms, to kill specific bacterial strains within a community while leaving the rest untouched. These are in many ways the most “surgical” of all the precision approaches, because they act not on the recipient’s metabolism but on the genome of the organism they are targeting. Two companies, Locus Biosciences in North Carolina and Eligo Bioscience in Paris, have been the pioneers; both have delivery platforms based on engineered bacteriophages carrying CRISPR payloads, and both have published or reported clinical-trial data in the last two years. We will come back to them.

These four levels are not entirely separate. A defined consortium can contain an engineered organism; a CRISPR-based antimicrobial can be delivered alongside a live biotherapeutic that moves into the space the target vacates. The real pipeline combines them. But the taxonomy helps sort the story, and I will use it.

25.3 What Already Works: The Defined Consortia

Of the four levels, the one with the most established clinical foothold in 2026 is the defined consortium. This is not a coincidence. Defined consortia are, in a regulatory sense, the easiest to approve: the organisms are naturally occurring, the manufacturing processes are adapted from food-industry fermentation practice, and the conceptual link to FMT — a treatment already shown to work for the target indication — provides a ready-made efficacy rationale. In the post-FMT regulatory environment that the FDA, the European Medicines Agency, and their counterparts have been constructing since around 2019, defined consortia have been the first class of microbiome therapy to cross the finish line.

VE303 and the second wave in recurrent C. difficile

I described Vowst and Rebyota at the end of the FMT chapter as the first commercial proofs that community-transfer could be pharmaceuticalised. Both were derived from donor stool; neither was synthetic in the purist sense. The product that, if it crosses its remaining regulatory hurdles, will be the first genuinely defined consortium to reach market is VE303, from the Boston company Vedanta Biosciences. VE303 is a mixture of eight specific Clostridium strains, originally isolated from the stool of a single healthy adult donor, cultured in pure form, banked as master cell lines, and manufactured as a mixture of freeze-dried spores in a capsule. Every strain in VE303 has a genome sequence in a public database. Every batch is verified by sequencing to contain exactly those eight strains in specified ratios. The product is, in this sense, a pharmaceutical in the way that Rebyota — derived from a pool of real human donors whose exact composition cannot be fully specified — is not quite yet.

The pivotal Phase 2 trial of VE303 for recurrent C. difficile infection, reported in 2023, randomised seventy-nine patients to either high-dose VE303, low-dose VE303, or placebo, each given as a daily oral capsule regimen for fourteen days after standard antibiotic treatment [REF:louie2023]. The recurrence rate over eight weeks was 13.8 percent in the high-dose arm, 37 percent in the low-dose arm, and 45.5 percent in the placebo arm. The confidence interval for the high-dose comparison just excluded zero, but the pattern — dose-dependent, ecologically plausible, and of a magnitude comparable to Vowst and Rebyota — was considered convincing enough that the product entered a Phase 3 programme. That programme was still running at the time of writing; an approval in 2026 or 2027 is plausible but not certain.

What makes VE303 more than just a third entry in the C. difficile market is its conceptual status. For the first time, a community-based therapy is being approved on the basis of exactly which organisms are in it, not on the basis of which donor it came from. This is the first post-donor microbiome therapy. If VE303 is approved, the model of microbiome engineering that it represents — identify the active components from a natural community, culture them separately, manufacture them as a defined mixture — becomes the default template for the next generation. A handful of similar products, targeting inflammatory bowel disease, hepatic encephalopathy, and cancer immunotherapy augmentation, are behind VE303 in the pipeline.

The inflammatory bowel disease consortium front

The FMT literature in ulcerative colitis that I reviewed in Chapter 24 showed a real but modest signal — remission rates around 25 to 30 percent against 5 to 10 percent with placebo, with the best results from the most intensive multi-donor pooled protocols. The defined-consortium answer to that literature is to ask: what are the specific organisms in the pool that are actually driving the benefit, and can we reconstruct those and omit the rest?

Several groups have taken a stab at this question. The 4D Pharma consortium MRx0518, built around a single Enterococcus gallinarum isolate; the Finch Therapeutics product CP101, a capsulised whole-stool preparation that was closer to FMT than to true consortium engineering; the VE202 consortium, sixteen strains designed to induce regulatory T cells. Not all of them have prospered. Finch Therapeutics filed for bankruptcy in 2023 after failing to secure funding for its Phase 3 programme, a reminder that the commercial environment for microbiome therapeutics remains unforgiving. MRx0518 and VE202 have both had trials that have produced mixed data, and both are undergoing protocol revisions at the time of writing.

The broader lesson from this front is worth stating plainly. The technology of building defined consortia works. The difficulty is that the diseases being targeted — ulcerative colitis, Crohn’s disease, irritable bowel syndrome — are more biologically complex than recurrent C. difficile, in ways that no amount of microbiome manipulation by itself is likely to fully resolve. A defined consortium that restores community diversity may modify these diseases; it is unlikely, on current evidence, to cure them. Realism about what the technology can do is going to matter, in the next five years, as much as the technology itself.

25.4 The Engineered Commensals: Synlogic and the Bacteria That Eat Poisons

The second major precision-engineering strategy — genetic modification of a safe commensal to carry out a defined therapeutic function — has had a more complicated history. The most visible pipeline has been Synlogic’s, and the Synlogic story is worth telling in some detail because it illustrates, in a single company’s arc, what can be done with this technology, what remains hard, and what happens when a genuinely elegant scientific idea meets the blunt reality of clinical trial design.

The Synlogic strategy, in a sentence, is this: take a safe, well-characterised commensal (E. coli Nissle 1917), engineer it to metabolise a toxic compound that accumulates in a specific inborn error of metabolism, deliver it as an oral capsule, and use the engineered bacterium as a living “metabolic sink” that converts the toxin into something harmless in the patient’s gut, before the toxin can be absorbed systemically. The elegance of the idea is that it turns a cell into a drug: not in the metaphorical sense in which all cell therapies are drugs, but in the literal sense that the cell has been given an enzymatic activity it did not previously have, and that activity is the therapeutic mechanism.

SYNB1020 and the hyperammonaemia setback

The first Synlogic product to reach clinical trials was SYNB1020, a Nissle 1917 strain engineered to consume ammonia and convert it to arginine. The target indication was urea-cycle disorders and hepatic encephalopathy — conditions in which ammonia accumulates in the blood to neurotoxic levels because the normal liver pathway for ammonia disposal is either genetically defective (urea-cycle disorders) or overwhelmed by liver failure (encephalopathy). A Phase 1 trial in healthy volunteers demonstrated that the engineered strain transited the gut, expressed the engineered pathway, and altered nitrogen metabolism in a direction consistent with the intended mechanism [REF:kurtz2019]. The science worked.

The Phase 1b/2a trial in patients with cirrhosis and hyperammonaemia, however, did not meet its efficacy endpoint and was terminated in 2019. The reasons are instructive. The engineered organism performed its metabolic function as designed; the problem was that it did not perform enough of it, for long enough, in the right place, to move the clinical endpoint. The gut is a harsh and crowded ecosystem. An introduced organism, even a well-adapted one like Nissle 1917, does not colonise stably in most adults; it transits through over days, doing what engineering it has while it is present, and is then out-competed by the resident community. The “dose” of the therapeutic, in the sense of how much ammonia-consuming enzyme is actually acting in the relevant anatomical compartment for the relevant amount of time, turned out to be lower than mouse models had suggested. This is a recurring lesson in the engineered-commensal space: the mouse gut and the human gut are not equivalent bioreactors, and the extrapolation from one to the other is still being learned.

SYNB1618 and phenylketonuria

The second Synlogic product, SYNB1618, was engineered to degrade phenylalanine — the amino acid that accumulates to neurotoxic levels in phenylketonuria (PKU), the inherited metabolic disease familiar from the heel-prick test given to every newborn in developed countries. PKU patients live, for their entire lives, on a diet that excludes most protein-containing foods, a regime that is particularly punishing for children and adolescents. An oral therapeutic that could degrade dietary phenylalanine in the gut before it could be absorbed — effectively decoupling diet from blood phenylalanine levels — would be transformative.

SYNB1618, and its successor SYNB1934, made it through Phase 2 trials with a signal of benefit in blood phenylalanine reduction. But the Phase 3 trial, known as Synpheny-3, was terminated in 2023 on the recommendation of the data monitoring committee, after an interim analysis suggested that the study was unlikely to meet its primary endpoint at the planned sample size [REF:synlogic2023]. The termination was not because the product did not work — it did work, in the sense of reducing blood phenylalanine modestly — but because the effect was too small relative to placebo to be clinically compelling, and because during the trial’s life an alternative product (pegvaliase, an injectable phenylalanine-degrading enzyme) had moved onto the market and made the bar for approval higher. Synlogic announced, shortly afterwards, that it was winding down its engineered-bacterium programmes and re-focusing on other technologies. The company’s share price, which had exceeded ten dollars at its peak, fell below a dollar.

The Synlogic story is not, however, a failure of the underlying science. The trials demonstrated that engineered E. coli Nissle 1917 could be administered safely to humans over long periods; that the engineered metabolic pathways functioned in the human gut; that the organisms did not colonise persistently (a safety feature, if a therapeutic limitation); and that the immunological, metabolic, and microbial-ecological consequences of the intervention were manageable. The lesson is not that engineered commensals do not work, but that the specific combination of delivery, residence time, and enzymatic capacity required to move a systemic clinical endpoint is a harder engineering problem than the founders initially estimated. Subsequent groups are attacking it with different chassis organisms (spore-forming Bacillus species, which survive the stomach more robustly; gut-adapted Bacteroides species, which colonise persistently if they can be made safe), different delivery schedules, and different indications. The Synlogic programme may have closed, but the engineered-commensal field is not, in 2026, demonstrably closing with it.

The wider engineered-commensal pipeline

Other companies and academic groups are pursuing the same general strategy for different targets. Novome Biotechnologies engineered a Bacteroides strain to degrade oxalate, the kidney-stone-forming metabolite that accumulates in hyperoxaluria; their programme reached Phase 1b but has since paused for funding reasons. ZBiotics, which operates in the consumer space rather than as a prescription pharmaceutical, has an engineered Bacillus product marketed to degrade acetaldehyde after alcohol consumption (a hangover prevention product, with modest evidence). The academic group of Timothy Lu at MIT, which originally span Synlogic out, has continued to publish on new chassis organisms and new genetic-circuit designs that offer more stable expression and more sophisticated behaviour — including “sense-and-respond” circuits in which the engineered organism only expresses its therapeutic payload when it detects a specific signal in its environment. None of these has yet reached the market. Several are plausibly five to ten years from doing so.

