Long Wenyan, Wang Xiaohua, Lu Liqin, Wei Zhengang, Yang Jijin
The Affiliated Hospital of Zunyi Medical University, Zunyi, 563099 China.
School of Medical Information Engineering, ZunyiMedical University, Zunyi, 563006 China.
J Diabetes Metab Disord. 2024 May 31;23(2):1899-1908. doi: 10.1007/s40200-024-01440-4. eCollection 2024 Dec.
To identify the independent risk variables that contribute to the emergence of microalbuminuria(MAU) in type 2 diabetes mellitus(T2DM), to develop two different prediction models, and to show the order of importance of the factors in the better prediction model combined with a SHAP(Shapley Additive exPlanations) plot.
Retrospective analysis of data from 981 patients with T2DM from March 2021 to March 2023. This dataset included socio-demographic characteristics, disease attributes, and clinical biochemical indicators. After preprocessing and variable screening, the dataset was randomly divided into training and testing sets at a 7:3 ratio. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance the training set. Subsequently, prediction models for MAU were constructed using two algorithms: Random Forest and BP neural network. The performance of these models was evaluated using k-fold cross-validation (k = 5), and metrics such as the area under the ROC curve (AUC), accuracy, precision, recall, specificity, and F1 score were utilized for assessment.
The final variables selected through multifactorial logistic regression analysis were age, BMI, stroke, diabetic retinopathy(DR), diabetic peripheral vascular disease (DPVD), 25 hydroxyvitamin D (25(OH)D), LDL cholesterol, neutrophil-to-lymphocyte ratio (NLR), and glycated haemoglobin (HbA1c) were used to construct the risk prediction models of Random Forest and BP neural network, respectively, and the Random Forest model demonstrated superior overall performance (AUC = 0.87, Accuracy = 0.80, Precision = 0.79, Recall = 0.84, Specificity = 0.76, F1 Score = 0.81). The SHAP feature matrix plot revealed that HbA1c, NLR, and 25(OH)D were the three most significant factors in predicting the development of MAU in T2DM, with 25(OH)D acting as an independent protective factor.
Effective identification of MAU in T2DM, therapeutic strategies for controllable high-risk factors, and prevention or delay of diabetic kidney disease(DKD) can all be achieved with the help of the risk prediction model developed in this study.
确定导致2型糖尿病（T2DM）患者出现微量白蛋白尿（MAU）的独立风险变量，开发两种不同的预测模型，并结合SHAP（Shapley加性解释）图展示在更好的预测模型中各因素的重要性顺序。
对2021年3月至2023年3月期间981例T2DM患者的数据进行回顾性分析。该数据集包括社会人口统计学特征、疾病属性和临床生化指标。经过预处理和变量筛选后，数据集以7:3的比例随机分为训练集和测试集。为解决类别不平衡问题，应用合成少数过采样技术（SMOTE）来平衡训练集。随后，使用随机森林和BP神经网络两种算法构建MAU的预测模型。使用k折交叉验证（k = 5）评估这些模型的性能，并利用ROC曲线下面积（AUC）、准确率、精确率、召回率、特异性和F1分数等指标进行评估。
通过多因素逻辑回归分析最终选择的变量为年龄、体重指数、中风、糖尿病视网膜病变（DR）、糖尿病外周血管疾病（DPVD）、25羟维生素D（25(OH)D）、低密度脂蛋白胆固醇、中性粒细胞与淋巴细胞比值（NLR）和糖化血红蛋白（HbA1c），分别用于构建随机森林和BP神经网络的风险预测模型，随机森林模型表现出更优的整体性能（AUC = 0.87，准确率 = 0.80，精确率 = 0.79，召回率 = 0.84，特异性 = 0.76，F1分数 = 0.81）。SHAP特征矩阵图显示，HbA1c、NLR和25(OH)D是预测T2DM患者MAU发生的三个最重要因素，其中25(OH)D为独立保护因素。
借助本研究开发的风险预测模型，可有效识别T2DM患者中的MAU，制定可控高危因素的治疗策略，并预防或延缓糖尿病肾病（DKD）。
