Key result
CMR-derived HeartAge-gap is linked to ~2% higher cardiovascular risk per excess year.
Why the study?
Does a CMR-derived machine-learning model for estimating cardiovascular biological age (HeartAge-gap) predict cardiovascular outcomes and mortality independently of chronological age?
Cohort (n=32,681)
Yes
Does a CMR-derived machine-learning model for estimating cardiovascular biological age (HeartAge-gap) predict cardiovascular outcomes and mortality independently of chronological age?
Hazard Ratio: 1.022 (95% CI 1.001–1.044)
p-value: p=0.048
A CMR-derived machine-learning model estimating cardiovascular biological age (HeartAge-gap) independently predicts adverse cardiovascular outcomes across both sexes and all-cause mortality in females.
Aims 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. Methods and Results HeartAge was developed using gradient-boosting regression in 3,760 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), 2,316 (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. Conclusion 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.
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Andreozzi et al. (2025) conducted a cohort in Cardiovascular risk (n=32,681). HeartAge-gap (difference between CMR-derived cardiovascular biological age and chronological age) vs. Chronological age was evaluated on Composite cardiovascular outcome (HR 1.022, 95% CI 1.001-1.044, p=0.048). Each one-year increase in CMR-derived HeartAge-gap was associated with the composite cardiovascular outcome in females (HR 1.022; 95% CI 1.001-1.044) and males (HR 1.017; 95% CI 1.002-1.033).
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