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.

Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota

A Corrigendum to this article was published on 04 May 2017

Abstract

In recent years, several associations between common chronic human disorders and altered gut microbiome composition and function have been reported1,2. In most of these reports, treatment regimens were not controlled for and conclusions could thus be confounded by the effects of various drugs on the microbiota, which may obscure microbial causes, protective factors or diagnostically relevant signals. Our study addresses disease and drug signatures in the human gut microbiome of type 2 diabetes mellitus (T2D). Two previous quantitative gut metagenomics studies of T2D patients that were unstratified for treatment yielded divergent conclusions regarding its associated gut microbial dysbiosis3,4. Here we show, using 784 available human gut metagenomes, how antidiabetic medication confounds these results, and analyse in detail the effects of the most widely used antidiabetic drug metformin. We provide support for microbial mediation of the therapeutic effects of metformin through short-chain fatty acid production, as well as for potential microbiota-mediated mechanisms behind known intestinal adverse effects in the form of a relative increase in abundance of Escherichia species. Controlling for metformin treatment, we report a unified signature of gut microbiome shifts in T2D with a depletion of butyrate-producing taxa3,4. These in turn cause functional microbiome shifts, in part alleviated by metformin-induced changes. Overall, the present study emphasizes the need to disentangle gut microbiota signatures of specific human diseases from those of medication.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

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

Figure 1: Type 2 diabetes is confounded by metformin treatment.
Figure 2: Gut microbiome signatures in metformin-naive T2D and in T1D.
Figure 3: Impact of metformin on the human gut microbiome.

Similar content being viewed by others

Accession codes

Primary accessions

European Nucleotide Archive

Sequence Read Archive

Data deposits

Raw nucleotide data can be found for all samples used in the study in the Sequence Read Archive (accession numbers: SRA045646 and SRA050230, CHN samples) and the European Nucleotide Archive (accession numbers: ERP002469, SWE samples; ERA000116, ERP003612, ERP002061 and ERP004605, MHD samples).

References

  1. Shreiner, A. B., Kao, J. Y. & Young, V. B. The gut microbiome in health and in disease. Curr. Opin. Gastroenterol. 31, 69–75 (2015)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Cho, I. & Blaser, M. J. The human microbiome: at the interface of health and disease. Nature Rev. Genet. 13, 260–270 (2012)

    Article  CAS  PubMed  Google Scholar 

  3. Qin, J. et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55–60 (2012)

    Article  ADS  CAS  PubMed  Google Scholar 

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

    Article  ADS  CAS  PubMed  Google Scholar 

  5. Schellenberg, E. S., Dryden, D. M., Vandermeer, B., Ha, C. & Korownyk, C. Lifestyle interventions for patients with and at risk for type 2 diabetes: a systematic review and meta-analysis. Ann. Intern. Med. 159, 543–551 (2013)

    Article  PubMed  Google Scholar 

  6. Larsen, N. et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS ONE 5, e9085 (2010)

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  7. Zhang, X. et al. Human gut microbiota changes reveal the progression of glucose intolerance. PLoS ONE 8, e71108 (2013)

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. de Vos, W. M. & Nieuwdorp, M. Genomics: A gut prediction. Nature 498, 48–49 (2013)

    Article  ADS  CAS  PubMed  Google Scholar 

  9. 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 

  10. 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 

  11. Napolitano, A. et al. Novel gut-based pharmacology of metformin in patients with type 2 diabetes mellitus. PLoS ONE 9, e100778 (2014)

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  12. Le Chatelier, E. et al. Richness of human gut microbiome correlates with metabolic markers. Nature 500, 541–546 (2013)

    Article  CAS  PubMed  Google Scholar 

  13. Sato, J. et al. Gut dysbiosis and detection of “live gut bacteria” in blood of Japanese patients with type 2 diabetes. Diabetes Care 37, 2343–2350 (2014)

    Article  CAS  PubMed  Google Scholar 

  14. Cabreiro, F. et al. Metformin retards aging in C. elegans by altering microbial folate and methionine metabolism. Cell 153, 228–239 (2013)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Gerritsen, J. et al. Characterization of Romboutsia ilealis gen. nov., sp. nov., isolated from the gastro-intestinal tract of a rat, and proposal for the reclassification of five closely related members of the genus Clostridium into the genera Romboutsia gen. nov., Intestinibacter gen. nov., Terrisporobacter gen. nov. and Asaccharospora gen. nov. Int. J. Syst. Evol. Microbiol. 64, 1600–1616 (2014)

    Article  CAS  PubMed  Google Scholar 

  16. Song, Y. L., Liu, C. X., McTeague, M., Summanen, P. & Finegold, S. M. Clostridium bartlettii sp. nov., isolated from human faeces. Anaerobe 10, 179–184 (2004)

