The electroretinography to identify biomarkers of idiopathic hypersomnia and narcolepsy type 1

Abstract

SummaryHypersomnia spectrum disorders are underdiagnosed and poorly treated due to their heterogeneity and absence of biomarkers. The electroretinography has been proposed as a proxy of central dysfunction and has proved to be valuable to differentiate certain psychiatric disorders. Hypersomnolence is a shared core feature in central hypersomnia and psychiatric disorders. We therefore aimed to identify biomarkers by studying the electroretinography profile in patients with narcolepsy type 1, idiopathic hypersomnia and in controls. Cone, rod and retinal ganglion cells electrical activity were recorded with flash‐electroretinography in non‐dilated eye of 31 patients with idiopathic hypersomnia (women 84%, 26.6 ± 5.9 years), 19 patients with narcolepsy type 1 (women 63%, 36.6 ± 12.7 years) and 43 controls (women 58%, 30.6 ± 9.3 years). Reduced cone a‐wave amplitude (p = 0.039) and prolonged cone (p = 0.022) and rod b‐wave (p = 0.009) latencies were observed in patients with narcolepsy type 1 as compared with controls, while prolonged photopic negative response‐wave latency (retinal ganglion cells activity) was observed in patients with idiopathic hypersomnia as compared with controls (p = 0.033). The rod and cone b‐wave latency clearly distinguished narcolepsy type 1 from idiopathic hypersomnia and controls (area under the curve > 0.70), and the photopic negative response‐wave latency distinguished idiopathic hypersomnia and narcolepsy type 1 from controls with an area under the curve > 0.68. This first original study shows electroretinography anomalies observed in patients with hypersomnia. Narcolepsy type 1 is associated with impaired cone and rod responses, whereas idiopathic hypersomnia is associated with impaired retinal ganglion cells response, suggesting different phototransduction alterations in both hypersomnias. Although these results need to be confirmed with a larger sample size, the electroretinography may be a promising tool for clinicians to differentiate hypersomnia subtypes.