Evidence mapPaperPMID 42069513Full record

ArticleBMC medical imaging2026

Vascular ultrasound-based risk stratification model for atherosclerotic cardiovascular disease in patients with type 2 diabetes mellitus.

Cuie Chen, Qiuxiao Xu, Hong Xu, Yiyun Xu, Xueling He, Minhao Lin, Yuan Li, Yinhua Li, Lijuan Liu

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Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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9 authors.

Cuie ChenDepartment of Ultrasound, Affiliated Hospital of Guangdong Medical University, ZhanJiang, Guangdong Province, China.
Qiuxiao XuDepartment of Ultrasound, Affiliated Hospital of Guangdong Medical University, ZhanJiang, Guangdong Province, China.
Hong XuDepartment of Ultrasound, Affiliated Hospital of Guangdong Medical University, ZhanJiang, Guangdong Province, China.
Yiyun XuDepartment of Ultrasound, The Second Affiliated Hospital of Guangdong Medical University, ZhanJiang, Guangdong Province, China.
Xueling HeDepartment of Ultrasound, Affiliated Hospital of Guangdong Medical University, ZhanJiang, Guangdong Province, China.
Minhao LinDepartment of Ultrasound, Affiliated Hospital of Guangdong Medical University, ZhanJiang, Guangdong Province, China.
Yuan LiDepartment of Ultrasound, Affiliated Hospital of Guangdong Medical University, ZhanJiang, Guangdong Province, China.
Yinhua LiDepartment of Ultrasound, The Second Affiliated Hospital of Guangdong Medical University, ZhanJiang, Guangdong Province, China.
Lijuan LiuDepartment of Ultrasound, Affiliated Hospital of Guangdong Medical University, ZhanJiang, Guangdong Province, China. nmyl0901@163.com.

Funding

Zhanjiang Science and Technology Bureau, China 2025A502038
6 · The paper itself

Abstract

backgroundThis 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.

methodsA 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.

resultsCIT, 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.

conclusionsCIT 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.

Indexed as

AtherosclerosisCarotid Intima-Media ThicknessDiabetes Mellitus, Type 2AgedCarotid-Femoral Pulse Wave VelocityFemaleHumansMaleMiddle AgedNomogramsPulse Wave AnalysisRisk AssessmentRisk FactorsROC CurveVascular StiffnessAtherosclerotic cardiovascular disease riskCarotid-femoral pulse wave velocityCarotid intima thicknessNomogramType 2 diabetes mellitus

Identifiers

PMID42069513
PMCPMC13288563

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.