Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

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 850–858 (2017)Cite this article

Subjects

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Metformin treatment promotes rapid changes in the composition of the gut microbiota.
Figure 2: Metformin treatment promotes the growth of gut bacteria.
Figure 3: Metformin-altered microbiota improves glucose tolerance.
Figure 4: Metformin treatment promotes functional shifts in the gut microbiota.
Figure 5: Direct metformin–microbiota interactions identified using an in vitro gut simulator.

Similar content being viewed by others

Accession codes

Primary accessions

Sequence Read Archive

References

  1. Nathan, D.M. et al. Medical management of hyperglycemia in type 2 diabetes: a consensus algorithm for the initiation and adjustment of therapy: a consensus statement of the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care 32, 193–203 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Pernicova, I. & Korbonits, M. Metformin—mode of action and clinical implications for diabetes and cancer. Nat. Rev. Endocrinol. 10, 143–156 (2014).

    Article  CAS  PubMed  Google Scholar 

  3. Zhou, G. et al. Role of AMP-activated protein kinase in mechanism of metformin action. J. Clin. Invest. 108, 1167–1174 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Shaw, R.J. et al. The kinase LKB1 mediates glucose homeostasis in liver and therapeutic effects of metformin. Science 310, 1642–1646 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Fullerton, M.D. et al. Single phosphorylation sites in Acc1 and Acc2 regulate lipid homeostasis and the insulin-sensitizing effects of metformin. Nat. Med. 19, 1649–1654 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Foretz, M. et al. Metformin inhibits hepatic gluconeogenesis in mice independently of the LKB1/AMPK pathway via a decrease in hepatic energy state. J. Clin. Invest. 120, 2355–2369 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Madiraju, A.K. et al. Metformin suppresses gluconeogenesis by inhibiting mitochondrial glycerophosphate dehydrogenase. Nature 510, 542–546 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Miller, R.A. et al. Biguanides suppress hepatic glucagon signalling by decreasing production of cyclic AMP. Nature 494, 256–260 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. McCreight, L.J., Bailey, C.J. & Pearson, E.R. Metformin and the gastrointestinal tract. Diabetologia 59, 426–435 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Duca, F.A. et al. Metformin activates a duodenal Ampk-dependent pathway to lower hepatic glucose production in rats. Nat. Med. 21, 506–511 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Stepensky, D., Friedman, M., Raz, I. & Hoffman, A. Pharmacokinetic-pharmacodynamic analysis of the glucose-lowering effect of metformin in diabetic rats reveals first-pass pharmacodynamic effect. Drug Metab. Dispos. 30, 861–868 (2002).

    Article  CAS  PubMed  Google Scholar 

  12. Buse, J.B. et al. The primary glucose-lowering effect of metformin resides in the gut, not the circulation. Results from short-term pharmacokinetic and 12-week dose-ranging studies. Diabetes Care 39, 198–205 (2016).

    Article  CAS  PubMed  Google Scholar 

  13. Shin, N.R. et al. An increase in the Akkermansia spp. population induced by metformin treatment improves glucose homeostasis in diet-induced obese mice. Gut 63, 727–735 (2014).

    Article  CAS  PubMed  Google Scholar 

  14. Zhang, X. et al. Modulation of gut microbiota by berberine and metformin during the treatment of high-fat diet-induced obesity in rats. Sci. Rep. 5, 14405 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lee, H. & Ko, G. Effect of metformin on metabolic improvement and gut microbiota. Appl. Environ. Microbiol. 80, 5935–5943 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Karlsson, F.H. et al. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498, 99–103 (2013).

    Article  CAS  PubMed  Google Scholar 

  17. Forslund, K. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262–266 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. de la Cuesta-Zuluaga, J. et al. Metformin is associated with higher relative abundance of mucin-degrading Akkermansia muciniphila and several short-chain fatty acid–producing microbiota in the gut. Diabetes Care 40, 54–62 (2017).

