Abstract 12631: Phenome-Wide Prediction of Incident Circulatory Diseases Using Machine Learning-Derived Features From Retinal Imaging

Abstract

Purpose: The retina is a window for assessment of cardiovascular health. Here, we performed machine learning-based assessment of retinal vascular density (RVD) from fundus photos, and combined this with retinal layer thicknesses from optical coherence tomography (OCT) imaging, to predict incident circulatory conditions. Methods: We utilized UK Biobank participant retinal fundus images (N=54,813), and OCT images (N=44,823). Deep-learning of fundus photos was used to remove poor-quality images and segment the retinal microvasculature to calculate RVD. Using the Topcon Segmentation algorithm, we quantified retinal layer thicknesses. We performed Cox survival analyses, separately associating RVD and retinal layer thicknesses with 130 circulatory conditions from Phecode ICD-9 and ICD-10 composite phenotypes (median 10 year follow-up), adjusting for age, sex, smoking, ethnicity, height, weight, and spherical equivalent. False Discovery Rate (FDR<0.05) correction was performed to identify significant associations. Results: Participants’ mean age was 56 (SD 8 years), 55% were female, and 56% were non-smokers. Associations were observed for each SD of photoreceptor thinning with incident abdominal aortic aneurysms (HR 1.47, P=6e-6), peripheral vascular disease (HR 1.32, P=4e-5), nonhypertensive heart failure (HR 1.20, P=2.8e-6), myocardial infarction (HR 1.17, P=8e-7), cerebrovascular disease (HR 1.15, P=1.9e-5) and hypertension (HTN) (HR 1.09, P=1e-12). Each SD of lower RVD was associated with incident HTN (HR 1.15, P=3.8e-39) and hypertensive heart disease (HR 2.07, P=6e-4). Further incident heart failure analysis additionally adjusting for RVD, prevalent HTN, and type 2 diabetes identified a persistent association for photoreceptor thinning. Conclusions: Our results indicate that photoreceptor thinning and RVD from retinal imaging can serve as a biomarker for future cardiovascular conditions, independent of other common risk factors.