The direction of travel in this subfield is clear enough. The first generation of engineered commensals was built on the question “can we do this at all?” and the answer was, roughly, yes. The second generation is being built on the question “can we do this well enough to move a clinical endpoint?” and the answer depends on the target. For diseases where a relatively small metabolic perturbation is enough to matter — certain inborn errors of metabolism, gut-luminal toxins, locally-acting immune modulators — the technology is plausibly close. For diseases that require sustained, systemic, large-magnitude metabolic shifts, the engineering problem remains hard.

25.5 The CRISPR Antimicrobials: Killing One Species at a Time

The fourth precision-engineering strategy is, in my reading, the most technologically radical, and the one with the most transformative near-term potential if the engineering problems can be solved. It is also, probably not coincidentally, the one that most clearly illustrates why precision engineering is genuinely different from antibiotics, probiotics, or FMT.

The observation on which the strategy rests is that CRISPR-Cas systems — the bacterial immune systems we have spent the last fifteen years repurposing for gene editing in human and plant cells — are, in their native context, sequence-specific antimicrobials. A bacterium encoding a CRISPR-Cas system that has captured a sequence from a phage it previously encountered will, on re-encountering that phage, cut the phage DNA to pieces. The specificity is set by a short guide RNA that matches twenty nucleotides of the target sequence; the cutting is done by the Cas nuclease. Repurpose the system so that the guide RNA matches a unique, essential sequence in the genome of a target pathogen rather than a phage, and the Cas nuclease will cut the pathogen’s own chromosome, causing the bacterium to lyse. Deliver the CRISPR-Cas system to the target pathogen using a bacteriophage that naturally infects that pathogen’s species, and you have a sequence-specific antibiotic: it kills only organisms whose genomes match the guide RNA, and spares everything else in the community.

This is, in the language of medicine, an astonishing prospect. Every other antimicrobial we have — every antibiotic, every antifungal, every antiviral, every phage — is either broad-spectrum (and therefore collateral) or narrow-spectrum in a species-at-best way. A CRISPR antimicrobial can be, in principle, strain-specific: it can kill the pathogenic Escherichia coli that produces an extended-spectrum beta-lactamase while leaving the commensal Escherichia coli that does not. It can eliminate an antibiotic-resistance gene from a community without killing any of the organisms carrying it (a variant strategy, using Cas systems that cut plasmids rather than chromosomes, that is also being pursued). It can target a virulence factor rather than a species. It is, in the deepest sense, a therapy that can be programmed.

Locus Biosciences and LBP-EC01

Locus Biosciences, founded in North Carolina in 2015 and partly acquired by Swiss pharmaceutical company Roche in 2024, has been the most clinically advanced of the CRISPR-antimicrobial companies. Their lead product, LBP-EC01, is a cocktail of three engineered bacteriophages, each carrying a CRISPR-Cas3 system with guide RNAs targeting essential and virulence-related sequences in uropathogenic E. coli — the most common cause of urinary tract infection. The choice of Cas3 rather than Cas9 is deliberate: Cas3 is a processive nuclease that shreds DNA progressively along the chromosome after the initial cut, making bacterial escape through repair or mutation much less likely than with Cas9.

In 2024, Locus reported the results of its ELIMINATE Phase 2 trial in patients with recurrent urinary tract infections [REF:locus2024]. The trial met its primary safety endpoint — no drug-related serious adverse events — and showed a reduction in E. coli burden in the urinary tract over seven days of treatment compared with placebo, with the specificity of the effect (preservation of other urinary and gut commensals) confirmed by sequencing. A Phase 3 programme is now in preparation. If LBP-EC01 is approved — and in 2026 this looks plausible within the next two to three years — it will be the first CRISPR-based antimicrobial to reach clinical practice. The implications extend well beyond urinary tract infection. Drug-resistant E. coli is one of the five bacterial species responsible for the majority of deaths from antimicrobial resistance globally [REF:murray2022]; a therapeutic that can selectively eliminate the pathogenic strains without damaging the commensal community would, at scale, be a substantial addition to the global antimicrobial-resistance toolkit.

Eligo Bioscience and the “eligobiotic” platform

The French company Eligo Bioscience, founded in 2014 by scientists at the Rockefeller University and the Pasteur Institute, has pursued a similar concept with a different delivery strategy. Rather than using intact engineered phages, Eligo uses phage-derived capsids that deliver a CRISPR-Cas9 payload but cannot replicate within their target cells — a design that should, in principle, reduce the risk of the therapy propagating uncontrollably through the community. Their lead programme targets the toxin-producing Staphylococcus aureus strains implicated in certain skin and systemic infections, with a particular focus on strains producing Panton-Valentine leukocidin. A further programme addresses the colibactin-producing strains of E. coli that have recently been implicated in colorectal cancer initiation. First-in-human trials for the lead programme were scheduled to begin in late 2025 or early 2026 [REF:eligo2025]. As of this writing, their status is not publicly confirmed.

Eligo also reported, in 2023, the first in vivo demonstration of microbiome base-editing in a mammalian gut — using a non-cutting, sequence-specific deaminase delivered via phage capsid to alter a specific DNA position in a target strain of E. coli in the intestines of live mice, without killing the organism or any of its neighbours. The result is conceptually important. It demonstrates that the CRISPR-toolbox can be applied, in the crowded and chaotic environment of a real mammalian microbiome, not just to destroy specific organisms but to edit them in place — leaving the organism alive but altering a specific function. What that opens up, in therapeutic terms, is not yet clear. But the demonstration itself is a proof of principle that will, in the next decade, be iterated on by many groups.

The specific risk profile of CRISPR antimicrobials

The precision of CRISPR antimicrobials is their greatest advantage. It is also the source of their characteristic risks, which differ in interesting ways from the risks of conventional antimicrobials.

First, resistance. A sequence-specific antimicrobial can, in principle, be defeated by a single mutation in the target sequence. In practice, the careful design of multi-target cocktails (LBP-EC01 has three phages each targeting different sequences) and the choice of essential, conserved genes as targets minimise this risk, but it is real and will require monitoring in long-term use. Second, off-target effects. A guide RNA sequence chosen to match a unique site in the target pathogen must be verified, computationally and experimentally, not to match other sites in other organisms in the community. Bioinformatic screening for off-target matches is now routine, but the reference microbiome data on which such screening depends is incomplete, and novel off-target effects in an individual patient’s specific microbial community cannot be ruled out in advance. Third, phage-host immunology. The bacteriophage delivery vehicle is itself a foreign biological entity, and repeated administration can induce host antibodies that reduce the effectiveness of subsequent doses — the same problem that AAV-based human gene therapies face, adapted to the microbial context. Fourth, horizontal gene transfer. The most serious theoretical concern is that a CRISPR-bearing bacteriophage might pass its payload to a non-target organism via horizontal transfer, producing unintended effects elsewhere in the community. The engineering strategies in current use (replication-incompetent capsids, narrow-host-range phages, inducible systems) are designed specifically to minimise this risk, but the field is not yet mature enough to be certain that all routes of unintended propagation have been closed.

None of these risks is disqualifying. All of them are specific to precision antimicrobials, and all of them are addressable with engineering attention. The regulatory frameworks are still catching up; the CRISPR antimicrobial is a genuinely novel regulatory object, neither drug in the traditional small-molecule sense nor cell therapy in the gene-therapy sense, and the agencies are feeling their way.

25.6 The Regulatory Landscape: Why Approval Is Slower Than the Science

At this point in the chapter I want to pivot from the science to the regulation, because in a field as new as this one the regulatory trajectory is not a downstream consequence of the science; it is a load-bearing determinant of what can be developed, for what indications, and how quickly. The reader who has followed pharmaceutical development at all is familiar with the FDA/EMA/TGA model for small-molecule drugs: Phase 1 for safety, Phase 2 for preliminary efficacy, Phase 3 for pivotal efficacy, then filing for approval, then post-marketing surveillance. Microbiome therapeutics fit awkwardly into this model, and the regulatory agencies have, over the last decade, been building a parallel framework that inherits some of the conventional drug model and some of the biologics and cell-therapy models.

The FDA’s Live Biotherapeutic Product framework

The FDA’s principal category for microbiome therapeutics is the Live Biotherapeutic Product (LBP), a term introduced in agency guidance documents around 2016 and refined several times since. An LBP is a biological product that contains live organisms — bacteria, fungi, or, in some readings, phage — that is applicable to the prevention, treatment, or cure of a disease in humans, and that is not a vaccine. LBPs are regulated by the FDA’s Center for Biologics Evaluation and Research (CBER), the same centre that handles vaccines and blood products, rather than by the drug centre. The LBP framework imposes a set of manufacturing standards (current Good Manufacturing Practice, adapted for live-organism processes), a set of characterisation requirements (whole-genome sequencing of master cell banks, identity and purity testing of each batch, stability testing to establish shelf-life), and a clinical-trial framework that is broadly similar to that for small-molecule drugs with some additions for the live-organism dimension (assessment of shedding from treated subjects, long-term follow-up for unanticipated ecological effects).

The LBP framework covers defined consortia and single-strain products but does not neatly accommodate either genetically modified organisms (which also come under the gene-therapy framework, adding complexity) or phage-based CRISPR antimicrobials (which sit at the intersection of LBP, gene-therapy, and antimicrobial regulation). In practice, these more complex products require case-by-case regulatory pathways, with considerable bilateral discussion between sponsor and agency about what data are required and what endpoints are acceptable. This case-by-case negotiation adds years to development programmes.

The European and Australian contexts

The European Medicines Agency has a broadly similar framework under the heading of Advanced Therapy Medicinal Products (ATMPs), with extensions to accommodate microbial therapeutics. The UK’s Medicines and Healthcare products Regulatory Agency (MHRA), post-Brexit, has been notable for somewhat greater flexibility, positioning itself explicitly as a jurisdiction that will consider novel microbiome therapeutics on expedited pathways where the science warrants it. Australia’s Therapeutic Goods Administration has, to this date, regulated FMT under its Biologicals framework and has been slower to build a parallel LBP regime; this has, in practice, resulted in most Australian patients being treated with imported products rather than domestically developed ones. China’s National Medical Products Administration approved its first defined-consortium product in 2024 on the basis of Chinese-only clinical-trial data, and has positioned itself to compete with the FDA for LBP approvals in the second half of this decade. Regulatory fragmentation, as with most pharmaceutical categories, means that a single product often has three or four different approval pathways in different markets, with substantial duplication of effort.

The specific regulatory complications of precision engineering

Beyond the general complications of live-organism therapies, precision microbiome engineering creates specific regulatory challenges that the agencies are still working through.

The first is the question of genetic containment. A genetically modified live organism, if it persists or spreads in the environment, is in principle a piece of genetically modified material released without controlled end-point — a category that, for agricultural organisms, is highly regulated. Applied to a therapeutic bacterium in the gut of a treated patient, the same logic generates questions about shedding, environmental release, and possible horizontal gene transfer to the general microbial population. The current regulatory response is to require that engineered strains be auxotrophic (dependent on a nutrient they cannot synthesise and that is not available in the wild) so that they cannot survive outside the treated gut, and to require tracking studies showing that engineered DNA does not persist in the recipient’s stool beyond the treatment period. These requirements are scientifically reasonable but add considerable complexity and cost to the engineering itself.

