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

Abstract

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.

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Fig. 1: Experimental strategy for linking diet, the gut microbiome and cardiometabolic disease risk in the MLVS.
Fig. 2: Mediterranean dietary pattern and taxonomic and functional profiles of the gut microbiome.
Fig. 3: Associations of the Mediterranean dietary pattern with overall gut microbiome configuration and with individual gut microbial species abundances.
Fig. 4: The Mediterranean dietary pattern is associated with microbial processes involved in plant polysaccharide degradation and short-chain fatty acid production.
Fig. 5: Prevotella copri carriage modulates the protective association between the Mediterranean dietary pattern and cardiometabolic disease risk.

Data availability

All the microbiome data have been published previously28,60 and are publicly available (https://www.nature.com/articles/s41564-017-0084-4#Sec22). All the metadata from the Health Professionals Follow-Up Study are available through a request for external collaboration and upon approvals of a letter of intent and a research proposal. Details for how to request an external collaboration with the Health Professionals Follow-Up Study can be found at https://sites.sph.harvard.edu/hpfs/for-collaborators/. The Harvard University Food Composition Database is publicly available at https://regepi.bwh.harvard.edu/health/nutrition/. Figures 25, Extended Data Figs. 110, Supplementary Tables 1 and 38 and Supplementary Figs. 1 and 2 are associated with the microbiome and metadata. Source data are provided with this paper.

Code availability

This study mainly relies on open-source bioBakery tools, particularly MetaPhlAn 2, HUMAnN 2 and MaAsLin 2, which are available at https://huttenhower.sph.harvard.edu/tools/. The analysis-specific programs are available through http://huttenhower.sph.harvard.edu/meddiet2020.

References

  1. 1.

    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).

    Google Scholar 

  2. 2.

    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).

  3. 3.

    Koeth, R. A. et al. Intestinal microbiota metabolism of l-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat. Med. 19, 576–585 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Kurilshikov, A. et al. Gut microbial associations to plasma metabolites linked to cardiovascular phenotypes and risk. Circ. Res. 124, 1808–1820 (2019).

    CAS  PubMed  Google Scholar 

  5. 5.

    Forslund, K. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262–266 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Pedersen, H. K. et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 535, 376–381 (2016).

    CAS  PubMed  Google Scholar 

  7. 7.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    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).

    CAS  PubMed  Google Scholar 

  9. 9.

    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).

    CAS  PubMed  Google Scholar 

  10. 10.

    Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).

    CAS  PubMed  Google Scholar 

  11. 11.

    David, L. A. et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505, 559–563 (2014).

    CAS  PubMed  Google Scholar 

  12. 12.

    Smits, S. A. et al. Seasonal cycling in the gut microbiome of the Hadza hunter-gatherers of Tanzania. Science 357, 802–806 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Sonnenburg, J. L. & Backhed, F. Diet–microbiota interactions as moderators of human metabolism. Nature 535, 56–64 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15.

    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  PubMed Central  Google Scholar 

  16. 16.

    Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).

    CAS  Google Scholar 

  17. 17.

    Vatanen, T. et al. The human gut microbiome in early-onset Type 1 diabetes from the TEDDY study. Nature 562, 589–594 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    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  Google Scholar 

  20. 20.

    Wu, G. D. et al. Linking long-term dietary patterns with gut microbial enterotypes. Science 334, 105–108 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Willett, W. C. et al. Mediterranean diet pyramid: a cultural model for healthy eating. Am. J. Clin. Nutr. 61, 1402S–1406S (1995).

    CAS  PubMed  Google Scholar 

  23. 23.

    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  Google Scholar 

  24. 24.

    American Diabetic Association 4. Lifestyle management: standards of medical care in diabetes—2018. Diabetes Care 41, S38–S50 (2018).

    Google Scholar 

  25. 25.

    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).

    CAS  PubMed  Google Scholar 

  26. 26.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Abu-Ali, G. S. et al. Metatranscriptome of human faecal microbial communities in a cohort of adult men. Nat. Microbiol. 3, 356–366 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    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).

    CAS  PubMed  Google Scholar 

  32. 32.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Tett, A. et al. The Prevotella copri complex comprises four distinct clades underrepresented in westernized populations. Cell Host Microbe 26, 666–679 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    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  Google Scholar 

  35. 35.

    Vangay, P. et al. US immigration westernizes the human gut microbiome. Cell 175, 962–972 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    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).

    CAS  PubMed  Google Scholar 

  37. 37.

    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  PubMed Central  Google Scholar 

  38. 38.

    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  Google Scholar 

  39. 39.

    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  PubMed Central  Google Scholar 

  40. 40.

