Key result
A nomogram integrating carotid intima thickness, carotid-femoral pulse wave velocity, and estimated glomerular filtration rate demonstrated excellent discrimination for identifying high 10-year ASCVD risk in patients with type 2 diabetes, achieving an AUC of 0.875.
Why the study?
This study aimed to investigate whether an ultrasound-based risk stratification model integrating carotid intima thickness and carotid-femoral pulse wave velocity could aid in ASCVD risk stratification in patients with type 2 diabetes mellitus.
Does an ultrasound-based model integrating CIT and cfPWV accurately identify high 10-year ASCVD risk in patients with T2DM?
Cross-Sectional (n=105)
No
Does an ultrasound-based model integrating CIT and cfPWV accurately identify high 10-year ASCVD risk in patients with T2DM?
An ultrasound-based nomogram integrating CIT, cfPWV, and eGFR shows strong potential for identifying T2DM patients at high 10-year ASCVD risk.
Answers are grounded in this paper and cite their sources.
May aid ASCVD risk stratification in T2DM but should not change practice; leaves open prognostic value pending prospective validation.
BACKGROUND: This study aimed to investigate the ability of an ultrasound-based risk stratification model integrating carotid intima thickness (CIT) and carotid-femoral pulse wave velocity (cfPWV) to aid in risk stratification and assessment of atherosclerotic cardiovascular disease (ASCVD) in patients with type 2 diabetes mellitus (T2DM), thereby providing an objective basis for identifying high-risk individuals and informing individualized management strategies. METHODS: A total of 105 patients with T2DM were enrolled in this study. According to the 10-year ASCVD risk score, patients were further classified into T2DM patients with low-to-moderate burden of other cardiovascular risk factors and T2DM patients with high burden of other cardiovascular risk factors. CIT was measured using high-resolution ultrasound to assess vascular structure, while cfPWV was evaluated using the automatic measurement of arterial stiffness (AMAS) system to assess vascular function. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to identify independent risk factors of high ASCVD risk. Based on these risk factors, individual discriminative models and a nomogram were constructed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were used to evaluate model performance, and differences among models were assessed using the DeLong test. RESULTS: CIT, cfPWV, and estimated glomerular filtration rate (eGFR) were identified as independent risk factors of high 10-year ASCVD risk in patients with T2DM. The areas under the curve (AUCs) for the CIT model, cfPWV model, eGFR model, combined CIT-cfPWV model, and the nomogram were approximately 0.781, 0.808, 0.797, 0.831, and 0.875, respectively. The constructed nomogram demonstrated excellent discrimination, calibration, and clinical applicability. CONCLUSIONS: CIT and cfPWV show strong potential for identifying T2DM patients at high ASCVD risk as estimated by the China-PAR model. Incorporating these parameters into vascular evaluation may aid in risk stratification and provide a robust basis for individualized clinical intervention strategies. Prospective studies are needed to validate their prognostic value for future ASCVD events.
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Chen et al. (2026) conducted a cross-sectional in Type 2 diabetes mellitus (n=105). Vascular ultrasound-based risk stratification model (CIT and cfPWV) was evaluated on Discriminative performance for identifying high 10-year ASCVD risk (AUC 0.875). A nomogram integrating carotid intima thickness, carotid-femoral pulse wave velocity, and estimated glomerular filtration rate demonstrated excellent discrimination for identifying high 10-year ASCVD risk in patients with type 2 diabetes, achieving an AUC of 0.875.
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