Li Wenxiang, Huang Guangyi, Tang Ningning, Lu Peng, Jiang Li, Lv Jian, Qin Yuanjun, Lin Yunru, Xu Fan, Lei Daizai
Department of Ophthalmology, the People's Hospital of Guangxi Zhuang Autonomous Region & Guangxi Key Laboratory of Eye Health & Guangxi Health Commission Key Laboratory of Ophthalmology and Related Systemic Diseases Artificial Intelligence Screening Technology & Institute of Ophthalmic Diseases, Guangxi Academy of Medical Sciences, Nanning, 530021, People's Republic of China.
Department of Ophthalmology, The People's Hospital of Guangxi Zhuang Autonomous Region, 6 Taoyuan Road, Qingxiu District, Nanning, 530000, China.
Environ Sci Pollut Res Int. 2023 Oct;30(48):105181-105193. doi: 10.1007/s11356-023-29887-7. Epub 2023 Sep 15.
The phenomenon of population aging has brought forth the challenge of frailty. Nevertheless, the contribution of environmental exposure to frailty remains ambiguous. Our objective was to investigate the association between phenols, phthalates (PAEs), and polycyclic aromatic hydrocarbons (PAHs) with frailty. We constructed a 48-item frailty index using data from the National Health and Nutrition Examination Survey (NHANES). The exposure levels of 20 organic contaminants were obtained from the survey circle between 2005 and 2016. The association between individual organic contaminants and the frailty index was assessed using negative binomial regression models. The combined effect of organic contaminants was examined using weighted quantile sum (WQS) regression. Dose-response patterns were modeled using generalized additive models (GAMs). Additionally, an interpretable machine learning approach was employed to develop a predictive model for the frailty index. A total of 1566 participants were included in the analysis. Positive associations were observed between exposure to MIB, P02, ECP, MBP, MHH, MOH, MZP, MC1, and P01 with the frailty index. WQS regression analysis revealed a significant increase in the frailty index with higher levels of the mixture of organic contaminants (aOR, 1.12; 95% CI, 1.05-1.20; p < 0.001), with MIB, ECP, COP, MBP, P02, and P01 identified as the major contributors. Dose-response relationships were observed between MIB, ECP, MBP, P02, and P01 exposure with an increased risk of frailty (both with p < 0.05). The developed predictive model based on organic contaminants exposure demonstrated high performance, with an R of 0.9634 and 0.9611 in the training and testing sets, respectively. Furthermore, the predictive model suggested potential synergistic effects in the MIB-MBP and P01-P02 pairs. Taken together, these findings suggest a significant association between exposure to phthalates and PAHs with an increased susceptibility to frailty.
人口老龄化现象带来了衰弱问题的挑战。然而，环境暴露对衰弱的影响仍不明确。我们的目标是研究酚类、邻苯二甲酸酯（PAEs）和多环芳烃（PAHs）与衰弱之间的关联。我们利用美国国家健康与营养检查调查（NHANES）的数据构建了一个包含48个项目的衰弱指数。20种有机污染物的暴露水平来自2005年至2016年的调查周期。使用负二项回归模型评估个体有机污染物与衰弱指数之间的关联。使用加权分位数和（WQS）回归检查有机污染物的综合效应。使用广义相加模型（GAMs）对剂量反应模式进行建模。此外，采用一种可解释的机器学习方法来开发衰弱指数的预测模型。共有1566名参与者纳入分析。观察到暴露于甲基异丁基酮（MIB）、P02、2-乙基己基邻苯二甲酸酯（ECP）、邻苯二甲酸丁苄酯（MBP）、2-甲基己醇（MHH）、2-甲基-1-己醇（MOH）、邻苯二甲酸二正丙酯（MZP）、邻苯二甲酸二环己酯（MC1）和邻苯二甲酸二异壬酯（P01）与衰弱指数呈正相关。WQS回归分析显示，随着有机污染物混合物水平升高，衰弱指数显著增加（调整后比值比，1.12；95%置信区间，1.05 - 1.20；p < 0.001），其中MIB、ECP、邻苯二甲酸二正辛酯（COP）、MBP、P02和P01被确定为主要贡献因素。观察到MIB、ECP、MBP、P02和P01暴露与衰弱风险增加之间存在剂量反应关系（两者p < 0.0）。基于有机污染物暴露开发的预测模型表现良好，训练集和测试集的R分别为0.9634和0.9611。此外，预测模型表明MIB - MBP和P01 - P02对之间存在潜在的协同效应。综上所述，这些发现表明邻苯二甲酸酯和PAHs暴露与衰弱易感性增加之间存在显著关联。
