Abstract: Objective To analyze the risk factors of type 2 diabetic peripheral neuropathy (DPN) and to construct a clinical prediction model for DPN. Methods A retrospective review covered 581 patients with type 2 diabetes treated in the Department of Endocrinology and Metabolism Diseases of Changchun University of Traditional Chinese Medicine from September 2020 to November 2023; 296 patients without diabetic kidney disease were classified as NDPD group and 285 patients with diabetic kidney disease were classified as DPN group. The clinical data of patients were collected; univariate analysis was performed followed by multivariate Logistic regression analysis to identify the variables with statistically significant differences to find independent risk factors. R software was used to construct a nomogram, and plot the receiver operating characteristic (ROC) curve and then calculated the cut-off value, and the discrimination of the model was represented by the area under curve (AUC) value. The calibration diagram of the model was drawn, and the Hosmer-Lemeshow test combined with the calibration curve was used to evaluate the prediction accuracy of the model. Results Seven risk factors were selected as age, disease duration, smoking history, hemoglobinA1c(HbA1c), total cholesterol(TC),triglyceride(TG), low density lipo-protein-cholesterol(LDL)and a prediction model was preliminarily established based on the above risk factors. The AUC value of the area under the ROC curve was 0.722 (95% CI: 0.673-0.771), and the cut-off value was 0.477 (0.620, 0.729) indicating that the model had certain predictive capacity and accuracy for DPN. The results of Hosmer-Lemeshow test showed that χ²=10.683, P=0.220, indicating that the model fit was good. The results of the calibration chart showed that the prediction curve and the calibration curve had a good degree of coincidence, indicating that the accuracy of the model was good. Conclusions The risk factors for peripheral neuropathy in patients with type 2 diabetes mellitus include age, course of disease, smoking history, HbA1c, TC, TG, LDL. The clinical prediction model based on these factors can provide a reference for early clinical screening and early identification of DPN patients.

Key words: type 2 diabetic mellitus, diabetic peripheral neuropathy, clinical prediction models