Abstract

Microbiome-wide association studies on large population cohorts have highlighted associations between the gut microbiome and complex traits, including type 2 diabetes (T2D) and obesity1. However, the causal relationships remain largely unresolved. We leveraged information from 952 normoglycemic individuals for whom genome-wide genotyping, gut metagenomic sequence and fecal short-chain fatty acid (SCFA) levels were available2, then combined this information with genome-wide-association summary statistics for 17 metabolic and anthropometric traits. Using bidirectional Mendelian randomization (MR) analyses to assess causality3, we found that the host-genetic-driven increase in gut production of the SCFA butyrate was associated with improved insulin response after an oral glucose-tolerance test (P = 9.8 × 10−5), whereas abnormalities in the production or absorption of another SCFA, propionate, were causally related to an increased risk of T2D (P = 0.004). These data provide evidence of a causal effect of the gut microbiome on metabolic traits and support the use of MR as a means to elucidate causal relationships from microbiome-wide association findings.

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Data availability

The LifeLines-DEEP metagenomic sequencing data are available at the European Genome-phenome Archive (EGA) under accession code EGAS00001001704. Genotype and phenotype data can be requested from the Lifelines Biobank at https://www.lifelines.nl/researcher/biobank-lifelines/application-process/. Summary statistics for metabolic traits were downloaded from the MAGIC, GIANT and DIAGRAM websites (see URLs).

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Acknowledgements

We thank the participants and staff of the LL-DEEP cohort for their collaboration, the UMCG Genomics Coordination center, the UG Center for Information Technology and their sponsors BBMRI-NL and TarGet for storage and compute infrastructure. We are also grateful to M. J. Bonder for help in formatting summary statistics; to R. K. Weersma and Y. Li for discussions; and to K. Mc Intyre for editing the manuscript. Part of this work was conducted by using the UK Biobank resource under application no. 9161. This project was funded by IN-CONTROL CVON grant CVON2012-03 to M.G.N., A.Z., L.A.B.J. and J.F.; Top Institute Food and Nutrition (TiFN, Wageningen, the Netherlands) grant TiFN GH001 to C.W.; the Netherlands Organization for Scientific Research (NWO) grants NWO-VENI 016.176.006 to M.O., NWO-VIDI 864.13.013 to J.F. and NWO-VIDI 016.Vidi.178.056 to A.Z.; NWO Spinoza Prizes SPI 92-266 to C.W. and SPI 94-212 to M.G.N.; European Research Council (ERC) starting grant ERC no. 715772 to A.Z.; FP7/2007-2013/ERC Advanced Grant (agreement 2012-322698) to C.W.; ERC Consolidator Grant ERC no. 310372 to M.G.N.; Tripartite Immunometabolism consortium (TrIC)–Novo Nordisk Foundation grant NNF15CC0018486 to M.I.M.; and Wellcome grants 090532, 098381, 106130 and 203141 to M.I.M. A.Z. is also supported by a Rosalind Franklin Fellowship from the University of Groningen. M.I.M. is supported as a Wellcome Senior Investigator and a National Institute of Health Research Senior Investigator. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The views expressed in this article are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

Author information

Author notes

  1. These authors contributed equally: Serena Sanna, Natalie R. van Zuydam, Anubha Mahajan.

  2. These authors jointly supervised this work: Cisca Wijmenga, Mark I. McCarthy

Affiliations

  1. Department of Genetics, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands

    • Serena Sanna
    • , Alexander Kurilshikov
    • , Arnau Vich Vila
    • , Urmo Võsa
    • , Lude Franke
    • , Alexandra Zhernakova
    • , Jingyuan Fu
    •  & Cisca Wijmenga
  2. Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK

    • Natalie R. van Zuydam
    • , Anubha Mahajan
    •  & Mark I. McCarthy
  3. Oxford Centre for Diabetes Endocrinology and Metabolism, Churchill Hospital, University of Oxford, Oxford, UK

    • Natalie R. van Zuydam
    • , Anubha Mahajan
    •  & Mark I. McCarthy
  4. Department of Gastroenterology and Hepatology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands

    • Arnau Vich Vila
  5. Maastricht University Medical Center, Division Gastroenterology-Hepatology, NUTRIM School for Nutrition, and Translational Research in Metabolism, Maastricht, the Netherlands

    • Zlatan Mujagic
    • , Ad A. M. Masclee
    •  & Daisy M. A. E. Jonkers
  6. Department of Internal Medicine, Radboud Institute of Molecular Life Sciences (RIMLS) and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, Nijmegen, the Netherlands

    • Marije Oosting
    • , Leo A. B. Joosten
    •  & Mihai G. Netea
  7. Department of Pediatrics, Groningen, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands

