Purpose: To provide structural and functional evidence of inner retinal loss in diabetes prior to vascular changes and interpret the structure-function relationship in the context of an established neural model.
Methods: Data from one eye of 505 participants (134 with diabetes and no clinically evident vascular alterations of the retina) were included in this analysis. The data were collected as part of a large population-based study. Functional tests included best-corrected visual acuity, Pelli-Robson contrast sensitivity, mesopic microperimetry, and frequency doubling technology perimetry (FDT). Macular optical coherence tomography volume scans were collected for all participants. To interpret the structure-function relationship in the context of a neural model, ganglion cell layer (GCL) thickness was converted to local ganglion cell (GC) counts.
Results: The GCL and inner plexiform layer were significantly thinner in participants with diabetes (P < 0.05), with no significant differences in the macular retinal nerve fiber layer or the outer retina. All functional tests except microperimetry showed a significant loss in diabetic patients (P < 0.05). Both FDT and microperimetry showed a significant relationship with the GC count (P < 0.05), consistent with predictions from a neural model for partial summation conditions. However, the FDT captured additional significant damage (P = 0.03) unexplained by the structural loss.
Conclusions: Functional and structural measurements support early neuronal loss in diabetes. The structure-function relationship follows the predictions from an established neural model. Functional tests could be improved to operate in total summation conditions in the macula, becoming more sensitive to early loss.
Retinal ganglion cells
ASJC Scopus subject areas
Cellular and Molecular Neuroscience
Dive into the research topics of 'Evidence for Structural and Functional Damage of the Inner Retina in Diabetes With No Diabetic Retinopathy'. Together they form a unique fingerprint.Student theses
Jul 2017Supervisor: Chakravarthy, U. (Supervisor) & Hogg, R. (Supervisor)
Student thesis: Doctoral Thesis › Doctor of Philosophy