Vision Changes in Diabetes

University Of California Berkeley, Berkeley CA

Investigators

Linked publications & trials

Abstract

Our long-term goal is to establish functional indicators of eye health and predictors of vision loss in diabetes. Diabetic eye complications are the leading cause of blindness in US adults aged 25-74. Early diagnosis and development of effective preventatives and treatments of diabetic retinopathy are essential to save sight. Clinical trials aimed at either prevention or early treatments will rely heavily on the discovery of sensitive methods to identify patients and retinal locations at risk, as well as to evaluate treatment effects. Classical vision measures (e.g. visual acuity) fail to reveal functional change even in the early stages of retinopathy, and none tests multiple local retinal locations except visual fields. We have successfully revealed such specific local retinal functional changes with the multifocal electroretinogram (mfERG), which allows the simultaneous recording of responses from over 100 small retinal patches across the macula. Our multivariate model including mfERG delays achieved 86% sensitivity and 84% specificity for prediction of future retinopathy at specific locations in eyes with some retinopathy at baseline. The prediction of retinal location relies solely on the mfERG delays. Now we propose to create new models of priority clinical importance (a) to predict the onset of retinopathy and its retinal location in eyes with no prior retinopathy; (b) to locally predict future sight-threatening diabetic macular edema. As a separate aim we will explore two retinal mechanisms of the mfERG delays that are so crucial for prediction in diabetic patients. These studies will add significantly to understanding retinal sites of diabetes actions. The ability to predict the retinal locations of future retinopathy provides clinicians a powerful tool to screen, follow up, and even consider early prophylactic treatment of the retinal tissue in diabetic patients. It also aids identification of 'at risk' populations for clinical trials of candidate therapies, which may greatly reduce their cost by decreasing the size of the needed sample and the duration of the trial.