Multimodal Deep Learning Reveals the Modular Genetic Architecture of Cardiovascular Aging
Insights
Artificial intelligence estimates biological age from multiple cardiovascular data types. These AI-derived cardiovascular ages represent distinct aging pathways, not a single measure of senescence.
Area of Science:
- Cardiovascular Medicine
- Artificial Intelligence
- Genetics
Background:
- Chronological age is a primary cardiovascular disease risk factor.
- Individual cardiovascular aging varies significantly across different organs and biological pathways.
Purpose of the Study:
- To utilize deep learning to estimate biological age from diverse cardiovascular data streams.
- To investigate modality-specific cardiovascular aging phenotypes and their associated disease profiles.
- To identify genetic underpinnings of distinct cardiovascular aging axes.
Main Methods:
- Deep learning models analyzed 12-lead electrocardiograms, cardiac MRI, carotid ultrasound, and retinal fundus photographs from over 100,000 UK Biobank participants.
- Phenome-wide association studies (PheWAS) linked biological age gaps to disease profiles.
- Genome-wide association studies (GWAS) and cross-trait analyses identified genetic loci and supported partial genetic separation of aging axes.
- Integration with myocardial single-cell transcriptomic data identified cellular contexts for genetic signals.
Main Results:
- AI-derived biological age gaps showed limited overlap across electrical, structural, macrovascular, and microvascular domains.
- Each aging axis was associated with distinct disease profiles, including atrial fibrillation, heart failure, and diabetic retinopathy.
- GWAS identified 38 independent loci with largely modality-specific genetic signals.
- Genetic analyses supported partial separation of the four cardiovascular aging axes.
Conclusions:
- AI-derived cardiovascular age is a composite of distinct, measurable phenotypes (electrical, myocardial, macrovascular, microvascular), rather than a singular biomarker of aging.
- These findings offer a multi-dimensional approach to understanding cardiovascular aging and disease risk.
- Modality-specific genetic and disease associations highlight the heterogeneity of cardiovascular aging.
Abstract:
Age is the dominant risk factor for cardiovascular disease, yet individuals of the same chronological age can differ markedly in the organs and biological pathways through which cardiovascular vulnerability emerges. We used deep learning to estimate biological age from four cardiovascular data streams in more than 100,000 UK Biobank participants - 12-lead electrocardiograms, cardiac magnetic resonance imaging, carotid ultrasound, and retinal fundus photographs - representing electrical, structural, macrovascular, and microvascular domains. The resulting biological age gaps showed limited overlap across modalities in participants with complete phenotyping, and phenome-wide analyses linked each axis to distinct disease profiles. Genome-wide association analyses identified 38 independent loci, with largely modality-specific signals involving electrophysiologic, myocardial structural, vascular regulatory, and ocular/microvascular pathways. Cross-trait LD score regression and polygenic risk scores further supported partial genetic separation of the four axes, with modality-specific associations for atrial fibrillation, heart failure, peripheral arterial disease, hypertension, diabetes, and diabetic retinopathy in UK Biobank and broadly concordant patterns in All of Us. Integration with myocardial single-cell transcriptomic data nominated distinct cellular contexts for these genetic signals. These findings suggest that AI-derived cardiovascular age is not a single biomarker of systemic senescence, but a family of related phenotypes that decompose cardiovascular aging into measurable electrical, myocardial, macrovascular, and microvascular modules.
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