2型糖尿病患者发生微量白蛋白尿预测模型的建立与验证研究

背景　糖尿病肾病早期发病隐匿,多数患者在诊断时肾脏已发生不可逆性损害,早期诊断和治疗对于阻止或者延缓糖尿病肾病的发生、发展具有重要作用,构建一个简单、有效的个性化风险预测模型可为糖尿病肾病的早期诊断和治疗提供重要参考。目的　分析影响2型糖尿病（T2DM）患者发生微量白蛋白尿（MAU）的独立危险因素,构建一个简单、有效的个性化临床预测模型,预测T2DM患者发生MAU的风险。方法　选取2014年3月至2016年3月于空军军医大学第一附属医院内分泌科住院的T2DM患者1 311例,为建立和验证预测模型将研究对象分为两部分,2014年3月至2015年9月的数据作为开发队列（933例）,2015年10月至2016年3月的数据作为验证队列（378例）。收集患者的基本特征、实验室检查、辅助检查和药物使用情况,并依据尿微量白蛋白/肌酐（UACR）诊断正常白蛋白尿（NAU）和MAU。应用LASSO回归优化筛选变量,通过多因素Logistic回归分析建立预测模型,并绘制列线图。采用受试者工作特征（ROC）曲线、校准曲线和Hosmer-Lemeshow拟合优度检验验证和评价预测模型的区分度和校准度;决策曲线分析（DCA）评估预测模型的临床有效性。结果　使用LASSO回归分析筛选出7个预测变量,包括糖尿病病程、收缩压（SBP）、空腹血糖（FPG）、三酰甘油（TG）、血肌酐（Scr）、胱抑素C（Cys C）、糖尿病视网膜病变（DR）。多因素Logistic回归分析结果显示,SBP≥140 mm Hg、FPG≥7.0 mmol/L、TG≥1.7 mmol/L、Scr>106 μmol/L、Cys C>1.09 mg/L、合并DR是T2DM患者发生MAU的危险因素（P<0.05）。依据预测变量绘制列线图,构建预测模型。预测模型预测开发队列T2DM患者发生MAU的ROC曲线下面积（AUC）为0.762〔95%CI（0.734,0.789）〕,预测验证队列T2DM患者发生MAU的AUC为0.734〔95%CI（0.686,0.777）〕。Hosmer-Lemeshow拟合优度检验显示出较好的拟合度（开发队列P=0.377;验证队列P=0.217）。DCA结果显示阈值概率>20%,预测模型在临床上是有益的。结论　包含7个预测变量（糖尿病病程、SBP、FPG、TG、Scr、Cys C、DR）的列线图预测模型可用于预测T2DM患者发生MAU的风险。

Construction and Verification of a Predictive Model for Microalbuminuriain Type 2 Diabetes Mellitus Patients

Background　The early onset of diabetic kidney disease （DKD） is insidious,and most patients have irreversible kidney impairment at the time of diagnosis. Early diagnosis and treatment greatly contribute to the prevention or delay the development of DKD. Hence,construction of a simple and effective personalized risk prediction model will significantly help the early diagnosis and treatment of DKD. Objective　To identify the risk factors independently associated with microalbuminuria（MAU） in type 2 diabetes mellitus （T2DM） patients,and to use them to develop a simple and effective personalized risk prediction model for MAU in T2DM. Methods　T2DM participants（n=1 311） were recruited from Department of Endocrinology,the First Affiliated Hospital of Air Force Medical University,and assigned those who were hospitalized between March 2014 and September 2015,and between October 2015 and March 2016 to a development sub-cohort（n=933）,and a validation sub-cohort（n=378）,for the convenience of developing and validating a predictive model for MAU. Demographics,results of laboratory and auxiliary examinations,pharmacological treatment,and prevalence of albuminuria（UACR<30 mg/g） or MAU （30 mg/g 106 μmol/L,Cys C>1.09 mg/L,and DR were risk factors for MAU in T2DM patients （P<0.05）. The predictive model was constructed by drawing nomogram according to the predictors. The nomogram model demonstrated very well discrimination with the development sub-cohort AUC of 0.762〔95%CI（0.734,0.789）〕,while the internal validation AUC was 0.734〔95%CI（0.686,0.777）〕. The Hosmer-Lemeshow test showed perfect fitting degree （internal validation：P=0.377;external validation：P=0.236）. Decision curve analysis showed a risk threshold of 20% and demonstrated a clinically effective predictive model. Conclusion　The nomogram model containing seven predictors（diabetes duration,SBP,FPG,TG,Scr,Cys C,and DR）could be used to predict the risk of MAU in T2DM patients.