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Multi-omic spatial effects on high-resolution AI-derived retinal thickness
V E Jackson1,2, Y Wu3, R Bonelli1,2,4
1Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Victoria, Australia.
Nature Communications
|February 5, 2025
Summary
AI analysis of retinal thickness reveals novel genetic links to systemic diseases. This high-resolution imaging provides new insights into macular health and disease biomarkers.
Area of Science:
- Ophthalmology
- Genetics
- Biomarkers
Background:
- Retinal thickness is a key indicator of eye health and a potential biomarker for systemic diseases.
- Optical coherence tomography (OCT) is routinely used for retinal thickness measurements in clinical eyecare.
Purpose of the Study:
- To leverage artificial intelligence (AI) for high-resolution analysis of macular retinal thickness using UK Biobank OCT images.
- To identify novel genetic and metabolic associations with fine-scale retinal thickness patterns.
Main Methods:
- Processed over 29,000 macular points from UK Biobank OCT images using a convolutional neural network.
- Analyzed common genomic variants, metabolomic, blood, and immune biomarkers, disease PheCodes, and genetic scores against a fine-scale macular thickness grid.
Main Results:
- Discovered multiple novel genetic loci, including four on the X chromosome, associated with retinal thickness.
- Identified associations between retinal thinning and systemic disorders like multiple sclerosis.
- Found spatial clustering of metabolite associations within the retina, with parafoveal thickness being particularly sensitive to systemic factors.
Conclusions:
- AI-driven analysis of retinal thickness offers enhanced discovery power and resolution.
- Macular retinal thickness patterns are linked to genetic factors and systemic diseases.
- Parafoveal thickness may serve as a sensitive indicator of systemic health insults.
