Causal relationship between particulate matter 2.5 and diabetes: two sample Mendelian randomization
BackgroundsMany 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 relati...
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Frontiers Media S.A.
2023-08-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1164647/full |
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| author | Joyce Mary Kim Joyce Mary Kim Eunji Kim Eunji Kim Do Kyeong Song Yi-Jun Kim Ji Hyen Lee Ji Hyen Lee Eunhee Ha Eunhee Ha Eunhee Ha Eunhee Ha |
| author_facet | Joyce Mary Kim Joyce Mary Kim Eunji Kim Eunji Kim Do Kyeong Song Yi-Jun Kim Ji Hyen Lee Ji Hyen Lee Eunhee Ha Eunhee Ha Eunhee Ha Eunhee Ha |
| author_sort | Joyce Mary Kim |
| collection | DOAJ |
| description | BackgroundsMany 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).MethodsWe 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 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.ResultsFrom 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 machine learning 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).ConclusionWe identified the hypothesis that there is a causal relationship between PM2.5 and diabetes in the European population, using MR methods. |
| first_indexed | 2024-03-12T15:18:49Z |
| format | Article |
| id | doaj.art-5025799c1496469fb6a98562e3a5d25d |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-03-12T15:18:49Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-5025799c1496469fb6a98562e3a5d25d2023-08-11T07:27:05ZengFrontiers Media S.A.Frontiers in Public Health2296-25652023-08-011110.3389/fpubh.2023.11646471164647Causal relationship between particulate matter 2.5 and diabetes: two sample Mendelian randomizationJoyce Mary Kim0Joyce Mary Kim1Eunji Kim2Eunji Kim3Do Kyeong Song4Yi-Jun Kim5Ji Hyen Lee6Ji Hyen Lee7Eunhee Ha8Eunhee Ha9Eunhee Ha10Eunhee Ha11Graduate Program in System Health Science and Engineering, College of Medicine, Ewha Womans University, Seoul, Republic of KoreaDepartment of Environmental Medicine, School of Medicine, Ewha Womans University, Seoul, Republic of KoreaGraduate Program in System Health Science and Engineering, College of Medicine, Ewha Womans University, Seoul, Republic of KoreaDepartment of Environmental Medicine, School of Medicine, Ewha Womans University, Seoul, Republic of KoreaDepartment of Internal Medicine, School of Medicine, Ewha Womans University, Seoul, Republic of KoreaDepartment of Environmental Medicine, School of Medicine, Ewha Womans University, Seoul, Republic of KoreaInstitute of Ewha-SCL for Environmental Health (IESEH), College of Medicine, Ewha Womans University, Seoul, Republic of KoreaDepartment of Pediatrics, College of Medicine, Ewha Womans University, Seoul, Republic of KoreaGraduate Program in System Health Science and Engineering, College of Medicine, Ewha Womans University, Seoul, Republic of KoreaDepartment of Environmental Medicine, School of Medicine, Ewha Womans University, Seoul, Republic of KoreaInstitute of Ewha-SCL for Environmental Health (IESEH), College of Medicine, Ewha Womans University, Seoul, Republic of KoreaDepartment of Medical Science, College of Medicine, Ewha Medical Research Institute, Ewha Womans University, Seoul, Republic of KoreaBackgroundsMany 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).MethodsWe 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 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.ResultsFrom 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 machine learning 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).ConclusionWe identified the hypothesis that there is a causal relationship between PM2.5 and diabetes in the European population, using MR methods.https://www.frontiersin.org/articles/10.3389/fpubh.2023.1164647/fullparticulate matter 2.5diabetesgenetics epidemiologyenvironmental epidemiologytwo sample Mendelian randomizationGWAS |
| spellingShingle | Joyce Mary Kim Joyce Mary Kim Eunji Kim Eunji Kim Do Kyeong Song Yi-Jun Kim Ji Hyen Lee Ji Hyen Lee Eunhee Ha Eunhee Ha Eunhee Ha Eunhee Ha Causal relationship between particulate matter 2.5 and diabetes: two sample Mendelian randomization Frontiers in Public Health particulate matter 2.5 diabetes genetics epidemiology environmental epidemiology two sample Mendelian randomization GWAS |
| title | Causal relationship between particulate matter 2.5 and diabetes: two sample Mendelian randomization |
| title_full | Causal relationship between particulate matter 2.5 and diabetes: two sample Mendelian randomization |
| title_fullStr | Causal relationship between particulate matter 2.5 and diabetes: two sample Mendelian randomization |
| title_full_unstemmed | Causal relationship between particulate matter 2.5 and diabetes: two sample Mendelian randomization |
| title_short | Causal relationship between particulate matter 2.5 and diabetes: two sample Mendelian randomization |
| title_sort | causal relationship between particulate matter 2 5 and diabetes two sample mendelian randomization |
| topic | particulate matter 2.5 diabetes genetics epidemiology environmental epidemiology two sample Mendelian randomization GWAS |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2023.1164647/full |
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