Miao Dan Dan, Pan En Chun, Zhang Qin, Sun Zhong Ming, Qin Yu, Wu Ming
Department of Chronic Disease Prevention and Control, Huai'an City Center for Disease Control and Prevention, Huai'an 223001, Jiangsu, China.
Department of Chronic Disease Prevention and Control, Jiangsu Provincial Center for Disease Control and Prevention, Nanjing 210009, Jiangsu, China.
Biomed Environ Sci. 2017 Feb;30(2):106-112. doi: 10.3967/bes2017.014.
To develop a risk model for predicting later development of diabetic nephropathy (DN) in Chinese people with type 2 diabetes mellitus (T2DM) and evaluate its performance with independent validation.
We used data collected from the project 'Comprehensive Research on the Prevention and Control of Diabetes', which was a community-based study conducted by the Jiangsu Center for Disease Control and Prevention in 2013. A total of 11,771 eligible participants were included in our study. The endpoint was a clear diagnosis of DN. Data was divided into two components: a training set for model development and a test set for validation. The Cox proportional hazard regression was used for survival analysis in men and women. The model's performance was evaluated by discrimination and calibration.
The incidence (cases per 10,000 person-years) of DN was 9.95 (95% CI; 8.66-11.43) in women and 11.28 (95% CI; 9.77-13.03) in men. Factors including diagnosis age, location, body mass index, high-density-lipoprotein cholesterol, creatinine, hypertension, dyslipidemia, retinopathy, diet control, and physical activity were significant in the final model. The model showed high discrimination and good calibration.
The risk model for predicting DN in people with T2DM can be used in clinical practice for improving the quality of risk management and intervention.
建立一个预测中国2型糖尿病（T2DM）患者糖尿病肾病（DN）后期发生风险的模型，并通过独立验证评估其性能。
我们使用了从“糖尿病防控综合研究”项目收集的数据，该项目是江苏省疾病预防控制中心于2013年开展的一项基于社区的研究。共有11771名符合条件的参与者纳入我们的研究。终点是明确诊断为DN。数据分为两个部分：用于模型开发的训练集和用于验证的测试集。采用Cox比例风险回归对男性和女性进行生存分析。通过区分度和校准来评估模型的性能。
女性DN的发病率（每10000人年的病例数）为9.95（95%CI：8.66 - 11.43），男性为11.28（95%CI：9.77 - 13.03）。包括诊断年龄、地区、体重指数、高密度脂蛋白胆固醇、肌酐、高血压、血脂异常、视网膜病变、饮食控制和体育活动等因素在最终模型中具有显著性。该模型显示出高区分度和良好的校准。
预测T2DM患者DN的风险模型可用于临床实践，以提高风险管理和干预的质量。
