Table 3_Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms.xlsx

ObjectiveTo develop a self-reportable risk assessment tool for elderly type 2 diabetes mellitus (T2DM) patients, evaluating risks of diabetic nephropathy (DN), retinopathy (DR), peripheral neuropathy (DPN), and diabetic foot (DF) using machine learning, thereby providing new insights and tools for the screening and intervention of these complications.
Materials and methodsData from 1,448 T2DM patients at Xi’an No.9 Hospital were used. After preprocessing, five machine learning algorithms (XGBoost, LightGBM, Random Forest, TabPFN, CatBoost) were applied. Models were trained on 70% of the data and evaluated on 30%, with performance assessed by multiple metrics and SHAP analysis for feature importance.
ResultsThe analysis identified 33 risk factors, including 6 shared risk factors (UACR for DN and DR; diabetes duration for DR, DPN, and DF; IBILI for DF and DPN; history of DN for DR and DF; U-Cr for DR and DF; MCHC for DN and DPN) and 27 unique risk factors. Model performance was robust: for DN, TabPFN achieved an AUC of 0.905 and Random Forest an accuracy of 0.878; for DR, LightGBM attained an AUC of 0.794; for DPN, both TabPFN and CatBoost achieved a perfect recall of 1.000 and F1-score of 0.915; and for DF, LightGBM attaining the highest AUC of 0.704. SHAP analysis highlighted key features for each complication, such as UACR and Y-protein for DN, diabetes duration and TPOAB for DR, history of DN and IBILI for DF, and diabetes duration and SBP for DPN.
ConclusionThis study employed interpretable machine learning to characterize risk factor profiles for multiple T2DM complications, identifying both common and distinct factors associated with major complications. The findings provide a foundation for exploring personalized risk management strategies and highlight the potential of data-driven approaches to inform early intervention research in T2DM complications.

研究目的：开发一款可自我报告的老年2型糖尿病（Type 2 Diabetes Mellitus, T2DM）患者风险评估工具，采用机器学习方法评估糖尿病肾病（Diabetic Nephropathy, DN）、糖尿病视网膜病变（Retinopathy, DR）、糖尿病周围神经病变（Peripheral Neuropathy, DPN）及糖尿病足（Diabetic Foot, DF）的发病风险，以期为上述并发症的筛查与干预提供全新的研究视角与实用工具。
材料与方法：本研究纳入西安市第九医院的1448例T2DM患者临床数据。经预处理后，采用5种机器学习算法，即极端梯度提升树（XGBoost）、轻量级梯度提升机（LightGBM）、随机森林（Random Forest）、TabPFN、类别提升树（CatBoost）开展建模。以70%的数据作为训练集、30%作为测试集对模型进行训练与评估，通过多维度指标衡量模型性能，并采用SHAP分析解析特征重要性。
研究结果：本分析共识别出33项风险因素，其中包括6项共有风险因素：UACR（尿白蛋白/肌酐比值）可同时关联糖尿病肾病与糖尿病视网膜病变；糖尿病病程可关联糖尿病视网膜病变、糖尿病周围神经病变与糖尿病足；IBILI可关联糖尿病足与糖尿病周围神经病变；糖尿病肾病病史可关联糖尿病视网膜病变与糖尿病足；U-Cr（尿肌酐）可关联糖尿病视网膜病变与糖尿病足；平均红细胞血红蛋白浓度（MCHC）可关联糖尿病肾病与糖尿病周围神经病变。另有27项特异性风险因素。模型性能表现稳健：针对糖尿病肾病，TabPFN的受试者工作特征曲线下面积（AUC）达0.905，随机森林的准确率达0.878；针对糖尿病视网膜病变，LightGBM的AUC为0.794；针对糖尿病周围神经病变，TabPFN与CatBoost的召回率均为1.000，F1分数均为0.915；针对糖尿病足，LightGBM的AUC最高，达0.704。SHAP分析明确了各并发症对应的关键特征，例如与糖尿病肾病相关的UACR与Y蛋白、与糖尿病视网膜病变相关的糖尿病病程与甲状腺过氧化物酶抗体（Thyroid Peroxidase Antibody, TPOAB）、与糖尿病足相关的糖尿病肾病病史与IBILI、与糖尿病周围神经病变相关的糖尿病病程与收缩压（Systolic Blood Pressure, SBP）。
结论：本研究采用可解释性机器学习方法，刻画了2型糖尿病多种并发症的风险因素谱，识别出与主要并发症相关的共有与特异性风险因素。研究结果为探索个性化风险管理策略奠定了基础，同时凸显了数据驱动方法在2型糖尿病并发症早期干预研究中的应用潜力。