Insulin resistance is a forerunner state of ischaemic cardiovascular disease and type 2 diabetes. Here we show how the human gut microbiome impacts the serum metabolome and associates with insulin resistance in 277 non-diabetic Danish individuals. The serum metabolome of insulin-resistant individuals is characterized by increased levels of branched-chain amino acids (BCAAs), which correlate with a gut microbiome that has an enriched biosynthetic potential for BCAAs and is deprived of genes encoding bacterial inward transporters for these amino acids. Prevotella copri and Bacteroides vulgatus are identified as the main species driving the association between biosynthesis of BCAAs and insulin resistance, and in mice we demonstrate that P. copri can induce insulin resistance, aggravate glucose intolerance and augment circulating levels of BCAAs. Our findings suggest that microbial targets may have the potential to diminish insulin resistance and reduce the incidence of common metabolic and cardiovascular disorders.
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European Nucleotide Archive
Raw nucleotide data can be found for all samples used in the study in the European Nucelotide Archive (accession numbers: ERP003612, ERP004605, MetaHIT samples; ERP014713, 16S rDNA from mouse experiment). The metabolomics data has been deposited in the MetaboLights database (http://www.ebi.ac.uk/metabolights/) under accession number: MTBLS351.
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The authors wish to thank S. Castillo, M. Sysi-Aho, A. Ruskeepää, U. Lahtinen, A. Forman, T. Lorentzen, B. Andreasen, G. J. Klavsen, M. J. Nielsen, B. Pedersen, M. T. F. Damgaard and L. B. Rosholm for technical assistance, D. R. Mende and J. R. Kultima for their help in data processing and tool provision, C. Ekstøm and S. Ditlevsen for statistical and mathematical assistance, respectively, and T. F. Toldsted and G. Lademann for management assistance. C. B. Newgard and A. Vaag are thanked for critical comments on our manuscript. The present study is initiated and funded by the European Community’s Seventh Framework Program (FP7/2007-2013): MetaHIT, grant agreement HEALTH-F4-2007-201052. Additional funding came from The Lundbeck Foundation Centre for Applied Medical Genomics in Personalized Disease Prediction, Prevention and Care (LuCamp, http://www.lucamp.org), Metagenopolis grant ANR-11-DPBS-0001 and FP7 METACARDIS HEALTH-F4-2012-305312. J.R., S.V.-S. and G.F. are funded by the Rega institute for Medical Research, KU Leuven, the Agency for Innovation by Science and Technology (IWT), Marie Curie Actions FP7 People COFUND - Proposal 267139 and the Fund for Scientific Research Flanders (FWO). M.O. is also supported by Academy of Finland (Centre of Excellence in Molecular Systems Immunology and Physiology Research, Decision No. 250114) and EU FP7 Project TORNADO (project 222720). F.H. has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 600375. The Center for Biological Sequence Analysis and the Novo Nordisk Foundation Center for Basic Metabolic Research have in addition received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no. 115317 (DIRECT), resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007-2013) and EFPIA companies’ in kind contribution. The Novo Nordisk Foundation Center for Protein Research received funding from the Novo Nordisk Foundation (grant agreement NNF14CC0001). The Novo Nordisk Foundation Center for Basic Metabolic Research is an independent Research Center at the University of Copenhagen partially funded by an unrestricted donation from the Novo Nordisk Foundation (http://www.metabol.ku.dk).
The authors declare no competing financial interests.
Reviewer Information Nature thanks J. Garrett, L. Groop, C. Lozupone, G. Siuzdak and the other anonymous reviewer(s) for their contribution to the peer review of this work.
Extended data figures and tables
Extended Data Figure 1 Distributions of continuous physiological traits for the 291 non-diabetic individuals, 75 type 2 diabetes patients and 75 matched non-diabetic controls.
An overview of the same traits is shown in Supplementary Table 1. The 75 non-diabetic controls are a subset of the 291 non-diabetic individuals matched to the type 2 diabetes patients by age, sex and BMI and used for comparative analyses.
Extended Data Figure 2 The number of metabolite clusters, species and microbiome functional modules significantly associated with HOMA-IR, HOMA-IRBMIadj, and metabolic syndrome.
a–c, Venn diagrams resuming the number of serum metabolite clusters (a), species (b) and microbiome functional modules (c) that are associated with the three HOMA-IR, HOMA-IRBMIadj and metabolic syndrome phenotypes at FDR < 0.1. d, The number of microbiome functional modules associated with HOMA-IR, gene richness and HOMA-IRGeneRichness.adj. The metabolite cluster associations are based on all 291 non-diabetic individuals whereas the species and KEGG module associations were estimated on the 277 non-diabetic individuals with microbiome data. MetS, metabolic syndrome.
Extended Data Figure 3 Fine-grained correlation profile of fasting serum metabolite clusters and physiological traits in 291 non-diabetic individuals.
Spearman correlations between all fasting serum metabolite clusters (top panel, molecular lipids; bottom panel, polar metabolites) and clinical phenotypes. The metabolites in each panel are clustered by their correlation profile (see dendrogram). The colour represents positive (blue) or negative (red) correlations and FDRs are denoted: +, FDR < 0.1; *, FDR < 0.01; **, FDR < 0.001. The names of the 19 metabolite clusters making up the IR- and IS-metabotypes are highlighted with blue (IR-metabotype) and red (IS-metabotype), respectively.
