Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals
Open accessAbstract
The gut microbiome is shaped by diet and influences host metabolism; however, these links are complex and can be unique to each individual. We performed deep metagenomic sequencing of 1,203 gut microbiomes from 1,098 individuals enrolled in the Personalised Responses to Dietary Composition Trial (PREDICT 1) study, whose detailed long-term diet information, as well as hundreds of fasting and same-meal postprandial cardiometabolic blood marker measurements were available. We found many significant associations between microbes and specific nutrients, foods, food groups and general dietary indices, which were driven especially by the presence and diversity of healthy and plant-based foods. Microbial biomarkers of obesity were reproducible across external publicly available cohorts and in agreement with circulating blood metabolites that are indicators of cardiovascular disease risk. While some microbes, such as Prevotella copri and Blastocystis spp., were indicators of favorable postprandial glucose metabolism, overall microbiome composition was predictive for a large panel of cardiometabolic blood markers including fasting and postprandial glycemic, lipemic and inflammatory indices. The panel of intestinal species associated with healthy dietary habits overlapped with those associated with favorable cardiometabolic and postprandial markers, indicating that our large-scale resource can potentially stratify the gut microbiome into generalizable health levels in individuals without clinically manifest disease. Analyses from the gut microbiome of over 1,000 individuals from the PREDICT 1 study, for which detailed long-term diet information as well as hundreds of fasting and same-meal postprandial cardiometabolic blood marker measurements are available, unveil new associations between specific gut microbes, dietary habits and cardiometabolic health.
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Microbiome-mediated links between diet and health have been prevalently investigated by considering the role of the abiotic components of foods.
“Microbiome-mediated links between diet and health have been prevalently investigated by considering the role of the abiotic components of foods. 35-41”
From Unexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome. (2024) · cites this paper as [38]
The Discussion (page not provided) says the study highlights a need to look beyond nutrients and single foods in diet–microbiome research, supporting the idea that those aspects have received attention. It does not establish that research has predominantly considered foods’ abiotic components rather than their biotic components. (LLM, conf=0.72)
Experimental — accuracy not yet measuredPresence of Ruminococcus gnavus as a biomarker of nonresponders in PRIMM-UK is in line with multiple reports of its association with poor cardiometabolic health and several diseases.
“whereas the presence of Ruminococcus gnavus as a biomarker of nonresponders in PRIMM-UK is in line with multiple reports of its association with poor cardiometabolic health20 and several diseases24–26.”
From Cross-cohort gut microbiome associations with immune checkpoint inhibitor response in advanced melanoma (2022) · cites this paper as [20]
The gut microbiome is unique in each individual, even when considering identical twins.
“The gut microbiome is unique in each individual, even when considering identical twins20,35”
From Cross-cohort gut microbiome associations with immune checkpoint inhibitor response in advanced melanoma (2022) · cites this paper as [20]
The human gut microbiome is influenced by diet and medication use.
“The human gut microbiome is itself shaped by a wide variety of environmental exposures, including diet (8, 9) and medication use (10–13)”
From Dietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response (2021) · cites this paper as [13]
The five PFS ≥12-associated species represent fiber degrading taxa capable of short chain fatty acid (SCFA) synthesis that has been linked to plant-based diets.
“These five species represent fiber degrading taxa capable of short chain fatty acid (SCFA) synthesis that has been linked to plant-based diets12,23,24.”
From Longitudinal gut microbiome changes in immune checkpoint blockade-treated advanced melanoma (2024) · cites this paper as [23]
Geography, diet, infection and drug use are among factors that shape the human gut microbiome.
“Multiple factors affect the composition of the human gut microbiome, including geography, diet, infection and drug usage56,61–64.”
From Intestinal microbiota signatures of clinical response and immune-related adverse events in melanoma patients treated with anti-PD-1 (2022) · cites this paper as [61]
A healthy diet is linked with a healthy microbiota.
