Objective: To investigate the risk factors of diabetic nephropathy (DN) in primary type 2 diabetes mellitus (T2DM) patients and to quantitatively analyze the risk of DN by nomogram modeling. Methods: A total of 1 588 primary T2DM patients from 17 townships and streets in Zhejiang Province were enrolled from June 2018 to August 2018 in this cross-sectional study, with an average age of (56.8±10.1) years (50.06% male) and a mean disease duration of 9 years. The clinical data, biochemical test results, and fundus photographs of all T2DM patients were collected, and logistic regression analysis was used to screen the risk factors of DN. Then, a nomogram model was used to quantitatively analyze the risk of DN. Results: DN occurred in 27.71% (440/1 588 cases) primary type 2 diabetes patients. Hemoglobin A1c (HbA1c) (OR=1.159, 95%CI 1.039-1.292), systolic blood pressure (OR=1.041, 95%CI 1.031-1.051), serum creatinine (Scr) (OR=1.011, 95%CI 1.004-1.017), serum globulin (GLOB) (OR=1.072, 95%CI 1.039-1.105), diabetic retinopathy (DR) (OR=1.463, 95%CI 1.073-1.996), education level of more than junior high school (OR=2.018, 95%CI 1.466-2.777), and moderate-intensity exercise (OR=0.751, 95%CI 0.586-0.961) were influencing factors of DN. Nomogram model analysis showed that the total score of each factor of DN ranged from 64-138 points, and the corresponding risk rate ranged from 0.1-0.9. The nomogram model also predicted a C-index value of 0.753 (95%CI 0.726-0.781) and an area under the receiver operating characteristic curve of DN of 0.753. Internal verification of the C-index reached 0.738. The model displayed medium predictive power and could be applied in clinical practice. Conclusions: HbA1c, systolic blood pressure, Scr, GLOB, DR, and more than a junior high school education are independent risk factors of DN. Nomogram modeling can more intuitively evaluate the risk of DN in primary T2DM patients.
目的： 探讨基层2型糖尿病患者糖尿病肾病（DN）发病的危险因素以及列线图模型对DN患病风险的预测价值。 方法： 采用横断面研究的方法，纳入2018年6至8月浙江省17个乡镇和街道符合研究标准的基层2型糖尿病患者1 588例，年龄（56.8±10.1）岁，男性占50.06%，平均病程9年。采集患者的临床资料、生化检测和眼底照相结果；采用logistic回归分析法筛选DN的危险因素，并应用列线图模型定量分析DN的患病风险。 结果： 1 588例基层2型糖尿病患者中440例（27.71%）发生DN。logistic回归分析显示糖化血红蛋白（OR=1.159，95%CI 1.039~1.292）、收缩压（OR=1.041，95%CI 1.031~1.051）、血清肌酐（OR=1.011，95%CI 1.004~1.017）、血清球蛋白（OR=1.072，95%CI 1.039~1.105）、糖尿病视网膜病变（OR=1.463，95%CI 1.073~1.996）、初中以上学历（OR=2.018，95%CI 1.466~2.777）、中等强度运动（OR=0.751，95%CI 0.586~0.961）是DN发病的影响因素。列线图模型分析显示，DN各影响因素评分的总分范围为64~138分，对应的风险率范围为0.1~0.9。列线图模型预测DN的C指数为0.753（95%CI 0.726~0.781），列线图模型预测DN的受试者工作特征曲线下面积为0.753，C指数的内部验证达到0.738，该模型具有中等预测能力。 结论： 糖化血红蛋白、收缩压、血清肌酐、血清球蛋白、糖尿病视网膜病变、初中以上学历为基层2型糖尿病患者发生DN的独立危险因素。列线图模型可以更加直观地评价基层2型糖尿病患者患DN的风险。.