Determinants of macular ganglion cell–inner plexiform layer thickness in normal Chinese adults

Abstract

Background Demographic, systemic and ocular factors may impact macular ganglion cell–inner plexiform layer (GCIPL) thickness measurements. This study aimed to investigate the influences of multiple potential determinants of macular GCIPL thickness in normal Chinese adults. Methods This was a retrospective study conducted on 225 normal eyes from 225 healthy Chinese adults. GCIPL thickness were obtained using Cirrus high-definition optical coherence tomography (OCT). The age, gender, laterality, spherical equivalent (SE) refractive error, intraocular pressure (IOP), axial length (AL), central cornea thickness (CCT), circumpapillary retinal nerve fibre layer (pRNFL) thickness and OCT signal strength were recorded and their respective effect on GCIPL thickness parameters were evaluated. Results The mean (± SD) average, minimum, superotemporal, superior, superonasal, inferonasal, inferior, and inferotemporal GCIPL thickness was 84.56\u2009±\u20095.36, 81.32\u2009±\u20095.58, 83.08\u2009±\u20095.37, 85.70\u2009±\u20095.95, 87.15\u2009±\u20096.26, 85.07\u2009±\u20096.11, 82.46\u2009±\u20095.76, and 83.88\u2009±\u20095.59\u2009μm, respectively. Determinants of thinner GCIPL thickness were older age ( P \u2009=\u20090.001–0.117; effects enhanced if age over 40\u2009years), thinner pRNFL (all P \u2009<\u20090.001), and weaker signal strength (all P \u2009<\u20090.001). No significant difference was found between males and females ( P \u2009=\u20090.069–0.842), and between right eyes and the left eyes ( P \u2009=\u20090.160–0.875) except that of superonasal GCIPL thickness ( P \u2009<\u20090.001). There was no significant correlation between GCIPL thickness and SE, IOP, CCT, and AL ( P \u2009=\u20090.135–0.968). Conclusions Individual determinants associated with thinner GCIPL thickness were older age (particularly over 40\u2009years of age), thinner pRNFL, and weaker OCT signal strength. This is relevant in comprehensively understanding the normative data and differentiating normal aging from abnormalities.