Wang Xian, Liu Xiaming, Zhao Jun, Chen Manyu, Wang Lidong
Graduate School of Chengde Medical College, Chengde, Hebei, People's Republic of China.
Department of Endocrinology and Immunology, Chengde Central Hospital Affiliated to Chengde Medical College, Chengde, Hebei, People's Republic of China.
Diabetes Metab Syndr Obes. 2024 Jan 12;17:215-225. doi: 10.2147/DMSO.S442925. eCollection 2024.
To investigate the predictors of diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM) patients and establish a nomogram model for predicting the risk of DKD.
The clinical data of T2DM patients, admitted to the Endocrinology Department of Chengde Central Hospital from October 2019 to September 2020 and divided into a case group or a control group based on whether they had DKD, were collected. The predictive factors of DKD were screened by univariate and multivariate analysis, and a nomogram prediction model was constructed for the risk of DKD in T2DM. Bootstrapping was used for model validation, receiver operating characteristic (ROC) curve and GiViTI calibration curve were used for evaluating the discrimination and calibration of prediction model, and decision analysis curve (DCA) was used for evaluating the practicality of model.
Predictors for DKD are diabetic retinopathy (DR), hypertension, history of gout, smoking history, using insulin, elevation of body mass index (BMI), triglyceride (TG), cystatin C (Cys-C), and reduction of 25 (OH) D. The nomogram prediction model based on the above nine predictors had good representativeness (Bootstrap method: precision: 0.866, Kappa: 0.334), differentiation [the area under curve (AUC) value: 0.868], and accuracy (GiViTI-corrected curved bands, P = 0.836); the DAC curve analysis showed that the prediction model, whose threshold probability was in the range of 0.10 to 0.70, had clinical practical value.
The risk of DKD in T2DM could be predicted accurately by DR, hypertension, history of gout, smoking history, using insulin, elevation of BMI, TG, Cys-C, and reduction of 25 (OH) D.
探讨2型糖尿病（T2DM）患者糖尿病肾病（DKD）的预测因素，并建立预测DKD风险的列线图模型。
收集2019年10月至2020年9月在承德市中心医院内分泌科住院的T2DM患者的临床资料，根据是否患有DKD分为病例组和对照组。通过单因素和多因素分析筛选DKD的预测因素，并构建T2DM患者DKD风险的列线图预测模型。采用自抽样法进行模型验证，受试者工作特征（ROC）曲线和GiViTI校准曲线用于评估预测模型的区分度和校准度，决策分析曲线（DCA）用于评估模型的实用性。
DKD的预测因素包括糖尿病视网膜病变（DR）、高血压、痛风病史、吸烟史、使用胰岛素、体重指数（BMI）升高、甘油三酯（TG）、胱抑素C（Cys-C）升高以及25（OH）D降低。基于上述9个预测因素的列线图预测模型具有良好的代表性（自抽样法：精度：0.866，Kappa：0.334）、区分度[曲线下面积（AUC）值：0.868]和准确性（GiViTI校正曲线带，P = 0.836）；DAC曲线分析表明，阈值概率在0.10至0.70范围内的预测模型具有临床实用价值。
通过DR、高血压、痛风病史、吸烟史、使用胰岛素、BMI升高、TG、Cys-C升高以及25（OH）D降低，可以准确预测T2DM患者发生DKD的风险。