The second is the question of off-target effects in a living, heterogeneous community. A small-molecule drug can be characterised in vitro for its target specificity and its off-target binding; a CRISPR antimicrobial can be characterised for its guide-RNA sequence specificity; but the behaviour of either in a specific patient’s actual microbial community cannot be fully predicted. The regulators are, understandably, asking for more and more patient-level community characterisation as part of trial designs, with 16S and shotgun sequencing of stool and other sites at multiple timepoints. This requirement, which did not exist for first-generation FMT approvals, is now standard for precision products. It is generating a large amount of data whose interpretation is not yet settled.

The third is the question of long-term surveillance. The residence time of an introduced organism in the gut can range from days (for most non-colonising commensals) to years (for engineered organisms specifically designed to colonise persistently). The regulators are, quite reasonably, asking for much longer post-marketing surveillance periods for precision-engineered products than for conventional biologics — up to ten years for some engineered-commensal products. This requirement is defensible but commercially discouraging.

The net effect of all these considerations is that precision microbiome therapeutics, while scientifically advancing quickly, are moving through clinical development at a slower pace than either their underlying science or their commercial advocates would prefer. This is probably, in the main, a good thing. The lessons of early gene therapy — the deaths in the 1999 Jesse Gelsinger trial, the leukaemias in the French SCID-X1 trial of 2002 — are what taught the field the value of regulatory conservatism when the therapy is both novel and biologically complex. Microbiome engineering is at a similar stage, and will probably benefit from being made to go slowly.

25.7 The Risks Specific to Precision Engineering

Throughout the preceding sections I have noted individual risk profiles for each strategy. Let me now draw them together, because the risk landscape of precision microbiome engineering is meaningfully different from, rather than simply smaller than, the risk landscape of FMT.

The risks of FMT, as the previous chapter described, are dominated by the transfer of unintended biological content from a human donor to a human recipient — pathogens, resistance genes, possibly metabolic or behavioural traits. The safety fix is better donor screening, and the residual risk is small but real. Precision engineering sidesteps these risks almost entirely: a manufactured product derived from defined strains or engineered organisms has, by design, no undefined passengers. The DeFilipp ESBL death that reshaped FMT safety could not, in principle, happen with VE303.

But precision engineering introduces its own set of risks, some of which are genuinely new to medicine.

First, unintended ecological cascade. A carefully chosen targeted intervention in a complex ecosystem can have consequences that the intervention’s designers did not anticipate, because the ecosystem contains feedback loops and compensatory mechanisms that are not captured in any current model. Kill a target pathogen with a CRISPR antimicrobial, and another organism may expand into the niche the pathogen vacated. Deliver an engineered commensal that produces a specific metabolite, and the metabolite may alter the behaviour of organisms elsewhere in the gut that were not anatomically or functionally considered in the engineering brief. Mouse models and computational simulations help, but the ecosystem in a specific patient at a specific time is always partly unknowable in advance. Adverse events in this category are, by construction, hard to predict. In the clinical trials to date they have not been a dominant signal, but the follow-up periods are too short to be confident.

Second, horizontal gene transfer. Bacteria exchange genetic material through plasmid conjugation, phage transduction, and natural transformation. An engineered gene delivered to one organism in the gut has, in principle, the possibility of being taken up by another organism and persisting, at low frequency, in the broader microbial population. The engineering strategies in use (chromosomal integration rather than plasmid carriage, elimination of mobile genetic elements from the chassis, use of degradation-targeting sequences) minimise this risk, but do not eliminate it. The consequence, if it occurred, would depend on what gene was transferred and to what organism. It could be benign; it could, in unlucky circumstances, create a novel resistance or virulence profile in a new host.

Third, unintended activation of the host immune system. Live bacteria, even probiotic ones, can trigger immune responses. Engineered organisms carrying novel protein sequences (the fluorescent marker proteins sometimes used for tracking, or the therapeutic enzymes themselves) have an additional source of potential immunogenicity. The Synlogic trials did not report serious immune-related adverse events, but the therapy was given for limited periods; a product intended for long-term or lifelong administration would have different stakes.

Fourth, off-target genome editing in the patient’s own cells. This is, in most cases, a theoretical concern rather than a demonstrated risk. CRISPR-Cas systems delivered by phage to bacterial targets are operating inside bacteria, not in the patient’s own cells, and the mechanism of delivery (phage infection) is biologically specific to bacteria. But the underlying concern — that CRISPR machinery in the gut might somehow reach a human cell and edit a human gene — is one that the regulators are asking sponsors to address explicitly, with tracking data showing that the editing machinery does not escape its intended compartment.

Fifth, and in some respects most subtly, manufacturing consistency. Live organisms are biological products, and biological products have lot-to-lot variability. A batch of VE303 that happens to have 2 percent lower levels of a specific strain than the specification calls for is not obviously defective, but it is, in ecological terms, a different product from the one characterised in the pivotal trial. The quality-control systems that the LBP framework requires are designed to minimise this variability, but they do not eliminate it. What happens when individual patients receive out-of-specification batches, and how such variability would be detected in routine clinical practice, are questions still being worked out.

Sixth, cost and access. This is not a biological risk, but it is a risk to the population-level benefit of the technology. The pharmaceutical manufacturing of live engineered products is expensive — more expensive than the manufacturing of most small-molecule drugs, and probably expensive on an ongoing basis rather than converging to low cost as patents expire. The market prices of Vowst and Rebyota in the US, at the time of writing, are in the tens of thousands of dollars per course. Whether precision microbiome therapies become generally accessible, or whether they join the list of modern therapeutics that are technically available but practically restricted to wealthy patients in wealthy countries, is a question that will be settled by health-system economics rather than by science.

25.8 What Is Plausibly Near, and What Is Not

I want to end with my own best reading of what is genuinely close, what is genuinely possible but further out, and what belongs more to the next generation than to this one. I have tried throughout this chapter to be sober; here I will be more so.

Near-term (2026–2030). Additional defined consortia will be approved for recurrent C. difficile infection, including VE303 or a successor. The first CRISPR-based antimicrobial, probably LBP-EC01 or a close competitor, will be approved, most likely for a narrow indication such as recurrent urinary tract infection caused by a specific E. coli. The first defined-consortium product for ulcerative colitis may reach approval, with modest efficacy comparable to the FMT trials. One or two engineered commensals for inborn errors of metabolism will reach late-phase trials, though approval will depend on effect sizes that have, so far, been smaller than hoped.

Medium-term (2030–2035). The move from whole-community FMT to defined consortia will largely complete for the major indications, leaving true FMT as a procedure mainly of historical and salvage importance. CRISPR antimicrobials will have been deployed, possibly at scale, for at least one antimicrobial-resistance problem (likely E. coli bloodstream infection or hospital-acquired Klebsiella pneumoniae). Engineered commensals will probably have achieved approval for at least one inborn error of metabolism. Early trials of “live sensor” organisms — engineered strains that detect specific biomarkers in the gut and either report them externally or respond with a therapeutic payload — will be underway. Base-editing of resident organisms in vivo, currently a laboratory proof of concept, may reach first-in-human trials.

Longer-term (2035–2045). The field’s central ambition — programmable microbial communities in which composition and function can be both measured and modified with precision comparable to current genetic medicine — will be approaching clinical reality for a narrow range of indications. Whole-community design, rather than single-strain modification, may become tractable as the underlying ecology is better understood. Some of the grander speculations — microbial communities engineered for the prevention of cardiovascular disease, for the modulation of ageing, for the alteration of behavioural phenotypes — will be either experimentally demonstrated or quietly abandoned, and it is not yet possible to say which of them will be which.

I have three cautionary notes to add to this optimistic arc. The first is that the gap between proof of concept and proof of benefit, in this field, is repeatedly turning out to be larger than its advocates expected. Synlogic’s arc is the cautionary tale here; the engineering worked, but the clinical benefit was too modest, too many times. The second is that precision microbiome engineering sits in a broader biomedical environment in which alternative technologies — monoclonal antibodies, small-molecule drugs, RNA therapeutics — are continuously improving, and the bar a microbiome therapy has to clear is rising. A microbiome therapy that would have been transformative if developed in 2015 may be merely useful if developed in 2030, because its competition has improved in the meantime. The third is that the underlying biology is still being learned. Every few years, a mechanism is uncovered that changes what we thought we knew — recent examples include the role of bacteriophage communities in shaping bacterial population structure in the gut, the recognition that small RNA signals travel between organisms in ways that were not previously modelled, and the emerging appreciation of biofilm structure in gut ecology. The microbiome is not a bag of bacteria. It is an ecosystem with layered structure, and every layer has surprises left in it.

25.9 Scalpel, Not Nuclear: A Final Framing

I opened this chapter with the contrast between nuclear option and scalpel, and I want to return to it at the end, because I think the metaphor captures something important about the choice that this field offers the reader.

FMT is, in a real sense, a brute-force solution to a problem we do not yet fully understand. It works because a complex adaptive system — a human gut community — has enough redundancy, enough internal cross-talk, and enough resilience that when you replace the whole system at once you can, under the right conditions, restore health. What you cannot do, with a nuclear option, is direct the restoration. You cannot say: please give me back the bile-acid-metabolising species that I lost to antibiotics, but leave everything else as it was. You take the whole donor community or none of it.

Precision engineering is the ambition to direct the restoration. To say, in effect: these specific organisms, performing these specific functions, in these specific amounts. It is, when it works, a fundamentally more sophisticated kind of medicine than FMT. It matches the way we think about modern pharmacology — active ingredients, dose-response, selectivity, characterisation. It also, when it does not work, fails in characteristic modern-pharmacology ways: the mechanism is right, the delivery is inadequate; the target is correct, the effect size is small; the drug does what was promised, but the disease has too many other causes for that one action to be enough.

The right reader to end this chapter with is the one who will, in a decade or two, have a choice between FMT and a precision product for some indication not yet approved. That reader, if they ask whether the precision product is better, is asking a question that the answer will depend on. Better at being characterised, standardised, regulated, manufactured at scale, and shipped without a donor: certainly. Better at accessing the full compensatory biology of a real human gut community, with its tens of thousands of genes and its millennia of coevolutionary fine-tuning: not necessarily, and in some cases probably not. A scalpel is more precise than a blender, but a blender covers more ground. The case for precision engineering is not that it makes FMT obsolete. It is that it complements FMT; that it takes the parts of the microbiome intervention space where the precision matters (specific metabolic functions, specific resistance elimination, specific single-organism delivery) and carries them forward into a pharmaceutical age, while leaving the parts where whole-community complexity matters — and they may turn out to be more of them than we currently think — to the more ancient technology of the transplant.

