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.
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The metagenomes are deposited in European Bioinformatics Institute European Nucleotide Archive under accession no. PRJEB39223. The non-metagenomic data used for analysis in this study are held by the Department of Twin Research at King’s College London. The data can be released to bona fide researchers using our normal procedures overseen by the Wellcome Trust and its guidelines as part of our core funding. We receive around 100 requests per year for our datasets and have three meetings per month with independent members to assess proposals. The application can be found at https://twinsuk.ac.uk/resources-for-researchers/access-our-data/. This means that data need to be anonymized and conform to GDPR standards.
Computational analyses were performed using the bioBakery suite of tools; species-level microbial abundances were computed using MetaPhlAn v.3.0 (https://github.com/biobakery/MetaPhlAn). Functional potential profiling was carried out with HUMAnN v.2.0 (https://github.com/biobakery/humann; Methods).
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We thank the participants of the PREDICT 1 study. We thank N. Atabaki-Pasdar for generating the liver fat score. We thank the staff of Zoe Global, the Department of Twin Research and the Massachusetts General Hospital and all the members of the Segata, Berry and Spector laboratories for their tireless work in contributing to the running of the study, data collection and data processing. We thank Nightingale Health and Affinity Biomarker Laboratories for their support and analytical work. This work was supported by Zoe Global and received support from grants from the Wellcome Trust (no. 212904/Z/18/Z) and Medical Research Council/British Heart Foundation Ancestry and Biological Informative Markers for Stratification of Hypertension (no. MR/M016560/1). The work was also supported by the European Research Council (ERC-STG project MetaPG-716575 to N.S.), MIUR ‘Futuro in Ricerca’ (grant no. RBFR13EWWI_001 to N.S.), the European H2020 program (ONCOBIOME-825410 and MASTER-818368 projects to N.S.), the National Cancer Institute of the National Institutes of Health (grant no. 1U01CA230551 to N.S.) and the Premio Internazionale Lombardia e Ricerca 2019 to N.S. S.E.B. was supported in part by a grant funded by the Biotechnology and Biological Sciences Research Council (grant no. BB/NO12739/1). P.W.F. was supported in part by grants from the European Research Council (grant no. CoG-2015_681742_NASCENT), Swedish Research Council (grant no. IRC15-0067) and Novo Nordisk Foundation. A.T.C. was supported in part as a Stuart and Suzanne Steele MGH Research Scholar. TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, Chronic Disease Research Foundation, Zoe Global and the National Institute for Health Research-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London.
T.D.S., S.E.B., A.M.V., F.A., P.W.F., C.H. and N.S. are consultants to Zoe Global. J.W., G.H., R.D., J.C.P., C.B., R.H., L.F., F.G. and S.D. are or have been employees of Zoe Global. The other authors declare no competing interests.
Peer review information Jennifer Sargent was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Alpha diversity linked with personal factors, habitual diet, fasting, and postprandial markers.
a, Microbiome alpha diversity computed using the Shannon index correlated markers from the four categories: personal, habitual diet, fasting, and post-prandial. Reported are the five strongest positive and negative Spearman correlations for each category with p < 0.05. All correlations and p-values available in the Supplementary Table 1. b, Inter-sample microbiome distances (beta-diversity) were substantially lower, that is closer, among samples from the same individuals (two weeks apart) compared to those amongst different individuals. Gut microbial communities in monozygotic twins were slightly more similar than in dizygotic twins (Mann–Whitney U test two-sided p = 0.06), which, in turn, were more similar than unrelated individuals (p < 1e-12), even after adjusting for age (p < 1e-12). c, After excluding twin status (that is non-twin, vs. mono vs. dizygotic twins) from the model, personal factors still accounted for the greatest proportion of variance explained in overall microbial diversity, followed by dietary habits, fasting and postprandial cardiometabolic blood markers (by cumulative stepwise dbRDA). d, Cumulative (left bars) contributions and individual (right bars) contributions for each metadata variable based on Bray-Curtis dissimilarity. Box plots show first and third quartiles (boxes) and the median (middle line), whiskers extends up-to 1.5× the interquartile range.
Extended Data Fig. 2 Species-level correlation with single foods.