    Article  CAS  PubMed  Google Scholar 

  17. Messori, S., Trevisi, P., Simongiovanni, A., Priori, D. & Bosi, P. Effect of susceptibility to enterotoxigenic Escherichia coli F4 and of dietary tryptophan on gut microbiota diversity observed in healthy young pigs. Vet. Microbiol. 162, 173–179 (2013)

    Article  CAS  PubMed  Google Scholar 

  18. Czyzyk, A., Tawecki, J., Sadowski, J., Ponikowska, I. & Szczepanik, Z. Effect of biguanides on intestinal absorption of glucose. Diabetes 17, 492–498 (1968)

    Article  CAS  PubMed  Google Scholar 

  19. Winter, S. E. et al. Host-derived nitrate boosts growth of E. coli in the inflamed gut. Science 339, 708–711 (2013)

    Article  ADS  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  ADS  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. De Vadder, F. et al. Microbiota-generated metabolites promote metabolic benefits via gut-brain neural circuits. Cell 156, 84–96 (2014)

    Article  CAS  PubMed  Google Scholar 

  23. Croset, M. et al. Rat small intestine is an insulin-sensitive gluconeogenic organ. Diabetes 50, 740–746 (2001)

    Article  CAS  PubMed  Google Scholar 

  24. Jørgensen, T. et al. A randomized non-pharmacological intervention study for prevention of ischaemic heart disease: baseline results Inter99. Eur. J. Cardiovasc. Prev. Rehabil. 10, 377–386 (2003)

    Article  PubMed  Google Scholar 

  25. WHO. Definition, Diagnosis and Classification of Diabetes Mellitus and its Complications. Part 1: Diagnosis and Classification of Diabetes Mellitus. Report No. WHO/NCD/NCS/99.2 (World Health Organization, 1999)

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

    Article  CAS  Google Scholar 

  27. Kultima, J. R. et al. MOCAT: a metagenomics assembly and gene prediction toolkit. PLoS ONE 7, e47656 (2012)

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

  28. Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006)

    CAS  PubMed  Google Scholar 

  29. Arumugam, M., Harrington, E. D., Foerstner, K. U., Raes, J. & Bork, P. SmashCommunity: a metagenomic annotation and analysis tool. Bioinformatics 26, 2977–2978 (2010)

    Article  CAS  PubMed  Google Scholar 

  30. Kanehisa, M. et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36, D480–D484 (2008)

    Article  CAS  PubMed  Google Scholar 

  31. Powell, S. et al. eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Nucleic Acids Res. 40, D284–D289 (2012)

    Article  CAS  PubMed  Google Scholar 

  32. Sunagawa, S. et al. Metagenomic species profiling using universal phylogenetic marker genes. Nature Methods 10, 1196–1199 (2013)

    Article  CAS  PubMed  Google Scholar 

  33. Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science 348, (2015)

  34. Nielsen, H. B. et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nature Biotechnol. 32, 822–828 (2014)

    Article  CAS  Google Scholar 

  35. Hildebrand, F. et al. LotuS: an efficient and user-friendly OTU processing pipeline. Microbiome 2, 30 (2014)

    Article  PubMed  PubMed Central  Google Scholar 

  36. Edgar, R. C. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nature Methods 10, 996–998 (2013)

    Article  CAS  PubMed  Google Scholar 

  37. Edgar, R. C. et al. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011)

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013)

    Article  CAS  PubMed  Google Scholar 

  40. Madden, T. in The NCBI Handbook [Internet]. (eds, McEntyre J. & Ostell J. ) Ch. 16 (National Center for Biotechnology Information, 2002) http://www.ncbi.nlm.nih.gov/books/NBK21097/

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

    MathSciNet  MATH  Google Scholar 

  42. Hothorn, T., Hornik, K., van de Wiel, M. A. & Zeileis, A. A Lego system for conditional inference. Am. Stat. 60, 257–263 (2006)

    Article  MathSciNet  Google Scholar 

  43. Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003)

    Article  Google Scholar 

  44. Wickham H. ggplot2: Elegant Graphics for Data Analysis. (Springer, 2009)

  45. Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26, 32–46 (2001)

    Google Scholar 

  46. Friedman, J. et al. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010)

    Article  PubMed  PubMed Central  Google Scholar 

  47. Abeel, T., Helleputte, T., Van de Peer, Y., Dupont, P. & Saeys, Y. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics 26, 392–398 (2010)

    Article  CAS  PubMed  Google Scholar 

  48. Hildebrand, F. et al. A comparative analysis of the intestinal metagenomes present in guinea pigs (Cavia porcellus) and humans (Homo sapiens). BMC Genomics 13, 514 (2012)