The next decade of microbiome therapeutics will, if I am reading the field correctly, see not the replacement of FMT by precision engineering but the bifurcation of the field into two parallel tracks. Precision therapies for indications where we understand the biology well enough to design a specific intervention; whole-community therapies for indications where we do not, and where the surest way to restore function is still to restore community. Both tracks will mature. Neither will make the other irrelevant. And the patient in front of me in ten years’ time will, I hope, be offered whichever of the two is right for their specific disease, their specific community, and their specific place in the long and still only-beginning story of how humans learned to live, deliberately, with the organisms that live with them.

Key Chapter References

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End of Chapter 25 draft.

The Inhabited Body — Bibliography by Chapter

Curated from Paperpile library (185 references) April 2026

References sorted by primary relevance to each chapter. Many references appear in multiple chapters; listed here under their primary home. Diabetes-focused references are concentrated in Chapters 22, 26, and 27 where they are most relevant, rather than scattered throughout.

Ch. 1 — You Are Never Alone

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Ch. 2 — Built Together: How Microbes Shaped Human Evolution

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Ch. 3 — Your First Inheritance: Colonisation Across a Lifetime

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- Chavarro JE et al. (2020). Association of Birth by Cesarean Delivery With Obesity and T2D Among Adult Women. JAMA Netw Open. doi:10.1001/jamanetworkopen.2020.2605

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Ch. 4 — Tools of Discovery

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- Kishikawa T et al. (2022). OMARU: a robust pipeline for metagenome-wide association study. NAR Genom Bioinform. doi:10.1093/nargab/lqac019

- D’Andreano S et al. (2021). Rapid real-time identification of fungi with long amplicon nanopore sequencing. Biol Methods Protoc. doi:10.1093/biomethods/bpaa026

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- Taxt AM et al. (2020). Rapid identification of pathogens, ARGs and plasmids in blood cultures by nanopore sequencing. Sci. Rep. doi:10.1038/s41598-020-64616-x

Ch. 5 — The Gut

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- Mallott EK et al. (2024). The primate gut microbiota contributes to interspecific differences in host metabolism. Microb. Genom. doi:10.1099/mgen.0.001322

- Corbin KD et al. (2023). Host-diet-gut microbiome interactions influence human energy balance. Nat. Commun. doi:10.1038/s41467-023-38778-x

- Collins J et al. (2018). Dietary trehalose enhances virulence of epidemic Clostridium difficile. Nature. doi:10.1038/nature25178

- Wikberg EC et al. (2020). Interactions between social groups of colobus monkeys explain similarities in their gut microbiomes. Anim. Behav. doi:10.1016/j.anbehav.2020.02.011

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Ch. 6 — The Skin

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Ch. 7 — The Respiratory Tract

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- Marrella V et al. (2024). Microbiota and immunity during respiratory infections: Lung and gut affair. Int. J. Mol. Sci. doi:10.3390/ijms25074051

- Jabeen MF et al. (2024). Species-level metagenomic and proteomic analysis of microbe-immune interactions in severe asthma. Allergy. doi:10.1111/all.16269

- Charalampous T et al. (2021). Respiratory metagenomics for secondary infection in COVID-19 ICUs. Genome Med. doi:10.1186/s13073-021-00991-y

- Woo S et al. (2020). Dynamics of Respiratory Microbiota during Mechanical Ventilation. J. Clin. Med. Res. doi:10.3390/jcm9030638

- Yeoh YK et al. (2021). Gut microbiota composition reflects disease severity in COVID-19. Gut. doi:10.1136/gutjnl-2020-323020

- Ren X et al. (2020). Intestinal microbiota in children with community-acquired pneumonia. Microb. Pathog. doi:10.1016/j.micpath.2020.104062

Ch. 8 — The Urogenital Microbiome

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- Sadeghpour Heravi F (2024). Host-vaginal microbiota interaction: shaping the vaginal microenvironment. Curr. Clin. Microbiol. Rep. doi:10.1007/s40588-024-00227-8

Ch. 9 — The Oral Microbiome

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Ch. 10 — The Mycobiome

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- D’Andreano S et al. (2021). Rapid identification of fungi with long amplicon nanopore sequencing. Biol Methods Protoc. doi:10.1093/biomethods/bpaa026

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Ch. 11 — The Brain Microbiome

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- Yong SJ (2024). What If Something Else Lives in Your Brain? The Science and Secrets of Brain Microbiome.

- Hu Z et al. (2023). The remarkable complexity of the brain microbiome in health and disease. bioRxiv. doi:10.1101/2023.02.06.527297

- Moné Y et al. (2023). Evidence for a bacterial component in the etiology of Alzheimer’s disease. Front. Cell. Infect. Microbiol. doi:10.3389/fcimb.2023.1123228

Ch. 13 — The Gut-Brain Axis

- Cox TO et al. (2026). Intestinal interoceptive dysfunction drives age-associated cognitive decline. Nature. doi:10.1038/s41586-026-10191-6

- Cheng YT & Mazmanian SK (2026). Gut microbes affect cognition during ageing. doi:10.1038/d41586-026-00492-1

- Cryan JF & Dinan TG (2012). Mind-altering microorganisms: the gut microbiota on brain and behaviour. Nat. Rev. Neurosci. doi:10.1038/nrn3346

- Cryan JF et al. (2020). The gut microbiome in neurological disorders. Lancet Neurol. doi:10.1016/S1474-4422(19)30356-4

- Torres-Fuentes C et al. (2017). The microbiota-gut-brain axis in obesity. Lancet Gastroenterol. Hepatol. doi:10.1016/S2468-1253(17)30147-4

- Fekete M et al. (2024). Gut-brain axis modulation on cognitive health: prebiotics, probiotics, and symbiotics. Nutrients. doi:10.3390/nu16060789

- Meyer K et al. (2022). Gut Microbiota and cognitive function in midlife. JAMA Netw. Open. doi:10.1001/jamanetworkopen.2021.43941

- DOE/PNNL (2020). Scientists explore links between genetics, gut microbiome and memory. Science Daily.

- Willyard C (2021). How gut microbes could drive brain disorders. Nature. doi:10.1038/d41586-021-00260-3

- Mehta RS et al. (2022). Midlife antibiotic use and subsequent cognitive function in women. PLoS One. doi:10.1371/journal.pone.0264649

Ch. 14 — Behavioural Modulation

- Hakimi MA et al. (2017). Toxoplasma effectors targeting host signaling and transcription. Clin. Microbiol. Rev. doi:10.1128/CMR.00005-17

- Johnson HJ & Koshy AA (2020). Latent toxoplasmosis effects on rodents and humans. MBio. doi:10.1128/mBio.02164-19

- de Haan L et al. (2021). Toxoplasma gondii seropositivity and cognitive function: systematic review and meta-analysis. JAMA Psychiatry. doi:10.1001/jamapsychiatry.2021.1590

- Stock AK et al. (2017). Humans with latent toxoplasmosis display altered reward modulation. Sci. Rep. doi:10.1038/s41598-017-10926-6

- Ortiz-Guerrero G et al. (2020). Cognitive impairment and neurodegeneration by Toxoplasma gondii. Brain Sci. doi:10.3390/brainsci10060369

- Rougier S et al. (2017). Lifelong Persistence of Toxoplasma Cysts: A Questionable Dogma? Trends Parasitol. doi:10.1016/j.pt.2016.10.007

- Sutterland AL et al. (2015). Toxoplasma gondii in schizophrenia, bipolar disorder, and addiction: meta-analysis. Acta Psychiatr. Scand. doi:10.1111/acps.12423

Ch. 15 — Metabolism, Hormones, and the Endocrine Microbiome

- Yue F et al. (2022). Constant light exposure alters gut microbiota and aggravates PCOS-like traits. Obesity. doi:10.1002/oby.23380

- Sarmiento J et al. (2022). Nocturnal Light Pollution Induces Weight Gain and Reshapes Colonic Microbiota. Int. J. Mol. Sci. doi:10.3390/ijms23031673

Ch. 16–17 — Bacteriophages

- Carroll-Portillo A & Lin HC (2019). Bacteriophage and the innate immune system. Microorganisms. doi:10.3390/microorganisms7120625

- Dutilh BE et al. (2014). Highly abundant bacteriophage in unknown sequences of human faecal metagenomes. Nat. Commun. doi:10.1038/ncomms5498

- Bondy-Denomy J & Davidson AR (2014). When a virus is not a parasite: beneficial effects of prophages. J. Microbiol. doi:10.1007/s12275-014-4083-3

- Atanasova NS et al. (2015). Comparison of lipid-containing bacterial and archaeal viruses. Adv. Virus Res. doi:10.1016/bs.aivir.2014.11.005

- Zheludev IN et al. (2024). Viroid-like colonists of human microbiomes [“Obelisks”]. bioRxiv. doi:10.1101/2024.01.20.576352

- Sidik S (2024). ‘Wildly weird’ RNA bits discovered infesting the microbes in our guts. Nature. doi:10.1038/d41586-024-00266-7

- Thompson J (n.d.). Weird ‘obelisks’ found in human gut may be virus-like entities. Sci. Am.

- Raoult D & Forterre P (2008). Redefining viruses: lessons from Mimivirus. Nat. Rev. Microbiol. doi:10.1038/nrmicro1858

Ch. 18 — Phage Therapy

- Romero-Calle D et al. (2019). Bacteriophages as alternatives to antibiotics in clinical care. Antibiotics. doi:10.3390/antibiotics8030138

- Kushwaha SO et al. (2024). Bacteriophages as potential substitute for antibiotics: Comprehensive review. Cell Biochem. Funct. doi:10.1002/cbf.4022

- Petrenko VA (2024). Phage display’s prospects for early diagnosis of prostate cancer. Viruses. doi:10.3390/v16020277

Ch. 19 — Antibiotics: Collateral Damage

- Baldanzi G et al. (2026). Antibiotic use and gut microbiome composition: 14,979 individuals. Nat. Med. doi:10.1038/s41591-026-04284-y

- Muñoz KA et al. (2024). A Gram-negative-selective antibiotic that spares the gut microbiome. Nature. doi:10.1038/s41586-024-07502-0

- Althubyani AA et al. (2024). Antibiotic-induced neuropsychiatric toxicity. Drugs Context. doi:10.7573/dic.2024-3-3

- Livanos AE et al. (2016). Antibiotic-mediated gut microbiome perturbation accelerates T1D in mice. Nat Microbiol. doi:10.1038/nmicrobiol.2016.140

- Patrick DM et al. (2020). Decreasing antibiotic use, gut microbiota, and asthma incidence in children. Lancet Respir. Med. doi:10.1016/S2213-2600(20)30052-7

- Llor C et al. (2011). Impact of rapid antigen testing in acute pharyngitis on antibiotic prescription. Br. J. Gen. Pract. doi:10.3399/bjgp11X572436

- von Dach E et al. (2020). CRP-guided antibiotic duration in Gram-negative bacteremia. JAMA. doi:10.1001/jama.2020.6348

- Rhee C et al. (2020). Antibiotic-resistant pathogens in culture-proven sepsis. JAMA Netw Open. doi:10.1001/jamanetworkopen.2020.2899

- Ternák G et al. (2022). Antibiotic consumption patterns and diabetes prevalence in Europe. Front. Endocrinol. doi:10.3389/fendo.2022.870465

- Shuai M et al. (2022). Human Gut Antibiotic Resistome and Progression of Diabetes. Adv. Sci. doi:10.1002/advs.202104965

- Jabbar A & Sajjad-Ur-Rehman (2013). Antibiotic residues in meat, milk and eggs. J Microb Biotech Food Sci.

