PDF(Pubmed)
Abstract：
Diabetic retinopathy (DR) is one of the leading causes of adult blindness in the United States. Although studies applying traditional statistical methods have revealed that heavy metals may be essential environmental risk factors for diabetic retinopathy, there is a lack of analyses based on machine learning (ML) methods to adequately explain the complex relationship between heavy metals and DR and the interactions between variables. Based on characteristic variables of participants with and without DR and heavy metal exposure data obtained from the NHANES database (2003-2010), a ML model was developed for effective prediction of DR. The best predictive model for DR was selected from 11 models by receiver operating characteristic curve (ROC) analysis. Further permutation feature importance (PFI) analysis, partial dependence plots (PDP) analysis, and SHapley Additive exPlanations (SHAP) analysis were used to assess the model capability and key influencing factors. A total of 1042 eligible individuals were randomly assigned to two groups for training and testing set of the prediction model. ROC analysis showed that the k-nearest neighbour (KNN) model had the highest prediction performance, achieving close to 100% accuracy in the testing set. Urinary Sb level was identified as the critical heavy metal affecting the predicted risk of DR, with a contribution weight of 1.730632 ± 1.791722, which was much higher than that of other heavy metals and baseline variables. The results of the PDP analysis and the SHAP analysis also indicated that antimony (Sb) had a more significant effect on DR. The interaction between age and Sb was more significant compared to other variables and metal pairs. We found that Sb could serve as a potential predictor of DR and that Sb may influence the development of DR by mediating cellular and systemic senescence. The study revealed that monitoring urinary Sb levels can be useful for early non-invasive screening and intervention in DR development, and also highlighted the important role of constructed ML models in explaining the effects of heavy metal exposure on DR.
摘要：
糖尿病视网膜病变（DR）是美国成人失明的主要原因之一。尽管应用传统统计方法的研究表明，重金属可能是糖尿病视网膜病变的基本环境危险因素，缺乏基于机器学习（ML）方法的分析来充分解释重金属与DR之间的复杂关系以及变量之间的相互作用。根据从NHANES数据库（2003-2010）获得的具有和不具有DR和重金属暴露数据的参与者的特征变量，建立了一种有效预测DR的ML模型。通过受试者工作特征曲线（ROC）分析，从11个模型中选择DR的最佳预测模型。进一步的排列特征重要性(PFI)分析，部分依赖图(PDP)分析，和Shapley加法扩张（SHAP）分析用于评估模型能力和关键影响因素。共有1042名符合条件的个体被随机分配到两组，用于预测模型的训练和测试集。ROC分析表明,k-近邻(KNN)模型具有最高的预测性能,在测试集中实现接近100%的准确度。尿Sb水平被确定为影响DR预测风险的关键重金属，贡献权重为1.730632±1.791722，远高于其他重金属和基线变量。PDP分析和SHAP分析的结果还表明,锑(Sb)对DR具有更显著的影响。与其他变量和金属对相比，年龄和Sb之间的相互作用更为显着。我们发现Sb可以作为DR的潜在预测因子，并且Sb可能通过介导细胞和全身衰老来影响DR的发展。研究表明，监测尿Sb水平可用于早期非侵入性筛查和干预DR的发展，并强调了构建的ML模型在解释重金属暴露对DR的影响方面的重要作用。
