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-cho-lesterol(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 cali-bration chart showed that the prediction curve and the calibration curve had a good degree of coincidence,indica-ting 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.