Abstract 4367427: A Multimodal Artificial Intelligence Signature of Advanced Cardiac and Vascular Aging Defines Elevated Risk of Cardiovascular Disease

Abstract

Background: Age is the strongest factor in current cardiovascular risk estimation tools, yet there is significant heterogeneity in chronological age as a proxy for biological decline. Artificial intelligence (AI) models can infer biological age from routinely collected, non-invasive biomedical data. Here, we introduce a novel cross-modal AI-aging framework that leverages cardiac (ECG) and microvascular (retinal fundus images) aging phenotypes and its association with cardiovascular outcomes. Methods: For model development, we used all available ECG and retinal fundus studies performed within the Yale New Haven Health System (YNHHS) from 2011- 2024 (train/validation/test split 80%/10%/10%). Separate convolutional neural network models (EfficientNetB3-based) were trained to predict age based on each modality [ Figure A ]. For individuals who had an ECG and retinal image within the same year, AI-age predictions were ensembled into a single cross-modal model. Predictive performance was evaluated using the mean absolute error (MAE) between predicted and chronological age in the test set. Individuals were categorized as ‘ Resilient Agers’ if their predicted age was ≥1 MAE unit younger than their chronological age, ‘ Accelerated Agers’ if ≥1 MAE unit older, and ‘Normal Agers’ otherwise. Cox proportional hazards models were fitted for chronic disease endpoints (hypertension, diabetes) as well as cardiovascular outcomes (MI, stroke, HF, death; together MACE) stratified by AI-aging phenotype. The study was externally validated in the UK Biobank. Results: In the YNHHS cohort (n=195,851 patients, mean age 56 years [IQR 41-69]), ECG-age MAE was 9.1 and retinal-age MAE was 7.3, with similar performance in the validation set (ECG MAE 9.7; retinal MAE 7.5). Cross-modal AI-age predictions in paired studies (n =12,667, mean age 55 [43-69]) had a lower MAE of 5.9 years. The cross-modal Accelerated Ager phenotype was significantly associated with 10-year incidence of MACE (HR 2.28, CI [1.43, 3.66]), independent of chronological age, and with a larger effect size than for ECG- or retinal-age models. This pattern was also observed for individual MACE endpoints and risk factors [ Figure B] . Conclusions: A unified ECG-retinal AI-age model delivers more accurate biologic-age estimates and identifies individuals whose 10-year MACE risk is doubled, independent of chronological age. This scalable, non-invasive framework offers a powerful tool for early cardiovascular risk stratification.

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