Abstract：
BACKGROUND: Machine learning models have promising applications in capturing the complex relationship between mixtures of exposures and outcomes.
OBJECTIVE: Our study aimed at introducing an explainable machine learning (EML) model to assess the association between metal mixtures with potentially opposing renal effects and renal function in middle-aged and older adults.
METHODS: This study extracted data from two cycle years of the National Health and Nutrition Examination Survey (NHANES). Participants aged 45 years or older with complete data on six metals (lead, cadmium, manganese, mercury, and selenium) and related covariates were enrolled. The EML model was developed by the optimized machine learning model together with Shapley Additive exPlanations (SHAP) to assess the chronic kidney disease (CKD) risk with metal mixtures. The results from EML were further compared in detail with multiple logistic regression (MLR) and Bayesian kernel machine regression (BKMR).
RESULTS: After adjusting for included covariates, MLR pointed out the lead and arsenic were generally positively associated with CKD, but manganese had a negative association. In the BKMR analysis, each metal was found to have a non-linear association with the risk of CKD, and interactions can exist between metals, especially for arsenic and lead. The EML ranked the feature importance: lead, manganese, arsenic and selenium were close behind in importance after gender, age or BMI for participants with CKD. Strong interactions between mercury and lead, manganese and cadmium and arsenic and manganese were identified by partial dependence plot (PDP) of SHAP and bivariate exposure-response effect plots of BKMR. The EML model determined the \"trigger point\" at which the risk of CKD abruptly changed.
CONCLUSIONS: Co-exposure to metals with different nephrotoxicity could have different joint association with renal function, and EML can be a powerful method for studying complex exposure mixtures.
OBJECTIVE: Our study aimed at introducing an explainable machine learning (EML) model to assess the association between metal mixtures with potentially opposing renal effects and renal function in middle-aged and older adults.
METHODS: This study extracted data from two cycle years of the National Health and Nutrition Examination Survey (NHANES). Participants aged 45 years or older with complete data on six metals (lead, cadmium, manganese, mercury, and selenium) and related covariates were enrolled. The EML model was developed by the optimized machine learning model together with Shapley Additive exPlanations (SHAP) to assess the chronic kidney disease (CKD) risk with metal mixtures. The results from EML were further compared in detail with multiple logistic regression (MLR) and Bayesian kernel machine regression (BKMR).
RESULTS: After adjusting for included covariates, MLR pointed out the lead and arsenic were generally positively associated with CKD, but manganese had a negative association. In the BKMR analysis, each metal was found to have a non-linear association with the risk of CKD, and interactions can exist between metals, especially for arsenic and lead. The EML ranked the feature importance: lead, manganese, arsenic and selenium were close behind in importance after gender, age or BMI for participants with CKD. Strong interactions between mercury and lead, manganese and cadmium and arsenic and manganese were identified by partial dependence plot (PDP) of SHAP and bivariate exposure-response effect plots of BKMR. The EML model determined the \"trigger point\" at which the risk of CKD abruptly changed.
CONCLUSIONS: Co-exposure to metals with different nephrotoxicity could have different joint association with renal function, and EML can be a powerful method for studying complex exposure mixtures.
摘要：
背景：机器学习模型在捕获曝光和结果混合之间的复杂关系方面具有很好的应用前景。
目的：我们的研究旨在引入一种可解释的机器学习（EML）模型，以评估金属混合物与中老年人潜在的相反肾脏作用与肾功能之间的关联。
方法：本研究从国家健康和营养调查（NHANES）的两个周期中提取了数据。45岁或以上的参与者，有六种金属(铅，镉,锰，水银,和硒）和相关协变量被纳入。EML模型是通过优化的机器学习模型与Shapley添加剂移植（SHAP）一起开发的，用于评估金属混合物的慢性肾脏疾病（CKD）风险。进一步将EML的结果与多元逻辑回归（MLR）和贝叶斯核机回归（BKMR）进行了详细比较。
结果：调整包含的协变量后，MLR指出铅和砷与CKD呈正相关,但是锰有负相关性。在BKMR分析中,发现每种金属与CKD的风险具有非线性关联，金属之间可以存在相互作用，尤其是砷和铅.EML对功能重要性进行了排名：铅，锰，在性别之后，砷和硒的重要性紧随其后，CKD患者的年龄或BMI。汞和铅之间强烈的相互作用，通过SHAP的部分依赖图（PDP）和BKMR的双变量暴露-响应效应图来鉴定锰和镉以及砷和锰。EML模型确定CKD风险突然改变的“触发点”。
结论：共同暴露于具有不同肾毒性的金属可能与肾功能有不同的联合关联，EML可以是研究复杂暴露混合物的强大方法。
目的：我们的研究旨在引入一种可解释的机器学习（EML）模型，以评估金属混合物与中老年人潜在的相反肾脏作用与肾功能之间的关联。
方法：本研究从国家健康和营养调查（NHANES）的两个周期中提取了数据。45岁或以上的参与者，有六种金属(铅，镉,锰，水银,和硒）和相关协变量被纳入。EML模型是通过优化的机器学习模型与Shapley添加剂移植（SHAP）一起开发的，用于评估金属混合物的慢性肾脏疾病（CKD）风险。进一步将EML的结果与多元逻辑回归（MLR）和贝叶斯核机回归（BKMR）进行了详细比较。
结果：调整包含的协变量后，MLR指出铅和砷与CKD呈正相关,但是锰有负相关性。在BKMR分析中,发现每种金属与CKD的风险具有非线性关联，金属之间可以存在相互作用，尤其是砷和铅.EML对功能重要性进行了排名：铅，锰，在性别之后，砷和硒的重要性紧随其后，CKD患者的年龄或BMI。汞和铅之间强烈的相互作用，通过SHAP的部分依赖图（PDP）和BKMR的双变量暴露-响应效应图来鉴定锰和镉以及砷和锰。EML模型确定CKD风险突然改变的“触发点”。
结论：共同暴露于具有不同肾毒性的金属可能与肾功能有不同的联合关联，EML可以是研究复杂暴露混合物的强大方法。
