The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk.

Biomarkers / metabolism Cardiometabolic Risk Factors Cardiovascular Diseases / epidemiology Diet Diet, Mediterranean / adverse effects Female Follow-Up Studies Gastrointestinal Microbiome / genetics Humans Male Microbiota / genetics Prevotella / genetics

Journal

Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015

Informations de publication

Date de publication:
02 2021
Historique:
received: 31 03 2020
accepted: 22 12 2020
pubmed: 13 2 2021
medline: 27 2 2021
entrez: 12 2 2021
Statut: ppublish

Résumé

To address how the microbiome might modify the interaction between diet and cardiometabolic health, we analyzed longitudinal microbiome data from 307 male participants in the Health Professionals Follow-Up Study, together with long-term dietary information and measurements of biomarkers of glucose homeostasis, lipid metabolism and inflammation from blood samples. Here, we demonstrate that a healthy Mediterranean-style dietary pattern is associated with specific functional and taxonomic components of the gut microbiome, and that its protective associations with cardiometabolic health vary depending on microbial composition. In particular, the protective association between adherence to the Mediterranean diet and cardiometabolic disease risk was significantly stronger among participants with decreased abundance of Prevotella copri. Our findings advance the concept of precision nutrition and have the potential to inform more effective and precise dietary approaches for the prevention of cardiometabolic disease mediated through alterations in the gut microbiome.

Identifiants

DOI: 10.1038/s41591-020-01223-3 PMID: 33574608 PMC: PMC8186452
pubmed: 33574608
doi: 10.1038/s41591-020-01223-3
pii: 10.1038/s41591-020-01223-3
pmc: PMC8186452
mid: NIHMS1699441
doi:

Substances chimiques

Biomarkers 0

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

333-343

Subventions

Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : P30DK046200
Organisme : NCI NIH HHS
ID : U01 CA152904
Pays : United States
Organisme : NCI NIH HHS
ID : U01 CA167552
Pays : United States
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : R00DK119412
Organisme : NHLBI NIH HHS
ID : R01 HL035464
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL060712
Pays : United States
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : R01CA202704
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : U01CA167552
Organisme : NIDDK NIH HHS
ID : R00 DK119412
Pays : United States
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : R01HL035464
Organisme : NIDDK NIH HHS
ID : K99 DK119412
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA202704
Pays : United States
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : K24DK098311
Organisme : NIDDK NIH HHS
ID : P30 DK046200
Pays : United States
Organisme : NIDDK NIH HHS
ID : K24 DK098311
Pays : United States
Organisme : NIDDK NIH HHS
ID : K23 DK125838
Pays : United States
Organisme : NIDCR NIH HHS
ID : U54 DE023798
Pays : United States
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : R01HL060712
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : U54DE023798
Organisme : U.S. Department of Health & Human Services | National Institutes of Health (NIH)
ID : U01CA152904