The figure shows the species-level correlations (Spearman) with single food quantities as estimated from the food frequency questionnaires. Only foods with at least 5 significant associations (q-value≤0.2) are displayed. Species are sorted by the number of significant associations, and the top 30 are reported in the figure.
Extended Data Fig. 3 Top foods, food groups, nutrients, and dietary patterns validated in the PREDICT 1 US cohort.
The application of the RF regression model trained on the PREDICT 1 UK cohort on the PREDICT 1 US participants, validating the associations with food-related variables found in the PREDICT 1 UK.
Extended Data Fig. 4 Performance for random Forest regression and classification on microbiome functional potential in predicting fasting measurements, total cholesterol and triglycerides in different lipoproteins.
The figure shows the performance of both RF regression and classification tasks trained on microbiome gene families profiles in predicting (a) the fasting measurements presented in Fig. 4a, sorted as in Fig. 4a. b, Predicting performances of the total cholesterol and (c) of triglycerides in different sizes of lipoproteins. For each lipoprotein, we considered its concentration values at both fasting and postprandial (6 h), and also the difference (rise) between the post-prandial concentration and the fasting one. Box plots show the distribution of the Spearman correlations (left axis) between real and predicted values using RF regression. Box plots show first and third quartiles (boxes) and the median (middle line), whiskers extends up-to 1.5× the interquartile range. Circles show the median AUC (right axis) of RF classification in predicting the bottom quartile of the distribution vs. the top quartile.
Extended Data Fig. 5 Distributions of BMI in each curatedMetagenomicData dataset.
The figure shows the distributions of BMI values for the datasets available in curatedMetagenomicData. This was used to further select those datasets with a comparable range of values (interquartile range between 3.5 and 7.5) as the one in the PREDICT 1 UK dataset (IQR of 5.5), to be used as validation datasets for the associations found. Box plots show first and third quartiles (boxes) and the median (middle line), whiskers extends up-to 1.5× the interquartile range.
Extended Data Fig. 6 Pairwise partial Spearman correlations between bacterial species and total lipids and cholesterol in lipoproteins.
a, The heatmap shows the species-level correlations with total lipids in lipoprotein variables at fasting, post-prandial (6 h), and the difference (rise) between the postprandial and fasting concentrations. The 30 species with the highest number of significant associations (FDR ≤ 0.2) are shown. The asterisk indicates a significant correlation between species and metadata variable using a t-test two-sided, corrected with FDR with q < 0.2. b, The heatmap shows the species-level correlations with total cholesterol in lipoprotein variables at fasting, post-prandial (6 h), and the difference (rise) between the postprandial and fasting concentrations. The 30 species with the highest number of significant associations (FDR ≤ 0.2) are shown. The asterisk indicates a significant correlation between species and metadata variable using a t-test two-sided, corrected with FDR with q < 0.2. All correlations, p-values, and q-values are available in the Supplementary Table 6.
Extended Data Fig. 7 Species-level correlations with triglycerides in lipoproteins.
The heatmap shows the species-level correlations with triglycerides in lipoprotein variables at fasting, post-prandial (6 h), and the difference (rise) between the postprandial and fasting concentrations. The 30 species with the highest number of significant associations (FDR ≤ 0.2) are shown. The asterisk indicates a significant correlation between species and metadata variable using a t-test two-sided, corrected with FDR with q < 0.2. All correlations, p-values, and q-values are available in the Supplementary Table 6.
Extended Data Fig. 8 Pairwise partial Spearman correlations between bacterial gene families and pathway abundances with clinical and metabolic risk scores, glycaemic and inflammatory measures, and lipoproteins.
a, The heatmap shows gene families correlations with the set of metadata presented in Fig. 5a–c reporting the top 2,000 genes selected among those with at least 20% prevalence on their number of significant correlations (q < 0.2). Gene families’ correlations are showing the same clusters as the species-level correlations in Fig. 5a–c. b, The heatmap shows pathway abundances correlations with the set of metadata presented in Fig. 5a–c reporting all the pathways at 20% prevalence (349 in total). Pathway abundances correlations are showing the same cluster structure as the species-level correlations in Fig. 5a–c.
Extended Data Fig. 9 Concordance of Random Forest scores with species-level partial correlations.
