Jiang Yu-Xin, Gui Si-Yu, Sun Xiao-Dong
Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China.
National Clinical Research Center for Eye Diseases, Shanghai 200080, China.
Int J Ophthalmol. 2025 Jul 18;18(7):1214-1230. doi: 10.18240/ijo.2025.07.04. eCollection 2025.
To investigate the associations between urinary dialkyl phosphate (DAP) metabolites of organophosphorus pesticides (OPPs) exposure and age-related macular degeneration (AMD) risk.
Participants were drawn from the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2008. Urinary DAP metabolites were used to construct a machine learning (ML) model for AMD prediction. Several interpretability pipelines, including permutation feature importance (PFI), partial dependence plot (PDP), and SHapley Additive exPlanations (SHAP) analyses were employed to analyze the influence from exposure features to prediction outcomes.
A total of 1845 participants were included and 137 were diagnosed with AMD. Receiver operating characteristic curve (ROC) analysis evaluated Random Forests (RF) as the best ML model with its optimal predictive performance among eleven models. PFI and SHAP analyses illustrated that DAP metabolites were of significant contribution weights in AMD risk prediction, higher than most of the socio-demographic covariates. Shapley values and waterfall plots of randomly selected AMD individuals emphasized the predictive capacity of ML with high accuracy and sensitivity in each case. The relationships and interactions visualized by graphical plots and supported by statistical measures demonstrated the indispensable impacts from six DAP metabolites to the prediction of AMD risk.
Urinary DAP metabolites of OPPs exposure are associated with AMD risk and ML algorithms show the excellent generalizability and differentiability in the course of AMD risk prediction.
探讨有机磷农药（OPPs）暴露的尿中二烷基磷酸酯（DAP）代谢物与年龄相关性黄斑变性（AMD）风险之间的关联。
研究对象来自2005年至2008年的美国国家健康与营养检查调查（NHANES）。尿DAP代谢物用于构建预测AMD的机器学习（ML）模型。采用了几种可解释性方法，包括排列特征重要性（PFI）、部分依赖图（PDP）和SHapley加性解释（SHAP）分析，以分析暴露特征对预测结果的影响。
共纳入1845名参与者，其中137人被诊断为AMD。受试者工作特征曲线（ROC）分析评估随机森林（RF）为11种模型中预测性能最佳的ML模型。PFI和SHAP分析表明，DAP代谢物在AMD风险预测中具有显著的贡献权重，高于大多数社会人口统计学协变量。随机选择的AMD个体的Shapley值和瀑布图强调了ML在每种情况下具有高精度和高敏感性的预测能力。通过图形绘制可视化并得到统计量支持的关系和相互作用证明了六种DAP代谢物对AMD风险预测的不可或缺的影响。
OPPs暴露的尿DAP代谢物与AMD风险相关，并且ML算法在AMD风险预测过程中显示出优异的泛化性和区分性。
