A streamlined CMR-derived machine-learning model for estimating cardiovascular biological age: development and...

Current models predicting cardiovascular biological age rely on radiomics or complex large feature sets including T1 and strain. We developed and validated a machine learning-based cardiovascular biological age estimate (HeartAge) using cardiovascular-magnetic-resonance (CMR) phenotypes and assessed the prognostic value of its deviation from chronological age (HeartAge-gap) for cardiovascular outcomes and mortality.

HeartAge was developed using gradient-boosting regression in 3760 healthy UK-Biobank participants based on readily extractable CMR phenotypes. HeartAge-gap was defined as the difference between HeartAge and chronological age. The association of HeartAge-gap with prevalent cardiovascular conditions and composite cardiovascular outcome or all-cause mortality was tested in 31 784 UK-Biobank participants (64 ± 7 years; 16 640 females) and validated in 897 Multi-Ethnic Study of Atherosclerosis (MESA) participants (60 ± 10 years; 472 females) using logistic and Cox regression, respectively. Over a median 5.5-year follow-up (IQR: 4.7-7.1), 2316 (7.3%) and 363 (1.1%) participants experienced the composite cardiovascular outcome and all-cause mortality, respectively. Each one-year increase in HeartAge-gap, was associated with the composite cardiovascular outcome in females (HR: 1.022, 95% CI: 1.001-1.044, P = 0.048) and males (HR: 1.017, 95% CI: 1.002-1.033, P = 0.027) independently of chronological age and confounders including, body-mass-index, ischaemic heart disease, diabetes, and hypertension. In females only, increased HeartAge-gap predicted all-cause mortality (HR: 1.061, 95% CI: 1.007-1.118, P = 0.027), regardless of chronological age. In female MESA participants only, increased HeartAge-gap predicted the cardiovascular outcome (HR: 1.113, 95% CI: 1.025-1.210, P = 0.011) independently of chronological age and other confounders.

A biologically older cardiovascular system was independently associated with adverse cardiovascular outcomes across both sexes. In females, advanced cardiovascular ageing also predicts all-cause mortality, irrespective of chronological age.