Multi-omic spatial effects on high-resolution AI-derived retinal thickness

作者：Victoria E. Jackson, Yingli Wu, Roberto Bonelli, Julia P. Owen, Liam W. Scott, Samaneh Farashi, Yasuki Kihara, Marin L. Gantner, Catherine Egan, Katie Williams, Brendan R. E. Ansell, Adnan Tufail, Aaron Lee, Melanie Bahlo · 发表于：Nature Communications · 年份：2025 · DOI：10.1038/s41467-024-55635-7 · 被引用次数：31 · 研究领域：Retinal Imaging and Analysis、Retinal Diseases and Treatments

Retinal thickness is a marker of retinal health and more broadly, is seen as a promising biomarker for many systemic diseases. Retinal thickness measurements are procured from optical coherence tomography (OCT) as part of routine clinical eyecare. We processed the UK Biobank OCT images using a convolutional neural network to produce fine-scale retinal thickness measurements across > 29,000 points in the macula, the part of the retina responsible for human central vision. The macula is disproportionately affected by high disease burden retinal disorders such as age-related macular degeneration and diabetic retinopathy, which both involve metabolic dysregulation. Analysis of common genomic variants, metabolomic, blood and immune biomarkers, disease PheCodes and genetic scores across a fine-scale macular thickness grid, reveals multiple novel genetic loci including four on the X chromosome; retinal thinning associated with many systemic disorders including multiple sclerosis; and multiple associations to correlated metabolites that cluster spatially in the retina. We highlight parafoveal thickness to be particularly susceptible to systemic insults. These results demonstrate the gains in discovery power and resolution achievable with AI-leveraged analysis. Results are accessible using a bespoke web interface that gives full control to pursue findings.