- Chowdhury S et al. (2015). Antibiotic residues in milk and eggs in Chittagong, Bangladesh. Vet World. doi:10.14202/vetworld.2015.467-471

- Boursi B et al. (2015). Recurrent antibiotic exposure may promote cancer formation. Eur. J. Cancer.

- Park SJ et al. (2021). Antibiotics use and diabetes incidence in Korean cohort. Sci. Rep. doi:10.1038/s41598-021-01125-5

- Nguyen K et al. (2013). Antibiotic use and resistance in Viet Nam. BMC Public Health. doi:10.1186/1471-2458-13-1158

Ch. 20 — Diet: Feeding the Ecosystem

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- Taylor BC et al. (2020). Consumption of fermented foods is associated with systematic differences in gut microbiome and metabolome. mSystems. doi:10.1128/mSystems.00901-19

- Thaiss CA et al. (2014). Transkingdom control of microbiota diurnal oscillations promotes metabolic homeostasis. Cell. doi:10.1016/j.cell.2014.09.048

- Thaiss CA et al. (2016). Microbiota diurnal rhythmicity programs host transcriptome oscillations. Cell. doi:10.1016/j.cell.2016.11.003

- Tomás-Barberán FA et al. (2017). Urolithins and metabotypes. Mol. Nutr. Food Res. doi:10.1002/mnfr.201500901

- Turroni S et al. (2016). Fecal metabolome of the Hadza hunter-gatherers. Sci. Rep. doi:10.1038/srep32826

- Walsh L, Hill C, Ross RP (2023). Impact of glyphosate (Roundup) on the composition and functionality of the gut microbiome. Gut Microbes. doi:10.1080/19490976.2023.2263935

- Wang Z et al. (2011). Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease. Nature. doi:10.1038/nature09922

- Wastyk HC et al. (2021). Gut-microbiota-targeted diets modulate human immune status. Cell. doi:10.1016/j.cell.2021.06.019

- Wu GD et al. (2011). Linking long-term dietary patterns with gut microbial enterotypes. Science. doi:10.1126/science.1208344

- Zeb F et al. (2020). Time-restricted feeding, metabolic risk, and circadian rhythm. Br. J. Nutr. doi:10.1017/S0007114519003428

- Zeevi D et al. (2015). Personalized nutrition by prediction of glycemic responses. Cell. doi:10.1016/j.cell.2015.11.001

- Zinöcker MK, Lindseth IA (2018). The Western diet-microbiome-host interaction and metabolic disease. Nutrients. doi:10.3390/nu10030365

Additional curated references (not yet cited in draft — reserve for possible inclusion)

- Li H et al. (2024). Resistant starch intake and weight loss by reshaping gut microbiota. Nat Metab. doi:10.1038/s42255-024-00988-y

- Lane MM et al. (2024). Ultra-processed food exposure and adverse health outcomes: umbrella review. BMJ. doi:10.1136/bmj-2023-077310

- Czarnecka K et al. (2021). Aspartame — True or False? Nutrients. doi:10.3390/nu13061957

- Mohr AE et al. (2024). Gut microbiome remodeling: protein pacing with intermittent fasting. Nat. Commun. doi:10.1038/s41467-024-48355-5

- Corbin KD et al. (2023). Host-diet-gut microbiome interactions and energy balance. Nat. Commun. doi:10.1038/s41467-023-38778-x

- Choi J et al. (2025). Yogurt promotes microbial diversity. Front. Nutr. doi:10.3389/fnut.2025.1609102

- Collins J et al. (2018). Dietary trehalose enhances virulence of epidemic C. difficile. Nature. doi:10.1038/nature25178

- Mesnage R & Antoniou MN (2017). Facts and fallacies in the debate on glyphosate toxicity. Front. Public Health. doi:10.3389/fpubh.2017.00316

Ch. 21 — Environment, Lifestyle, and the Modern World

- Huang D et al. (2022). Polystyrene microplastic exposure induces insulin resistance via dysbacteriosis. Sci. Total Environ. doi:10.1016/j.scitotenv.2022.155937

- Li D et al. (2020). Microplastic release from polypropylene feeding bottles. Nature Food. doi:10.1038/s43016-020-00171-y

- Zrimec J et al. (2021). Plastic-Degrading Potential across the Global Microbiome. MBio. doi:10.1128/mBio.02155-21

- Lu J et al. (2018). Triclosan induces multiple antibiotic resistance through genetic mutation. Environ. Int. doi:10.1016/j.envint.2018.06.004

- Short FL et al. (2021). Benzalkonium chloride antagonises aminoglycoside antibiotics. EBioMedicine. doi:10.1016/j.ebiom.2021.103653

- Singh R et al. (2019). Lifestyle-Induced Microbial Gradients: An Indian Perspective. Front. Microbiol. doi:10.3389/fmicb.2019.02874

- Oduaran OH et al. (2020). Gut Microbiome of Rural and Urban South African Cohort. bioRxiv.

- Tomiyama AJ (2019). Stress and Obesity. Annu. Rev. Psychol. doi:10.1146/annurev-psych-010418-102936

Ch. 22 — Drugs Beyond Antibiotics

Primary references cited in Chapter 22 draft

Foundational cross-class screens

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PPIs

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Metformin

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NSAIDs

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Statins

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Antipsychotics and antidepressants

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Laxatives

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Hormonal contraceptives and HRT

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Iron, vitamin D, fish oil, and herbal supplements

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- [REF:wu2025vitd] Wu CW, et al. (2025). Vitamin D3 Improves Hypothalamic-Pituitary-Adrenal Axis Function, Immunological Responses, and Gut Dysbiosis in Sleep Desynchrony. Brain Behav., 15(11), e71084. doi:10.1002/brb3.71084

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- [REF:kong2020berberine] Kong WJ, et al. (2020). Berberine in the treatment of metabolism-related chronic diseases: A drug cloud (dCloud) effect to target multifactorial disorders. Pharmacol. Ther., 209, 107496. doi:10.1016/j.pharmthera.2020.107496

Pharmacomicrobiomics — drugs metabolised by microbes

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Additional reserve references (not cited in current draft — kept for possible future inclusion)

- Hall RJ et al. (2024). Non-antibiotic pharmaceuticals are toxic against E. coli. NPJ Antimicrob. Resist. doi:10.1038/s44259-024-00028-5

- Chen H et al. (2025). Non-antibiotic medications and AMR in E. coli. NPJ Antimicrob. Resist. doi:10.1038/s44259-025-00144-w

- Imhann F et al. (2017). Proton pump inhibitors and other medications on gut microbiota. Gut Microbes. doi:10.1080/19490976.2017.1284732

- Le Bastard Q et al. (2021). Impact of non-antibiotic drugs on intestinal microbiome. Expert Rev. Mol. Diagn. doi:10.1080/14737159.2021.1952075

- Pyo JH et al. (2021). Proton pump inhibitors and fatty liver disease risk. J. Gastroenterol. Hepatol. doi:10.1111/jgh.15236

- Wu H et al. (2017). Metformin alters the gut microbiome in treatment-naive T2D. Nat. Med. doi:10.1038/nm.4345

Ch. 23 — Probiotics: Promise, Hype, and Hard Truths

- Suez J et al. (2018). Post-antibiotic gut mucosal microbiome reconstitution is impaired by probiotics. Cell. doi:10.1016/j.cell.2018.08.047

- Butler CC et al. (2020). Probiotic Use and Antibiotic Administration in Care Home Residents. JAMA. doi:10.1001/jama.2020.8556

- Li G et al. (2023). Probiotics supplementation and glycaemic control in T2DM: meta-analysis. J. Transl. Med. doi:10.1186/s12967-023-04306-0

- Gao K et al. (2023). Bifidobacterium longum WHH2270 ameliorates type 2 diabetes in rats. J. Food Sci. doi:10.1111/1750-3841.16727

- Forsyth CB et al. (2009). Lactobacillus GG treatment ameliorates alcohol-induced liver injury. Alcohol. doi:10.1016/j.alcohol.2008.12.009

- Maguire M & Maguire G (2019). Gut dysbiosis, leaky gut, and intestinal epithelial proliferation in neurological disorders. Rev. Neurosci. doi:10.1515/revneuro-2018-0024

Ch. 24 — Faecal Microbiota Transplantation

- Udayappan SD et al. (2014). Intestinal microbiota and FMT as treatment for insulin resistance and T2DM. Clin. Exp. Immunol. doi:10.1111/cei.12293

- [REF:eiseman1958] Eiseman B, Silen W, Bascom GS & Kauvar AJ (1958). Fecal enema as an adjunct in the treatment of pseudomembranous enterocolitis. Surgery, 44(5), 854–859.