    Amato, K. R. et al. Variable responses of human and non-human primate gut microbiomes to a Western diet. Microbiome 3, 53 (2015).

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    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  PubMed Central  Google Scholar 

  42. 42.

    Boerjan, W., Ralph, J. & Baucher, M. Lignin biosynthesis. Annu. Rev. Plant Biol. 54, 519–546 (2003).

    CAS  PubMed  Google Scholar 

  43. 43.

    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).

    CAS  PubMed  Google Scholar 

  44. 44.

    Jia, W., Xie, G. & Jia, W. Bile acid–microbiota crosstalk in gastrointestinal inflammation and carcinogenesis. Nat. Rev. Gastroenterol. Hepatol. 15, 111–128 (2018).

    CAS  PubMed  Google Scholar 

  45. 45.

    Yoshimoto, S. et al. Obesity-induced gut microbial metabolite promotes liver cancer through senescence secretome. Nature 499, 97–101 (2013).

    CAS  PubMed  Google Scholar 

  46. 46.

    Ferslew, B. C. et al. Altered bile acid metabolome in patients with nonalcoholic steatohepatitis. Dig. Dis. Sci. 60, 3318–3328 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Luis, A. S. et al. Dietary pectic glycans are degraded by coordinated enzyme pathways in human colonic bacteroides. Nat. Microbiol. 3, 210–219 (2018).

    CAS  PubMed  Google Scholar 

  48. 48.

    Hunter, D. J. Gene–environment interactions in human diseases. Nat. Rev. Genet. 6, 287–298 (2005).

    CAS  PubMed  Google Scholar 

  49. 49.

    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).

    CAS  PubMed  Google Scholar 

  50. 50.

    Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    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).

    CAS  PubMed  Google Scholar 

  52. 52.

    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  PubMed Central  Google Scholar 

  53. 53.

    Koropatkin, N. M., Cameron, E. A. & Martens, E. C. How glycan metabolism shapes the human gut microbiota. Nat. Rev. Microbiol. 10, 323–335 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Rooks, M. G. & Garrett, W. S. Gut microbiota, metabolites and host immunity. Nat. Rev. Immunol. 16, 341–352 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    De Vadder, F. et al. Microbiota-produced succinate improves glucose homeostasis via intestinal gluconeogenesis. Cell Metab. 24, 151–157 (2016).

    PubMed  Google Scholar 

  57. 57.

    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  PubMed Central  Google Scholar 

  58. 58.

    Lloyd-Price, J. et al. Strains, functions and dynamics in the expanded human microbiome project. Nature 550, 61–66 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl Acad. Sci. USA 111, E2329–E2338 (2014).

    CAS  PubMed  Google Scholar 

  60. 60.

    Mehta, R. S. et al. Stability of the human faecal microbiome in a cohort of adult men. Nat. Microbiol. 3, 347–355 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Willett, W. C. et al. Reproducibility and validity of a semiquantitative food frequency questionnaire. Am. J. Epidemiol. 122, 51–65 (1985).

    CAS  PubMed  Google Scholar 

  62. 62.

    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).

    CAS  PubMed  Google Scholar 

  63. 63.

    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).

    CAS  PubMed  Google Scholar 

  64. 64.

    Chasan-Taber, S. et al. Reproducibility and validity of a self-administered physical activity questionnaire for male health professionals. Epidemiology 7, 81–86 (1996).

    CAS  PubMed  Google Scholar 

  65. 65.

    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  Google Scholar 

  66. 66.

    McIver, L. J. et al. bioBakery: a meta’omic analysis environment. Bioinformatics 34, 1235–1237 (2018).

    CAS  PubMed  Google Scholar 

  67. 67.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68.

    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).

    CAS  PubMed  Google Scholar 

  69. 69.

    Buchfink, B., Xie, C. & Huson, D. H. Fast and sensitive protein alignment using DIAMOND. Nat. Methods 12, 59–60 (2015).

    CAS  PubMed  Google Scholar 

  70. 70.

    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).

    CAS  PubMed  Google Scholar 

  71. 71.

    Ye, Y. & Doak, T. G. A parsimony approach to biological pathway reconstruction/inference for genomes and metagenomes. PLoS Comput. Biol. 5, e1000465 (2009).

