In the field of cardiovascular risk assessment, aortic stiffness is considered a key predictive indicator, and carotid-femoral pulse wave velocity (cfPWV) is recognized as the gold standard for non-invasive assessment of atherosclerotic risk in the aorta. Due to challenges such as technical difficulty, cfPWV testing has not been widely implemented in China.

This study aimed to develop and validate a screening model for early atherosclerotic risk in the aorta based on cardiovascular risk factors, with the intention of replacing the complex measurement process of cfPWV and reducing reliance on traditional measurement methods.

A total of 878 participants recruited from the Health Checkup Center of the First Affiliated Hospital of Anhui Medical University between May and November 2023 were selected as research subjects, randomly divided into a model-building group (n=703) and a validation group (n=175) in an 8∶2 ratio. Patient general information, laboratory test results, and cfPWV were collected. Based on the cfPWV examination results and relevant guidelines, participants in the model-building group were divided into those without atherosclerotic risk in the aorta (n=503) and those with atherosclerotic risk in the aorta (n=200). Multifactorial Logistic regression analysis was used to screen variables and establish a nomogram assessment model. The receiver operating characteristic curve (ROC curve) for predicting the risk of atherosclerosis in the aorta was plotted for the model, and the model's discriminative ability and calibration were assessed using the area under the ROC curve (AUC) and the Hosmer-Lemeshow test, respectively. The Delong test was used to compare the AUCs of different models, and decision curve analysis (DCA) was used to assess the clinical utility of the model. Internal validation of the model was performed using the bootstrap method with 1 000 resampling iterations.

Participants with atherosclerotic risk in the model-building group were older, had higher BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), urea, fasting blood glucose (FBG), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), total cholesterol (TC), alanine aminotransferase (ALT), aspartate aminotransferase (AST), hemoglobin (Hb), and a higher proportion of alcohol consumption, dyslipidemia, and diabetes than those without atherosclerotic risk in the aorta. The glomerular filtration rate (GFR) and platelet count (PLT) were lower in those with atherosclerotic risk (P<0.05). Multifactorial Logistic regression analysis showed that age (OR=1.112, 95%CI=1.082-1.143), MAP (OR=1.146, 95%CI=1.107-1.188), Hb (OR=1.026, 95%CI=1.004-1.049), and FBG (OR=1.353, 95%CI=1.076-1.701) were independent risk factors for atherosclerosis in the aorta (P<0.05). A predictive modelⅠ was constructed using statistically significant indicators from the multifactorial logistic regression analysis (age, MAP, Hb, FBG), and models Ⅱ, Ⅲ, and Ⅳ were constructed by additionally including smoking, gender, and dyslipidemia, respectively. The AUCs for models Ⅰ to Ⅳ were 0.941 (95%CI=0.923-0.964, P<0.05), 0.941 (95%CI=0.922-0.962, P<0.05), 0.941 (95%CI=0.922-0.963, P<0.05), and 0.939 (95%CI=0.919-0.962, P<0.05), respectively. The Delong test showed no statistically significant difference in AUCs among models Ⅰ, Ⅱ, Ⅲ, and Ⅳ (P>0.05). A nomogram model was constructed using age, MAP, FBG, and Hb as predictive factors, with an AUC of 0.941 (95%CI=0.920-0.962) for the training set, sensitivity of 0.832, and specificity of 0.917. The AUC for the validation set was 0.961 (95%CI=0.914-1.000), with sensitivity of 0.872 and specificity of 0.964. DCA results indicated that the use of the early atherosclerosis screening model could benefit participants in clinical practice.

Based on four simple indexes of age, mean arterial pressure, hemoglobin and fasting blood glucose, a screening model for early aortic sclerosis risk was established, which provides a convenient and efficient method for early vascular function screening.