AI-driven biological clocks of cardiac aging: Should we integrate ECG, metabolomic, and epigenetic signatures?
Abstract
Background Cardiovascular diseases remain the leading cause of death globally, with age as the primary risk factor. Unlike chronological age, biological age aims to capture age-related physiological changes and may better predict health outcomes and disease risk. Recent advances in artificial intelligence have enabled the development of aging clocks, computational models that estimate biological age from diverse biomarker patterns. Main body This review examines three complementary approaches to quantifying cardiac and systemic aging. Epigenetic clocks, particularly second-generation models such as GrimAge, currently represent the most established molecular measures of biological aging and show strong associations with cardiovascular outcomes. AI-based electrocardiogram (ECG) clocks capture functional cardiac aging, with landmark studies demonstrating that ECG age gaps independently predict mortality and that accelerated ECG aging may confer up to 79% higher mortality risk. Metabolomic clocks provide complementary insight into systemic metabolic aging and reveal links between metabolic dysregulation and cardiovascular aging, including associations involving metabolites such as trimethylamine N-oxide (TMAO). However, current aging clocks face important limitations: single-modality approaches capture only one dimension of aging, the biological mechanisms underlying accelerated aging remain incompletely understood, and population generalizability remains limited. Conclusions Integration of ECG, metabolomic, and epigenetic data promises a paradigm shift from reactive disease management to proactive, personalized aging intervention. Understanding concordance and discordance between these complementary clocks may uncover distinct aging phenotypes and reveal modifiable biological pathways. Future research should focus on integrating multi-omics data within large longitudinal cohorts to build interpretable, population-generalizable models that can guide clinical interventions to extend healthspan and prevent cardiovascular disease.
Related
- Metabolite Profile and Biological Activities of Zanthoxylum nemorale: A Metabolomic and Multivariate Statistical Analysis Study
- Transcriptomic Signatures Suggest Epigenetic Programming of Molecular Defense in Biochar-Exposed Formica japonica
- Metabolomic Signatures of Longevity in Bivalves Reveal Reorganization of TCA Cycle and Amino Acid Metabolism
- Integrated transcriptomic and metabolomic analyses identify molecular signatures associated with meat quality differences between Yunling and Nubian goats.
- Kabuki Syndrome: Epigenetic Mechanisms, DNA Methylation Signatures, and Diagnostic Advances
BibTeX
@misc{akerblom2026aidriven,
title = {{AI-driven biological clocks of cardiac aging: Should we integrate ECG, metabolomic, and epigenetic signatures?}},
author = {Salla Åkerblom and Manyi Jia and Shamin Tahasildar and Amna Khamis and Philippe Froguel and Amélie Bonnefond and Declan O’regan and Nicolas Gambardella and Marc-Emmanuel Dumas},
howpublished = {HAL},
year = {2026},
month = jul,
doi = {10.21203/rs.3.rs-10155822/v2},
url = {https://hal.science/hal-05704880v1},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - AI-driven biological clocks of cardiac aging: Should we integrate ECG, metabolomic, and epigenetic signatures?
AU - Salla Åkerblom
AU - Manyi Jia
AU - Shamin Tahasildar
AU - Amna Khamis
AU - Philippe Froguel
AU - Amélie Bonnefond
AU - Declan O’regan
AU - Nicolas Gambardella
AU - Marc-Emmanuel Dumas
PY - 2026
DA - 2026/07/23
PB - HAL
DO - 10.21203/rs.3.rs-10155822/v2
UR - https://hal.science/hal-05704880v1
AB - Background Cardiovascular diseases remain the leading cause of death globally, with age as the primary risk factor. Unlike chronological age, biological age aims to capture age-related physiological changes and may better predict health outcomes and disease risk. Recent advances in artificial intelligence have enabled the development of aging clocks, computational models that estimate biological age from diverse biomarker patterns. Main body This review examines three complementary approaches to quantifying cardiac and systemic aging. Epigenetic clocks, particularly second-generation models such as GrimAge, currently represent the most established molecular measures of biological aging and show strong associations with cardiovascular outcomes. AI-based electrocardiogram (ECG) clocks capture functional cardiac aging, with landmark studies demonstrating that ECG age gaps independently predict mortality and that accelerated ECG aging may confer up to 79% higher mortality risk. Metabolomic clocks provide complementary insight into systemic metabolic aging and reveal links between metabolic dysregulation and cardiovascular aging, including associations involving metabolites such as trimethylamine N-oxide (TMAO). However, current aging clocks face important limitations: single-modality approaches capture only one dimension of aging, the biological mechanisms underlying accelerated aging remain incompletely understood, and population generalizability remains limited. Conclusions Integration of ECG, metabolomic, and epigenetic data promises a paradigm shift from reactive disease management to proactive, personalized aging intervention. Understanding concordance and discordance between these complementary clocks may uncover distinct aging phenotypes and reveal modifiable biological pathways. Future research should focus on integrating multi-omics data within large longitudinal cohorts to build interpretable, population-generalizable models that can guide clinical interventions to extend healthspan and prevent cardiovascular disease.
ER -