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This work was supported by R00DK119412 (D.D.W.), R01HL060712 (F.B.H.), P30DK046200 (F.B.H.), R01CA202704 (A.T.C. and C.H.), K24DK098311 (A.T.C.) and U54DE023798 (C.H.) from the National Institutes of Health (NIH), STARR Cancer Consortium award no. #I7-A714 to C.H., and a Pilot and Feasibility award to D.D.W. from the Boston Nutrition and Obesity Research Center funded by the National Institute of Diabetes and Digestive and Kidney Diseases (P30DK046200). The Men’s Lifestyle Validation Study was supported by U01CA152904 from the National Cancer Institute. The Health Professionals Follow-Up Study is supported by research grants nos. U01CA167552 and R01HL035464 from the NIH. The funding source had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; preparation, review or approval of the manuscript the decision to submit the manuscript for publication. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. We are indebted to the participants in the Health Professionals Follow-up Study for their continuing outstanding level of cooperation and to the staff of the Health Professionals Follow-up Study for their valuable contributions. The authors assume full responsibility for analyses and interpretation of these data.

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Contributions

Study conception, manuscript preparation and data analysis were provided by D.D.W. and C.H. All authors interpretated data and critically revised the manuscript for important intellectual content. Data and specimen collections were carried out by E.B.R., M.J.S., A.T.C. and C.H.; W.C.W., E.B.R., M.J.S., A.T.C. and C.H. obtained funding.

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Correspondence to Curtis Huttenhower.

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C.H. is a scientific advisor for Seres Therapeutics, Empress Therapeutics and ZOE Nutrition. Y.L. has received research support from the California Walnut Commission and SwissRe Management Ltd. The other authors declare no competing interests.

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Peer review information Michael Basson was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Mediterranean diet index and its individual components.

(a) Distribution of the Mediterranean diet (MedDiet) index in the study population. Each participant’s adherence to the MedDiet was evaluated by a 9-dimensional MedDiet index (Supplementary Table 2 and Methods) as previously described22,31. The total MedDiet index ranged from 0 (non-adherence) to 9 (perfect adherence). The index was calculated based on the intake levels of 9 items: vegetables, legumes, fruit, nuts, whole grains, red/processed meat (R/P meat), fish, alcohol, and the ratio of monounsaturated to saturated fat (M/S ratio). Participants who had a higher adherence to MedDiet consumed more beneficial components of the dietary pattern, including whole grains, vegetables, fruit, nuts, legumes, fish, monounsaturated fats (at the expense of saturated fats) and moderate alcohol drinking, but less red and processed meat, a detrimental component of the MedDiet index. (b) Correlations between the MedDiet index, its individual constituent food and nutrient contributors, and dairy food. Values in the figure are partial Spearman correlation coefficients with adjustment for total energy intake. As expected, the composite MedDiet score was positively correlated with ‘healthy’ contributing factors, negatively correlated with ‘unhealthy’ factors, and, importantly, not dominated by any one component. Source data

Extended Data Fig. 2 Principal coordinate analysis of species-level Bray-Curtis dissimilarity colored by the relative abundance of major taxonomic features.

(a) Principal coordinate analysis of species-level Bray-Curtis dissimilarity colored in correspondence to the relative abundance of Bacteroidetes and Firmicutes phyla. As expected, a majority of variation in the species-level compositional structure of the gut microbiome was driven by a tradeoff between Bacteroidetes versus Firmicutes phyla. (b) Principal coordinate analysis of species-level Bray-Curtis dissimilarity colored in correspondence to the relative abundance of 9 most abundant species-level features. The most prominent patterns of gut microbial taxonomic variation in the population included tradeoffs between the abundances of Eubacterium rectale and Bacteroides uniformis vs. Subdoligranulum unclassified and P. copri. Source data

Extended Data Fig. 3 Association between the adherence to a Mediterranean dietary pattern and microbiome taxonomic diversity.

The diversity of gut microbiome was quantified by Shannon diversity index. P for linear trend was derived from a general linear model with the Shannon diversity index as the dependent variable and the quartiles of the Mediterranean diet index as independent variables. The significance test was two-sided. Box plot centers show medians of the Shannon diversity index with boxes indicating their inter-quartile ranges (IQRs); upper and lower whiskers indicate 1.5 times the IQR from above the upper quartile and below the lower quartile, respectively. This analysis was conducted based on 925 metagenomes from 307 participants. Source data

Extended Data Fig. 4 Associations of the Mediterranean diet index and its components with species-level features.

Colors of the heatmap are in correspondence to the beta coefficient for dietary variables from linear mixed models in MaAsLin 2 with species-level feature as outcomes. All models included each participant’s identifier as random effects and simultaneously adjusted for total energy intake, age, physical activity level, smoking, probiotic use, medication use (including antibiotics, proton pump inhibitors, aspirin, statins and metformin), and the Bristol stool scale. Statistical significance is from the linear mixed model with multiple comparison adjustment using the Benjamini-Hochberg method to calculate q values (false discovery rate adjusted P value, exact q values in Source Data). These analyses were based on 925 metagenomes collected from 307 participants. All the statistical tests were two-sided. Source data

Extended Data Fig. 5 Associations of the Mediterranean diet index and its components with metagenomic pathways.

