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

Title
Causal relationship between particulate matter 2.5 and diabetes: two sample Mendelian randomization
Authors
Kim, Joyce MaryKim, EunjiSong, Do KyeongKim, Yi-JunLee, Ji HyenHa, Eunhee
Ewha Authors
하은희송도경이지현
SCOPUS Author ID
하은희scopus; 송도경scopus; 이지현scopus
Issue Date
2023
Journal Title
FRONTIERS IN PUBLIC HEALTH
ISSN
2296-2565JCR Link
Citation
FRONTIERS IN PUBLIC HEALTH vol. 11
Keywords
particulatematter 2.5diabetesgenetics epidemiologyenvironmental epidemiologytwo sample Mendelian randomizationGWAS
Publisher
FRONTIERS MEDIA SA
Indexed
SCIE; SSCI; SCOPUS WOS
Document Type
Article
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 (PM2.5) and diabetes based on genetic approaches are scarce. The study estimated the causal relationship between diabetes and PM2.5 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 PM2.5 from the Single nucleotide polymorphisms (SNPs) GWAS pipeline using pheasant-derived variables (Consortium = MRC-IEU, sample size: 423,796). The annual relationship 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 E ects. 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: Fromthe IVWmethod, 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 (beta = 0.016, P = 0.687). From the IVW fixed-e ect method (i.e., one of the MR machine learning mixture methods), we excluded outlier SNP (rs1537371) and showed the best predictivemodel (AUC = 0.72) with a causal relationship between PM2.5 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 PM2.5 and diabetes in the European population, using MR methods.
DOI
10.3389/fpubh.2023.1164647
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의과대학 > 의학과 > Journal papers
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