Retinomics : a window to multidisease prediction using retinal biomarkers from routine eye imaging

Abstract

Background: The aim of this study is to investigate the potential of retinal biomarkers (retinomics) derived from color fundus photography and optical coherence tomography for predicting multiple diseases. Methods: Using UK Biobank cohort data, we applied least absolute shrinkage and selection operator regression to address multicollinearity and identify key biomarkers. Cox proportional hazards models, with and without retinomic features. Detection rates (DR) across false positive rates (FPR: 5–40%) were assessed to ensure improved sensitivity without disproportionate false positives. Results: Three retinomic features emerged as top predictors: ganglion cell-inner plexiform layer (37 diseases), retinal pigment epithelium (33 diseases), and central subfield of inner segment/outer segment-RPE (32 diseases). Adding retinomics improved mean C-index from 0.653 to 0.693 (+ 6.4%) in baseline models (age and sex) and from 0.697 to 0.721 (+ 3.5%) in clinical models (traditional common risk factors). A simplified retinal model (retinomics + age/sex) achieved C-index ≥ 0.75 for 13 diseases. Retinomics enhanced prediction by > 5% for 24 diseases in baseline models and 12 diseases in clinical models. DR improvements across FPR ranges confirmed robust performance without excessive false positives. Conclusions: Retinomics universally enhanced disease prediction, with marked gains for conditions like cardiovascular and metabolic disorders. The onset of presbyopia (~ 50 years)—a common trigger for eye exams—aligns with escalating chronic disease risks, suggesting an opportunity to leverage routine eye care for broader health assessment. While requiring further validation, this approach demonstrates the potential to enhance health screening efficiency using existing ophthalmic infrastructure, offering particular value for resource-limited settings.