Colors of the heatmap are in correspondence to the beta coefficient for dietary variables from linear mixed models in MaAsLin 2 with metagenomic pathways as outcomes. All models included each participant’s identifier as random effects and simultaneously adjusted for total energy intake, age, physical activity level, smoking, probiotic use, medication use (including antibiotics, proton pump inhibitors, statins, aspirin and metformin), and the Bristol stool scale. Statistical significance is from the linear mixed model with multiple comparison adjustment using the Benjamini-Hochberg method to calculate q values (false discovery rate adjusted P value, exact q values in Source Data). These analyses were based on 925 metagenomes collected from 307 participants. All the statistical tests were two-sided. Source data

Extended Data Fig. 6 Associations of the Mediterranean diet index and its components with metagenomic enzymes.

Colors of the heatmap are in correspondence to the beta coefficient for dietary variables from linear mixed models in MaAsLin 2 with metagenomic enzymes as outcomes. All models included each participant’s identifier as random effects and simultaneously adjusted for total energy intake, age, physical activity level, smoking, probiotic use, medication use (including antibiotics, proton pump inhibitors, statins, aspirin and metformin), and the Bristol stool scale. Statistical significance is from the linear mixed model with multiple comparison adjustment using the Benjamini-Hochberg method to calculate q values (false discovery rate adjusted P value, exact q values in Source Data). These analyses were based on 925 metagenomes collected from 307 participants. All the statistical tests were two-sided. Source data

Extended Data Fig. 7 Associations of the Mediterranean diet index and its components with transcription levels of microbial enzymes.

Colors of the heatmap are in correspondence to the beta coefficient for dietary variables from linear mixed models in MaAsLin 2 with transcription levels of microbial enzymes (RNA/DNA ratio) as outcomes. All models included each participant’s identifier as random effects and simultaneously adjusted for total energy intake, age, physical activity level, smoking, probiotic use, and the Bristol stool scale. Statistical significance is from the linear mixed model with multiple comparison adjustment using the Benjamini-Hochberg method to calculate q values (false discovery rate adjusted P value, exact q values in Source Data). These analyses were based on 340 metatranscriptome and metagenome pairs from 96 participants. All the statistical tests were two-sided. Source data

Extended Data Fig. 8 Associations of the Mediterranean diet index with the cardiometabolic disease risk score and biomarkers.

P values were estimated from linear mixed model that included each participant’s identifier as random effects and simultaneously adjusted for total energy intake, age, physical activity level, smoking, probiotic use, Bristol stool scale, medication use (including antibiotics, statins, aspirin, proton pump inhibitors, statins, aspirin and metformin) and the first principal coordinates axis (PCo1) as fixed effects. This analysis was based on 468 blood samples from 304 participants. The shaded areas indicate 95% confidence intervals of values on the fitted linear trend lines. All the statistical tests were two-sided. Source data

Extended Data Fig. 9 Interaction between adherence to the Mediterranean diet and the abundance of highly abundant microbial species in relation to the cardiometabolic disease risk score.

P for interaction was derived from linear mixed models that included participant’s identifier as random effects, the Mediterranean diet index, individual microbial species and their product term, and simultaneously adjusted for total energy intake, age, physical activity level, smoking, probiotic use, the Bristol stool scale, and medication use (including antibiotics, statins, aspirin, proton pump inhibitors and metformin) as fixed effects. We performed two-sided likelihood ratio tests by comparing models with and without an interaction term to calculate P value for interaction (degree of freedom =1). This analysis was based on 468 blood samples from 304 participants. The shaded areas indicate 95% confidence intervals of values on the fitted linear trend lines. Source data

Extended Data Fig. 10 The gut microbial profile modifies associations of the MedDiet with individual biomarkers of cardiometabolic disease risk.

P for interaction was derived from a linear mixed model that included participant’s identifier as random effects, the MedDiet index, individual microbial species and their product term, and simultaneously adjusted for total energy intake, age, physical activity level, smoking, probiotic use, Bristol stool scale, and medication use (including antibiotics, statins, aspirin, proton pump inhibitors and metformin) as fixed effects. We performed two-sided likelihood ratio tests by comparing models with and without an interaction term to calculate P value for interaction (degree of freedom =1). This analysis was based on 468 blood samples from 304 participants. Source data

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Wang, D.D., Nguyen, L.H., Li, Y. et al. The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk. Nat Med 27, 333–343 (2021). https://doi.org/10.1038/s41591-020-01223-3

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