Abstract
BACKGROUND: Peripheral arterial disease (PAD) is a common macrovascular complication of type 2 diabetes mellitus (T2DM) that contributes to lower-limb morbidity and increased cardiovascular mortality. Early risk stratification is essential to guide screening and preventive measures; however, no comprehensive tool currently integrates demographic, clinical and hematologic factors to predict PAD in T2DM. METHODS: In this retrospective cohort study, 426 adults with T2DM treated between January 2020 and December 2024 were stratified by PAD status (PAD, n = 136; non-PAD, n = 290). Risk factors were identified by multivariable logistic regression. A nomogram was constructed using the rms package in R and internally validated via bootstrap resampling (n = 1 000). Discrimination was assessed by area under the receiver operating characteristic curve (AUC) and concordance index (C-index), and calibration by Hosmer-Lemeshow goodness-of-fit and calibration plots. RESULTS: Eleven independent predictors were incorporated: age; smoking; alcohol use; diabetes duration; systolic blood pressure; high-density lipoprotein cholesterol (HDL-C); low-density lipoprotein cholesterol (LDL-C); antihypertensive use; white blood cell count; platelet distribution width (PDW); and large platelet ratio (LPR). The nomogram achieved an AUC of 0.826 (95% CI 0.768-0.895), sensitivity of 78.6% and specificity of 89.6%. Internal validation yielded a bias-corrected C-index of 0.795 (95% CI 0.756-0.893), and Hosmer-Lemeshow P = 0.913, indicating good calibration. CONCLUSIONS: The proposed nomogram demonstrates robust discrimination and calibration for individualized PAD risk prediction in T2DM, supporting its potential to optimize targeted screening and preventive strategies pending external validation.