Extended Data Figure 4 Fine-grained correlation profile of IR- and metabolic-syndrome-associated microbial species and physiological traits in 277 non-diabetic individuals.
Spearman correlations between continuous physiological traits and the 81 species significantly associated (FDR < 0.1) with HOMA-IR, HOMA-IRBMIadj or metabolic syndrome phenotypes (Extended Data Fig. 1). The species are clustered by their correlation profile. The colour represents positive (blue) or negative (red) correlations and FDRs are denoted: +, FDR < 0.1; *, FDR < 0.01; **, FDR < 0.001.
Extended Data Figure 5 Correlations between IR- and metabolic syndrome-associated microbial species and fasting serum metabolite clusters in 277 non-diabetic individuals.
Spearman correlations between species that were significantly associated (FDR < 0.1) with HOMA-IR, HOMA-IRBMIadj or metabolic syndrome phenotypes and the 19 metabolite clusters making up the IR- and IS-metabotypes. The colour represents positive (blue) or negative (red) correlations and FDRs are denoted: +, FDR < 0.1; *, FDR < 0.01; **, FDR < 0.001. The left sidebar represents positive (blue) or negative (red) correlations between the species and the indicated phenotypes (FDR < 0.1). MetS, metabolic syndrome.
Extended Data Figure 6 Abundances of P. copri and B. vulgatus anti-correlate and their combined abundance correlates with HOMA-IR in 277 non-diabetic individuals.
a, b, The abundances of T2DCAG00385: P. copri (orange) and T2DCAG00050: B. vulgatus (blue), shown for all non-diabetic individuals arranged by decreasing P. copri abundance and increasing B. vulgatus abundance (a), and arranged by total abundance of both species with HOMA-IR levels shown above (b).
Extended Data Figure 7 Correlations between microbial species and both HOMA-IR and the BCAA-containing metabolite cluster (M10) in 277 non-diabetic individuals.
a, b, Spearman correlations between species and both the BCAA-containing metabolite cluster (a, M10) and insulin resistance (b, HOMA-IR) in individuals with detectable abundances of the respective species. FDRs of 0.1 and 0.05 are denoted with dotted and dashed lines, respectively. Colour intensity represents mean species abundance in individuals where the species was observed.
Extended Data Figure 8 Microbial driver species for associations between microbiome functional modules and insulin resistance in 277 non-diabetic individuals.
The five most important microbial species driving the association between the indicated microbiome functional modules and insulin resistance (HOMA-IR) are shown (see Supplementary Table 9 for effect sizes). Each species is highlighted with a different colour. The left sidebar represents positive (blue) or negative (red) associations between the functional modules and the indicated phenotypes (FDR <0.1). MetS, metabolic syndrome.
Extended Data Figure 9 An in-depth view of the microbial BCAA biosynthesis pathway and BCAA inward transport system, illustrating the correlations between microbial KEGG orthologous gene groups and serum metabolites with human insulin resistance.
KEGG orthologous gene groups (squares) and metabolites (circles) are coloured by their Spearman correlation with HOMA-IR in the non-diabetic individuals (n = 277 for KEGG orthologous gene groups, n = 291 for metabolites), or coloured grey if no information was available. The network is adapted from KEGG pathway maps (pathways hsa00290 and hsa02010).
Extended Data Figure 10 Oral glucose tolerance test after two weeks of P. copri or sham gavaging and 16S rDNA amplicon sequencing of faecal samples from mice after three weeks of treatment with P.copri or sham.
a, Oral glucose tolerance test. The P. copri-gavaged mice (n = 12) had significantly higher serum glucose levels compared to sham-gavaged controls (n = 12, P = 0.02, Mann–Whitney U-test for AUC) after two weeks of the gavage challenge. Mean ± s.e.m. is depicted. Stars indicate significant differences at individual time points (repeated measurements two-way ANOVA): *P < 0.05; **P < 0.01. b, Plasma insulin was measured before and 15 min post glucose bolus, P = 0.80, Mann–Whitney U-test, bars represents mean ± s.e.m., n = 12. c, Microbiota taxa summary plots on family level after the two given time points, that is, pre high-fat diet (HFD) and post HFD plus gavage. HFD feeding significantly changed the microbial community (adonis P = 0.001) while bacterial gavaging had negligible effect. Data represent mean values. n = 12 per group (one sample from the sham group at time point −3 weeks did not go successfully through the 16S rDNA amplicon sequencing and is therefore represented by 11 samples). ‘Unclassified’ refers to reads that could not be classified to any taxonomy. ‘Other’ refers to reads that could not be classified at family level. d, P. copri changes in mouse faecal samples after P. copri gavaging as determined by quantitative PCR. Bars show the change in P. copri levels relative to before P. copri or sham challenge (bars represents mean ± s.e.m., n = 12, P = 0.0058, Mann–Whitney U-test).
This file contains full legends for Supplementary Tables 1-17, Supplementary Results and Discussion and a list of additional MetaHIT consortium members. (PDF 531 kb)
This file contains Supplementary Tables 1-17 – see Supplementary Information document for full legends. (XLSX 647 kb)
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Pedersen, H., Gudmundsdottir, V., Nielsen, H. et al. Human gut microbes impact host serum metabolome and insulin sensitivity. Nature 535, 376–381 (2016). https://doi.org/10.1038/nature18646
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