Commentaires et corrections

Type : CommentIn
Type : CommentIn

Références

US Burden of Disease Collaborators et al.The State of US Health, 1990–2016: burden of diseases, injuries and risk factors among US States. JAMA 319, 1444–1472 (2018).
doi: 10.1001/jama.2018.0158
GBD 2016 DALYs & HALE Collaborators. Global, regional, and national disability-adjusted life-years (DALYs) for 359 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990–2017: a systematic analysis for the global burden of disease study 2017. Lancet 392, 1859–1922 (2018).
Koeth, R. A. et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat. Med. 19, 576–585 (2013).
pubmed: 23563705 pmcid: 3650111 doi: 10.1038/nm.3145
Kurilshikov, A. et al. Gut microbial associations to plasma metabolites linked to cardiovascular phenotypes and risk. Circ. Res. 124, 1808–1820 (2019).
pubmed: 30971183 doi: 10.1161/CIRCRESAHA.118.314642
Forslund, K. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262–266 (2015).
pubmed: 26633628 pmcid: 4681099 doi: 10.1038/nature15766
Pedersen, H. K. et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 535, 376–381 (2016).
pubmed: 27409811 doi: 10.1038/nature18646
Thingholm, L. B. et al. Obese individuals with and without type 2 diabetes show different gut microbial functional capacity and composition. Cell Host Microbe 26, 252–264 (2019).
pubmed: 31399369 pmcid: 7720933 doi: 10.1016/j.chom.2019.07.004
Haro, C. et al. Two healthy diets modulate gut microbial community improving insulin sensitivity in a human obese population. J. Clin. Endocrinol. Metab. 101, 233–242 (2016).
pubmed: 26505825 doi: 10.1210/jc.2015-3351
Kovatcheva-Datchary, P. et al. Dietary fiber-induced improvement in glucose metabolism is associated with increased abundance of Prevotella. Cell Metab. 22, 971–982 (2015).
pubmed: 26552345 doi: 10.1016/j.cmet.2015.10.001
Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).
pubmed: 26590418
David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).
pubmed: 24336217 doi: 10.1038/nature12820
Smits, S. A. et al. Seasonal cycling in the gut microbiome of the Hadza hunter-gatherers of Tanzania. Science 357, 802–806 (2017).
pubmed: 28839072 pmcid: 5891123 doi: 10.1126/science.aan4834
Sonnenburg, J. L. & Backhed, F. Diet–microbiota interactions as moderators of human metabolism. Nature 535, 56–64 (2016).
pubmed: 27383980 pmcid: 5991619 doi: 10.1038/nature18846
Faith, J. J., McNulty, N. P., Rey, F. E. & Gordon, J. I. Predicting a human gut microbiota’s response to diet in gnotobiotic mice. Science 333, 101–104 (2011).
pubmed: 21596954 pmcid: 3303606 doi: 10.1126/science.1206025
Turnbaugh, P. J. et al. The effect of diet on the human gut microbiome: a metagenomic analysis in humanized gnotobiotic mice. Sci. Transl. Med. 1, 6ra14 (2009).
pubmed: 20368178 pmcid: 2894525 doi: 10.1126/scitranslmed.3000322
Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).
doi: 10.1126/science.aad3503 pubmed: 27126039
Vatanen, T. et al. The human gut microbiome in early-onset Type 1 diabetes from the TEDDY study. Nature 562, 589–594 (2018).
pubmed: 30356183 pmcid: 6296767 doi: 10.1038/s41586-018-0620-2
Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).
pubmed: 22699611 pmcid: 3376388 doi: 10.1038/nature11053
De Filippo, C. et al. Impact of diet in shaping gut microbiota revealed by a comparative study in children from Europe and rural Africa. Proc. Natl Acad. Sci. USA 107, 14691–14696 (2010).
pubmed: 20679230 doi: 10.1073/pnas.1005963107 pmcid: 2930426
Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011).
pubmed: 21885731 pmcid: 3368382 doi: 10.1126/science.1208344
Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016).
pubmed: 27126040 pmcid: 5240844 doi: 10.1126/science.aad3369
Willett, W. C. et al. Mediterranean diet pyramid: a cultural model for healthy eating. Am. J. Clin. Nutr. 61, 1402S–1406S (1995).
pubmed: 7754995 doi: 10.1093/ajcn/61.6.1402S
Van Horn, L. et al. Recommended dietary pattern to achieve adherence to the American Heart Association/American College of Cardiology (AHA/ACC) guidelines: a scientific statement from the American Heart Association. Circulation 134, e505–e529 (2016).
pubmed: 27789558
American Diabetic Association 4. Lifestyle management: standards of medical care in diabetes—2018. Diabetes Care 41, S38–S50 (2018).
doi: 10.2337/dc18-S004
Estruch, R. et al. Primary prevention of cardiovascular disease with a Mediterranean diet supplemented with extra-virgin olive oil or nuts. New Engl. J. Med. 378, e34 (2018).