    • Jingyuan Fu
  8. K.G. Jebsen Coeliac Disease Research Centre, Department of Immunology, University of Oslo, Oslo, Norway

    • Cisca Wijmenga
  9. Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK

    • Mark I. McCarthy

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Contributions

S.S. performed statistical analyses on the LifeLines and 500FG cohorts; N.R.v.Z. and A.M. performed statistical analyses on UK Biobank and DIAGRAM studies; A.K. and A.V.V. processed raw microbiome data in Lifelines-DEEP and 500FG; U.V. and L.F. downloaded and harmonized the summary statistics from the GIANT, MAGIC and DIAGRAM consortia; L.F., and C.W. provided LifeLines-DEEP data; Z.M., A.A.M.M. and D.M.A.E.J. provided critical input in manuscript revisions; M.O., L.A.B.J. and M.G.N. provided 500FG data; S.S., N.R.v.Z. and M.I.M. wrote the manuscript, to which J.F., A.Z. and C.W. provided critical input; S.S., N.R.v.Z., A.M., C.W. and M.I.M. designed the study. All authors read, revised and approved the manuscript.

Competing interests

M.I.M. serves on advisory panels for Pfizer, NovoNordisk and Zoe Global; has received honoraria from Pfizer, NovoNordisk and Eli Lilly; has stock options in Zoe Global; and has received research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier and Takeda. All other authors declare no competing financial interests.

Corresponding authors

Correspondence to Serena Sanna or Cisca Wijmenga or Mark I. McCarthy.

Integrated supplementary information

  1. Supplementary Figure 1 Distribution of variance explained for microbiome features in 500FG.

    Distribution of variance explained of microbiome features in 445 normo-glycemic 500FG individuals by genetic predictors selected in LL-DEEP GWAS at different P-value thresholds, when predictors are selected from the full HRC imputed data (green), or when restricted to HapMap2 SNPs and proxies (red). Distributions are represented as boxplots, where the box hinges represent the 25th and 75th percentile, the central line of the box represents the median value, and the points outliers (default options in the boxplot() function in R).

  2. Supplementary Figure 2 Clustering of metabolic traits through genetic correlation.

    Clustering of metabolic traits generated using the R function hclust(“complete”) and a dissimilarity metric of (1-ρg)/2, where ρg is the genetic correlation.

  3. Supplementary Figure 3 MR plot showing the causal effect of microbiome PWY-5022 on the glucose-stimulated insulin response.

    In this plot, each point is a genetic predictor of PWY-5022. The x-axis plot shows their effect size on PWY-5022 as estimated in LL-DEEP cohort (952 samples), and the y-axis shows the allelic effect on glucose-stimulated insulin response as estimated in MAGIC (4213 samples). Whiskers represent 95% confidence intervals of these effects. The slope of the red line represents the causal effect estimated by the Mendelian Randomization analysis with the IVW test, with dashed lines corresponding to 95% confidence interval (as given in Supplementary Table 5)

  4. Supplementary Figure 4 Forest plot of the PWY-5022 causal effect estimated with different statistical tests.

    The forest plot shows the causal effect (in mU/mmol) of PWY-5022 on glucose-stimulated insulin response parameter AUCinsulin/AUCglucose estimated with different MR tests, using 8 genetic predictors and their effects from LL-DEEP (952 samples) and MAGIC (4213 samples) summary statistics. Bars represent 95% confidence intervals. The annexed table indicates the two-sided pvalues for the respective MR and pleiotropy statistical tests.

  5. Supplementary Figure 5 Forest plot of the fecal propionate causal effect on T2D, estimated with different statistical tests.

    The forest plot shows the causal effect (in log(OR) units) of fecal propionate on T2D estimated with different MR tests, using 3 genetic predictors and their effects from LL-DEEP (898 samples) and DIAGRAM (26,676 T2D cases and 132,532 controls) summary statistics. Bars represent 95% confidence intervals. The annexed table indicates the two-sided pvalues for the respective MR and pleiotropy statistical tests.

  6. Supplementary Figure 6 Forest plot of the fecal propionate causal effect on BMI, estimated with different statistical tests.

    The forest plot shows the causal effect of fecal propionate on BMI (in SD units) estimated with different MR tests using 3 genetic predictors and their effects from LL-DEEP (898 samples) and GIANT (339,224 samples) summary statistics. Bars represent 95% confidence intervals. The annexed table indicates the two-sided pvalues for the respective MR and pleiotropy statistical tests.

Supplementary information

  1. Supplementary Text and Figures

    Supplementary Figures 1–6 and Supplementary Tables 1 and 3–11

  2. Reporting Summary

  3. Supplementary Table 2

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https://doi.org/10.1038/s41588-019-0350-x

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