Causal relationship between particulate matter 2.5 and diabetes: two sample Mendelian randomization

Joyce Mary Kim; Eunji Kim; Do Kyeong Song; Yi-Jun Kim; Ji Hyen Lee; Eunhee Ha

doi:10.3389/fpubh.2023.1164647

Causal relationship between particulate matter 2.5 and diabetes: two sample Mendelian randomization

Front Public Health. 2023 Aug 10:11:1164647. doi: 10.3389/fpubh.2023.1164647. eCollection 2023.

Authors

Joyce Mary Kim^{1

2}, Eunji Kim^{1

2}, Do Kyeong Song³, Yi-Jun Kim², Ji Hyen Lee^{4

5}, Eunhee Ha^{1

2

4

6}

Affiliations

¹ Graduate Program in System Health Science and Engineering, College of Medicine, Ewha Womans University, Seoul, Republic of Korea.
² Department of Environmental Medicine, School of Medicine, Ewha Womans University, Seoul, Republic of Korea.
³ Department of Internal Medicine, School of Medicine, Ewha Womans University, Seoul, Republic of Korea.
⁴ Institute of Ewha-SCL for Environmental Health (IESEH), College of Medicine, Ewha Womans University, Seoul, Republic of Korea.
⁵ Department of Pediatrics, College of Medicine, Ewha Womans University, Seoul, Republic of Korea.
⁶ Department of Medical Science, College of Medicine, Ewha Medical Research Institute, Ewha Womans University, Seoul, Republic of Korea.

Abstract

Backgrounds: Many studies have shown particulate matter has emerged as one of the major environmental risk factors for diabetes; however, studies on the causal relationship between particulate matter 2.5 (PM_2.5) and diabetes based on genetic approaches are scarce. The study estimated the causal relationship between diabetes and PM_2.5 using two sample mendelian randomization (TSMR).

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 PM_2.5 from the Single nucleotide polymorphisms (SNPs) GWAS pipeline using pheasant-derived variables (Consortium = MRC-IEU, sample size: 423,796). The annual relationship of PM_2.5 (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 Mendelian Randomization (MR) methods: Inverse Variance Weighted regression (IVW), Egger, and weighted median for causal relationship 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 PM_2.5 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 machine learning mixture methods), we excluded outlier SNP (rs1537371) and showed the best predictive model (AUC = 0.72) with a causal relationship between PM_2.5 and diabetes (OR: 1.028, 95% CI: 1.006-1.049, P = 0.012).

Conclusion: We identified the hypothesis that there is a causal relationship between PM_2.5 and diabetes in the European population, using MR methods.

Keywords: GWAS; diabetes; environmental epidemiology; genetics epidemiology; particulate matter 2.5; two sample Mendelian randomization.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Air Pollution* / adverse effects
Diabetes Mellitus*
Genome-Wide Association Study
Humans
Mendelian Randomization Analysis
Particulate Matter / adverse effects

Substances

Particulate Matter

Grants and funding

This research was supported by the BK21 FOUR (Fostering Outstanding Universities for Research) funded by the Ministry of Education (MOE, Korea) and National Research Foundation of Korea (NRF-5199990614253, Education Research Center for 4IR-Based Health Care).