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Causal relationship between particulate matter 2.5 and diabetes: two sample Mendelian randomization
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 re...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10450337/ https://www.ncbi.nlm.nih.gov/pubmed/37637811 http://dx.doi.org/10.3389/fpubh.2023.1164647 |
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author | Kim, Joyce Mary Kim, Eunji Song, Do Kyeong Kim, Yi-Jun Lee, Ji Hyen Ha, Eunhee |
author_facet | Kim, Joyce Mary Kim, Eunji Song, Do Kyeong Kim, Yi-Jun Lee, Ji Hyen Ha, Eunhee |
author_sort | Kim, Joyce Mary |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10450337 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104503372023-08-26 Causal relationship between particulate matter 2.5 and diabetes: two sample Mendelian randomization Kim, Joyce Mary Kim, Eunji Song, Do Kyeong Kim, Yi-Jun Lee, Ji Hyen Ha, Eunhee Front Public Health Public Health 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. Frontiers Media S.A. 2023-08-10 /pmc/articles/PMC10450337/ /pubmed/37637811 http://dx.doi.org/10.3389/fpubh.2023.1164647 Text en Copyright © 2023 Kim, Kim, Song, Kim, Lee and Ha. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Public Health Kim, Joyce Mary Kim, Eunji Song, Do Kyeong Kim, Yi-Jun Lee, Ji Hyen Ha, Eunhee 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 |
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 | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10450337/ https://www.ncbi.nlm.nih.gov/pubmed/37637811 http://dx.doi.org/10.3389/fpubh.2023.1164647 |
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