“Generally, a healthy diet is linked with a healthy microbiota, 127”
From Key determinants of success in fecal microbiota transplantation: From microbiome to clinic (2023) · cites this paper as [127]
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Self-citationHealthy Plant-Based Diets Are Associated with Lower Risk of All-Cause Mortality in US Adults
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Accessible, curated metagenomic data through ExperimentHubPreprint
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Heart Disease and Stroke Statistics—2022 Update: A Report From the American Heart Association
Tsao, C. W., Aday, A. W., Almarzooq, Z. I., Alonso, A., Beaton, A. Z., Bittencourt, M. S., Boehme, A. K., Buxton, A. E., Carson, A. P., Commodore-Mensah, Y., et al. (2022) · Circulation · doi:10.1161/cir.0000000000001052 · Cited by 5,987 · see citing works
Blanco-Míguez, A., Beghini, F., Cumbo, F., McIver, L. J., Thompson, K. N., Zolfo, M., Manghi, P., Dubois, L., Huang, K. D., Thomas, A. M., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01688-w · Cited by 1,442 · see citing works
Self-citationDietary fiber and probiotics influence the gut microbiome and melanoma immunotherapy response
Spencer, C. N., McQuade, J. L., Gopalakrishnan, V., McCulloch, J. A., Vetizou, M., Cogdill, A. P., Khan, M. A. W., Zhang, X., White, M. G., Peterson, C. B., et al. (2021) · Science · doi:10.1126/science.aaz7015 · Cited by 966 · see citing works
Self-citationThe gut microbiome as a modulator of healthy ageing
Ghosh, T. S., Shanahan, F., & O’Toole, P. W. (2022) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-022-00605-x · Cited by 817 · see citing works
Prevotella Diversity, Niches and Interactions with the Human Host
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Self-citationThe person-to-person transmission landscape of the gut and oral microbiomes
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Self-citationMcCulloch, J. A., Davar, D., Rodrigues, R. R., Badger, J. H., Fang, J. R., Cole, A. M., Balaji, A. K., Vetizou, M., Prescott, S. M., Fernandes, M. R., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01698-2 · Cited by 472 · see citing works
Lee, K. A., Thomas, A. M., Bolte, L. A., Björk, J. R., de Ruijter, L. K., Armanini, F., Asnicar, F., Blanco-Miguez, A., Board, R., Calbet-Llopart, N., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01695-5 · Cited by 464 · see citing works
Self-citationRETRACTED ARTICLE: Role of the gut microbiome in chronic diseases: a narrative review
Vijay, A., & Valdes, A. M. (2022) · European Journal of Clinical Nutrition · doi:10.1038/s41430-021-00991-6 · Cited by 361 · see citing works
Self-citationRetractedIaniro, G., Punčochář, M., Karcher, N., Porcari, S., Armanini, F., Asnicar, F., Beghini, F., Blanco-Míguez, A., Cumbo, F., Manghi, P., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01964-3 · Cited by 366 · see citing works
Self-citationChen, L., Zhernakova, D. V., Kurilshikov, A., Andreu-Sánchez, S., Wang, D., Augustijn, H. E., Vich Vila, A., Study, L. C., Weersma, R. K., Medema, M. H., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-02014-8 · Cited by 327 · see citing works
Gut microbiota in the pathogenesis and therapeutic approaches of diabetes
Crudele, L., Gadaleta, R. M., Cariello, M., & Moschetta, A. (2023) · eBioMedicine · doi:10.1016/j.ebiom.2023.104821 · Cited by 251 · see citing works
Rethinking healthy eating in light of the gut microbiome
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Leveraging diet to engineer the gut microbiome
Wolter, M., Grant, E. T., Boudaud, M., Steimle, A., Pereira, G. V., Martens, E. C., & Desai, M. S. (2021) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-021-00512-7 · Cited by 211 · see citing works
Host genetic regulation of human gut microbial structural variation
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Longitudinal gut microbiome changes in immune checkpoint blockade-treated advanced melanoma
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Self-citationUnexplored microbial diversity from 2,500 food metagenomes and links with the human microbiome.
