Objective  To screen the risk factors of cardiovascular complications of diabetes mellitus, and build a predictive model to provide a basis for early prevention and slow down the progression of the disease.

Methods  Based on the chronic disease cohort of the natural population in the Beijing-Tianjin-Hebei community, the baseline diabetic patients from 2017 to 2019 were selected as the study objects, and the occurrence of cardiovascular complication was determined  as the outcome index based on the chronological order of the self-reported illness. The model was randomly divided into a training set  and a test set at 7 ∶ 3 to build and verify the model, respectively. Lasso-Logistic regression model was used to screen risk factors, which were incorporated into the multi-factor Logistic regression model to build a prediction model for the risk of cardiovascular complication of diabetes, and a nomogram was drawn for visualization. The receiver operating characteristic curve (ROC) was drawn and the area under the curve (AUC) was calculated. The Hosmer-Lemeshow tests and plotting calibration curve to assess the calibration degree, and the prediction model was evaluated and verified.

Results  A total of 813 patients with type 2 diabetes were included, with an average age of (62.6±10.4) years, including 569 patients in the training set and 244 patients in the test set. There were differences in the level of hypersensitive C-reactive protein between the two groups (P=0.028), and there were no statistically significant differences in other basic characteristics. Multivariate Logistic regression analysis showed that the risk factors for cardiovascular complications of diabetes mellitus included age [OR=1.040, 95%CI (1.010, 1.073), P=0.010], hypertension [OR=2.211, 95%CI (1.263, 3.975), P=0.006], duration of diabetes [OR=1.063, 95%CI (1.028, 1.099), P<0.001], level of fasting blood glucose [OR=1.186, 95%CI (1.075, 1.309), P=0.001], dyslipidemia [OR=2.051, 95%CI (1.167, 3.583), P=0.012], family history of cardiovascular disease [OR=2.794, 95%CI (1.650, 4.774), P<0.001] and smoking [OR=1.975, 95%CI (1.133, 3.462), P=0.017]; the protective factor was level of serum bilirubin [OR=0.940, 95%CI (0.889, 0.991), P=0.027]. The nomogram shows that patients with type 2 diabetes can calculate the probability of developing cardiovascular complications of diabetes based on the dynamic changes in the eight predictors in the model. The AUC of the training set and the test set were 0.803 and 0.820, the P-values of the Hosmer-Lemeshow test were 0.776 and 0.554, respectively, and the average absolute error between the calibration curve and the ideal curve was 0.013, which proved that the differentiation and calibration degree of the prediction model were good.

Conclusions  In this study, the risk prediction model of diabetic cardiovascular complication built on the basis of community natural population has good effect, and provides a convenient and feasible tool for the early prediction and early warning of cardiovascular complication in diabetic.

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