    Article  CAS  PubMed  Google Scholar 

  19. Karlsson, F.H., Nookaew, I. & Nielsen, J. Metagenomic data utilization and analysis (MEDUSA) and construction of a global gut microbial gene catalogue. PLoS Comput. Biol. 10, e1003706 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Everard, A. et al. Cross-talk between Akkermansia muciniphila and intestinal epithelium controls diet-induced obesity. Proc. Natl. Acad. Sci. USA 110, 9066–9071 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Plovier, H. et al. A purified membrane protein from Akkermansia muciniphila or the pasteurized bacterium improves metabolism in obese and diabetic mice. Nat. Med. 23, 107–113 (2017).

    Article  CAS  PubMed  Google Scholar 

  22. Dao, M.C. et al. Akkermansia muciniphila and improved metabolic health during a dietary intervention in obesity: relationship with gut microbiome richness and ecology. Gut 65, 426–436 (2016).

    Article  CAS  PubMed  Google Scholar 

  23. Park, S.K., Kim, M.S., Roh, S.W. & Bae, J.W. Blautia stercoris sp. nov., isolated from human faeces. Int. J. Syst. Evol. Microbiol. 62, 776–779 (2012).

    Article  CAS  PubMed  Google Scholar 

  24. Korem, T. et al. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples. Science 349, 1101–1106 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wong, J.M., de Souza, R., Kendall, C.W., Emam, A. & Jenkins, D.J. Colonic health: fermentation and short chain fatty acids. J. Clin. Gastroenterol. 40, 235–243 (2006).

    Article  CAS  PubMed  Google Scholar 

  27. Koh, A., De Vadder, F., Kovatcheva-Datchary, P. & Bäckhed, F. From dietary fiber to host physiology: short-chain fatty acids as key bacterial metabolites. Cell 165, 1332–1345 (2016).

    Article  CAS  PubMed  Google Scholar 

  28. Ridlon, J.M., Harris, S.C., Bhowmik, S., Kang, D.J. & Hylemon, P.B. Consequences of bile salt biotransformations by intestinal bacteria. Gut Microbes 7, 22–39 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Caspary, W.F. et al. Alteration of bile acid metabolism and vitamin-B12-absorption in diabetics on biguanides. Diabetologia 13, 187–193 (1977).

    Article  CAS  PubMed  Google Scholar 

  30. Scarpello, J.H., Hodgson, E. & Howlett, H.C. Effect of metformin on bile salt circulation and intestinal motility in type 2 diabetes mellitus. Diabet. Med. 15, 651–656 (1998).

    Article  CAS  PubMed  Google Scholar 

  31. Zhernakova, A. et al. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352, 565–569 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Cotillard, A. et al. Dietary intervention impact on gut microbial gene richness. Nature 500, 585–588 (2013).

    Article  CAS  PubMed  Google Scholar 

  33. Chen, J., Wang, R., Li, X.F. & Wang, R.L. Bifidobacterium adolescentis supplementation ameliorates visceral fat accumulation and insulin sensitivity in an experimental model of the metabolic syndrome. Br. J. Nutr. 107, 1429–1434 (2012).

    Article  CAS  PubMed  Google Scholar 

  34. Desai, M.S. et al. A dietary fiber-deprived gut microbiota degrades the colonic mucus barrier and enhances pathogen susceptibility. Cell 167, 1339–1353.e21 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Roopchand, D.E. et al. Dietary polyphenols promote growth of the gut bacterium Akkermansia muciniphila and attenuate high-fat diet-induced metabolic syndrome. Diabetes 64, 2847–2858 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Anhê, F.F. et al. A polyphenol-rich cranberry extract protects from diet-induced obesity, insulin resistance and intestinal inflammation in association with increased Akkermansia spp. population in the gut microbiota of mice. Gut 64, 872–883 (2015).

    Article  CAS  PubMed  Google Scholar 

  37. Greer, R.L. et al. Akkermansia muciniphila mediates negative effects of IFNγ on glucose metabolism. Nat. Commun. 7, 13329 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zhang, H., Sparks, J.B., Karyala, S.V., Settlage, R. & Luo, X.M. Host adaptive immunity alters gut microbiota. ISME J. 9, 770–781 (2015).