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- [REF:vannood2013] van Nood E, Vrieze A, Nieuwdorp M et al. (2013). Duodenal infusion of donor feces for recurrent Clostridium difficile. N. Engl. J. Med., 368(5), 407–415. doi:10.1056/NEJMoa1205037

- [REF:vrieze2012] Vrieze A, Van Nood E, Holleman F et al. (2012). Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome. Gastroenterology, 143(4), 913–916.e7. doi:10.1053/j.gastro.2012.06.031

- [REF:alang2015] Alang N & Kelly CR (2015). Weight gain after fecal microbiota transplantation. Open Forum Infect. Dis., 2(1), ofv004. doi:10.1093/ofid/ofv004

- [REF:paramsothy2017] Paramsothy S, Kamm MA, Kaakoush NO et al. (2017). Multidonor intensive faecal microbiota transplantation for active ulcerative colitis: a randomised placebo-controlled trial. Lancet, 389(10075), 1218–1228. doi:10.1016/S0140-6736(17)30182-4

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- [REF:kao2017] Kao D, Roach B, Silva M et al. (2017). Effect of oral capsule- vs colonoscopy-delivered fecal microbiota transplantation on recurrent Clostridium difficile infection: a randomized clinical trial. JAMA, 318(20), 1985–1993. doi:10.1001/jama.2017.17077

- [REF:kang2017] Kang D-W, Adams JB, Gregory AC et al. (2017). Microbiota Transfer Therapy alters gut ecosystem and improves gastrointestinal and autism symptoms: an open-label study. Microbiome, 5(1), 10. doi:10.1186/s40168-016-0225-7

- [REF:kang2019] Kang D-W, Adams JB, Coleman DM et al. (2019). Long-term benefit of Microbiota Transfer Therapy on autism symptoms and gut microbiota. Sci. Rep., 9(1), 5821. doi:10.1038/s41598-019-42183-0

- [REF:defilipp2019] DeFilipp Z, Bloom PP, Torres Soto M et al. (2019). Drug-resistant E. coli bacteremia transmitted by fecal microbiota transplant. N. Engl. J. Med., 381(21), 2043–2050. doi:10.1056/NEJMoa1910437

- [REF:ekekezie2020] Ekekezie C, Perler BK, Wexler A, Duff C, Lillis CJ & Kelly CR (2020). Understanding the scope of do-it-yourself fecal microbiota transplant. Am. J. Gastroenterol., 115(4), 603–607. doi:10.14309/ajg.0000000000000499

- [REF:ianiro2020covid] Ianiro G, Mullish BH, Kelly CR et al. (2020). Reorganisation of faecal microbiota transplant services during the COVID-19 pandemic. Gut, 69(9), 1555–1563. doi:10.1136/gutjnl-2020-321829

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- [REF:davar2021] Davar D, Dzutsev AK, McCulloch JA et al. (2021). Fecal microbiota transplant overcomes resistance to anti-PD-1 therapy in melanoma patients. Science, 371(6529), 595–602. doi:10.1126/science.abf3363

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- [REF:feuerstadt2022] Feuerstadt P, Louie TJ, Lashner B et al. (2022). SER-109, an oral microbiome therapy for recurrent Clostridioides difficile infection. N. Engl. J. Med., 386(3), 220–229. doi:10.1056/NEJMoa2106516

- [REF:khanna2025] Khanna S, Yoho D, Van Handel D et al. (2025). Prevention of recurrent C. difficile infection by fecal microbiota, live-jslm (REBYOTA) administered via colonoscopy: 6-month data from the single-arm phase IIIb CDI-SCOPE trial. Ther. Adv. Gastroenterol., 18, 17562848251396744. doi:10.1177/17562848251396744

Ch. 25 — Precision Microbiome Engineering

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- [REF:louie2023] Louie T, Golan Y, Khanna S et al. (2023). VE303, a defined bacterial consortium, for prevention of recurrent Clostridioides difficile infection: a randomized clinical trial. JAMA, 329(16), 1356–1366. doi:10.1001/jama.2023.4314

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- [REF:citorik2014] Citorik RJ, Mimee M & Lu TK (2014). Sequence-specific antimicrobials using efficiently delivered RNA-guided nucleases. Nat. Biotechnol., 32(11), 1141–1145. doi:10.1038/nbt.3011

- [REF:neil2021] Neil K, Allard N, Roy P et al. (2021). High-efficiency delivery of CRISPR-Cas9 by engineered probiotics enables precise microbiome editing. Mol. Syst. Biol., 17(10), e10335. doi:10.15252/msb.202110335

- [REF:pdaia2023] U.S. Food and Drug Administration, CBER (2016, updated 2023). Early clinical trials with live biotherapeutic products: chemistry, manufacturing, and control information — guidance for industry.

- [REF:hanson2021] Hanson BM & Weinstock GM (2021). The importance of the microbiome in epidemiologic research. Ann. Epidemiol., 35, 75–79. doi:10.1016/j.annepidem.2019.05.007

Ch. 26 — Inflammatory and Autoimmune Disease

Core references used in Chapter 26 draft

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- [REF:haneishi2023] Haneishi Y, Furuya Y, Hasegawa M et al. (2023). Inflammatory bowel diseases and gut microbiota. Int. J. Mol. Sci., 24(4), 3817. doi:10.3390/ijms24043817

- [REF:darfeuille2004] Darfeuille-Michaud A, Boudeau J, Bulois P et al. (2004). High prevalence of adherent-invasive Escherichia coli associated with ileal mucosa in Crohn’s disease. Gastroenterology, 127(2), 412–421. doi:10.1053/j.gastro.2004.04.061

- [REF:henke2019] Henke MT, Kenny DJ, Cassilly CD, Vlamakis H, Xavier RJ & Clardy J (2019). Ruminococcus gnavus, a member of the human gut microbiome associated with Crohn’s disease, produces an inflammatory polysaccharide. Proc. Natl. Acad. Sci. USA, 116(26), 12672–12677. doi:10.1073/pnas.1904099116

- [REF:hugot2001] Hugot JP, Chamaillard M, Zouali H et al. (2001). Association of NOD2 leucine-rich repeat variants with susceptibility to Crohn’s disease. Nature, 411(6837), 599–603. doi:10.1038/35079107

- [REF:leibovitzh2024] Leibovitzh H, Nayeri S, Borowski K et al. (2024). Inflammatory bowel disease associated with primary sclerosing cholangitis is associated with an altered gut microbiome and bile acid profile. J. Crohns Colitis, 18(12), 1957–1966. doi:10.1093/ecco-jcc/jjae096

- [REF:sendid2023] Sendid B, Cornu M, Cordier C, Bouckaert J, Colombel JF & Poulain D (2023). From ASCA breakthrough in Crohn’s disease and Candida albicans research to thirty years of investigations about their meaning in human health. Autoimmun. Rev., 23(2), 103486. doi:10.1016/j.autrev.2023.103486

- [REF:fuchtbauer2025] Füchtbauer JD, Berentsen EM, Lindhardt CL et al. (2025). Gut mycobiome in inflammatory bowel disease: a systematic review of case-control studies. Inflamm. Bowel Dis., 31(11), 3213–3224. doi:10.1093/ibd/izaf217

- [REF:hsu2023] Hsu C, Ghannoum M, Cominelli F & Di Martino L (2023). Mycobiome and inflammatory bowel disease: role in disease pathogenesis, current approaches and novel nutritional-based therapies. Inflamm. Bowel Dis., 29(3), 470–479. doi:10.1093/ibd/izac156

- [REF:shao2022] Shao TY, Haslam DB, Bennett RJ & Way SS (2022). Friendly fungi: symbiosis with commensal Candida albicans. Trends Immunol., 43(9), 706–717. doi:10.1016/j.it.2022.07.003

- [REF:fasano2012] Fasano A (2012). Leaky gut and autoimmune diseases. Clin. Rev. Allergy Immunol., 42(1), 71–78. doi:10.1007/s12016-011-8291-x

- [REF:divincenzo2023] Di Vincenzo F, Del Gaudio A, Petito V, Lopetuso LR & Scaldaferri F (2023). Gut microbiota, intestinal permeability, and systemic inflammation: a narrative review. Intern. Emerg. Med., 19(2), 275–293. doi:10.1007/s11739-023-03374-w

- [REF:mu2017] Mu Q, Kirby J, Reilly CM & Luo XM (2017). Leaky gut as a danger signal for autoimmune diseases. Front. Immunol., 8, 598. doi:10.3389/fimmu.2017.00598

- [REF:vatanen2018] Vatanen T, Franzosa EA, Schwager R et al. (2018). The human gut microbiome in early-onset type 1 diabetes from the TEDDY study. Nature, 562(7728), 589–594. doi:10.1038/s41586-018-0620-2

- [REF:stewart2018teddy] Stewart CJ, Ajami NJ, O’Brien JL et al. (2018). Temporal development of the gut microbiome in early childhood from the TEDDY study. Nature, 562(7728), 583–588. doi:10.1038/s41586-018-0617-x

- [REF:luppi2024] Luppi S, Aldegheri L, Azzalini E et al. (2024). Unravelling the role of gut and oral microbiota in the pediatric population with type 1 diabetes mellitus. Int. J. Mol. Sci., 25(19), 10611. doi:10.3390/ijms251910611

- [REF:asantebaadu2025] Asante Baadu F, Yalley C, Ohene Yeboah AR et al. (2025). Microbiome imbalance and pediatric type 1 diabetes mellitus: an updated systematic review of gut dysbiosis evidence. Cureus, 17(8), e89279. doi:10.7759/cureus.89279

- [REF:jangi2016] Jangi S, Gandhi R, Cox LM et al. (2016). Alterations of the human gut microbiome in multiple sclerosis. Nat. Commun., 7, 12015. doi:10.1038/ncomms12015

- [REF:ordonezrodriguez2023] Ordoñez-Rodriguez A, Roman P, Rueda-Ruzafa L, Campos-Rios A & Cardona D (2023). Changes in gut microbiota and multiple sclerosis: a systematic review. Int. J. Environ. Res. Public Health, 20(5), 4624. doi:10.3390/ijerph20054624

- [REF:schwerdtfeger2025] Schwerdtfeger LA, Jangi S, Yeh WZ et al. (2025). Gut microbiota and metabolites are linked to disease progression in multiple sclerosis. Cell Rep. Med., 6(4), 102055. doi:10.1016/j.xcrm.2025.102055

- [REF:scher2013] Scher JU, Sczesnak A, Longman RS et al. (2013). Expansion of intestinal Prevotella copri correlates with enhanced susceptibility to arthritis. eLife, 2, e01202. doi:10.7554/eLife.01202

- [REF:jiang2022] Jiang L, Shang M, Yu S et al. (2022). A high-fiber diet synergizes with Prevotella copri and exacerbates rheumatoid arthritis. Cell. Mol. Immunol., 19(12), 1414–1424. doi:10.1038/s41423-022-00934-6

- [REF:abdelsalam2023] Abdelsalam NA, Hegazy SM & Aziz RK (2023). The curious case of Prevotella copri. Gut Microbes, 15(2), 2249152. doi:10.1080/19490976.2023.2249152

- [REF:kim2023] Kim CH (2023). Complex regulatory effects of gut microbial short-chain fatty acids on immune tolerance and autoimmunity. Cell. Mol. Immunol., 20(4), 341–350. doi:10.1038/s41423-023-00987-1

- [REF:catassi2024] Catassi G, Aloi M, Giorgio V, Gasbarrini A, Cammarota G & Ianiro G (2024). The role of microbiome in the development of gluten-related disorders. Best Pract. Res. Clin. Gastroenterol., 72, 101951. doi:10.1016/j.bpg.2024.101951

- [REF:elmouzan2022] El Mouzan M, Assiri A, Al Sarkhy A et al. (2022). Microbiota profile of new-onset celiac disease in children in Saudi Arabia. Gut Pathog., 14(1), 37. doi:10.1186/s13099-022-00493-1

- [REF:belvoncikova2022] Belvoncikova P, Maronek M & Gardlik R (2022). Gut dysbiosis and fecal microbiota transplantation in autoimmune diseases. Int. J. Mol. Sci., 23(18), 10729. doi:10.3390/ijms231810729

- [REF:aleman2023] Aleman RS, Moncada M & Aryana KJ (2023). Leaky gut and the ingredients that help treat it: a review. Molecules, 28(2), 619. doi:10.3390/molecules28020619

Additional references (not cited in current draft; kept for later sub-sections on SLE, ankylosing spondylitis, and the STI-antibiotic-autoimmunity tangent)

- Tomofuji Y et al. (2021). Gut microbiome of systemic lupus erythematosus in Japanese. Ann. Rheum. Dis. doi:10.1136/annrheumdis-2021-220687

- Huang R et al. (2020). Intestinal microbiome in ankylosing spondylitis patients. J. Med. Microbiol. doi:10.1099/jmm.0.001107

- Dedrick S et al. (2020). Gut microbiota and environmental factors in T1D pathogenesis. Front. Endocrinol. doi:10.3389/fendo.2020.00078

- Van Dijck C et al. (2021). Antibacterial mouthwash for STIs in MSM on PrEP (PReGo). Lancet Infect. Dis. doi:10.1016/S1473-3099(20)30778-7

- Kenyon C et al. (2023). Asymptomatic STI management in Europe. Lancet Reg. Health Eur. doi:10.1016/j.lanepe.2023.100743

Ch. 27 — Metabolic Disease and Obesity

Note: This is the primary home for the diabetes/obesity references. The bibliography is rich here — reflecting the author’s research interest — and will provide substantial material for a detailed chapter.

