Causal relationships among the gut microbiome, short-chain fatty acids and metabolic diseases - PubMed (original) (raw)

. 2019 Apr;51(4):600-605.

doi: 10.1038/s41588-019-0350-x. Epub 2019 Feb 18.

Natalie R van Zuydam # 2 3, Anubha Mahajan # 2 3, Alexander Kurilshikov 4, Arnau Vich Vila 4 5, Urmo Võsa 4, Zlatan Mujagic 6, Ad A M Masclee 6, Daisy M A E Jonkers 6, Marije Oosting 7, Leo A B Joosten 7, Mihai G Netea 7, Lude Franke 4, Alexandra Zhernakova 4, Jingyuan Fu 4 8, Cisca Wijmenga # 9 10, Mark I McCarthy # 11 12 13

Affiliations

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

Serena Sanna et al. Nat Genet. 2019 Apr.

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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Conflict of interest statement

Competing Interests statement

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

Figures

Figure 1.

Figure 1.. Schematic representation of the study

Figure 1 is a schematic representation of our study, highlighting for each step the research question we want to answer, the analysis workflow, and the data used. We first aimed to identify which microbiome feature (taxa, microbiome pathway or short-chain fatty acid (SCFA)) correlated with metabolic traits in the LifeLines-DEEP (LL-DEEP) cohort (Step 1). We then performed genome-wide association (GWA) analysis in LL-DEEP to identify genetic predictors of those microbiome features (Step 2), and used the genetic predictors to estimate causal relationships through bidirectional Mendelian Randomization analysis and effect sizes for metabolic traits extracted from summary statistics of large GWA studies (Step 3). Finally, we validated our causality results using the UK Biobank (Step 4).

Figure 2.

Figure 2.. Causal effect of butyrate-producing activity of the gut on glucose-stimulated insulin response

a) Schematic representation of the Mendelian Randomization analysis results: genetic predisposition to higher abundance of butyrate-producing microbiome pathway PWY-5022 (4-aminobutanoate degradation V pathway) is associated with insulin response after glucose challenge. The causal effect of PWY-5022 was also seen on other insulin response parameters, and the forest plot in panel (b) represents the magnitude of the effect on each parameter per one-standard-deviation increase in pathway abundance, as estimated in the inverse-variance weighted Mendelian Randomization (MR) analysis. MR analysis was carried out using up to nine genetic predictors and their effect size from LL-DEEP (952 samples) and MAGIC summary statistics (trait specific sample sizes are: AUCinsulin/AUCglucose = 4213; insulin at 30 min = 4,409; AUCinsulin = 4,324; correct insulin response = 4,789; insulin increase at 30 min = 4,447; Disposition index = 5,130) (Methods, Supplementary Table 4 and 5). Corresponding two-sided _P_-values are given in the annexed table. (c) Correlation plots with PWY-5022 abundance and the bacteria correlating the most with it in 950 LL-DEEP samples (subset of the 952 normo-glycemic samples for which presence of those bacteria was detected). The Spearman correlation coefficient ρ is given in blue in each panel.

Figure 3.

Figure 3.. Causal effect of fecal propionate on type 2 diabetes (T2D)

a) Schematic representation of the Mendelian Randomization analysis results: genetic predisposition to higher fecal propionate levels is associated with increased risk of T2D. b) A forest plot depicts the magnitude of the causal effect on T2D per each one-standard-deviation increase in fecal butyrate levels, as estimated by the inverse-variance weighted Mendelian Randomization (MR) analysis. MR analysis was carried out using the three genetic predictors derived in LifeLines-DEEP (LL-DEEP) and their effects in the discovery data set (DIAGRAM; 26,676 T2D cases and 132,532 controls) and in the replication cohort (UK Biobank; 19,119 T2D cases and 423,698 controls). The effect derived combining the two causal effects (from discovery and replication) with an inverse-variance weighted meta-analysis approach is also given. Corresponding two-sided _P_-values are listed in the annexed table. OR, odd’s ratio.

Comment in

References

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