    Article  CAS  PubMed  Google Scholar 

  39. Collado, M.C., Derrien, M., Isolauri, E., de Vos, W.M. & Salminen, S. Intestinal integrity and Akkermansia muciniphila, a mucin-degrading member of the intestinal microbiota present in infants, adults, and the elderly. Appl. Environ. Microbiol. 73, 7767–7770 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Kong, F. et al. Gut microbiota signatures of longevity. Curr. Biol. 26, R832–R833 (2016).

    Article  CAS  PubMed  Google Scholar 

  41. Tremaroli, V. et al. Roux-en-Y gastric bypass and vertical banded gastroplasty induce long-term changes on the human gut microbiome contributing to fat mass regulation. Cell Metab. 22, 228–238 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Everard, A. et al. Microbiome of prebiotic-treated mice reveals novel targets involved in host response during obesity. ISME J. 8, 2116–2130 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Fernández-Real, J.M. & Manco, M. Effects of iron overload on chronic metabolic diseases. Lancet Diabetes Endocrinol. 2, 513–526 (2014).

    Article  CAS  PubMed  Google Scholar 

  44. Logie, L. et al. Cellular responses to the metal-binding properties of metformin. Diabetes 61, 1423–1433 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Wahlström, A. et al. Induction of farnesoid X receptor signaling in germ-free mice colonized with a human microbiota. J. Lipid Res. 58, 412–419 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wahlström, A., Sayin, S.I., Marschall, H.U. & Bäckhed, F. Intestinal crosstalk between bile acids and microbiota and its impact on host metabolism. Cell Metab. 24, 41–50 (2016).

    Article  CAS  PubMed  Google Scholar 

  47. Schaap, F.G., Trauner, M. & Jansen, P.L. Bile acid receptors as targets for drug development. Nat. Rev. Gastroenterol. Hepatol. 11, 55–67 (2014).

    Article  CAS  PubMed  Google Scholar 

  48. De Vadder, F. et al. Microbiota-produced succinate improves glucose homeostasis via intestinal gluconeogenesis. Cell Metab. 24, 151–157 (2016).

    Article  CAS  PubMed  Google Scholar 

  49. Bäckhed, F., Manchester, J.K., Semenkovich, C.F. & Gordon, J.I. Mechanisms underlying the resistance to diet-induced obesity in germ-free mice. Proc. Natl. Acad. Sci. USA 104, 979–984 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Vioque, J. et al. Reproducibility and validity of a food frequency questionnaire among pregnant women in a Mediterranean area. Nutr. J. 12, 26 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 33 (Suppl. 1), S62–S69 (2010).

  52. Langmead, B. & Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Love, M.I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    PubMed  PubMed Central  Google Scholar 

  54. Young, M.D., Wakefield, M.J., Smyth, G.K. & Oshlack, A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 11, R14 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Kindt, R. & Coe, R. Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies (World Agroforestry Centre, Nairobi, Kenya, 2005).

  56. McMurdie, P.J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Csárdi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal. Complex Syst. 1695, 1695 (2006).

    Google Scholar 

  58. Altschul, S.F., Gish, W., Miller, W., Myers, E.W. & Lipman, D.J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    Article  CAS  PubMed  Google Scholar 

  59. Tatusova, T., Ciufo, S., Fedorov, B., O'Neill, K. & Tolstoy, I. RefSeq microbial genomes database: new representation and annotation strategy. Nucleic Acids Res. 42, D553–D559 (2014).

    Article  CAS  PubMed  Google Scholar 

  60. Li, J. et al. An integrated catalog of reference genes in the human gut microbiome. Nat. Biotechnol. 32, 834–841 (2014).

    Article  CAS  PubMed  Google Scholar 

  61. Wichmann, A. et al. Microbial modulation of energy availability in the colon regulates intestinal transit. Cell Host Microbe 14, 582–590 (2013).

