Causal relationship between particulate matter 2.5 and diabetes: two sample Mendelian randomization
- 주제(키워드) particulatematter 2.5 , diabetes , genetics epidemiology , environmental epidemiology , two sample Mendelian randomization , GWAS
- 주제(기타) Public, Environmental & Occupational Health
- 설명문(일반) [Kim, Joyce Mary; Kim, Eunji; Ha, Eunhee] Ewha Womans Univ, Coll Med, Grad Program Syst Hlth Sci & Engn, Seoul, South Korea; [Kim, Joyce Mary; Kim, Eunji; Kim, Yi-Jun; Ha, Eunhee] Ewha Womans Univ, Sch Med, Dept Environm Med, Seoul, South Korea; [Song, Do Kyeong] Ewha Womans Univ, Sch Med, Dept Internal Med, Seoul, South Korea; [Lee, Ji Hyen; Ha, Eunhee] Ewha Womans Univ, Inst Ewha SCL Environm Hlth IESEH, Coll Med, Seoul, South Korea; [Lee, Ji Hyen] Ewha Womans Univ, Coll Med, Dept Pediat, Seoul, South Korea; [Ha, Eunhee] Ewha Womans Univ, Ewha Med Res Inst, Coll Engn, Dept Med Sci, Seoul, South Korea
- 등재 SCIE, SSCI, SCOPUS
- OA유형 Green Published, gold
- 발행기관 FRONTIERS MEDIA SA
- 발행년도 2023
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000211507
- 본문언어 영어
- Published As https://doi.org/10.3389/fpubh.2023.1164647
- PubMed 37637811
초록/요약
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.
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