Causal Association between Rheumatoid Arthritis with the Increased Risk of Type 2 Diabetes: A Mendelian Randomization Analysis

Abstract

Objective. This study aimed to examine whether rheumatoid arthritis (RA) is causally associated with type 2 diabetes (T2D). Methods. We performed a two-sample Mendelian randomization (MR) analysis using the inverse-variance weighted (IVW), weighted median, and MR-Egger regression methods. We used the publicly available summary statistics datasets from a genome-wide association studies (GWAS) meta-analysis of 5,539 autoantibody-positive individuals with RA and 20,169 controls of European descent, and a GWAS dataset of 10,247 individuals with T2D and 53,924 controls, overwhelmingly of European descent as outcomes. Results. We selected 10 single-nucleotide polymorphisms from GWAS data on RA as instrumental variables to improve the inference. The IVW method supported a causal association between RA and T2D (beta=0.044, standard error [SE]=0.022, p=0.047). The MR-Egger analysis showed a causal association between RA and T2D (beta=0.093, SE=0.033, p=0.023). In addition, the weighted median approach supported a causal association between RA and T2D (beta=0.056, SE=0.025, p=0.028). The association between RA and T2D was consistently observed using IVW, MR Egger, and weighted median methods. Cochran's Q test indicated no evidence of heterogeneity between instrumental variable estimates based on individual variants and MR-Egger regression revealed that directional pleiotropy was unlikely to have biased the results (intercept=-0.030; p=0.101). Conclusion. MR analysis supports that RA may be causally associated with an increased risk of T2D.