Causal relationship between PM 2.5 and diabetes mellitus: Two sample Mendelian Randomization using MR-Base platform

BACKGROUND AND AIM: Many studies have shown air pollution has emerged as one of the major environmental risk factors for diabetes mellitus; however, studies on the causal relationship between air pollution and diabetes mellitus based on genetic approaches are scarce. The study estimated the causal relationship between diabetes and fine particulate matter (PM2.5) using Mendelian randomization (MR). METHODS: We collected genetic data from European ancestry publicly available genome wide association studies (GWAS) summary data through the MR-BASE repository. The IEU GWAS information output (PM2.5) from the Single nucleotide polymorphisms (SNPs) GWAS pipeline using pheasant-derived variables (Consortium=MRC-IEU, sample size: 423,796). The annual estimates of PM2.5 (2010) were modeled for each address using a Land Use Regression model developed as part of the European Study of Cohorts for Air Pollution Effects. Diabetes GWAS information (Consortium=MRC-IEU, sample size: 461,578) were used, and the genetic variants were used as the instrumental variables (IVs). We performed three representative MR methods: Inverse Variance Weighted regression (IVW), Egger, and Weighted median for causal inference using genetic variants. Furthermore, we used a novel method called MR Mixture to identify outlier SNPs. RESULTS: From the IVW method, we revealed the causal relationship between PM2.5 and diabetes (Odds ratio [OR]: 1.041, 95% CI: 1.008-1.076, p=0.016), and the finding was substantiated by the absence of any directional horizontal pleiotropy through MR-Egger regression (β=0.016, p=0.687). From the IVW fixed-effect method (i.e. one of the MR Mixture methods), we excluded outlier SNP (rs1537371) and showed the best predictive model (AUC=0.72) with a causal relationship between PM2.5 and diabetes (OR: 1.028, 95% CI: 1.006-1.049, p=0.012). CONCLUSIONS: We identified the hypothesis that there is a causal relationship between PM2.5 and diabetes in the European population, using MR methods. KEYWORDS: Causal relationship; Particulate matter; Diabetes; Mendelian Randomization