Computational Risk Modeling of Carotid Atherosclerosis in Type 2 Diabetes: Insights from Doppler-Based Datat

Type 2 diabetes mellitus (T2DM) is a major risk factor for the development of atherosclerotic disease. Carotid Doppler ultrasound allows non-invasive detection of subclinical vascular alterations, offering a potential tool for early risk stratification. This study aimed to evaluate the association between T2DM and carotid atherosclerosis and to develop a computational predictive model integrating Doppler-derived data and clinical risk factors. Methods: A cross-sectional study involving 611 patients undergoing cervical Doppler ultrasound was conducted. Demographic data, cardiovascular risk factors, intima-media thickness (IMT), and carotid plaque presence were analyzed. Associations between T2DM and vascular alterations were assessed using chi-square and Mann-Whitney U tests. A multivariate logistic regression model was constructed to predict the presence of atherosclerotic changes, followed by ROC curve analysis to evaluate predictive performance. An ordinal regression analysis assessed predictors of stenosis severity. Results: T2DM was significantly associated with carotid plaque presence (82.4% vs. 67.6%; p=0.015), greater plaque burden (2.12 vs. 1.55 plaques/patient; p=0.001), and higher prevalence of stenosis >50% (13.7% vs. 4.9%; p=0.037). The multivariate predictive model identified T2DM (OR=2.56; 95% CI: 1.48–4.42; p<0.01), dyslipidemia (OR=1.87; p=0.02), and age (OR=1.04 per year; p<0.001) as independent predictors of carotid atherosclerotic alterations. The ROC curve analysis yielded an AUC of 0.836, demonstrating excellent discriminative ability. IMT, age, and hypertension were independently associated with increasing degrees of stenosis. Conclusion: T2DM significantly increases the risk of carotid atherosclerosis. The developed computational model, integrating clinical and Doppler-derived data, demonstrated robust predictive performance for vascular risk stratification in diabetic patients. These findings support the incorporation of vascular imaging and computational tools into preventive cardiovascular strategies.