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A scoping review on aging and cardiovascular diseases - Molecular mediators and artificial intelligence-based
Antonio M Sudoso1, Lorenzo Ciarpaglini1, Diego Scuppa1
1Department of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Via Ariosto 25, 00185 Roma, RM, Italy.
Insights
Artificial intelligence (AI) can estimate biological age from ECGs and other data, offering better cardiovascular risk prediction than chronological age. This AI-derived biological age shows promise for assessing heart health, though standardization and validation are ongoing.
Area of Science:
- Cardiovascular Medicine
- Biomedical Engineering
- Artificial Intelligence
Background:
- Cardiovascular diseases (CVDs) are the leading global cause of death, with risk escalating with age.
- Biological age, a measure of physiological decline, offers more accurate cardiovascular risk prediction than chronological age.
- Artificial Intelligence (AI) advances enable biological age estimation from diverse data, but synthesis in cardiovascular medicine is lacking.
Purpose of the Study:
- To comprehensively synthesize existing evidence on AI-derived biological age in cardiovascular medicine.
- To evaluate the role of AI-estimated biological age in predicting cardiovascular outcomes.
- To identify current trends and future directions in AI applications for cardiovascular aging research.
Main Methods:
- Systematic literature search of PubMed and Scopus (2019-2025).
- Inclusion of original research applying AI/machine learning to estimate biological age or aging biomarkers in humans.
- Narrative synthesis of findings, organized by AI methods and data sources, focusing on cardiovascular outcomes.
Main Results:
- AI-derived biological age, especially from ECGs, provides prognostic information beyond chronological age.
- Deep learning models identified an 'age gap' linked to increased risks of heart failure, atrial fibrillation, and stroke.
- Retinal imaging and biomarker-based models confirm the systemic nature of aging; generative models offer insights into aging trajectories.
Conclusions:
- AI-based biological age estimation is a promising biomarker for cardiovascular risk assessment.
- Evidence across different data modalities is consistent, supporting AI's potential in cardiology.
- Further standardization, prospective validation, and enhanced interpretability are crucial for clinical implementation.
Background:
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, and their risk increases with age. Biological age reflects physiological decline more accurately than chronological age and may improve cardiovascular risk prediction. Recent advances in Artificial Intelligence (AI) have enabled estimation of biological age from electrocardiograms (ECGs), imaging, biomarkers, and omics data. However, the existing evidence on AI-derived biological age in cardiovascular medicine has not been comprehensively synthesized.
Methods:
PubMed and Scopus were searched for studies published between 2019 and 2025. Eligible studies included original research applying AI or machine learning to estimate biological age or aging biomarkers in human participants and relating these estimates to cardiovascular outcomes. Data were extracted on study design, AI methods, input data sources, biological age metrics, cardiovascular endpoints, and validation strategies. Findings were synthesized narratively and organized by methodological approaches.
Results:
AI-derived biological age, particularly ECG-predicted age, consistently provided prognostic information beyond chronological age. Deep learning models identified an age gap associated with increased risks of heart failure, atrial fibrillation and stroke. Retinal imaging-based biological age and biomarker-based machine learning models further supported the systemic nature of aging. Emerging approaches such as generative models offer insights into aging trajectories and lifestyle-linked aging phenotypes. Advances in interpretability using explainable AI highlighted ECG features that contribute most to aging predictions.
Conclusions:
AI-based estimation of biological age is a promising biomarker for cardiovascular risk assessment. Although evidence across modalities is consistent, further standardization, prospective validation, and improved interpretability are needed to support clinical implementation.
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