    Article  CAS  PubMed  Google Scholar 

  62. Lee, Y.S. et al. Insulin-like peptide 5 is a microbially regulated peptide that promotes hepatic glucose production. Mol. Metab. 5, 263–270 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Van den Abbeele, P. et al. Microbial community development in a dynamic gut model is reproducible, colon region specific, and selective for Bacteroidetes and Clostridium cluster IX. Appl. Environ. Microbiol. 76, 5237–5246 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Salonen, A. et al. Comparative analysis of fecal DNA extraction methods with phylogenetic microarray: effective recovery of bacterial and archaeal DNA using mechanical cell lysis. J. Microbiol. Methods 81, 127–134 (2010).

    Article  CAS  PubMed  Google Scholar 

  65. Murphy, N.R. & Hellwig, R.J. Improved nucleic acid organic extraction through use of a unique gel barrier material. Biotechniques 21, 934–936, 938–939 (1996).

    Article  CAS  PubMed  Google Scholar 

  66. Zoetendal, E.G. et al. Isolation of RNA from bacterial samples of the human gastrointestinal tract. Nat. Protoc. 1, 954–959 (2006).

    Article  CAS  PubMed  Google Scholar 

  67. Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize Implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).

    Article  CAS  PubMed  Google Scholar 

  68. Franceschini, A. et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41, D808–D815 (2013).

    Article  CAS  PubMed  Google Scholar 

  69. Team, R.C.R. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/ (2015).

  70. Champely, S. pwr: Basic Functions for Power Analysis. R package version 1.1-3. http://CRAN.R-project.org/package=pwr (2015).

  71. Leucht, S., Helfer, B., Gartlehner, G. & Davis, J.M. How effective are common medications: a perspective based on meta-analyses of major drugs. BMC Med. 13, 253 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.2-1 http://CRAN.R-project.org/package=vegan (2015).

  73. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

Download references

Acknowledgements

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

  1. Hao Wu and Eduardo Esteve: These authors contributed equally to this work.

Authors and Affiliations

  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

Authors
  1. Hao Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Eduardo Esteve
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Valentina Tremaroli
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Muhammad Tanweer Khan
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Robert Caesar
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Louise Mannerås-Holm
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Marcus Ståhlman
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Lisa M Olsson
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Matteo Serino
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Mercè Planas-Fèlix
    View author publications

    You can also search for this author inPubMed Google Scholar

  11. Gemma Xifra
    View author publications

    You can also search for this author inPubMed Google Scholar

  12. Josep M Mercader
    View author publications

    You can also search for this author inPubMed Google Scholar

  13. David Torrents
    View author publications

    You can also search for this author inPubMed Google Scholar

  14. Rémy Burcelin
    View author publications

    You can also search for this author inPubMed Google Scholar

  15. Wifredo Ricart
    View author publications

    You can also search for this author inPubMed Google Scholar

  16. Rosie Perkins
    View author publications

    You can also search for this author inPubMed Google Scholar

  17. José Manuel Fernàndez-Real
    View author publications

    You can also search for this author inPubMed Google Scholar

  18. Fredrik Bäckhed
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

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.

Corresponding authors

Correspondence to José Manuel Fernàndez-Real or Fredrik Bäckhed.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–4. (PDF 781 kb)

Supplementary Table 1

Diet information for the 40 subjects with T2D enrolled in this study. (XLSX 16 kb)

Supplementary Table 2

Summary of quality filtering and mapping to both the MEDUSA gene and genome catalogues. (XLSX 29 kb)

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. (XLSX 39 kb)

Supplementary Table 4

List of KOs that were significantly affected in the fecal metagenome across all sampling points and corresponding KEGG pathway annotations. (XLSX 224 kb)

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. (XLSX 15 kb)

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. (XLSX 243 kb)

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. (XLSX 69 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, H., Esteve, E., Tremaroli, V. et al. Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat Med 23, 850–858 (2017). https://doi.org/10.1038/nm.4345

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nm.4345

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Microbiology