pubmed: 29897866 doi: 10.1056/NEJMoa1800389
Ghosh, T. S. et al. Mediterranean diet intervention alters the gut microbiome in older people reducing frailty and improving health status: the NU-AGE 1-year dietary intervention across five European countries. Gut 69, 1218–1228 (2020).
pubmed: 32066625 doi: 10.1136/gutjnl-2019-319654
Meslier, V. et al. Mediterranean diet intervention in overweight and obese subjects lowers plasma cholesterol and causes changes in the gut microbiome and metabolome independently of energy intake. Gut 69, 1258–1268 (2020).
pubmed: 32075887 doi: 10.1136/gutjnl-2019-320438
Abu-Ali, G. S. et al. Metatranscriptome of human faecal microbial communities in a cohort of adult men. Nat. Microbiol. 3, 356–366 (2018).
pubmed: 29335555 pmcid: 6557121 doi: 10.1038/s41564-017-0084-4
Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).
pubmed: 26418763 doi: 10.1038/nmeth.3589
Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018).
pubmed: 30377376 pmcid: 6235447 doi: 10.1038/s41592-018-0176-y
Fung, T. T. et al. Diet-quality scores and plasma concentrations of markers of inflammation and endothelial dysfunction. Am. J. Clin. Nutr. 82, 163–173 (2005).
pubmed: 16002815 doi: 10.1093/ajcn/82.1.163
Pasolli, E. et al. Extensive unexplored human microbiome diversity revealed by over 150,000 genomes from metagenomes spanning age, geography and lifestyle. Cell 176, 649–662 (2019).
pubmed: 30661755 pmcid: 6349461 doi: 10.1016/j.cell.2019.01.001
Tett, A. et al. The Prevotella copri complex comprises four distinct clades underrepresented in westernized populations. Cell Host Microbe 26, 666–679 (2019).
pubmed: 31607556 pmcid: 6854460 doi: 10.1016/j.chom.2019.08.018
De Filippis, F. et al. Distinct genetic and functional traits of human intestinal Prevotella copri strains are associated with different habitual diets. Cell Host Microbe 25, 444–453 (2019).
pubmed: 30799264 doi: 10.1016/j.chom.2019.01.004
Vangay, P. et al. US immigration westernizes the human gut microbiome. Cell 175, 962–972 (2018).
pubmed: 30388453 pmcid: 6498444 doi: 10.1016/j.cell.2018.10.029
Dethlefsen, L. & Relman, D. A. Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proc. Natl Acad. Sci. USA 108, 4554–4561 (2011).
pubmed: 20847294 doi: 10.1073/pnas.1000087107
Chung, W. S. et al. Modulation of the human gut microbiota by dietary fibres occurs at the species level. BMC Biol. 14, 3 (2016).
pubmed: 26754945 pmcid: 4709873 doi: 10.1186/s12915-015-0224-3
Martinez-Medina, M. et al. Western diet induces dysbiosis with increased E. coli in CEABAC10 mice, alters host barrier function favouring AIEC colonisation. Gut 63, 116–124 (2014).
pubmed: 23598352 doi: 10.1136/gutjnl-2012-304119
Gomez-Arango, L. F. et al. Low dietary fiber intake increases Collinsella abundance in the gut microbiota of overweight and obese pregnant women. Gut Microbes 9, 189–201 (2018).
pubmed: 29144833 pmcid: 6219589 doi: 10.1080/19490976.2017.1406584
Amato, K. R. et al. Variable responses of human and non-human primate gut microbiomes to a Western diet. Microbiome 3, 53 (2015).
pubmed: 26568112 pmcid: 4645477 doi: 10.1186/s40168-015-0120-7
Foerster, J. et al. The influence of whole grain products and red meat on intestinal microbiota composition in normal weight adults: a randomized crossover intervention trial. PLoS ONE 9, e109606 (2014).
pubmed: 25299601 pmcid: 4192132 doi: 10.1371/journal.pone.0109606
Boerjan, W., Ralph, J. & Baucher, M. Lignin biosynthesis. Annu. Rev. Plant Biol. 54, 519–546 (2003).
pubmed: 14503002 doi: 10.1146/annurev.arplant.54.031902.134938
Koh, A., De Vadder, F., Kovatcheva-Datchary, P. & Backhed, F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell 165, 1332–1345 (2016).
pubmed: 27259147 doi: 10.1016/j.cell.2016.05.041
Jia, W., Xie, G. & Jia, W. Bile acid–microbiota crosstalk in gastrointestinal inflammation and carcinogenesis. Nat. Rev. Gastroenterol. Hepatol. 15, 111–128 (2018).
pubmed: 29018272 doi: 10.1038/nrgastro.2017.119
Yoshimoto, S. et al. Obesity-induced gut microbial metabolite promotes liver cancer through senescence secretome. Nature 499, 97–101 (2013).
pubmed: 23803760 doi: 10.1038/nature12347
Ferslew, B. C. et al. Altered bile acid metabolome in patients with nonalcoholic steatohepatitis. Dig. Dis. Sci. 60, 3318–3328 (2015).
pubmed: 26138654 pmcid: 4864493 doi: 10.1007/s10620-015-3776-8
Luis, A. S. et al. Dietary pectic glycans are degraded by coordinated enzyme pathways in human colonic bacteroides. Nat. Microbiol. 3, 210–219 (2018).
