Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug

  • Nature Medicine volume 23, pages 850858 (2017)
  • doi:10.1038/nm.4345
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Metformin is widely used in the treatment of type 2 diabetes (T2D), but its mechanism of action is poorly defined. Recent evidence implicates the gut microbiota as a site of metformin action. In a double-blind study, we randomized individuals with treatment-naive T2D to placebo or metformin for 4 months and showed that metformin had strong effects on the gut microbiome. These results were verified in a subset of the placebo group that switched to metformin 6 months after the start of the trial. Transfer of fecal samples (obtained before and 4 months after treatment) from metformin-treated donors to germ-free mice showed that glucose tolerance was improved in mice that received metformin-altered microbiota. By directly investigating metformin–microbiota interactions in a gut simulator, we showed that metformin affected pathways with common biological functions in species from two different phyla, and many of the metformin-regulated genes in these species encoded metalloproteins or metal transporters. Our findings provide support for the notion that altered gut microbiota mediates some of metformin's antidiabetic effects.

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We thank C. Arvidsson, S. Nordin-Larsson, C. Wennberg, and U. Enqvist for superb mouse husbandry. The administrative and technical help of J.M. Moreno Navarrete, E. Huertos, M. Sabater, and O. Rovira is also acknowledged. The strain Akkermansia muciniphila DSM22959 was kindly provided by W. de Vos (Wageningen University and Helsinki University). The strain Bifidobacterium adolescentis L2-32 was kindly provided by K. Scott (The Rowett Institute of Nutrition and Health, University of Aberdeen). Whole-genome shotgun sequencing was performed at the Genomics Core Facility at the Sahlgrenska Academy, University of Gothenburg. The computations for metagenomics analyses were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) through Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX). This study was supported by the Swedish Diabetes Foundation; Swedish Research Council; Swedish Heart Lung Foundation; Torsten Söderberg's Foundation; Göran Gustafsson's Foundation; Inga Britt and Arne Lundberg's Foundation; Swedish Foundation for Strategic Research; Knut and Alice Wallenberg Foundation; the Novo Nordisk Foundation; the regional agreement on medical training and clinical research (ALF) between Region Västra Götaland and Sahlgrenska University Hospital; the Ministerio de Economía y Competitividad (PI11-00214 and PI15/01934); and FEDER funds. CIBEROBN Fisiopatología de la Obesidad y Nutrición is an initiative from the Instituto de Salud Carlos III from Spain. M.P.-F. is funded by the Obra Social Fundación la Caixa fellowship under the Severo Ochoa 2013 program. J.M.M. was supported by the Sara Borrell Fellowship from the Instituto Carlos III, EFSD/Lilly Research Fellowship and Beatriu de Pinós Fellowship from the Agency for Management of University and Research Grants (AGAUR). F.B. is a recipient of ERC Consolidator Grant (European Research Council, Consolidator grant 615362—METABASE).

Author information

Author notes

    • Hao Wu
    •  & Eduardo Esteve

    These authors contributed equally to this work.


  1. Department of Molecular and Clinical Medicine, Wallenberg Laboratory, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.

    • Hao Wu
    • , Valentina Tremaroli
    • , Muhammad Tanweer Khan
    • , Robert Caesar
    • , Louise Mannerås-Holm
    • , Marcus Ståhlman
    • , Lisa M Olsson
    • , Rosie Perkins
    •  & Fredrik Bäckhed
  2. Department of Diabetes, Endocrinology and Nutrition, Institut d'Investigació Biomèdica de Girona, Hospital Josep Trueta, Girona, Spain.

    • Eduardo Esteve
    • , Gemma Xifra
    • , Wifredo Ricart
    •  & José Manuel Fernàndez-Real
  3. Departament de Medicina, Facultat de Medicina, University of Girona, Girona, Spain.

    • Eduardo Esteve
    • , Gemma Xifra
    • , Wifredo Ricart
    •  & José Manuel Fernàndez-Real
  4. Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Madrid, Spain.

    • Eduardo Esteve
    • , Gemma Xifra
    • , Wifredo Ricart
    •  & José Manuel Fernàndez-Real
  5. IRSD, Université de Toulouse, INSERM, INRA, ENVT, UPS, Toulouse, France.

    • Matteo Serino
  6. Barcelona Supercomputing Center (BSC), Joint BSC–CRG–IRB Research Program in Computational Biology, Barcelona, Spain.

    • Mercè Planas-Fèlix
    • , Josep M Mercader
    •  & David Torrents
  7. Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.

    • David Torrents
  8. Institut National de la Santé et de la Recherche Médicale (INSERM), Toulouse, France.

    • Rémy Burcelin
  9. Université Paul Sabatier (UPS), Unité Mixte de Recherche 1048, Institut de Maladies Métaboliques et Cardiovasculaires, Toulouse, France.

    • Rémy Burcelin
  10. Sahlgrenska University Hospital, Gothenburg, Sweden.

    • Fredrik Bäckhed
  11. Novo Nordisk Foundation Center for Basic Metabolic Research, Section for Metabolic Receptology and Enteroendocrinology, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark.

    • Fredrik Bäckhed


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F.B., J.M.F.-R., V.T., and R.B. conceived and designed the study. E.E., M.P.-F., G.X., J.M.M., D.T., W.R., and J.M.F.-R. recruited cohort individuals and performed the clinical study. H.W., V.T., and F.B. conducted the bioinformatics study, analyzed all results unless otherwise indicated. H.W., V.T., R.P., and F.B. wrote the paper. M.T.K. performed the in vitro gut simulator and bacterial growth experiments. R.C. and L.M.-H. performed and analyzed the fecal microbiota transplantation experiments. M. Ståhlman performed the metabolomics experiments. M. Serino and V.T. extracted the bacterial DNA and discussed the results. L.M.O. and V.T. extracted the bacterial RNA and coordinated the metagenomics and metatranscriptomics sequencing. All authors commented on the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to José Manuel Fernàndez-Real or Fredrik Bäckhed.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–4.

Excel files

  1. 1.

    Supplementary Table 1

    Diet information for the 40 subjects with T2D enrolled in this study.

  2. 2.

    Supplementary Table 2

    Summary of quality filtering and mapping to both the MEDUSA gene and genome catalogues.

  3. 3.

    Supplementary Table 3

    List of microbial strains and genera in the fecal metagenomes that were significantly increased or decreased by metformin treatment across all sampling points.

  4. 4.

    Supplementary Table 4

    List of KOs that were significantly affected in the fecal metagenome across all sampling points and corresponding KEGG pathway annotations.

  5. 5.

    Supplementary Table 5

    List of bacterial strains that were significantly increased or decreased in the gut simulator based on WGS sequencing at both DNA and RNA levels.

  6. 6.

    Supplementary Table 6

    List of KOs that were significantly increased or decreased in the metagenome and metatranscriptome from the in vitro gut simulator after exposure to metformin using fecal samples from donors 13 and 49 and corresponding KEGG pathway annotations.

  7. 7.

    Supplementary Table 7

    Annotation of the 207 and 200 genes in A. muciniphila and B. wadsworthia, respectively, that were significantly regulated by exposure to metformin in the in vitro gut simulator.