BACKGROUND: Chronological age inadequately reflects aging variability and cardiovascular risk. Biological age derived from routine complete blood count (CBC) parameters may provide a more actionable marker.
OBJECTIVE: To develop a machine learning model of biological age using CBC data (HemeAge) and evaluate associations with mortality and major adverse cardiovascular events (MACE) in two large cohorts.
METHODS: An XGBoost model was trained on 53,355 NHANES participants (1999-2010) to predict chronological age from CBC parameters. The model was applied to 109,844 Houston Methodist CVD Registry patients, generating "delta age" (predicted minus chronological age). Patients were classified as Resilient (delta < -10), Proportionate (-10 ≤ delta ≤ 10), or Accelerated (delta > 10). Cox models assessed mortality and MACE risk, adjusting for demographics and clinical factors.
RESULTS: Red cell distribution width, mean cell volume, and neutrophil count were key age predictors. Accelerated aging associated with increased mortality risk (HR 3.05, 95% CI 2.41-3.85) and MACE (HR 1.37, 95% CI 1.24-1.51) versus proportionate aging. Resilient aging conferred reduced risk for mortality (HR 0.59, 95% CI 0.52-0.68) and MACE (HR 0.76, 95% CI 0.72-0.81). Associations were strongest in midlife (ages 40-80) and for death and heart failure outcomes and persisted across age-stratified and continuous models.
CONCLUSIONS: HemeAge independently predicts mortality and cardiovascular risk beyond chronological age. These accessible hematologic markers may enhance risk stratification and inform targeted prevention strategies.