Objective：To analyze the independent risk factors affecting type 2 diabetes mellitus-carotid atherosclerosis（T2DM-CAS） patients complicated with diabetic kidney disease（DKD），and construct a personalized clinical prediction model to predict the risk of DKD in T2DM-CAS patients. Methods：A total of 883 patients with T2DM were selected in the study，and their basic characteristics，laboratory tests，auxiliary examinations and concomitant diseases of the patients were collected. LASSO regression was applied to screen the optimized variables by running cyclic coordinate descent. Multivariate logistic regression analyses were applied to build a prediction model，which incorporated the selected features. It relied on the receiver operating characteristic curve（ROC curve），calibration curves，and Hosmer-Lemeshow test to validate and evaluate the discrimination and calibration of the clinical prediction model；while the decision curve analysis（DCA） was used to evaluate its clinical validity. Results：A multivariable prediction model included diabetes duration，systolic blood pressure（SBP），fasting plasma glucose（FPG），triglycerides（TG），blood urea nitrogen（BUN），serum creatinine（Scr），cystatin C（Cys C） and diabetic retinopathy（DR）. This clinical prediction model demonstrated very good discrimination with an AUC of 0.831（95%CI=0.800-0.863），while the internal validation AUC was 0.825（95%CI=0.766-0.884）. The Hosmer-Lemeshow test showed very good fitting degree（P=0.822）. DCA showed the risk threshold of 30% and demonstrated a clini－cally effective prediction model. Conclusion：A Nomogram model with eight clinical predictor variables can be used to predict the risk of DKD in T2DM-CAS patients.