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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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 2–5, Extended Data Figs. 1–10, Supplementary Tables 1 and 3–8 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.
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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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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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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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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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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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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DOI: https://doi.org/10.1038/s41591-020-01223-3
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