Abstract
Purpose :
The retina shares developmental, physiological and anatomical structures with the brain. Hence, retinal imaging is used to examine neurodegenerative disease. Previous studies have shown that patients with Alzheimer’s disease have anatomical changes and functional visual defects in retinal structures. Advances in noninvasive optical coherence tomography (OCT) allows the retinal neurodegeneration to be quantified in vivo. Our study aimed to examine the association of specific retinal sublayer thicknesses with brain magnetic resonance imaging (MRI) markers.
Methods :
UK Biobank participants aged 40 to 69 years old completed a detailed baseline questionnaire and underwent ophthalmic and brain imaging assessments. Retinal nerve fibre layer (RNFL), ganglion cell- inner plexiform layer (GC-IPL), ganglion cell complex (GCC) and total macular thicknesses were obtained from spectral domain (SD) OCT, while total brain, grey-matter, white-matter and hippocampus volumes were assessed using MRI scans. Multivariate linear regression models were constructed to evaluate associations of retinal layers with brain MRI markers, adjusting for demographic factors and vascular risk factors.
Results :
Thinner GC-IPL, GCC and total macular were all significantly associated with smaller brain volume, standardized mean difference in z-score per SD decrease in retinal sublayer thickness, respectively: (-0.12; 95% CI -0.17 to -0.07), (-0.12; 95% CI -0.17 to -0.07), and (-0.12; 95% CI -0.18 to -0.07). Similarly, thinner GC-IPL, GCC and total macular were associated with smaller grey and white volume. No association was found between RNFL thickness and total brain, grey or white volumes.
Conclusions :
Markers of retinal neurodegeneration, as reflected by thinning of GC-IPL, GCC and total macular thickness were associated with smaller brain, grey-matter and white-matter volumes. Our findings suggest that changes in the retinal OCT may be used as novel ocular biomarkers to identify neurodegenerative changes in the brain structure.
This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.