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Vascular aging, characterized by progressive structural and functional deterioration of the vasculature, serves as a critical pathophysiological nexus between chronological aging and cardiovascular disease (CVD). This study establishes a quantitative vascular age model to decode individualized vascular senescence patterns, thereby enabling early identification of accelerated aging phenotypes for targeted intervention. We collected physical examination records from 2009 to 2019 and a total of 8578 participants aged 20-70 years were enrolled in this study. We constructed sex-specific basic vascular age models based on healthy individuals by Klemera-Doubal method and calculated the normalized cardiovascular age acceleration (NCAA, η) as an estimate of vascular aging status. The association between η and CVD risk were evaluated across subgroups. Furthermore, we developed expanded models by incorporating traditional CVD risk factors that were significantly associated with η index. Male with lower values of η, which meant relatively higher vascular aging velocity, had a higher risk of CVD adjusted by chronological age (HR = 1.21, 95% CI = 1.01-1.45). In subgroup analysis, η index exhibited age- and sex-specific associations with traditional CVD risk factors. After adding body mass index, fasting blood glucose, and triglycerides significantly related to η in male, the CVD prediction by expand η were improved in age-adjusted model (HR = 1.25, 95% CI = 1.04-1.50). The vascular age model emerges as a robust composite biomarker for CVD risk stratification. Our findings establish an evidence-based framework for precision prevention, prioritizing high-risk phenotypes for early intervention to mitigate CVD burden.