Zhang Bowen, Chen Liang, Li Tao
Department of Anesthesiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, China; Laboratory of Mitochondrial Metabolism and Perioperative Medicine, National-Local Joint Engineering Research Centre of Translational Medicine of Anesthesiology, West China Hospital, Sichuan University, Chengdu 610041, China; Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China.
Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu 610041, China.
Ecotoxicol Environ Saf. 2025 Mar 1;292:117945. doi: 10.1016/j.ecoenv.2025.117945. Epub 2025 Feb 22.
Exposure to three primary xenoestrogens (XEs), including phthalates, parabens, and phenols, has been strongly associated with chronic kidney disease (CKD). An interpretable machine learning (ML) model was developed to predict CKD using data from the National Health and Nutrition Examination Survey (NHANES) database spanning from 2007 to 2016. Four ML algorithms-random forest classifier (RF), XGBoost (XGB), k-nearest neighbors (KNN), and support vector machine (SVM)-were used alongside traditional logistic regression to predict CKD. The study included 6910 U.S. adults, with XGB showing the highest predictive accuracy, achieving an area under the curve (AUC) of 0.817 (95 % CI: 0.789, 0.844). The selected model was interpreted using Shapley additive explanations (SHAP) and partial dependence plot (PDP). The SHAP method identified key predictive features for CKD in urinary metabolites of XEs-methyl paraben (MeP), mono-(carboxynonyl) phthalate (MCNP), and triclosan (TCS)-and suggested personalized CKD care should focus on XE control. PDP results confirmed that, within certain ranges, MeP levels positively impacted the model, MCNP levels negatively impacted it, and TCS had a mixed effect. The synergistic effects suggested that managing urinary MeP levels could be essential for the effective control of CKD. In summary, our research highlights the significant predictive potential of XEs for CKD, especially MeP, MCNP, and TCS.
接触三种主要的外源性雌激素（XEs），包括邻苯二甲酸盐、对羟基苯甲酸酯和酚类，与慢性肾脏病（CKD）密切相关。利用2007年至2016年美国国家健康与营养检查调查（NHANES）数据库中的数据，开发了一种可解释的机器学习（ML）模型来预测CKD。使用四种ML算法——随机森林分类器（RF）、XGBoost（XGB）、k近邻（KNN）和支持向量机（SVM）——以及传统逻辑回归来预测CKD。该研究纳入了6910名美国成年人，其中XGB显示出最高的预测准确性，曲线下面积（AUC）达到0.817（95%CI：0.789，0.844）。使用夏普利加法解释（SHAP）和偏依赖图（PDP）对所选模型进行了解释。SHAP方法确定了XEs尿液代谢物中CKD的关键预测特征——对羟基苯甲酸甲酯（MeP）、单（羧基壬基）邻苯二甲酸酯（MCNP）和三氯生（TCS），并建议个性化的CKD护理应侧重于控制XEs。PDP结果证实，在一定范围内，MeP水平对模型有正向影响，MCNP水平对模型有负向影响，TCS有混合效应。协同效应表明，控制尿液中MeP水平可能对有效控制CKD至关重要。总之，我们的研究突出了XEs对CKD的显著预测潜力，尤其是MeP、MCNP和TCS。
