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. 2026 Feb 27;27(3):515-526.
doi: 10.1093/ehjci/jeaf337.

A streamlined CMR-derived machine-learning model for estimating cardiovascular biological age: development and validation in the UK-biobank and multi-ethnic study of atherosclerosis

Pier-Giorgio Masci  1   2 , Gianni Andreozzi  3 , Esther Puyol-Anton  1   2 , Ashkan Abdollahi  4 , Richard Mospan  1 , Bram Ruijsink  1 , Marina Cecelja  2   5 , Phillip J Chowienczyk  2   6 , Aqeel T Mohamed  7   8 , Alistair Young  1   2 , Bharath Ambale  9 , Amedeo Chiribiri  1   2 , Geoff Tison  10 , Claire J Steves  2   5 , Joao A C Lima  4 , Reza Razavi  1   2 , Valentina Lorenzoni  3 , Andrew King  1   2
Affiliations

A streamlined CMR-derived machine-learning model for estimating cardiovascular biological age: development and validation in the UK-biobank and multi-ethnic study of atherosclerosis

Pier-Giorgio Masci et al. Eur Heart J Cardiovasc Imaging. .

Abstract

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 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.

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.

Keywords: ageing; biological age; cardiac magnetic resonance imaging; cardiovascular outcome.

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Conflict of interest statement

Conflict of interest: Dr Pier-Giorgio Masci was a consultant at the Perspectum Diagnostic Ltd (Oxford—UK)—consultancy ended November 2022. Dr Andrew King and Dr Esther Puyol-Anton have received royalties/licenses from Perspectum Diagnostic Ltd (Oxford—UK) within the last 36 months. Professor Reza Razavi is currently Chief Executive Officer of Fraiya Ltd (London—UK). Professor Alistair Young is an Associate Editor, European Heart Journal, Cardiovascular Imaging.

Comment in

  • Imaging the aging heart.
    Tahasildar S, O'Regan DP. Tahasildar S, et al. Eur Heart J Cardiovasc Imaging. 2026 Feb 27;27(3):527-528. doi: 10.1093/ehjci/jeaf354. Eur Heart J Cardiovasc Imaging. 2026. PMID: 41367296 No abstract available.

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