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

Nature volume 528, pages 262–266 (2015)Cite this article

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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.

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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.

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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).

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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

Author notes

  1. Kristoffer Forslund, Falk Hildebrand, Trine Nielsen, Gwen Falony and Emmanuelle Le Chatelier: These authors contributed equally to this work.

Authors and Affiliations

  1. European Molecular Biology Laboratory, Structural and Computational Biology Unit, Heidelberg, 69117, Germany

    Kristoffer Forslund, Falk Hildebrand, Shinichi Sunagawa, Anita Yvonne Voigt, Rajna Hercog, Paul Igor Costea, Jens Roat Kultima & Peer Bork

  2. VIB Center for the Biology of Disease, Katholieke Universiteit Leuven, Leuven, 3000, Belgium

    Falk Hildebrand, Gwen Falony, Sara Vieira-Silva & Jeroen Raes

  3. Department of Bioscience Engineering, Vrije Universiteit Brussel, Brussels, 1040, Belgium

    Falk Hildebrand & Jeroen Raes

  4. The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark

    Trine Nielsen, Manimozhiyan Arumugam, Henrik Vestergaard, Torben Hansen & Oluf Pedersen

  5. Department of Microbiology and Immunology, Rega Institute for Medical Research, Laboratory of Molecular Bacteriology, Katholieke Universiteit Leuven, Leuven, 3000, Belgium

    Gwen Falony, Sara Vieira-Silva & Jeroen Raes

  6. MICALIS, Institut National de la Recherche Agronomique, Jouy en Josas, 78352, France

    Emmanuelle Le Chatelier, Edi Prifti, Florence Levenez, Joël Dore & S. Dusko Ehrlich

  7. Metagenopolis, Institut National de la Recherche Agronomique, Jouy en Josas, 78352, France

    Emmanuelle Le Chatelier, Edi Prifti, Florence Levenez, Joël Dore & S. Dusko Ehrlich

  8. Institute of Cardiometabolism and Nutrition, Paris, 75013, France

    Edi Prifti

  9. Department of Systems Biology, Center for Biological Sequence Analysis, Technical University of Denmark, Kongens Lyngby, 2800, Denmark

    Valborg Gudmundsdottir, Helle Krogh Pedersen, H. Bjørn Nielsen & Søren Brunak

  10. Department of Biology, University of Copenhagen, Copenhagen, 2100, Denmark

    Karsten Kristiansen & Jun Wang

  11. Department of Applied Tumor Biology, Institute of Pathology, University Hospital Heidelberg, Heidelberg, 69120, Germany

    Anita Yvonne Voigt

  12. Molecular Medicine Partnership Unit, University of Heidelberg and European Molecular Biology Laboratory, Heidelberg, 69120, Germany

    Anita Yvonne Voigt & Peer Bork

  13. Bejing Genomics Institute (BGI)-Shenzhen, Shenzhen, 518083, China

    Junhua Li & Jun Wang

  14. Research Centre for Prevention and Health, Capital Region of Denmark, 2600, Glostrup, Denmark

    Torben Jørgensen

  15. Department of Public Health, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2600, Denmark

    Torben Jørgensen

  16. Faculty of Medicine, University of Aalborg, Aalborg, 9100, Denmark

    Torben Jørgensen

  17. Novo Nordisk Foundation Center for Protein Research, Disease Systems Biology, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 2200, Denmark

    Søren Brunak

  18. Faculty of Health Sciences, University of Southern Denmark, Odense, 5000, Denmark

    Torben Hansen

  19. Princess Al Jawhara Albrahim Center of Excellence in the Research of Hereditary Disorders, King Abdulaziz University, Jeddah, 80205, Saudi Arabia

    Jun Wang

  20. Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau, China

    Jun Wang

  21. Department of Medicine and State Key Laboratory of Pharmaceutical Biotechnology, University of Hong Kong, Hong Kong

    Jun Wang

  22. Centre for Host-Microbiome Interactions, Dental Institute Central Office, Guy’s Hospital, King’s College London, London SE1 9RT , UK,

    S. Dusko Ehrlich

  23. Max Delbrück Centre for Molecular Medicine, Berlin, 13125, Germany

    Peer Bork

  24. Department of Bioinformatics, University of Wuerzburg, 97074, Würzburg, Germany

    Peer Bork

Authors
  1. Kristoffer Forslund
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  2. Falk Hildebrand
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Consortia

MetaHIT consortium

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.

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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
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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)

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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

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