Aller au contenu principal
Accès ouvert déclaré 2025 article

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

1Citations signalées — pas une note de qualité
11Institutions déclarées
3Pays d’affiliation déclarés

Résumé fourni par la source

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.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.

Contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
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
Date Crossref
04/12/2025
Éditeur
Oxford University Press (OUP)
Type
journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude et ne compte pas comme une seconde source scientifique indépendante.

Institutions déclarées

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Sujets associés

Genetic Associations and EpidemiologyCardiovascular Disease and AdiposityArtificial Intelligence in Healthcare

BNTIC News n’est pas le producteur de ces données. Recherche à la demande dans Crossref, OpenAIRE, DOAJ, Europe PMC, HAL, DataCite, ROR et la Banque mondiale, sans clé ; OpenAlex reste optionnel. Aucun service payant requis, aucune donnée externe enregistrée en base. Sources et limites.