Evidence map›Paper›PMID 41872600›Full record

ArticleNature medicine2026

Diet-microbiome associations in 10,068 individuals from the Human Phenotype Project to guide personalized nutrition.

Tomer Segev, Daniel Barak, Liron Zahavi, Anastasia Godneva, Michal Rein, David Krongauz, Dorit Samocha-Bonet, Hagai Rossman, Adina Weinberger, Eran Segal

Abstract read
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In one paragraph

Article in Nature medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Gut microbiota in health and disease.Molecular biomedicine · 2026
    Review
  5. Review
  6. Diet-microbiome associations.Nature reviews. Gastroenterology & hepatology · 2026
    Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Tomer SegevDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.ORCID http://orcid.org/0009-0005-2492-5344
Daniel BarakDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Liron ZahaviDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.ORCID http://orcid.org/0000-0001-8867-1287
Anastasia GodnevaDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
Michal ReinDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
David KrongauzDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.ORCID http://orcid.org/0000-0003-4017-1122
Dorit Samocha-BonetClinical Diabetes, Appetite and Metabolism Laboratory, Garvan Institute of Medical Research, Sydney, New South Wales, Australia.ORCID http://orcid.org/0000-0001-9422-7852
Hagai RossmanPheno.AI, Tel Aviv, Israel.ORCID http://orcid.org/0000-0002-5613-8004
Adina WeinbergerDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.ORCID http://orcid.org/0000-0003-2231-4687
Eran SegalDepartment of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel. eran.segal@weizmann.ac.il.ORCID http://orcid.org/0000-0002-6859-1164

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diet is a major environmental factor influencing the human gut microbiome. However, the effects of specific foods and dietary patterns on microbial composition, diversity and function is not fully understood, limiting progress toward personalized dietary strategies. Here, leveraging 10,068 participants from the Human Phenotype Project with app-based diet logs and shotgun metagenomics, we predicted diet-microbiome associations at species-level resolution. Diet significantly predicted microbial diversity (richness r = 0.26, Shannon Index r = 0.24), the relative abundance of 669 of 724 species tested (92.4%, false discovery rate <0.05), and 313 of 320 pathways (97.8%, false discovery rate <0.05). Feature attribution identified distinct food-microbe links, including coffee with Lawsonibacter asaccharolyticus (r = 0.43), yogurt with Streptococcus thermophilus (r = 0.42) and milk with Bifidobacterium species (r = 0.31-0.36). In parallel, broader dietary patterns, especially the degree of food processing, emerged as predictors of microbial diversity and composition. We also show that diet-microbiome associations persist over four years, with 82.5% of species exhibiting significant longitudinal tracking between predicted and observed abundances. Finally, we developed an exploratory analysis for simulating personalized dietary interventions with predicted microbiome shift effects that are associated with improvements in cardiometabolic health. Our findings demonstrate that diet is strongly associated with microbiome composition, diversity and function, and highlight its potential for guiding personalized interventions.

Indexed as

DietGastrointestinal MicrobiomePrecision MedicineAdultBifidobacteriumCoffeeFemaleHumansMaleMetagenomicsMilkPhenotypeStreptococcus thermophilusYogurtCoffee

Identifiers

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.