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

Data deposits

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).

Author information

Author notes

    • Helle Krogh Pedersen
    • , Valborg Gudmundsdottir
    • , Henrik Bjørn Nielsen
    • , Tuulia Hyotylainen
    •  & Trine Nielsen

    These authors contributed equally to this work.

    • MetaHIT Consortium

    Lists of participants and their affiliations appear in the Supplementary Information.


  1. Center for Biological Sequence Analysis, Dept. of Systems Biology, Technical University of Denmark, DK-2800 Kongens Lyngby, Denmark

    • Helle Krogh Pedersen
    • , Valborg Gudmundsdottir
    • , Henrik Bjørn Nielsen
    • , Damian R. Plichta
    • , Lars I. Hellgren
    • , Susanne Brix
    •  & Søren Brunak
  2. University of Örebro, SE-702 81 Örebro, Sweden

    • Tuulia Hyotylainen
  3. Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland

    • Tuulia Hyotylainen
    •  & Matej Oresic
  4. VTT Technical Research Centre of Finland, FI-02044 Espoo, Finland

    • Tuulia Hyotylainen
    • , Ismo Mattila
    • , Päivi Pöhö
    •  & Matej Oresic
  5. The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark

    • Trine Nielsen
    • , Manimozhiyan Arumugam
    • , Torben Hansen
    •  & Oluf Pedersen
  6. Laboratory of Genomics and Molecular Biomedicine, Department of Biology, University of Copenhagen, DK-2100 Copenhagen, Denmark

    • Benjamin A. H. Jensen
    • , Jacob Bak Holm
    • , Karsten Kristiansen
    •  & Jun Wang
  7. European Molecular Biology Laboratory, 69117 Heidelberg, Germany

    • Kristoffer Forslund
    • , Falk Hildebrand
    • , Shinichi Sunagawa
    •  & Peer Bork
  8. Department of Bioscience Engineering, Vrije Universiteit Brussel, 1050 Brussels, Belgium

    • Falk Hildebrand
    •  & Jeroen Raes
  9. Center for the Biology of Disease, VIB, 3000 Leuven, Belgium

    • Falk Hildebrand
    • , Gwen Falony
    • , Sara Vieira-Silva
    •  & Jeroen Raes
  10. MGP MetaGénoPolis, INRA, Université Paris-Saclay, 78350 Jouy en Josas, France

    • Edi Prifti
    • , Emmanuelle Le Chatelier
    • , Florence Levenez
    • , Joel Doré
    •  & S. Dusko Ehrlich
  11. Institute of Cardiometabolism and Nutrition (ICAN), 75013 Paris, France

    • Edi Prifti
  12. Department of Microbiology and Immunology, Rega Institute, KU Leuven, 3000 Leuven, Belgium

    • Gwen Falony
    • , Sara Vieira-Silva
    •  & Jeroen Raes
  13. Micalis Institute, INRA, AgroParisTech, Université Paris-Saclay, 78350 Jouy-en-Josas, France

    • Joel Doré
  14. Steno Diabetes Center, DK-2820 Gentofte, Denmark

    • Ismo Mattila
    • , Kajetan Trošt
    •  & Matej Oresic
  15. Faculty of Pharmacy, University of Helsinki, FI-00014 Helsinki, Finland

    • Päivi Pöhö
  16. Institute of Microbiology, ETH Zurich, CH-8092 Zurich, Switzerland

    • Shinichi Sunagawa
  17. Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark

    • Torben Jørgensen
    •  & Oluf Pedersen
  18. Research Centre for Prevention and Health, Centre for Health, Capital region, Glostrup Hospital, DK-2600 Glostrup, Denmark

    • Torben Jørgensen
  19. BGI-Shenzhen, 518083 Shenzhen, China

    • Karsten Kristiansen
    •  & Jun Wang
  20. Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah, Saudi Arabia

    • Jun Wang
  21. Macau University of Science and Technology, Avenida Wai long, Taipa, Macau

