Age ≥ 60 years was the strongest independent risk factor for carotid plaque formation (OR 14.04), and a developed 12-factor risk prediction model demonstrated good discriminative ability with an AUC of 0.80.
Case-Control (n=12,391)
No
A newly developed risk stratification model using routine health examination data accurately predicts carotid plaque formation, enabling early identification of high-risk individuals for atherosclerosis.
Effect estimate: OR 14.04 (95% CI 11.33-17.41)
p-value: p=<0.001
Background Carotid plaque serves as an early window into atherosclerosis; however, more convenient tools for plaque risk stratification are currently lacking. This study aimed to investigate the risk factors for carotid plaque occurrence, establish a predictive model, and develop a risk assessment scale. Methods A total of 12,391 individuals who underwent health examinations at the Physical Examination Center of the First Affiliated Hospital of Xinjiang Medical University between January 2024 and March 2025 were retrospectively enrolled. After applying inclusion and exclusion criteria, Least Absolute Shrinkage and Selection Operator (LASSO) regression was performed. The cohort was then randomly divided into a development set ( n = 7,434) and a validation set ( n = 4,957) to construct a binary multivariate logistic regression model. Results In the multivariate regression model adjusted for confounding factors within the development set, female sex (OR = 0.59) and high-density lipoprotein cholesterol (HDL-c) 1.55 mmol/L (OR = 0.80) were associated with a reduced risk of plaque. Age 45–59 years (OR = 5.19), age ≥60 years (OR = 14.04), and smoking (OR = 1.37) were independently associated.
Wu et al. (Fri,) conducted a case-control in Carotid plaque (n=12,391). Risk factors (Age ≥ 60 years) vs. Age 18-44 years was evaluated on Carotid plaque formation (OR 14.04, 95% CI 11.33-17.41, p=<0.001). Age ≥ 60 years was the strongest independent risk factor for carotid plaque formation (OR 14.04), and a developed 12-factor risk prediction model demonstrated good discriminative ability with an AUC of 0.80.