The aim of this study is to analyze the effect of serum metabolites on diabetic nephropathy (DN) and predict the prevalence of DN through a machine learning approach. The dataset consists of 548 patients from April 2018 to April 2019 in Second Affiliated Hospital of Dalian Medical University (SAHDMU). We select the optimal 38 features through a Least absolute shrinkage and selection operator (LASSO) regression model and a 10-fold cross-validation. We compare four machine learning algorithms, including eXtreme Gradient Boosting (XGB), random forest, decision tree and logistic regression, by AUC-ROC curves, decision curves, calibration curves. We quantify feature importance and interaction effects in the optimal predictive model by Shapley Additive exPlanations (SHAP) method. The XGB model has the best performance to screen for DN with the highest AUC value of 0.966. The XGB model also gains more clinical net benefits than others and the fitting degree is better. In addition, there are significant interactions between serum metabolites and duration of diabetes. We develop a predictive model by XGB algorithm to screen for DN. C2, C5DC, Tyr, Ser, Met, C24, C4DC, and Cys have great contribution in the model, and can possibly be biomarkers for DN.

翻译：本研究旨在分析血清代谢物对糖尿病肾病（DN）的影响，并通过机器学习方法预测DN的患病率。数据集包含2018年4月至2019年4月大连医科大学附属第二医院（SAHDMU）的548名患者。我们通过最小绝对收缩与选择算子（LASSO）回归模型和10折交叉验证筛选出最优的38个特征。我们通过AUC-ROC曲线、决策曲线和校准曲线比较了四种机器学习算法，包括极端梯度提升（XGB）、随机森林、决策树和逻辑回归。我们利用Shapley加性解释（SHAP）方法量化了最优预测模型中的特征重要性和交互效应。XGB模型在筛查DN方面表现最佳，AUC值最高达0.966。该模型在临床净收益方面也优于其他模型，且拟合度更佳。此外，血清代谢物与糖尿病病程之间存在显著的交互作用。我们基于XGB算法开发了用于筛查DN的预测模型。C2、C5DC、Tyr、Ser、Met、C24、C4DC和Cys在该模型中贡献显著，可能成为DN的生物标志物。