    • Jun Wang
  22. Department of Medicine and State Key Laboratory of Pharmaceutical Biotechnology, University of Hong Kong, Hong Kong

    • Jun Wang
  23. Faculty of Health Sciences, University of Southern Denmark, DK-5000 Odense, Denmark

    • Torben Hansen
  24. Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, 69120 Heidelberg, Germany

    • Peer Bork
  25. Max Delbrück Centre for Molecular Medicine, D-13125 Berlin, Germany

    • Peer Bork
  26. Department of Bioinformatics, University of Wuerzburg, D-97074 Würzburg, Germany

    • Peer Bork
  27. Novo Nordisk Foundation Center for Protein Research, Disease Systems Biology, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark

    • Søren Brunak
  28. King’s College London, Centre for Host–Microbiome Interactions, Dental Institute Central Office, Guy’s Hospital, SE1 9RT London, UK

    • S. Dusko Ehrlich


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O.P., S.D.E. and P.B. devised the study. O.P., S.D.E., S.Bru. and H.B.N. designed the study protocol and supervised all parts of the project. H.B.N. and S.Bru. led the data integration, the bioinformatics analyses and did the primary interpretation of analytical outcomes in close collaboration with H.K.P. and V.G. H.K.P., V.G., B.A.H.J., T.Hy., E.P., D.P., S.S., F.H., K.F., J.B.H. and S.V.-S. performed data analyses. T.N., T.Ha. and O.P. composed the clinical protocol, carried out phenotyping of study participants including collection of biological samples and physiological data generation and interpretation. F.L. performed DNA extraction and J.D. supervised DNA extraction. J.W. supervised DNA sequencing and gene profiling. M.O., T.Hy., I.M., K.T. and P.P. performed profiling of serum metabolomics and serum lipidomics. B.A.H.J., K.K., J.B.H. and S.Bri. performed mouse experiments. H.B.N., H.K.P. and V.G. drafted the first versions of the paper with critical and substantial contributions from O.P., S.Bru., T.N., J.R., K.F., F.H., M.O., L.I.H., D.P., G.F., P.B. and S.D.E. All authors approved the final version. MetaHIT consortium members provided support and constructive criticism throughout MetaHIT research operations.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Søren Brunak or Matej Oresic or S. Dusko Ehrlich or Oluf Pedersen.

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

Extended data figures

  1. 1.

    Distributions of continuous physiological traits for the 291 non-diabetic individuals, 75 type 2 diabetes patients and 75 matched non-diabetic controls.

  2. 2.

    The number of metabolite clusters, species and microbiome functional modules significantly associated with HOMA-IR, HOMA-IRBMIadj, and metabolic syndrome.

  3. 3.

    Fine-grained correlation profile of fasting serum metabolite clusters and physiological traits in 291 non-diabetic individuals.

  4. 4.

    Fine-grained correlation profile of IR- and metabolic-syndrome-associated microbial species and physiological traits in 277 non-diabetic individuals.

  5. 5.

    Correlations between IR- and metabolic syndrome-associated microbial species and fasting serum metabolite clusters in 277 non-diabetic individuals.

  6. 6.

    Abundances of P. copri and B. vulgatus anti-correlate and their combined abundance correlates with HOMA-IR in 277 non-diabetic individuals.

  7. 7.

    Correlations between microbial species and both HOMA-IR and the BCAA-containing metabolite cluster (M10) in 277 non-diabetic individuals.

  8. 8.

    Microbial driver species for associations between microbiome functional modules and insulin resistance in 277 non-diabetic individuals.

  9. 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.

  10. 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.

Supplementary information

PDF files

  1. 1.

    Supplementary Information

    This file contains full legends for Supplementary Tables 1-17, Supplementary Results and Discussion and a list of additional MetaHIT consortium members.

Excel files

  1. 1.

    Supplementary Data

    This file contains Supplementary Tables 1-17 – see Supplementary Information document for full legends.

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