To determine whether biological age, estimated using phenotypic age (PhenoAge) and PhenoAge acceleration, predicts in-hospital and intermediate-term outcomes after acute myocardial infarction (AMI) in older adults.

We conducted a retrospective cohort study of 192 consecutive adults [median age: 71 (62–78) years] admitted with AMI to a national tertiary center in 2022. PhenoAge metrics were derived from routine admission laboratory parameters. Patients were followed until 30 August 2025. Outcomes included in-hospital mortality and two composite endpoints: (1) all-cause mortality, non-fatal myocardial infarction, or revascularization; and (2) the above plus hospitalization for heart failure. Logistic regression models and Cox proportional hazards regression models were adjusted for baseline cardiovascular comorbidities.

PhenoAge acceleration was independently associated with in-hospital mortality (OR = 1.051, 95% CI: 1.018–1.084, P = 0.002), whereas chronological age was not, and PhenoAge also demonstrated independent association (OR = 1.042, 95% CI: 1.013–1.072, P = 0.004). Over a median follow-up of 1285 days, all three aging metrics were independently associated with both composite endpoints and heart failure hospitalization in multivariable analyses. Receiver operating characteristic analyses demonstrated similar discriminative performance across metrics for intermediate-term outcomes [area under the curve (AUC) = 0.63–0.71]. PhenoAge demonstrated significant incremental value over chronological age [net reclassification improvement (NRI) = 0.38–0.43, integrated discrimination improvement (IDI) = 0.059–0.069, P ≤ 0.005].

PhenoAge acceleration independently predicted in-hospital mortality following AMI, suggesting it captures acute physiological vulnerability not reflected by chronological age. Although PhenoAge acceleration showed comparable intermediate-term discriminatory performance to chronological age, PhenoAge demonstrated significant incremental prognostic value beyond chronological age. Biological aging assessment may help identify biologically vulnerable patients at presentation and support personalized risk stratification in geriatric cardiology. These findings support further prospective evaluation of biological aging metrics in older adults with acute coronary syndromes.