- Qin J et al. (2012). Metagenome-wide association study of gut microbiota in T2D. Nature. doi:10.1038/nature11450

- de Vos WM & Nieuwdorp M (2013). A gut prediction. Nature. doi:10.1038/nature12251

- Hartstra AV et al. (2014). Role of the Microbiome in Obesity and T2D. Diabetes Care. doi:10.2337/dc14-0769

- Tilg H & Moschen AR (2014). Microbiota and diabetes: an evolving relationship. Gut. doi:10.1136/gutjnl-2014-306928

- Barlow GM et al. (2015). Role of the Gut Microbiome in Obesity and DM. Nutr. Clin. Pract. doi:10.1177/0884533615609896

- Cai L et al. (2015). T2D Biomarkers of Human Gut Microbiota. PLoS One. doi:10.1371/journal.pone.0140827

- Sabatino A et al. (2017). Intestinal Microbiota in T2D and CKD. Curr. Diab. Rep. doi:10.1007/s11892-017-0841-z

- Tong X et al. (2018). Gut Microbiota during amelioration of T2D with hyperlipidemia by metformin. MBio. doi:10.1128/mBio.02392-17

- Liu G et al. (2018). Pumpkin polysaccharide modifies gut microbiota during alleviation of T2D. Int. J. Biol. Macromol. doi:10.1016/j.ijbiomac.2018.04.127

- Nie X et al. (2020). Gut microbiome and visceral fat accumulation. Comput. Struct. Biotechnol. J. doi:10.1016/j.csbj.2020.09.026

- Liu X et al. (2021). GWAS for gut metagenome in Chinese adults. Cell Discov. doi:10.1038/s41421-020-00239-w

- Wang Z et al. (2024). Gut Microbiota and blood metabolites related to fiber and T2D. Circ. Res. doi:10.1161/circresaha.123.323634

- Peng D et al. (2023). Dendrobium officinale derivatives in a T2D mouse model. Food Chem. doi:10.1016/j.foodchem.2022.133974

- Neri-Rosario D et al. (2023). Dysbiosis signatures and T2D progression: machine learning. Front. Endocrinol. doi:10.3389/fendo.2023.1170459

- Gravdal K et al. (2023). Gut microbiota in pre-diabetes and treatment-naïve T2D. BMC Endocr. Disord. doi:10.1186/s12902-023-01432-0

- Hu J et al. (2023). Gut microbiota signatures in T1D and T2D. EClinicalMedicine. doi:10.1016/j.eclinm.2023.102132

- Zhang L et al. (2023). Intestinal Microbiota in Early and Late Stages of DKD. Microbiol Spectr. doi:10.1128/spectrum.00382-23

- Lu X et al. (2023). Gut microbiota in diabetic nephropathy. Sci. Rep. doi:10.1038/s41598-023-39444-4

- Chen X et al. (2023). Dendrobium officinale polysaccharide alleviates T2DM. J. Agric. Food Chem. doi:10.1021/acs.jafc.3c02429

- Cuadrat RRC et al. (2023). Gut microbiota and vascular stiffness. Sci. Rep. doi:10.1038/s41598-023-40178-6

- Antwi J (2023). Precision Nutrition for Obesity and T2D. Curr. Nutr. Rep. doi:10.1007/s13668-023-00491-y

- Ofosu FK et al. (2023). Fermented sorghum improves T2D via gut microbiota. J. Funct. Foods. doi:10.1016/j.jff.2023.105666

- Han CY et al. (2023). Benaglutide effects on Gut Microbiota in T2DM. Diab. Metab. Syndr. Obes. doi:10.2147/DMSO.S418757

- Niu X et al. (2023). Metformin + liraglutide and gut microbiota in T2D. Int. Microbiol. doi:10.1007/s10123-023-00380-y

- Zhou H et al. (2023). Diet composition influences cholesterol via gut Microbiota in ApoE-/- mice. J. Agric. Food Chem. doi:10.1021/acs.jafc.3c00810

- Pierce DR et al. (2023). Experimental hookworm infection and insulin resistance. Nat. Commun. doi:10.1038/s41467-023-40263-4

- Hughes S (2022). Gut microbiome product implicated in heart failure risk.

- Melville NA (2022). Gut microbiome species predict T2D.

- Jie Z et al. (2023). Three-bacteria consortium inhibits atherosclerosis in mice. iScience. doi:10.1016/j.isci.2023.106960

References added during Ch. 27 drafting (new citations)

- Ley RE, Bäckhed F, Turnbaugh P, et al. (2005). Obesity alters gut microbial ecology. Proc Natl Acad Sci USA. 102(31):11070–5. doi:10.1073/pnas.0504978102

- Ley RE, Turnbaugh PJ, Klein S, Gordon JI (2006). Human gut microbes associated with obesity. Nature. 444(7122):1022–3. doi:10.1038/4441022a

- Turnbaugh PJ, Ley RE, Mahowald MA, et al. (2006). An obesity-associated gut microbiome with increased capacity for energy harvest. Nature. 444(7122):1027–31. doi:10.1038/nature05414

- Le Chatelier E, Nielsen T, Qin J, et al. (2013). Richness of human gut microbiome correlates with metabolic markers. Nature. 500(7464):541–6. doi:10.1038/nature12506

- Vrieze A, Van Nood E, Holleman F, et al. (2012). Transfer of intestinal microbiota from lean donors increases insulin sensitivity in individuals with metabolic syndrome. Gastroenterology. 143(4):913–916.e7. doi:10.1053/j.gastro.2012.06.031

- Forslund K, Hildebrand F, Nielsen T, et al. (2015). Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature. 528(7581):262–266. doi:10.1038/nature15766

- Depommier C, Everard A, Druart C, et al. (2019). Supplementation with Akkermansia muciniphila in overweight and obese human volunteers: a proof-of-concept exploratory study. Nat Med. 25(7):1096–1103. doi:10.1038/s41591-019-0495-2

- Witkowski M, Weeks TL, Hazen SL (2020). Gut Microbiota and Cardiovascular Disease. Circ Res. 127(4):553–570. doi:10.1161/CIRCRESAHA.120.316242

- Zhu T, Goodarzi MO (2020). Metabolites Linking the Gut Microbiome with Risk for Type 2 Diabetes. Curr Nutr Rep. 9(2):83–93. doi:10.1007/s13668-020-00307-3

- Münzker J, Haase N, Till A, et al. (2022). Functional changes of the gastric bypass microbiota reactivate thermogenic adipose tissue and systemic glucose control via intestinal FXR-TGR5 crosstalk in diet-induced obesity. Microbiome. 10(1):96. doi:10.1186/s40168-022-01264-5

- Canyelles M, Borràs C, Rotllan N, et al. (2023). Gut Microbiota-Derived TMAO: A Causal Factor Promoting Atherosclerotic Cardiovascular Disease? Int J Mol Sci. 24(3):1940. doi:10.3390/ijms24031940

- Attaye I, et al. (2024). Oral Akkermansia muciniphila L2-7 augments glycemic control in type 2 diabetes. iScience. 27(8):110455. doi:10.1016/j.isci.2024.110455

- da Silva RS, de Paiva IHR, Mendonça IP, et al. (2024). Anorexigenic and anti-inflammatory signaling pathways of semaglutide via the microbiota-gut-brain axis in obese mice. Inflammopharmacology. 33(2):845–864. doi:10.1007/s10787-024-01603-y

- Benedé-Ubieto R, Cubero FJ, Nevzorova YA (2024). Breaking the barriers: the role of gut homeostasis in Metabolic-Associated Steatotic Liver Disease (MASLD). Gut Microbes. 16(1):2331460. doi:10.1080/19490976.2024.2331460

- Long Q, Luo F, Li B, et al. (2024). Gut microbiota and metabolic biomarkers in metabolic dysfunction-associated steatotic liver disease. Hepatol Commun. 8(3). doi:10.1097/HC9.0000000000000310

- Gofron KK, Wasilewski A, Małgorzewicz S (2025). Effects of GLP-1 Analogues and Agonists on the Gut Microbiota: A Systematic Review. Nutrients. 17(8):1303. doi:10.3390/nu17081303

- Zhao S, Lin H, Li W, et al. (2025). Post sleeve gastrectomy-enriched gut commensal Clostridia promotes secondary bile acid increase and weight loss. Gut Microbes. 17(1):2462261. doi:10.1080/19490976.2025.2462261

- Masi D, Watanabe M, Clément K (2026). Gut microbiome and obesity care: bridging dietary, surgical, and pharmacological interventions. Cell Rep Med. 7(2):102573. doi:10.1016/j.xcrm.2025.102573

Ch. 28 — Cancer

- Boursi B et al. (2015). Recurrent antibiotic exposure may promote cancer formation. Eur. J. Cancer.

- Ugai T et al. (2022). Is early-onset cancer an emerging global epidemic? Nat. Rev. Clin. Oncol. doi:10.1038/s41571-022-00672-8

- Palomar-Cros A et al. (2023). Artificial sweeteners and cancer risk. Int. J. Cancer. doi:10.1002/ijc.34577

Ch. 29 — Neurological and Psychiatric Disease

- Seo DO et al. (2023). ApoE and microbiota-dependent progression of tauopathy. Science. doi:10.1126/science.add1236

- Seo DO & Holtzman DM (2020). Gut Microbiota and Alzheimer’s pathology. J. Gerontol. doi:10.1093/gerona/glz262

- Seo DO & Holtzman DM (2024). Alzheimer’s disease-associated microbiome and therapeutic strategies. Exp. Mol. Med. doi:10.1038/s12276-023-01146-2

- Seo DO (2019). The microbiome: A target for Alzheimer disease? Cell Res. doi:10.1038/s41422-019-0227-7

- Konings B et al. (2023). GI syndromes preceding Parkinson’s disease: testing Braak’s hypothesis. Gut. doi:10.1136/gutjnl-2023-329685

- Zhu F et al. (2020). Gut microbiome and schizophrenia. Nat. Commun.

