Yi Xingxu, Song Shasha, Cui Zhiqian, Li Ming, Huang Yuxin, Zhang Zichen, Kuang Lingmei, Su Hong
Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, Anhui, China.
Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, China.
Medicine (Baltimore). 2025 May 23;104(21):e42582. doi: 10.1097/MD.0000000000042582.
Several recent observational studies have found associations between nitrogen dioxide (NO2) exposure and the risk of Parkinson's disease (PD), but the causal relationship between them remains unclear. Our objective is to employ a 2-sample Mendelian randomization (MR) approach to determine the causal effect of NO2 exposure on the risk of PD. MR analyses were performed using genome-wide association studies (GWAS) data on NO2 exposure (n = 456,380) and PD GWAS data (33,674 cases and 449,056 controls). Inverse variance weighting (IVW) was the primary analytical method used to examine causal effects, coupled with the MR-Egger, weighted median, weighted model and MR pleiotropy residual sum and outlier (MR-PRESSO). The main results of the IVW method (odds ratio: 4.701; 95% CI: 1.127-19.615, P = .034) showed evidence for a causal relationship between NO2 exposure and the risk of PD. Heterogeneity analyses was conducted using the MR-Egger method (Cochran's Q = 1.155; P = .764) and IVW (Cochran's Q = 1.356; P = .852) demonstrated no statistically significant heterogeneity among the selected SNPs. We employed MR-Egger regression (β intercept = -0.026; SE = 0.058; P = .684) and the MR-PRESSO global test (P = .840), which revealed no significant impact of pleiotropy on the results of the MR evaluation. Based on MR analysis, higher levels of NO2 exposure are causally associated with an increased risk of PD. Consequently, mitigating air pollution could be an important strategy for reducing the risk of PD.
最近的几项观察性研究发现二氧化氮（NO₂）暴露与帕金森病（PD）风险之间存在关联，但它们之间的因果关系仍不明确。我们的目标是采用两样本孟德尔随机化（MR）方法来确定NO₂暴露对PD风险的因果效应。使用关于NO₂暴露的全基因组关联研究（GWAS）数据（n = 456,380）和PD的GWAS数据（33,674例病例和449,056例对照）进行MR分析。逆方差加权（IVW）是用于检验因果效应的主要分析方法，同时结合了MR-Egger、加权中位数、加权模型和MR多效性残差和异常值（MR-PRESSO）。IVW方法的主要结果（优势比：4.701；95%置信区间：1.127 - 19.615，P = 0.034）表明NO₂暴露与PD风险之间存在因果关系的证据。使用MR-Egger方法（ Cochr an's Q = 1.155；P = 0.76）和IVW（ Cochr an's Q = 1.356；P = 0.852）进行的异质性分析表明，所选单核苷酸多态性（SNP）之间没有统计学上的显著异质性。我们采用MR-Egger回归（β截距 = -0.026；标准误 = 0.058；P = 0.684）和MR-PRESSO全局检验（P = 0.84），结果显示多效性对MR评估结果没有显著影响。基于MR分析，更高水平的NO₂暴露与PD风险增加存在因果关联。因此，减轻空气污染可能是降低PD风险的重要策略。
