Abstract
OBJECTIVE: To compare the predictive value of triglyceride glucose index (TyG) and the ratio of serum uric acid (SUA) to high-density lipoprotein cholesterol (HDL-C) (UHR) for lower extremity atherosclerotic disease (LEAD) in type 2 diabetes (T2DM) patients. METHODS: 303 patients with T2DM were divided into LEAD group (n=192) and non-LEAD group (n=111) based on the results of lower extremity vascular color Doppler ultrasound. All patients were divided into a training set and a validation set at a 7:3 ratio. In the training set, Least absolute shrinkage and selection operator (LASSO) regression was applied to screen for predictive factors of LEAD, and a multivariate logistic regression model was constructed to analyze the predictive factors, with a nomogram being plotted. The discriminative ability and calibration of the model were evaluated using the receiver operating characteristic (ROC) curve area under the curve (AUC) and calibration curves in both the training and validation sets. Decision curve analysis (DCA) was used to evaluate the clinical net benefit. RESULTS: The variables selected by the LASSO regression included age, pulse pressure difference (PP), TyG, and UHR. The multivariate logistic regression model indicated that age, PP, TyG, and UHR were predictive factors for LEAD in T2DM patients (P<0.05). ROC curve analysis suggested that the discriminatory ability was in the following order: the nomogram model (AUC=0.872), TyG (AUC=0.751), and UHR (AUC=0.709), which were greater than that of age and PP. TyG and UHR cut-off values were 9.836 and 216.248, respectively. The specificities of TyG and UHR were 0.760 and 0.547, and the sensitivities were 0.629 and 0.807, respectively. The calibration curve showed the model's predictions matched actual conditions. DCA verified the model's clinical benefit. CONCLUSION: Both TyG and UHR have good predictive value and are suitable for screening LEAD in T2DM patients.