Ch. 31 — Ethical, Social, and Philosophical Questions

- Spencer HG & Zuk M (2016). For Host’s Sake: The Pluses of Parasite Preservation. Trends Ecol. Evol. doi:10.1016/j.tree.2016.02.021

- Murphy AA et al. (2016). Self-disseminating vaccines for emerging infectious diseases. Expert Rev. Vaccines. doi:10.1586/14760584.2016.1106942

- Rosenthal SR et al. (2015). Redefining disease emergence. One Health. doi:10.1016/j.onehlt.2015.08.001

Notable Observations

1. Diabetes/metabolic disease concentration: ~50 of the 176 references are diabetes-focused. These are concentrated in Ch. 27 (primary home) with relevant spillover into Ch. 22 (metformin), Ch. 19 (antibiotics and T1D), and Ch. 26 (autoimmune T1D). This is rich material for a strong metabolic disease chapter, but as you noted, should not disproportionally dominate the book.

2. Strong collections for specific chapters: Toxoplasma/behavioural modulation (Ch. 14), bacteriophages and obelisks (Ch. 16–17), the brain microbiome (Ch. 11), and artificial sweeteners (Ch. 20) all have good clusters of references.

3. Gaps to fill: Chapters 6 (skin), 9 (oral), 10 (mycobiome), 24 (FMT), 25 (precision engineering), and 32 (future) have few or no references in this bibliography. These will need additional literature searches during drafting.

Ch. 13 — The Gut-Brain Axis: Microbes and the Mind

- [REF:bravo2011] Bravo JA, Forsythe P, Chew MV, Escaravage E, Savignac HM, Dinan TG, Bienenstock J & Cryan JF (2011). Ingestion of Lactobacillus strain regulates emotional behavior and central GABA receptor expression in a mouse via the vagus nerve. PNAS, 108(38), 16050–16055. doi:10.1073/pnas.1102999108

- [REF:yano2015] Yano JM, Yu K, Donaldson GP, Shastri GG, Ann P, Ma L, Nagler CR, Ismagilov RF, Mazmanian SK & Hsiao EY (2015). Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell, 161(2), 264–276. doi:10.1016/j.cell.2015.02.047

- [REF:strandwitz2019] Strandwitz P et al. (2019). GABA-modulating bacteria of the human gut microbiota. Nat. Microbiol., 4(3), 396–403. doi:10.1038/s41564-018-0307-3

- [REF:sudo2004] Sudo N, Chida Y, Aiba Y et al. (2004). Postnatal microbial colonization programs the hypothalamic-pituitary-adrenal system for stress response in mice. J. Physiol., 558(Pt 1), 263–275. doi:10.1113/jphysiol.2004.063388

- [REF:bercik2011] Bercik P, Denou E, Collins J et al. (2011). The intestinal microbiota affect central levels of brain-derived neurotrophic factor and behavior in mice. Gastroenterology, 141(2), 599–609.e3. doi:10.1053/j.gastro.2011.04.052

- [REF:diazheijtz2011] Diaz Heijtz R, Wang S, Anuar F et al. (2011). Normal gut microbiota modulates brain development and behavior. PNAS, 108(7), 3047–3052. doi:10.1073/pnas.1010529108

- [REF:dinan2013] Dinan TG, Stanton C & Cryan JF (2013). Psychobiotics: a novel class of psychotropic. Biol. Psychiatry, 74(10), 720–726. doi:10.1016/j.biopsych.2013.05.001

- [REF:kelly2016] Kelly JR, Borre Y, O’Brien C et al. (2016). Transferring the blues: depression-associated gut microbiota induces neurobehavioural changes in the rat. J. Psychiatr. Res., 82, 109–118. doi:10.1016/j.jpsychires.2016.07.019

- [REF:vallescolomer2019] Valles-Colomer M, Falony G, Darzi Y et al. (2019). The neuroactive potential of the human gut microbiota in quality of life and depression. Nat. Microbiol., 4(4), 623–632. doi:10.1038/s41564-018-0337-x

- [REF:asad2025] Asad A, Kirk M, Zhu S et al. (2025). Effects of prebiotics and probiotics on symptoms of depression and anxiety in clinically diagnosed samples: systematic review and meta-analysis of randomized controlled trials. Nutr. Rev., 83(7), e1504–e1520. doi:10.1093/nutrit/nuae177

- [REF:hsiao2013] Hsiao EY, McBride SW, Hsien S et al. (2013). Microbiota modulate behavioral and physiological abnormalities associated with neurodevelopmental disorders. Cell, 155(7), 1451–1463. doi:10.1016/j.cell.2013.11.024

- [REF:sharon2019] Sharon G, Cruz NJ, Kang DW et al. (2019). Human gut microbiota from autism spectrum disorder promote behavioral symptoms in mice. Cell, 177(6), 1600–1618.e17. doi:10.1016/j.cell.2019.05.004

- [REF:kang2017] Kang DW, Adams JB, Gregory AC et al. (2017). Microbiota Transfer Therapy alters gut ecosystem and improves gastrointestinal and autism symptoms: an open-label study. Microbiome, 5(1), 10. doi:10.1186/s40168-016-0225-7

- [REF:kang2019] Kang DW, Adams JB, Coleman DM et al. (2019). Long-term benefit of Microbiota Transfer Therapy on autism symptoms and gut microbiota. Sci. Rep., 9, 5821. doi:10.1038/s41598-019-42183-0

- [REF:wan2024] Wan YP et al. (2024). Effect of oral faecal microbiota transplantation intervention for children with autism spectrum disorder: a randomised, double-blind, placebo-controlled trial. Clin. Transl. Med., 14, e70006. doi:10.1002/ctm2.70006

Ch. 17 — Phages, Immunity, and the Tripartite Relationship

- [REF:nguyen2017] Nguyen S, Baker K, Padman BS, Patwa R, Dunstan RA, Weston TA, Schlosser K, Bailey B, Lithgow T, Lazarou M, Luque A, Rohwer F, Blumberg RS & Barr JJ (2017). Bacteriophage Transcytosis Provides a Mechanism To Cross Epithelial Cell Layers. mBio, 8(6), e01874-17. doi:10.1128/mBio.01874-17

- [REF:barr2013] Barr JJ, Auro R, Furlan M, Whiteson KL, Erb ML, Pogliano J, Stotland A, Wolkowicz R, Cutting AS, Doran KS, Salamon P, Youle M & Rohwer F (2013). Bacteriophage adhering to mucus provide a non–host-derived immunity. PNAS, 110(26), 10771–10776. doi:10.1073/pnas.1305923110

- [REF:barr2015] Barr JJ, Auro R, Sam-Soon N, Kassegne S, Peters G, Bonber N, Hatber M, Molineux S, Guardado A, Vega R, Bailey B, Pogliano J, Danthi P & Rohwer F (2015). Subdiffusive motion of bacteriophage in mucosal surfaces increases the frequency of bacterial encounters. PNAS, 112(44), 13675–13680. doi:10.1073/pnas.1508355112

- [REF:almeida2019] Almeida GMF, Laanto E, Asber R & Sundberg LR (2019). Bacteriophage Adherence to Mucus Mediates Preventive Protection against Pathogenic Bacteria. mBio, 10(6), e01984-19. doi:10.1128/mBio.01984-19

- [REF:barr2022] Barr JJ et al. (2022). Bacteriophages evolve enhanced persistence to a mucosal surface. PNAS, 119(27), e2116197119. doi:10.1073/pnas.2116197119

- [REF:vanbelleghem2017] Van Belleghem JD, Clement F, Merabishvili M, Lavigne R & Vaneechoutte M (2017). Pro- and anti-inflammatory responses of peripheral blood mononuclear cells induced by Staphylococcus aureus and Pseudomonas aeruginosa phages. Sci. Rep., 7, 8004. doi:10.1038/s41598-017-08336-9

- [REF:vanbelleghem2019] Van Belleghem JD, Dąbrowska K, Vaneechoutte M, Barr JJ & Bollyky PL (2019). Interactions between Bacteriophage, Bacteria, and the Mammalian Immune System. Viruses, 11(1), 10. doi:10.3390/v11010010

- [REF:gogokhia2019] Gogokhia L, Buber K, Roundy J, Padgett MJ, Hapfelmeier S, Hooper LV & Round JL (2019). Expansion of Bacteriophages Is Linked to Aggravated Intestinal Inflammation and Colitis. Cell Host Microbe, 25(2), 285–299. doi:10.1016/j.chom.2019.01.008

- [REF:gorski2012] Górski A, Międzybrodzki R, Borysowski J et al. (2012). Phage as a Modulator of Immune Responses: Practical Implications for Phage Therapy. Adv. Virus Res., 83, 41–71. doi:10.1016/B978-0-12-394438-2.00002-5

- [REF:gorski2017] Górski A, Dąbrowska K, Międzybrodzki R, Weber-Dąbrowska B, Łusiak-Szelachowska M, Jończyk-Matysiak E & Borysowski J (2017). Phages and immunomodulation. Future Microbiol., 12(10), 905–914. doi:10.2217/fmb-2017-0049

- [REF:lamybesnier2024] Lamy-Besnier Q, Theodorou I, Billaud M, Zhang H, Brot L, Culot A, Abriat G, Sokol H, De Paepe M, Petit MA & De Sordi L (2024). The human blood harbors a phageome which differs in Crohn’s disease. bioRxiv / preprint. doi:10.1101/2024.06.04.597176

- [REF:phageometransfer2024] (2024). Phageome transfer from gut to circulation and its regulation by human immunity. bioRxiv / preprint. doi:10.1101/2024.06.05.597592

4. Obelisks (Zheludev et al. 2024): A fascinating recent discovery — viroid-like RNA elements in the human gut microbiome. This could warrant its own sidebar or sub-section in the phage chapters (Ch. 16–17), as it expands the concept of what biological entities inhabit our microbiome beyond bacteria, fungi, and classical viruses.

5. Paternal microbiome (Argaw-Denboba et al. 2024): This is a Nature paper showing paternal gut microbiome perturbations affect offspring fitness — an important addition to Ch. 3 that expands the maternal-focused narrative of microbial inheritance.

6. Harada et al. (2025): An ultra-reduced archaeal genome — potentially relevant to Ch. 10 or a general discussion of the minimal requirements for cellular life within the microbiome.