pubmed: 29255254 doi: 10.1038/s41564-017-0079-1
Hunter, D. J. Gene–environment interactions in human diseases. Nat. Rev. Genet. 6, 287–298 (2005).
pubmed: 15803198 doi: 10.1038/nrg1578
Shi, Y. et al. A genome-wide association study identifies new susceptibility loci for non-cardia gastric cancer at 3q13.31 and 5p13.1. Nat. Genet. 43, 1215–1218 (2011).
pubmed: 22037551 doi: 10.1038/ng.978
Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019).
pubmed: 31142855 pmcid: 6650278 doi: 10.1038/s41586-019-1237-9
Wegner, K. et al. Rapid analysis of bile acids in different biological matrices using LC-ESI-MS/MS for the investigation of bile acid transformation by mammalian gut bacteria. Anal. Bioanal. Chem. 409, 1231–1245 (2017).
pubmed: 27822648 doi: 10.1007/s00216-016-0048-1
de Aguiar Vallim, T. Q., Tarling, E. J. & Edwards, P. A. Pleiotropic roles of bile acids in metabolism. Cell Metab. 17, 657–669 (2013).
pubmed: 23602448 pmcid: 3654004 doi: 10.1016/j.cmet.2013.03.013
Koropatkin, N. M., Cameron, E. A. & Martens, E. C. How glycan metabolism shapes the human gut microbiota. Nat. Rev. Microbiol. 10, 323–335 (2012).
pubmed: 22491358 pmcid: 4005082 doi: 10.1038/nrmicro2746
Rooks, M. G. & Garrett, W. S. Gut microbiota, metabolites and host immunity. Nat. Rev. Immunol. 16, 341–352 (2016).
pubmed: 27231050 pmcid: 5541232 doi: 10.1038/nri.2016.42
Koren, O. et al. A guide to enterotypes across the human body: meta-analysis of microbial community structures in human microbiome datasets. PLoS Comput. Biol. 9, e1002863 (2013).
pubmed: 23326225 pmcid: 3542080 doi: 10.1371/journal.pcbi.1002863
De Vadder, F. et al. Microbiota-produced succinate improves glucose homeostasis via intestinal gluconeogenesis. Cell Metab. 24, 151–157 (2016).
pubmed: 27411015 doi: 10.1016/j.cmet.2016.06.013
De Angelis, M. et al. Effect of whole-grain barley on the human fecal microbiota and metabolome. Appl. Environ. Microbiol. 81, 7945–7956 (2015).
pubmed: 26386056 pmcid: 4616929 doi: 10.1128/AEM.02507-15
Lloyd-Price, J. et al. Strains, functions and dynamics in the expanded human microbiome project. Nature 550, 61–66 (2017).
pubmed: 28953883 pmcid: 5831082 doi: 10.1038/nature23889
Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl Acad. Sci. USA 111, E2329–E2338 (2014).
pubmed: 24843156 doi: 10.1073/pnas.1319284111 pmcid: 4050606
Mehta, R. S. et al. Stability of the human faecal microbiome in a cohort of adult men. Nat. Microbiol. 3, 347–355 (2018).
pubmed: 29335554 pmcid: 6016839 doi: 10.1038/s41564-017-0096-0
Willett, W. C. et al. Reproducibility and validity of a semiquantitative food frequency questionnaire. Am. J. Epidemiol. 122, 51–65 (1985).
pubmed: 4014201 doi: 10.1093/oxfordjournals.aje.a114086
Rimm, E. B. et al. Reproducibility and validity of an expanded self-administered semiquantitative food frequency questionnaire among male health professionals. Am. J. Epidemiol. 135, 1114–1136 (1992).
pubmed: 1632423 doi: 10.1093/oxfordjournals.aje.a116211
Feskanich, D. et al. Reproducibility and validity of food intake measurements from a semiquantitative food frequency questionnaire. J. Am. Diet. Assoc. 93, 790–796 (1993).
pubmed: 8320406 doi: 10.1016/0002-8223(93)91754-E
Chasan-Taber, S. et al. Reproducibility and validity of a self-administered physical activity questionnaire for male health professionals. Epidemiology 7, 81–86 (1996).
pubmed: 8664406 doi: 10.1097/00001648-199601000-00014
Trichopoulou, A., Costacou, T., Bamia, C. & Trichopoulos, D. Adherence to a Mediterranean diet and survival in a Greek population. New Engl. J. Med. 348, 2599–2608 (2003).
pubmed: 12826634 doi: 10.1056/NEJMoa025039
McIver, L. J. et al. bioBakery: a meta’omic analysis environment. Bioinformatics 34, 1235–1237 (2018).
pubmed: 29194469 doi: 10.1093/bioinformatics/btx754
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
pubmed: 22388286 pmcid: 3322381 doi: 10.1038/nmeth.1923
Suzek, B. E., Huang, H., McGarvey, P., Mazumder, R. & Wu, C. H. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics 23, 1282–1288 (2007).
pubmed: 17379688 doi: 10.1093/bioinformatics/btm098
Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).
pubmed: 25402007 doi: 10.1038/nmeth.3176
Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Res. 44, D471–D480 (2016).
pubmed: 26527732 doi: 10.1093/nar/gkv1164
Ye, Y. & Doak, T. G. A parsimony approach to biological pathway reconstruction/inference for genomes and metagenomes. PLoS Comput. Biol. 5, e1000465 (2009).
pubmed: 19680427 pmcid: 2714467 doi: 10.1371/journal.pcbi.1000465

