Objective:

To develop a deep-learning model that estimates retinal age from fundus photographs and assess its association with specific cardiometabolic conditions, including diabetes and cardiovascular diseases.

Approach:

Key Findings:

• The model achieved a mean absolute error of 2.78 years in internal validation and 3.39 years in the primary external cohort, suggesting strong predictive capability.
• Larger retinal age gaps were associated with diabetes medication use and a history of stroke or cardiac disease, indicating potential clinical implications.
• The model outperformed comparator models in the primary external cohort, demonstrating its effectiveness.

Interpretation:

The retinal age model shows promise as a biomarker for biological aging and potential clinical utility in risk stratification, though its clinical significance remains uncertain and requires further investigation.

Limitations:

• The model was trained on a relatively healthy population, limiting generalizability to patients with chronic conditions, and self-reported data for disease status may introduce bias.
• The study was observational and cross-sectional, relying on self-reported data for disease status, which may affect the accuracy of findings.
• Performance declined in a more heterogeneous external cohort, indicating the need for validation in diverse populations to ensure applicability.

Conclusion:

Further prospective studies are needed to validate the clinical utility of retinal age estimates, establish thresholds for clinical use, and explore the integration of retinal age gap in clinical decision-making.

Sources:

• Communications Medicine
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