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.
Ng, M. et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 384, 766–781 (2014).
Brown, J. M. & Hazen, S. L. Microbial modulation of cardiovascular disease. Nat. Rev. Microbiol. 16, 171–181 (2018).
Le Chatelier, E. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013).
Sze, M. A. & Schloss, P. D. Looking for a signal in the noise: revisiting obesity and the microbiome. mBio 7, e01018-16 (2016).
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.e20 (2019).
Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).
Gilbert, J. A. et al. Current understanding of the human microbiome. Nat. Med. 24, 392–400 (2018).
Berry, S. E. et al. Human postprandial responses to food and potential for precision nutrition. Nat. Med. 26, 964–973 (2020).
Zeevi, D. et al. Personalized nutrition by prediction of glycemic responses. Cell 163, 1079–1094 (2015).
Mendes-Soares, H. et al. Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals. Am. J. Clin. Nutr. 110, 63–75 (2019).
Falony, G. et al. Population-level analysis of gut microbiome variation. Science 352, 560–564 (2016).
Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016).
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.e10 (2019).
Schirmer, M. et al. Linking the human gut microbiome to inflammatory cytokine production capacity. Cell 167, 1897 (2016).
Fu, J. et al. The gut microbiome contributes to a substantial proportion of the variation in blood lipids. Circ. Res. 117, 817–824 (2015).
Berry, S. et al. Personalised REsponses to DIetary Composition Trial (PREDICT): an intervention study to determine inter-individual differences in postprandial response to foods. Preprint at https://protocolexchange.researchsquare.com/article/pex-802/v1 (2020).
Xie, H. et al. Shotgun metagenomics of 250 adult twins reveals genetic and environmental impacts on the gut microbiome. Cell Syst. 3, 572–584.e3 (2016).
Atabaki-Pasdar, N. et al. Predicting and elucidating the etiology of fatty liver disease: a machine learning modeling and validation study in the IMI DIRECT cohorts. PLoS Med. 17, e1003149 (2020).
Vojinovic, D. et al. Relationship between gut microbiota and circulating metabolites in population-based cohorts. Nat. Commun. 10, 5813 (2019).
Vadiveloo, M., Dixon, L. B., Mijanovich, T., Elbel, B. & Parekh, N. Development and evaluation of the US Healthy Food Diversity index. Br. J. Nutr. 112, 1562–1574 (2014).
Guenther, P. M. et al. Update of the healthy eating index: HEI-2010. J. Acad. Nutr. Diet. 113, 569–580 (2013).
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).
Reedy, J. et al. Higher diet quality is associated with decreased risk of all-cause, cardiovascular disease, and cancer mortality among older adults. J. Nutr. 144, 881–889 (2014).
Mitrou, P. N. et al. Mediterranean dietary pattern and prediction of all-cause mortality in a US population: results from the NIH-AARP Diet and Health Study. Arch. Intern. Med. 167, 2461–2468 (2007).
Satija, A. et al. Plant-based dietary patterns and incidence of type 2 diabetes in US men and women: results from three prospective cohort studies. PLoS Med. 13, e1002039 (2016).
Vadiveloo, M., Parekh, N. & Mattei, J. Greater healthful food variety as measured by the US Healthy Food Diversity index is associated with lower odds of metabolic syndrome and its components in US adults. J. Nutr. 145, 564–571 (2015).
Onvani, S., Haghighatdoost, F., Surkan, P. J., Larijani, B. & Azadbakht, L. Adherence to the Healthy Eating Index and Alternative Healthy Eating Index dietary patterns and mortality from all causes, cardiovascular disease and cancer: a meta-analysis of observational studies. J. Hum. Nutr. Diet. 30, 216–226 (2017).
Redondo-Useros, N. et al. Associations of probiotic fermented milk (PFM) and yogurt consumption with Bifidobacterium and Lactobacillus components of the gut microbiota in healthy adults. Nutrients 11, 651 (2019).
Sakamoto, M., Iino, T., Yuki, M. & Ohkuma, M. Lawsonibacter asaccharolyticus gen. nov., sp. nov., a butyrate-producing bacterium isolated from human faeces. Int. J. Syst. Evol. Microbiol. 68, 2074–2081 (2018).
Satija, A. et al. Healthful and unhealthful plant-based diets and the risk of coronary heart disease in U.S. adults. J. Am. Coll. Cardiol. 70, 411–422 (2017).
Monteiro, C. A. et al. The UN Decade of Nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutr. 21, 5–17 (2018).
Nielsen, H. B. et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat. Biotechnol. 32, 822–828 (2014).
