Department of Ophthalmology, Anqing Second People's Hospital, 79 Guanyuemiao Street, Anqing, 246004, China.
Department of Ophthalmology, The Second Affiliated Hospital of Anhui Medical University, 678 Furong Road, Hefei, 230601, China.
Sci Rep. 2024 Jun 6;14(1):13049. doi: 10.1038/s41598-024-63916-w.
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.
糖尿病性视网膜病变（DR）是美国成年人失明的主要原因之一。虽然应用传统统计方法的研究表明，重金属可能是糖尿病性视网膜病变的重要环境风险因素，但缺乏基于机器学习（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。基于来自 NHANES 数据库（2003-2010 年）的患有和不患有 DR 以及重金属暴露数据的参与者的特征变量，开发了一种用于有效预测 DR 的 ML 模型。通过接收者操作特征曲线（ROC）分析，从 11 个模型中选择了用于 DR 的最佳预测模型。进一步进行置换特征重要性（PFI）分析、部分依赖图（PDP）分析和 SHapley Additive exPlanations（SHAP）分析，以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个组，用于预测模型的训练集和测试集。ROC 分析表明，k-最近邻（KNN）模型具有最高的预测性能，在测试集中的准确率接近 100%。尿锑水平被确定为影响 DR 预测风险的关键重金属，其贡献权重为 1.730632±1.791722，远高于其他重金属和基线变量。PDP 分析和 SHAP 分析的结果也表明，锑（Sb）对 DR 的影响更大。与其他变量和金属对相比，年龄与 Sb 之间的相互作用更为显著。我们发现 Sb 可作为 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 的发生。该研究表明，监测尿锑水平对于早期非侵入性筛查和干预 DR 的发展可能是有用的，并且突出了构建的 ML 模型在解释重金属暴露对 DR 的影响方面的重要作用。