    Article  PubMed  PubMed Central  Google Scholar 

  49. Hildebrand, F. et al. Inflammation-associated enterotypes, host genotype, cage and inter-individual effects drive gut microbiota variation in common laboratory mice. Genome Biol. 14, R4 (2013)

    Article  PubMed  PubMed Central  Google Scholar 

  50. Haraldsdóttir, J. et al. Portionsstorleker - Nordiska standardportioner av mat och livsmedel (Nordisk Ministerråd, 1998)

  51. Biltoft-Jensen, A. et al. Danskernes kostvaner 2000–2002. DFVF publication No. 11 (Danmarks Fødevareforskning, Afdeling for Ernæring, 2005)

  52. Møller, A. et al. Fødevaredatabanken version 5.0. Fødevareinformatik, Institut for Fødevaresikkerhed og Ernæring, Fødevaredirektoratethttp://www.foodcomp.dk (2002)

  53. Lauritsen, J. FoodCalc. www.ibt.ku.dk/jesper/FoodCalc/ (2004)

Download references

Acknowledgements

The authors wish to thank A. Forman, T. Lorentzen, B. Andreasen, G. J. Klavsen and M. J. Nielsen for technical assistance, and T. F. Toldsted and G. Lademann for management assistance. J. Nielsen and F. Bäckhed are thanked for providing access to T2D metagenome data and metformin treatment status before publication4. V. Benes and the GeneCore facility of EMBL Heidelberg are thanked for their assistance with the metformin signature validation experiments, as is Y. Yuan for assistance with computer infrastructure. This research has received funding from European Community’s Seventh Framework Program (FP7/2007-2013): MetaHIT, grant agreement HEALTH-F4-2007-201052, MetaCardis, grant agreement HEALTH-2012-305312, International Human Microbiome Standards, grant agreement HEALTH-2010-261376, as well as from the Metagenopolis grant ANR-11-DPBS-0001, from the European Research Council CancerBiome project, contract number 268985, and from the European Union HORIZON 2020 programme, under Marie Skłodowska-Curie grant agreement 600375. Additional funding came from The Lundbeck Foundation Centre for Applied Medical Genomics in Personalized Disease Prediction, Prevention and Care (LuCamp, http://www.lucamp.org), the Novo Nordisk Foundation (grant NNF14CC0001), and the European Molecular Biology Laboratory (EMBL). 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). Additional funding for the validation experiments was provided by the Innovation Fund Denmark through the MicrobDiab project.

Author information

Authors and Affiliations

Authors

Consortia

Contributions

O.P., S.D.E. and P.B. devised the project, designed the study protocol and supervised all phases of the project. T.N., T.H., T.J., H.V., J.L. and O.P. carried out patient phenotyping and clinical data analyses. T.N. and F.L. performed sample collection and DNA extraction. J.D. supervised DNA extraction, J.W., K.K. supervised DNA sequencing and gene profiling, A.Y.V. and R.H. performed additional microbial DNA extraction and amplicon sequencing. J.R., H.B.N., S.B., S.D.E., P.B. and O.P. designed and supervised the data analyses. K.F., F.H., G.F., E.L.C., S.S., E.P., S.S.-V., V.G., H.K.P, M.A., P.I.C., J.R.K. and H.B.N performed the data analyses. K.F., F.H., T.N., P.B, S.D.E. and O.P. wrote the paper. All authors contributed to data interpretation, discussions and editing of the paper. All authors are members of the MetaHIT consortium. Additional consortium members contributed to the design and execution of the study.

Corresponding authors

Correspondence to S. Dusko Ehrlich, Peer Bork or Oluf Pedersen.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

A list of participants and their affiliations appears in the Supplementary Information.

Extended data figures and tables

Extended Data Figure 1 Validation of meta-analysis pipeline on simulated data.

