Peri-aortic fat to assess cardiovascular aging using an AI-driven radiomic biomarker

Background and Aims Chronological age is central to cardiovascular disease (CVD) risk estimation but poorly reflects interindividual heterogeneity in cardiovascular aging. This study developed a peri-aortic adipose tissue (PAAT) radiomic cardiovascular age (CV-Age) biomarker from routine chest CT and tested whether the PAAT age gap (ΔAge = CV-Age − chronological age) stratifies major adverse cardiovascular events (MACE) and improves risk classification. Methods Four chest CT cohorts with different protocols were utilized to train (n = 4451) and externally validate (n = 44 214) a CV-Age model. Associations between ΔAge decile groups (resilient ≤10th; accelerated ≥90th) and incident 5-point MACE (myocardial infarction, stroke, revascularization, heart failure, and death) were assessed using survival models. Clinical utility was evaluated by substituting CV-Age for chronological age in PREVENT to quantify risk reclassification. Results A 31-feature radiomic signature capturing PAAT volume, attenuation, and texture heterogeneity predicted age with good accuracy (training MAE 2.2 years; primary external validation MAE 2.7 years). In a separate higher-risk cohort, predictions showed a positive ΔAge (median +5.4 years), reflecting elevated baseline cardiovascular burden. ΔAge-stratified event-free survival differed across groups (log-rank P < .001); compared with normative agers, accelerated agers had higher MACE risk (HR 1.51, 95% CI 1.37–1.66), and resilient agers had lower risk (HR 0.70, 0.63–0.77). Substituting CV-Age into PREVENT improved risk reclassification (categorical NRI +0.03, P < .01, continuous NRI +0.04, P < .05). Conclusions Radiomic profiling of PAAT on routine chest CT yields a scalable imaging biomarker of cardiovascular aging. CV-Age can help improve identification of higher-risk individuals missed by age-driven risk estimation, supporting its role as a risk-enrichment tool.