Carlino, N., Blanco-Míguez, A., Punčochář, M., Mengoni, C., Pinto, F., Tatti, A., Manghi, P., Armanini, F., Avagliano, M., Barcenilla, C., et al. (2024) · Cell · doi:10.1016/j.cell.2024.07.039 · Cited by 146 · see citing works
Self-citationGut OncoMicrobiome Signatures (GOMS) as next-generation biomarkers for cancer immunotherapy
Thomas, A. M., Fidelle, M., Routy, B., Kroemer, G., Wargo, J. A., Segata, N., & Zitvogel, L. (2023) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-023-00785-8 · Cited by 141 · see citing works
Self-citationFackelmann, G., Manghi, P., Carlino, N., Heidrich, V., Piccinno, G., Ricci, L., Piperni, E., Arrè, A., Bakker, E., Creedon, A. C., et al. (2025) · Nature Microbiology · doi:10.1038/s41564-024-01870-z · Cited by 142 · see citing works
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Xiao, M., Zhang, C., Duan, H., Narbad, A., Zhao, J., Chen, W., Zhai, Q., Yu, L., & Tian, F. (2024) · npj Biofilms and Microbiomes · doi:10.1038/s41522-024-00524-6 · Cited by 120 · see citing works
Cardiometabolic health, diet and the gut microbiome: a meta-omics perspective
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Self-citationTomita, Y., Goto, Y., Sakata, S., Imamura, K., Minemura, A., Oka, K., Hayashi, A., Jodai, T., Akaike, K., Anai, M., et al. (2022) · OncoImmunology · doi:10.1080/2162402x.2022.2081010 · Cited by 100 · see citing works
Jarvis, P. R. E., Cardin, J. L., Nisevich-Bede, P. M., & McCarter, J. P. (2023) · Metabolism · doi:10.1016/j.metabol.2023.155640 · Cited by 86 · see citing works
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Self-citationShuai, M., Zuo, L.-S.-Y., Miao, Z., Gou, W., Xu, F., Jiang, Z., Ling, C.-W., Fu, Y., Xiong, F., Chen, Y.-M., et al. (2021) · eBioMedicine · doi:10.1016/j.ebiom.2021.103284 · Cited by 61 · see citing works
Targeting nonalcoholic fatty liver disease via gut microbiome-centered therapies
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Microbiota alters the metabolome in an age- and sex- dependent manner in mice
Brown, K., Thomson, C. A., Wacker, S., Drikic, M., Groves, R., Fan, V., Lewis, I. A., & McCoy, K. D. (2023) · Nature Communications · doi:10.1038/s41467-023-37055-1 · Cited by 49 · see citing works
Gut microbiome strain-sharing within isolated village social networks
Beghini, F., Pullman, J., Alexander, M., Shridhar, S. V., Prinster, D., Singh, A., Matute Juárez, R., Airoldi, E. M., Brito, I. L., & Christakis, N. A. (2025) · Nature · doi:10.1038/s41586-024-08222-1 · Cited by 51 · see citing works
Self-citationGut microbiome and amyotrophic lateral sclerosis: a systematic review of current evidence
Sun, J., Huang, T., Debelius, J. W., & Fang, F. (2021) · Journal of Internal Medicine · doi:10.1111/joim.13336 · Cited by 43 · see citing works
Maternal and food microbial sources shape the infant microbiome of a rural Ethiopian population
Manara, S., Selma-Royo, M., Huang, K. D., Asnicar, F., Armanini, F., Blanco-Miguez, A., Cumbo, F., Golzato, D., Manghi, P., Pinto, F., et al. (2023) · Current Biology · doi:10.1016/j.cub.2023.04.011 · Cited by 30 · see citing works
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Self-citationThe holobiont mind: A bridge between 4E-cognition and the microbiome
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- Authors
- Asnicar, F., Berry, S. E., Valdes, A. M., Nguyen, L. H., Piccinno, G., Drew, D. A., Leeming, E., Gibson, R., Le Roy, C., Khatib, H. A., Francis, L., Mazidi, M., Mompeo, O., Valles-Colomer, M., Tett, A., Beghini, F., Dubois, L., Bazzani, D., Thomas, A. M., Mirzayi, C., Khleborodova, A., Oh, S., Hine, R., Bonnett, C., Capdevila, J., Danzanvilliers, S., Giordano, F., Geistlinger, L., Waldron, L., Davies, R., Hadjigeorgiou, G., Wolf, J., Ordovás, J. M., Gardner, C., Franks, P. W., Chan, A. T., Huttenhower, C., Spector, T. D., & Segata, N.
