Neuroretinal dysfunction revealed by a flicker electroretinogram correlated with peripheral nerve dysfunction and parameters of atherosclerosis in patients with diabetes

• Main Authors: Miyuka Kawai, Tatsuhito Himeno, Yuka Shibata, Nobuhiro Hirai, Yuriko Asada‐Yamada, Emi Asano‐Hayami, Yohei Ejima, Rina Kasagi, Eriko Nagao, Yukako Sugiura‐Roth, Hiromi Nakai‐Shimoda, Takayuki Nakayama, Yuichiro Yamada, Takahiro Ishikawa, Yoshiaki Morishita, Masaki Kondo, Shin Tsunekawa, Yoshiro Kato, Jiro Nakamura, Hideki Kamiya
• Format: Article
• Language: English
• Published: Wiley 2021-07-01
• Series: Journal of Diabetes Investigation
• Subjects: Diabetic neuropathies; Electroretinography; Point‐of‐care testing
• Online Access: https://doi.org/10.1111/jdi.13465

Abstract Aims/Introduction Diabetic polyneuropathy (DPN) develops in the early stage of diabetes. However, no common diagnostic protocol has yet been established. Here, to verify that the flicker electroretinogram using a hand‐held device can detect the early dysfunction of the peripheral nervous system in patients with diabetes, we investigated the correlation between the progression of DPN and neuroretinal dysfunction. Materials and Methods In total, 184 participants with type 1 or 2 diabetes underwent a flicker electroretinogram (ERG) using a hand‐held device RETeval™ and nerve conduction study. Participants were also evaluated for intima‐media thickness, ankle‐brachial index, toe brachial index and brachial‐ankle pulse wave velocity. Parameters of the nerve conduction study were used to diagnose the severity according to Baba’s classification. A multiple regression analysis was used to examine the associations of ERG parameters with the severity of DPN categorized by Baba’s classification. Diagnostic properties of the device in DPN were evaluated using a receiver operating characteristic curve. Results A multiple regression model to predict the severity of DPN was generated using ERG. In the model, moderate‐to‐severe DPN was effectively diagnosed (area under the receiver operating characteristic curve 0.692, sensitivity 56.5%, specificity 78.3%, positive predictive value 70.6%, negative predictive value 66.1%, positive likelihood ratio 2.60, negative likelihood ratio 0.56). In the patients without diabetic retinopathy, the implicit time and amplitude in ERG significantly correlated with the parameters of the nerve conduction study, brachial‐ankle pulse wave velocity and intima‐media thickness. Conclusions Electroretinogram parameters obtained by the hand‐held device successfully predict the severity of DPN. The device might be useful to evaluate DPN.