a, As a positive control for the meta-analysis pipeline, true signal was removed from the data by randomly reshuffling sample labels. Artificial contrast was thereafter introduced between random groups containing as many such reshuffled samples as were in the original sets of T2D metformin+ (nCHN = 15, nMHD = 58, nSWE = 20) and T2D metformin− (nCHN = 56, nMHD = 17, nSWE = 33) samples in each original study subset, using the genus Akkermansia as an example feature. Samples randomly assigned to the sets of fake ‘metformin-treated’ and ‘control’ categories had their Akkermansia genus abundances adjusted to match the scale of the metformin effect on Escherichia genus abundance reported here (metformin-treated samples were roughly 150% as likely to have non-zero abundance, with a roughly threefold higher abundance where present), while retaining their data set origin labels. The full meta-analysis pipeline (study set blocked Kruskal–Wallis test, post-hoc Wilcoxon rank-sum test) was applied to these samples. Benjamini–Hochberg-corrected P values (FDR scores/Q values) from testing for a metformin effect on Akkermansia abundance are plotted in logarithmic scale on the vertical axis for 100 randomizations of the entire shuffled data set, either without (left box plot) or with (right box plot) the artificial Akkermansia metformin signal added after shuffling the data to remove original signal. Box plot borders show medians and quartiles, with points outside this range shown as vertical whisker lines and point markers. Whiskers extend to 1.58× interquartile range/. Horizontal guide lines are shown for ease of visualization corresponding to different false discovery rate thresholds. For randomly reshuffled data, no significant contrast is detected as expected, whereas the artificially introduced signal is reliably detected, roughly matching expectations from the definition of the false discovery rate itself. b, To investigate statistical power for the other medications tracked, five random sub-samplings were made of pairs of medicated and non-medicated samples at each increasing number of included sample pairs and the overall analysis was replicated for each. We tested each genus for significantly differential abundance between cases and controls (Kruskal–Wallis test followed by post-hoc Wilcoxon rank-sum test) at different Benjamini–Hochberg FDR significance cut-offs, which are represented by different colours. Of the total number of samples for which medication status was known, equal numbers (n) of medicated and unmedicated samples were chosen randomly in repeated iterations. This number n was varied up to its largest possible value (smallest of either number of medicated or unmedicated samples in the overall data set) and is shown on the x axis. The y axis shows the number of significant features relative to each cut-off. Error bars show ±1 s.d. of each set of five randomized samples. c, The graphs show Intestinibacter and Escherichia median and quartile abundances as box plots, whiskers extend to 1.58× interquartile range/, with samples that are extreme relative to the interquartile range shown as point markers, and with samples below detection threshold (DT) plotted at y = 0, in 21 additional T2D metformin+ and 9 additional T2D metformin− samples. Differences in abundance between sample categories are significant (Wilcoxon rank-sum test, Benjamini–Hochberg FDR < 0.1). All samples in which Intestinibacter was detected fall among the 9 out of 30 untreated rather than the 21 out of 30 metformin-treated samples, consistent with severe depletion under treatment; whereas Escherichia abundances increase under treatment, likewise consistent with observations from the main data set.

Extended Data Figure 2 Differences in physiological variables and microbiome characteristics between gut metagenome sample sets.

Chinese (n = 368), Danish MetaHIT (n = 383) and Swedish (n = 145). a, Several participant metadata variables are significantly different between cohorts. A subselection is shown as box plots displaying median and quartiles, with samples outside this range shown as point markers and whiskers. Whiskers extend to 1.58× interquartile range/. b, In a principal coordinates analysis ordination of Bray–Curtis distances between samples on bacterial family level, clear differences between samples from the different cohorts become apparent. These are largely explained by taxonomic differences as summarized at the phylum level. c, Box plots for gut microbial taxa show medians and quartiles of log-transformed read counts for mOTUs summarized at the level of bacterial genera for the three country subsets across sample categories, with samples outside this range shown as point markers and whiskers. Whiskers extend to 1.58× interquartile range/. For all box plots, tests for significant differences (Kruskal–Wallis test adjusted for study source) were performed, with P values shown at the head of each figure. Asterisks denote statistical significance of tests done for each country subset separately (***P < 0.001).

Extended Data Figure 3 Microbiome taxonomic composition comparison between gut metagenomes with particular focus on possible taxonomic restoration under metformin treatment for certain taxa.

T2D metformin− (n = 106), T2D metformin+ (n = 93) and ND control (n = 554). Box plots show medians and quartiles log-transformed read counts for mOTUs summarized at the level of bacterial genera, for the three country subsets across sample categories, with samples outside this range shown as point markers and whiskers. Whiskers extend to 1.58× interquartile range/. Tests for significant differences (Kruskal–Wallis test adjusted for study source) were performed, with P values shown at the head of each figure. Asterisks denote statistical significance of tests for each country subset separately (*P < 0.05; **P < 0.01; ***P < 0.001).

Extended Data Table 1 Analysis of variances

Supplementary information

Supplementary Information

This file contains a Supplementary Discussion, full legends for Supplementary Tables 1-16, Supplementary References and a list of additional MetaHIT consortium members. (PDF 669 kb)

Supplementary Tables

This file contains Supplementary Tables 1-16 – see Supplementary Information document for legends. (ZIP 465 kb)

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Forslund, K., Hildebrand, F., Nielsen, T. et al. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528, 262–266 (2015). https://doi.org/10.1038/nature15766

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature15766

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

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