The First Hospital of Jilin University, No.1 Xinmin Street, Changchun, 130012, China.
Jilin Provincial Institute for Drug Control, Changchun, 130022, China.
Sci Rep. 2024 Jul 12;14(1):16085. doi: 10.1038/s41598-024-67210-7.
Volatile organic compounds (VOCs) represent a significant component of air pollution. However, studies evaluating the impact of VOC exposure on chronic obstructive pulmonary disease (COPD) have predominantly focused on single pollutant models. This study aims to comprehensively assess the relationship between multiple VOC exposures and COPD. A large cross-sectional study was conducted on 4983 participants from the National Health and Nutrition Examination Survey. Four models, including weighted logistic regression, restricted cubic splines (RCS), weighted quantile sum regression (WQS), and the dual-pollution model, were used to explore the association between blood VOC levels and the prevalence of COPD in the U.S. general population. Additionally, six machine learning algorithms were employed to develop a predictive model for COPD risk, with the model's predictive capacity assessed using the area under the curve (AUC) indices. Elevated blood concentrations of benzene, toluene, ortho-xylene, and para-xylene were significantly associated with the incidence of COPD. RCS analysis further revealed a non-linear and non-monotonic relationship between blood levels of toluene and m-p-xylene with COPD prevalence. WQS regression indicated that different VOCs had varying effects on COPD, with benzene and ortho-xylene having the greatest weights. Among the six models, the Extreme Gradient Boosting (XGBoost) model demonstrated the strongest predictive power, with an AUC value of 0.781. Increased blood concentrations of benzene and toluene are significantly correlated with a higher prevalence of COPD in the U.S. population, demonstrating a non-linear relationship. Exposure to environmental VOCs may represent a new risk factor in the etiology of COPD.
挥发性有机化合物（VOCs）是空气污染的重要组成部分。然而，评估 VOC 暴露对慢性阻塞性肺疾病（COPD）影响的研究主要集中在单一污染物模型上。本研究旨在全面评估多种 VOC 暴露与 COPD 之间的关系。对来自国家健康和营养检查调查的 4983 名参与者进行了一项大型横断面研究。使用加权逻辑回归、限制性立方样条（RCS）、加权分位数和回归（WQS）和双重污染模型，研究了美国普通人群血液 VOC 水平与 COPD 患病率之间的关系。此外，还使用了六种机器学习算法来开发 COPD 风险预测模型，并使用曲线下面积（AUC）指数评估模型的预测能力。苯、甲苯、邻二甲苯和对二甲苯的血液浓度升高与 COPD 的发生显著相关。RCS 分析进一步显示，甲苯和间二甲苯的血液水平与 COPD 患病率之间存在非线性和非单调关系。WQS 回归表明，不同的 VOC 对 COPD 有不同的影响，苯和邻二甲苯的权重最大。在六种模型中，极端梯度提升（XGBoost）模型表现出最强的预测能力，AUC 值为 0.781。血液中苯和甲苯浓度的增加与美国人群 COPD 患病率的升高显著相关，表明存在非线性关系。环境 VOC 的暴露可能是 COPD 病因学中的一个新的危险因素。
