Association of retinal optical coherence tomography metrics and polygenic risk scores with cognitive function and future cognitive decline
Year: 2023
Type: article
Abstract: PURPOSE: To evaluate the potential of retinal optical coherence tomography (OCT) measurements and polygenic risk scores (PRS) to identify people at risk of cognitive impairment. METHODS: Using OCT images from 50 342 UK Biobank participants, we examined associations between retinal layer thickness and genetic risk for neurodegenerative disease and combined these metrics with PRS to predict baseline cognitive function and future cognitive deterioration. Multivariate Cox proportional hazard models were used to predict cognitive performance. P values for retinal thickness analyses are false-discovery-rate-adjusted. RESULTS: Higher Alzheimer's disease PRS was associated with a thicker inner nuclear layer (INL), chorio-scleral interface (CSI) and inner plexiform layer (IPL) (all p<0.05). Higher Parkinson's disease PRS was associated with thinner outer plexiform layer (p<0.001). Worse baseline cognitive performance was associated with thinner retinal nerve fibre layer (RNFL) (aOR=1.038, 95% CI (1.029 to 1.047), p<0.001) and photoreceptor (PR) segment (aOR=1.035, 95% CI (1.019 to 1.051), p<0.001), ganglion cell complex (aOR=1.007, 95% CI (1.002 to 1.013), p=0.004) and thicker ganglion cell layer (aOR=0.981, 95% CI (0.967 to 0.995), p=0.009), IPL (aOR=0.976, 95% CI (0.961 to 0.992), p=0.003), INL (aOR=0.923, 95% CI (0.905 to 0.941), p<0.001) and CSI (aOR=0.998, 95% CI (0.997 to 0.999), p<0.001). Worse future cognitive performance was associated with thicker IPL (aOR=0.945, 95% CI (0.915 to 0.999), p=0.045) and CSI (aOR=0.996, 95% CI (0.993 to 0.999) 95% CI, p=0.014). Prediction of cognitive decline was significantly improved with the addition of PRS and retinal measurements. CONCLUSIONS AND RELEVANCE: Retinal OCT measurements are significantly associated with genetic risk of neurodegenerative disease and may serve as biomarkers predictive of future cognitive impairment.
Source: British Journal of Ophthalmology
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
Awards: Personalizing Glaucoma Diagnosis by Disease Specific Patterns and Individual Eye Anatomy, Sociodemographic predictors of healthcare utilization and adverse outcomes in Medicare beneficiaries with glaucoma, Glaucoma Risk Prediction Using Machine Learning Integration of Image-Based Phenotypes and Genetic Associations, Uncovering novel biological processes and pathogenic mechanisms for glaucoma, Associating retinal nerve fiber layer thickness with glucose metabolism and diabetic retinopathy, G9000087432, G9000091133, P30 Core Grant for Vision Research, Understanding the clinical impact of cumulative genetic risk to glaucoma, Core Grant for Vision Research
OpenAlex ID: W4361302179