Beaumont, M. et al. Heritable components of the human fecal microbiome are associated with visceral fat. Genome Biol. 17, 189 (2016).
Pasolli, E. et al. Accessible, curated metagenomic data through ExperimentHub. Nat. Methods 14, 1023–1024 (2017).
Truong, D. T., Tett, A., Pasolli, E., Huttenhower, C. & Segata, N. Microbial strain-level population structure and genetic diversity from metagenomes. Genome Res. 27, 626–638 (2017).
D’Agostino, R. B. Sr et al. General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation 117, 743–753 (2008).
Kettunen, J. et al. Biomarker glycoprotein acetyls is associated with the risk of a wide spectrum of incident diseases and stratifies mortality risk in angiography patients. Circ. Genom. Precis. Med. 11, e002234 (2018).
Würtz, P. et al. Metabolite profiling and cardiovascular event risk: a prospective study of 3 population-based cohorts. Circulation 131, 774–785 (2015).
Hrebícek, J., Janout, V., Malincíková, J., Horáková, D. & Cízek, L. Detection of insulin resistance by simple quantitative insulin sensitivity check index QUICKI for epidemiological assessment and prevention. J. Clin. Endocrinol. Metab. 87, 144–147 (2002).
Thomas, A. M. et al. Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nat. Med. 25, 667–678 (2019).
Wojczynski, M. K. et al. High-fat meal effect on LDL, HDL, and VLDL particle size and number in the Genetics of Lipid-Lowering Drugs and Diet Network (GOLDN): an interventional study. Lipids Health Dis. 10, 181 (2011).
Skeggs, J. W. & Morton, R. E. LDL and HDL enriched in triglyceride promote abnormal cholesterol transport. J. Lipid Res. 43, 1264–1274 (2002).
Hodson, L., Skeaff, C. M. & Fielding, B. A. Fatty acid composition of adipose tissue and blood in humans and its use as a biomarker of dietary intake. Prog. Lipid Res. 47, 348–380 (2008).
Cohn, J. S. Postprandial lipemia: emerging evidence for atherogenicity of remnant lipoproteins. Can. J. Cardiol. 14, 18B–27B (1998).
Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011).
Huttenhower, C. et al. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).
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).
Pedersen, H. K. et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 535, 376–381 (2016).
Tett, A. et al. The Prevotella copri complex comprises four distinct clades underrepresented in westernized populations. Cell Host Microbe 26, 666–679.e7 (2019).
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.e3 (2019).
Clark, C. G., van der Giezen, M., Alfellani, M. A. & Stensvold, C. R. Recent developments in Blastocystis research. Adv. Parasitol. 82, 1–32 (2013).
Lukeš, J., Stensvold, C. R., Jirků-Pomajbíková, K. & Wegener Parfrey, L. Are human intestinal eukaryotes beneficial or commensals? PLoS Pathog. 11, e1005039 (2015).
Beghini, F. et al. Large-scale comparative metagenomics of Blastocystis, a common member of the human gut microbiome. ISME J. 11, 2848–2863 (2017).
Sokol, H. et al. Faecalibacterium prausnitzii is an anti-inflammatory commensal bacterium identified by gut microbiota analysis of Crohn disease patients. Proc. Natl Acad. Sci. USA 105, 16731–16736 (2008).
Hall, A. B. et al. A novel Ruminococcus gnavus clade enriched in inflammatory bowel disease patients. Genome Med. 9, 103 (2017).
Valles-Colomer, M. et al. The neuroactive potential of the human gut microbiota in quality of life and depression. Nat. Microbiol. 4, 623–632 (2019).
Kim, H., Caulfield, L. E. & Rebholz, C. M. Healthy plant-based diets are associated with lower risk of all-cause mortality in US adults. J. Nutr. 148, 624–631 (2018).
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).
Kurilshikov, A. et al. Gut microbial associations to plasma metabolites linked to cardiovascular phenotypes and risk. Circ. Res. 124, 1808–1820 (2019).
Ko, C.-W., Qu, J., Black, D. D. & Tso, P. Regulation of intestinal lipid metabolism: current concepts and relevance to disease. Nat. Rev. Gastroenterol. Hepatol. 17, 169–183 (2020).
McIver, L. J. et al. bioBakery: a meta’omic analysis environment. Bioinformatics 34, 1235–1237 (2018).
Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).
Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018).
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).
Kang, D. D. et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ 7, e7359 (2019).
Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Meth. 9, 357–359 (2012).
