Diabetic Eye Disease

Diabetes mellitus represents a growing international public health issue with a near quadrupling in its worldwide prevalence since 1980. Though it has many known microvascular complications, vision loss from diabetic retinopathy is one of the most devastating for affected individuals. In addition, there is increasing evidence to suggest that diabetic patients have a greater risk for glaucoma as well. Though the pathophysiology of glaucoma is not completely understood, both diabetes and glaucoma appear to share some common risk factors and pathophysiologic similarities with studies also reporting that the presence of diabetes and elevated fasting glucose levels are associated with elevated intraocular pressure-the primary risk factor for glaucomatous optic neuropathy. While no study has completely addressed the possibility of detection bias, most recent epidemiologic evidence suggests that diabetic populations are likely enriched with glaucoma patients. As the association between diabetes and glaucoma becomes better defined, routine evaluation for glaucoma in diabetic patients, particularly in the telemedicine setting, may become a reasonable consideration to reduce the risk of vision loss in these patients.

We present a novel fully automated algorithm for the detection of retinal diseases via optical coherence tomography (OCT) imaging. Our algorithm utilizes multiscale histograms of oriented gradient descriptors as feature vectors of a support vector machine based classifier. The spectral domain OCT data sets used for cross-validation consisted of volumetric scans acquired from 45 subjects: 15 normal subjects, 15 patients with dry age-related macular degeneration (AMD), and 15 patients with diabetic macular edema (DME). Our classifier correctly identified 100% of cases with AMD, 100% cases with DME, and 86.67% cases of normal subjects. This algorithm is a potentially impactful tool for the remote diagnosis of ophthalmic diseases.

Despite treatment advances, diabetic eye disease remains a leading cause of visual acuity (VA) loss worldwide. No methods to prospectively determine which patients will gain or lose vision exist, limiting individualized risk assessment and management. We investigated whether noninvasive, readily obtainable spectral domain optical coherence tomography parameters were correlated with VA in eyes with current or resolved center-involved diabetic macular edema (DME). Images were evaluated for disorganization of the retinal inner layers (DRIL), cysts, epiretinal membranes, microaneurysms, subretinal fluid, and outer layer disruption/reflectivity. DRIL affecting ≥50% of the 1-mm central retinal zone was associated with worse VA in all eyes, eyes with current edema, and eyes with resolved edema. Furthermore, early 4-month change in DRIL extent predicted VA change from baseline to 1 year. These data suggest that DRIL is a robust predictor of VA in eyes with present or previous DME and more highly correlated with VA than other widely used measures, such as retinal thickness. If further studies confirm DRIL as a predictive biomarker of future VA, physicians would gain a new tool of substantial clinical and investigative importance that could significantly change the approach to ophthalmic counseling and therapeutic management in patients with diabetes.

IMPORTANCE: Biomarkers that predict future visual acuity (VA) in eyes with baseline diabetic macular edema (DME) would substantively improve risk assessment, management decisions, and selection of eyes for clinical studies targeting DME. OBJECTIVE: To determine whether baseline or early change in the novel spectral domain-optical coherence tomography (SD-OCT) parameter disorganization of the retinal inner layers (DRIL) is predictive of VA in eyes with center-involved DME. DESIGN, SETTING, AND PARTICIPANTS: At a tertiary care referral center for diabetic eye disease, a retrospective, longitudinal cohort study obtained demographics, VA, and SD-OCT images from baseline, 4-month, and 8-month visits in 96 participants (120 eyes) with diabetes mellitus and baseline center-involved DME (SD-OCT central subfield thickness, ≥ 320 µm for men and ≥ 305 µm for women). Exclusion criteria included substantial media opacity, cataract surgery within 6 months, and nondiabetic retinal pathology affecting VA. On SD-OCT, the 1-mm-wide retinal area centered on the fovea was evaluated by masked graders for DRIL extent, cysts, hyperreflective foci, microaneurysms, cone outer segment tip visibility, and external limiting membrane or photoreceptor disruption and reflectivity. MAIN OUTCOMES AND MEASURES: Visual acuity and SD-OCT-derived retinal morphology. RESULTS: Greater DRIL extent at baseline correlated with worse baseline VA (point estimate, 0.04; 95% CI, 0.02-0.05 per 100 µm; P < .001). An increase in DRIL during 4 months was associated with VA worsening at 8 months (point estimate, 0.03; 95% CI, 0.02-0.05 per 100 µm; P < .001). A multivariate model that included a 4-month change in VA, DRIL, and external limiting membrane disruption was predictive of an 8-month VA change (r = 0.80). Each approximately 300-µm DRIL increase during 4 months predicted a 1-line, 8-month VA decline. When DRIL increased at least 250 µm at 4 months, no eyes had VA improvement of at least 1 line at 8 months. When DRIL decreased at least 250 µm at 4 months, no eyes had VA decline of at least 1 line at 8 months, and 77.7% had VA improvement of at least 1 line. CONCLUSIONS AND RELEVANCE: Disorganization of the retinal inner layers in the 1-mm foveal area is associated with VA, and change in DRIL predicts future change in VA. Early change in DRIL prospectively identifies eyes with a high likelihood of subsequent VA improvement or decline. Therefore, DRIL warrants further study as a robust, readily obtained, and noninvasive biomarker of future VA response in eyes with DME.