- Affiliations
- University of Trento (IT)
- King's College London (GB)
- University of Nottingham (GB)
- Harvard University (US)
- ZOE (United Kingdom) (GB)
- City University of New York (US)
- Tufts University (US)
- Stanford University (US)
- Broad Institute (US)
- Corresponding author
- Berry, Sarah E.
- Segata, Nicola
- ORCID
- Asnicar, Francesco
- Berry, Sarah E.
- Valdes, Ana M.
- Nguyen, Long H.
- Piccinno, Gianmarco
- Drew, David A.
- Leeming, Emily
- Gibson, Rachel
- Le Roy, Caroline
- Khatib, Haya Al
- Francis, Lucy
- Mazidi, Mohsen
- Valles-Colomer, Mireia
- Tett, Adrian
- Beghini, Francesco
- Dubois, Léonard
- Bazzani, Davide
- Thomas, Andrew Maltez
- Mirzayi, Chloe
- Oh, Sehyun
- Hine, Rachel
- Bonnett, Christopher
- Capdevila, Joan
- Danzanvilliers, Serge
- Giordano, Francesca
- Geistlinger, Ludwig
- Waldron, Levi
- Davies, Richard
- Hadjigeorgiou, George
- Wolf, Jonathan
- Ordovás, José M.
- Gardner, Christopher
- Franks, Paul W.
- Chan, Andrew T.
- Huttenhower, Curtis
- Spector, Tim D.
- Segata, Nicola
- Venue
- Nature Medicine
- Published
- 2021-01-11
- Volume
- 27
- Issue
- 2
- Pages
- 321-332
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC8353542
- OpenAlex
- W3118724893
- Semantic Scholar
- 762045d88330436e5de1517309656e6ffd253ebb
- Cited by
- 995
- References
- 95
- Citations by year
- 2026: 164 · 2025: 188 · 2024: 202 · 2023: 175 · 2022: 179 · 2021: 83 · 2012: 1
- FWCI
- 51.06
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- Research Portal (King's College London) (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (acceptedVersion)
- Repository@Nottingham (University of Nottingham) (acceptedVersion, other-oa)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by)
- Iris (University of Trento) (submittedVersion, other-oa)
- Lund University Publications (Lund University) (submittedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion)
- Funders
- Massachusetts General Hospital
- Wellcome Trust (212904, 212904/Z/18/Z, MR/M016560/1, 212904/Z/18/)
- Crohn's and Colitis Foundation (563526, 690389)
- National Institute for Health and Care Research (NF-SI-0507-10228, MR/M016560/1, NF-SI-0514-10027)
- British Heart Foundation (MR/M016560/1)
- King's College London
- European Commission (825410, 818368, MR/M016560/1, 212904/Z/18/Z)
- Ministero dell’Istruzione, dell’Università e della Ricerca (H2020, RBFR13EWWI_001)
- Novo Nordisk
- Vetenskapsrådet
- National Institutes of Health (1u01ca230551-01a1)
- Horizon 2020 Framework Programme (MASTER-818368, OncoBiome 825410, 818368, 825410)
- Medical Research Council (MR/N030125/1, 212904/Z/18/Z, MR/N01183X/1, MR/M016560/1)
- Biotechnology and Biological Sciences Research Council
- National Cancer Institute (U01 CA230551, R01CA230551)
- National Institute of Diabetes and Digestive and Kidney Diseases (K23DK125838, P30DK043351)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Nutritional Studies and Diet
- Keywords
- gut microbiota, host metabolism, diet, cardiometabolic health, postprandial blood glucose, postprandial lipemia, inflammation, obesity biomarkers, Prevotella copri, Blastocystis, plant-based diet, microbial biomarkers, cardiovascular risk, blood metabolites
- MeSH terms
- Gastrointestinal Microbiome, Adult, Blood Glucose, Child, Diet, Fasting, Feeding Behavior, Female, Food Microbiology, Glucose, Humans, Male, Middle Aged, Obesity, Biomarkers, Blastocystis, Prevotella, Postprandial Period, Metagenome, High-Throughput Nucleotide Sequencing, Microbiota
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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