Quince, C., Walker, A. W., Simpson, J. T., Loman, N. J. & Segata, N. Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. 35, 833–844 (2017).
Bingham, S. A. et al. Nutritional methods in the European Prospective Investigation of Cancer in Norfolk. Public Health Nutr. 4, 847–858 (2001).
Mulligan, A. A. et al. A new tool for converting food frequency questionnaire data into nutrient and food group values: FETA research methods and availability. BMJ Open 4, e004503 (2014).
McCance and Widdowson’s The Composition of Foods 7th edn (Public Health England, 2014).
Food Portion Sizes 3rd edn (Food Standards Agency, 2002).
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, 1114–1126 (1992).
Frankenfield, D. C., Muth, E. R. & Rowe, W. A. The Harris–Benedict studies of human basal metabolism: history and limitations. J. Am. Diet. Assoc. 98, 439–445 (1998).
McGuire, S. U.S. Department of Agriculture and U.S. Department of Health and Human Services, Dietary Guidelines for Americans, 2010. 7th Edition, Washington, DC: U.S. Government Printing Office, January 2011. Adv. Nutr. 2, 293–294 (2011).
World Health Organization & Brouwer, I. A. Effect of trans-fatty acid intake on blood lipids and lipoproteins: a systematic review and meta-regression analysis. World Health Organization https://apps.who.int/iris/handle/10665/246109 (2016).
Zhong, V. W. et al. Associations of dietary cholesterol or egg consumption with incident cardiovascular disease and mortality. JAMA 321, 1081–1095 (2019).
de Souza, R. J. et al. Intake of saturated and trans unsaturated fatty acids and risk of all cause mortality, cardiovascular disease, and type 2 diabetes: systematic review and meta-analysis of observational studies. BMJ 351, h3978 (2015).
Michaëlsson, K. et al. Milk intake and risk of mortality and fractures in women and men: cohort studies. BMJ 349, g6015 (2014).
Mazidi, M. et al. Consumption of dairy product and its association with total and cause specific mortality: a population-based cohort study and meta-analysis. Clin. Nutr. 38, 2833–2845 (2019).
Petsini, F., Fragopoulou, E. & Antonopoulou, S. Fish consumption and cardiovascular disease related biomarkers: a review of clinical trials. Crit. Rev. Food Sci. Nutr. 59, 2061–2071 (2019).
Rimm, E. B. et al. Seafood long-chain n-3 polyunsaturated fatty acids and cardiovascular disease: a science advisory from the American Heart Association. Circulation 138, e35–e47 (2018).
Kim, K. et al. Role of total, red, processed, and white meat consumption in stroke incidence and mortality: a systematic review and meta-analysis of prospective cohort studies. J. Am. Heart Assoc. 6, e005983 (2017).
Dairy and alternatives in your diet. NHS https://www.nhs.uk/live-well/eat-well/milk-and-dairy-nutrition/ (2018).
Matthews, J. N., Altman, D. G., Campbell, M. J. & Royston, P. Analysis of serial measurements in medical research. BMJ 300, 230–235 (1990).
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Pasolli, E., Truong, D. T., Malik, F., Waldron, L. & Segata, N. Machine learning meta-analysis of large metagenomic datasets: tools and biological insights. PLoS Comput. Biol. 12, e1004977 (2016).
Oksanen, J. et al. Vegan: Community ecology package. R package v.1.17-4 https://cran.r-project.org/web/packages/vegan/index.html (2010).
Costea, P. I. et al. Subspecies in the global human gut microbiome. Mol. Syst. Biol. 13, 960 (2017).
Dhakan, D. B. et al. The unique composition of Indian gut microbiome, gene catalogue, and associated fecal metabolome deciphered using multi-omics approaches. Gigascience 8, giz004 (2019).
Hansen, L. B. S. et al. A low-gluten diet induces changes in the intestinal microbiome of healthy Danish adults. Nat. Commun. 9, 4630 (2018).
Jie, Z. et al. The gut microbiome in atherosclerotic cardiovascular disease. Nat. Commun. 8, 845 (2017).
Zeller, G. et al. Potential of fecal microbiota for early-stage detection of colorectal cancer. Mol. Syst. Biol. 10, 766 (2014).
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.
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.
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.
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.
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.
Alpha diversity measures and their correlations with personal factors, habitual diet, fasting and postprandial markers
List of foods and their assigned food groups and health classification and nutrients normalized by daily energy intake that is calorie adjusted
Plant-based Diet Index, Healthy Food Diversity index, animal groups, and Alternate Mediterranean score description
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
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
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
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)
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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