Dataset Information

Causal associations of particulate matter 2.5 and cardiovascular disease: A two-sample mendelian randomization study.

ABSTRACT:

Background

According to epidemiological studies, particulate matter 2.5 (PM2.5) is a significant contributor to cardiovascular disease (CVD). However, making causal inferences is difficult due to the methodological constraints of observational studies. In this study, we used two-sample Mendelian randomization (MR) to examine the causal relationship between PM 2.5 and the risk of CVD.

Methods

Genome-wide association study (GWAS) statistics for PM2.5 and CVD were collected from the FinnGen and UK Biobanks. Mendelian randomization analyses were applied to explore the causal effects of PM2.5 on CVD by selecting single-nucleotide polymorphisms(SNP) as instrumental variables.

Results

The results revealed that a causal effect was observed between PM2.5 and coronary artery disease(IVW: OR 2.06, 95% CI 1.35, 3.14), and hypertension(IVW: OR 1.07, 95% CI 1.03, 1.12). On the contrary, no causal effect was observed between PM2.5 and myocardial infarction(IVW: OR 0.73, 95% CI 0.44, 1.22), heart failure(IVW: OR 1.54, 95% CI 0.96, 2.47), atrial fibrillation(IVW: OR 1.03, 95% CI 0.71, 1.48), and ischemic stroke (IS)(IVW: OR 0.98, 95% CI 0.54, 1.77).

Conclusion

We discovered that there is a causal link between PM2.5 and coronary artery disease and hypertension in the European population, using MR methods. Our discovery may have the significance of public hygiene to improve the understanding of air quality and CVD risk.

SUBMITTER: Cao Y

PROVIDER: S-EPMC10997086 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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Publications

Causal associations of particulate matter 2.5 and cardiovascular disease: A two-sample mendelian randomization study.

Cao Ye Y Feng Yi Y Xia Nan N Zhang Jiancheng J

PloS one 20240405 4

<h4>Background</h4>According to epidemiological studies, particulate matter 2.5 (PM2.5) is a significant contributor to cardiovascular disease (CVD). However, making causal inferences is difficult due to the methodological constraints of observational studies. In this study, we used two-sample Mendelian randomization (MR) to examine the causal relationship between PM 2.5 and the risk of CVD.<h4>Methods</h4>Genome-wide association study (GWAS) statistics for PM2.5 and CVD were collected from the ...[more]

PMID: 38578766

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Project 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.

Dataset Information

Causal associations of particulate matter 2.5 and cardiovascular disease: A two-sample mendelian randomization study.

Background

Methods

Results

Conclusion

Publications

Causal associations of particulate matter 2.5 and cardiovascular disease: A two-sample mendelian randomization study.

Similar Datasets

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