Liver Function and Risk of Type 2 Diabetes: Bidirectional Mendelian Randomization Study

Abstract

Liver dysfunction and type 2 diabetes (T2D) are consistently associated. However, it is currently unknown whether liver dysfunction contributes to, results from, or is merely correlated with T2D due to confounding. We used Mendelian randomization to investigate the presence and direction of any causal relation between liver function and T2D risk including up to 64,094 T2D case and 607,012 control subjects. Several biomarkers were used as proxies of liver function (i.e., alanine aminotransferase [ALT], aspartate aminotransferase [AST], alkaline phosphatase [ALP], and γ-glutamyl transferase [GGT]). Genetic variants strongly associated with each liver function marker were used to investigate the effect of liver function on T2D risk. In addition, genetic variants strongly associated with T2D risk and with fasting insulin were used to investigate the effect of predisposition to T2D and insulin resistance, respectively, on liver function. Genetically predicted higher circulating ALT and AST were related to increased risk of T2D. There was a modest negative association of genetically predicted ALP with T2D risk and no evidence of association between GGT and T2D risk. Genetic predisposition to higher fasting insulin, but not to T2D, was related to increased circulating ALT. Since circulating ALT and AST are markers of nonalcoholic fatty liver disease (NAFLD), these findings provide some support for insulin resistance resulting in NAFLD, which in turn increases T2D risk.

Figure 1

Study design and data sources used to investigate the effect of liver dysfunction (proxied by biomarkers: ALT, AST, ALP, and GGT) on T2D or secondary outcomes (fasting glucose, fasting insulin, LDLc, HDLc, total cholesterol, and triglycerides) (A) and the effect of predisposition to T2D or insulin resistance on circulating liver function biomarkers (B). As shown in A, the multivariable association of liver function markers with T2D risk (or related outcomes) was estimated by meta-analyzing results from each data source using logistic regression models (or linear regression models in the case of secondary outcomes) with participant-level data from relevant studies within the UCLEB consortium (BRHS, BWHHS, MRC NSHD) and summary-level data from the published meta-analyses of Kunutsor et al. (2013) (2) and Fraser et al. (2009) (27). We also estimated the association of liver function markers with T2D risk (or secondary outcomes) using an MR approach. In MR analysis, we used different data sources to estimate the SNP–liver function marker association (UCLEB consortium [BRHS, BWHHS, and MRC NSHD], Fenland study, and GWAS of liver function markers [Chambers et al., 2011] [28]) and SNP–T2D risk association (UCLEB consortium [BRHS, BWHS, CaPS, EAS, ELSA, MRC-NSHD, and WHII] and GWAS consortium) or SNP–secondary outcomes. As shown in B, the summary-level data for the association of SNP–T2D risk and SNP–fasting insulin for the reverse MR was extracted from GWAS consortia, and the association of SNP–liver function marker was extracted from Chambers et al. (2011) (28).

Figure 2

Multivariable and MR analysis of the effect of liver function on T2D (A) and MR analysis of the effect of T2D on liver function markers (B). Results from Fig. 1A correspond to OR of T2D per unit increase in standardized liver function markers (and 95% CI). Results from Fig. 1B correspond to change in standardized liver function markers per unit increase in log odds of T2D (and 95% CI). I-squared indicates between-study heterogeneity and is only presented when estimates for more than one study were available.