Auteurs

Dong D Wang (DD)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Long H Nguyen (LH)

Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.
ORCID: 0000-0002-5436-4219

Yanping Li (Y)

Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
ORCID: 0000-0002-0412-2748

Yan Yan (Y)

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Wenjie Ma (W)

Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.
ORCID: 0000-0001-9894-7072

Ehud Rinott (E)

Ben-Gurion University of the Negev, Negev, Israel.
ORCID: 0000-0003-3028-5492

Kerry L Ivey (KL)

Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
South Australian Health and Medical Research Institute, Infection and Immunity Theme, South Australia, Australia.
Department of Nutrition and Dietetics, College of Nursing and Health Sciences, Flinders University, Adelaide, Australia.

Iris Shai (I)

Ben-Gurion University of the Negev, Negev, Israel.

Walter C Willett (WC)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Frank B Hu (FB)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Eric B Rimm (EB)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Meir J Stampfer (MJ)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

Andrew T Chan (AT)

Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
Division of Gastroenterology, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA.
ORCID: 0000-0001-7284-6767

Curtis Huttenhower (C)

Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA. chuttenh@hsph.harvard.edu.
Broad Institute of MIT and Harvard, Boston, MA, USA. chuttenh@hsph.harvard.edu.
ORCID: 0000-0002-1110-0096

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Classifications MeSH

questionsmedicales.fr › Facteurs biologiques › Marqueurs biologiques › The gut microbiome modulates the protective...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Statistiques comme sujet › Probabilité › Risque › Facteurs de risque › Facteurs de risque de maladie cardiaque › Facteurs de risque cardiométabolique › The gut microbiome modulates the protective...
questionsmedicales.fr › Maladies cardiovasculaires › The gut microbiome modulates the protective...
questionsmedicales.fr › Phénomènes physiologiques › Alimentation et nutrition › Phénomènes physiologiques nutritionnels › Régime alimentaire › The gut microbiome modulates the protective...
questionsmedicales.fr › Phénomènes physiologiques › Alimentation et nutrition › Phénomènes physiologiques nutritionnels › Régime alimentaire › Régime méditerranéen › The gut microbiome modulates the protective...
questionsmedicales.fr › Environnement et santé publique › Santé publique › Méthodes épidémiologiques › Caractéristiques des études épidémiologiques › Études épidémiologiques › Études de cohortes › Études de suivi › The gut microbiome modulates the protective...
questionsmedicales.fr › Environnement et santé publique › Environnement › Écosystème › Biodiversité › Biote › Microbiote › Microbiome gastro-intestinal › The gut microbiome modulates the protective...
questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › The gut microbiome modulates the protective...
questionsmedicales.fr › Environnement et santé publique › Environnement › Écosystème › Biodiversité › Biote › Microbiote › The gut microbiome modulates the protective...
questionsmedicales.fr › Bactéries › Bactéries à Gram négatif › Bactéries anaérobies à Gram négatif › Bâtonnets à Gram négatif anaérobies droits, incurvés et spiralés › Bacteroidaceae › Prevotella › The gut microbiome modulates the protective...