Abstract
BackgroundUnderstanding how retinal morphological features are associated with the risk of developing depression and anxiety disorders holds significant implications for public health and early detection strategies. This study aims to investigate the association between optical coherence tomography (OCT)-detected retinal features and the incidence of depression and anxiety.MethodsThis cohort study was conducted using data from UK Biobank participants aged 40-70 years who underwent retinal OCT imaging at recruitment. Baseline retinal morphological features, including the retinal nerve fibre layer (RNFL), ganglion cell-inner plexiform layer (GCIPL), inner nuclear layer (INL), photoreceptor layer (PRL), retinal pigment epithelium (RPE), and macular thickness, were segmented from macular-centered OCT images. Depression and anxiety disorders were identified using International Classification of Diseases (ICD) codes. Cox proportional hazards regression models were used to assess the association of retinal features with incident depression and anxiety, with the full model adjusting for demographic, lifestyle, and comorbid factors.ResultsA total of 36,220 participants were included (mean [SD] age, 55.90 [8.22] years, 47.8% male). Over a median follow-up duration of 12.5 years, 1340 new cases of depression and 1373 new cases of anxiety were observed. In fully adjusted Cox hazard regression models, each standard deviation (SD) increase in GCIPL and macular thickness was associated with a lower risk of incident depression (HR [95%CI], 0.92 [0.87, 0.97]; and 0.91 [0.86, 0.96], respectively). The associations of GCIPL and macular thickness with incident depression were more pronounced among females. On the other hand, there was no association between retinal features and incident anxiety disorders in the fully adjusted model.ConclusionsThinner GCIPL and macular thickness were independently associated with increased risk of depression, especially in females. Our findings highlight a potential role of OCT-detected retinal features as additional biomarkers for at-risk stratification of depression.