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Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases


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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Fig. 1: Schematic representation of the study.
Fig. 2: Causal effect of butyrate-producing activity of the gut on the glucose-stimulated insulin response.
Fig. 3: Causal effect of fecal propionate on T2D.

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 Summary statistics for metabolic traits were downloaded from the MAGIC, GIANT and DIAGRAM websites (see URLs).


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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.

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Authors and Affiliations



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.

Corresponding authors

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

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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.

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Integrated supplementary information

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).

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.

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)

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.

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.

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

Supplementary Text and Figures

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

Reporting Summary

Supplementary Table 2

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Sanna, S., van Zuydam, N.R., Mahajan, A. et al. Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases. Nat Genet 51, 600–605 (2019).

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