Volcano plots of the scores assigned to each species by Random Forest and their partial correlation, showing an overall concordance between the two independent approaches. We considered the top 5 metadata variables for the six metadata categories: a, Foods, bacon (g) (corr. 0.49), garlic (g) (corr. 0.424), unsalted nuts (g) (0.422), dairy dessert (g) (corr. 0.421), salted nuts (g) (corr. 0.395). b, Food groups, nuts (corr. 0.468), tea and coffee (corr. 0.436), meat (corr. 0.42), legumes (corr. 0.374), vegetables (corr. 0.371). c, Nutrients, lactose (corr. 0.442), niacin (corr. 0.381), maltose (corr. 0.361), sucrose (corr. 0.344), total carbohydrates (corr. 0.324). d, Nutrients normalized by daily energy intake, magnesium (corr. 0.472), starch (corr. 0.436), total carbohydrates (corr. 0.422), non-starch polysaccharides (NSP) (corr. 0.421), lactose (corr. 0.414). e, Dietary patterns, healthy plant percentage (corr. 0.492), healthy PDI (corr. 0.472), hei score (corr. 0.47), HFD (corr. 0.408), total plants percentage (0.388). f, Lipoproteins, M-HDL-L 6 h rise (corr. 0.406), IDL-C 6 h (corr. 0.4), HDL-L 6 h rise (corr. 0.397), XL-HDL-C 0 h (corr. 0.395), Total Cholesterol 4 h rise (corr. 0.391).
Extended Data Fig. 10 Prevotella copri and/or Blastocystis presence are indicators of a more favourable postprandial glucose response to meals.
a–c, Differential analysis of visceral fat, HFD and glucose iAUC 2 h after standardised breakfast according to presence-absence of one and both of P. copri and Blastocystis. The analysis reveals that both these species are indicators of reduced visceral fat, good cholesterol and meal-driven increase of glucose. d,e, Differential analysis of C-peptide and triglycerides at different time points according to presence-absence of one and both of P. copri and Blastocystis. The distributions of the concentrations for C-peptide and triglycerides were typically lower when one or both are absent. An asterisk between two box plots represents a significant p-value (p < 0.05) according to the Mann-Whitney U test (two-sided, Supplementary Table 8). Box plots show first and third quartiles (boxes) and the median (middle line), whiskers extends up-to 1.5× the interquartile range. P-values are available in Supplementary Table 8.
Supplementary Table 1
Alpha diversity measures and their correlations with personal factors, habitual diet, fasting and postprandial markers
Supplementary Table 2
List of foods and their assigned food groups and health classification and nutrients normalized by daily energy intake that is calorie adjusted
Supplementary Table 3
Supplementary Table 4
Plant-based Diet Index, Healthy Food Diversity index, animal groups, and Alternate Mediterranean score description
Supplementary Table 5
Species-level partial correlations with food groups, nutrients normalized by daily energy intake, dietary patterns, and fasting and postprandial measures with the species identified in the PREDICT 1 UK participants. Partial correlations were computed using pcor.test (two-sided) with the parameter method=spearman and corrected for multiple-hypothesis testing with FDR
Supplementary Table 6
Species-level partial correlations with food groups, nutrients normalized by daily energy intake, dietary patterns, and fasting and postprandial measures with the species identified in the PREDICT 1 US participants. Partial correlations were computed using pcor.test (two-sided) with the parameter method=spearman and corrected for multiple-hypothesis testing with FDR
Supplementary Table 7
Random forest regression and classification performances measured as Pearson and Spearman correlations for the regression task and AUC for the classification task for the model trained and tested with 80/20 training and testing random splitting over 100 folds for foods, food groups, nutrients, nutrients normalized by average energy intake, dietary patterns, and fasting and postprandial measures
Supplementary Table 8
P values from the Mann–Whitney U-test between presence and absence of Prevotella copri, Blastocystis and P. copri and Blastocystis (first tab). Effect size measured as the ratio of the medians for P. copri and Blastocystis presence/absence (second tab)
Supplementary Table 9
Correlations, ranks, and average ranks for determining the two sets of positive and negative bacterial species according to their correlations with a balanced set of personal, habitual diet, fasting and postprandial metadata
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Asnicar, F., Berry, S.E., Valdes, A.M. et al. Microbiome connections with host metabolism and habitual diet from 1,098 deeply phenotyped individuals. Nat Med 27, 321–332 (2021). https://doi.org/10.1038/s41591-020-01183-8
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