Background:
Retinal nerve fiber layer thickness, as a new visual indicator that may help diagnose mental disorders, is gaining attention from researchers. However, the causal relationship between retinal nerve fiber layer thickness and mental disorders is still to be effectively proved.
Methods:
A bidirectional Two-sample Mendelian randomization analysis was utilized to analyse aggregated data from large-scale genome-wide association studies, we selected genetic loci for retinal nerve fiber layer thickness in independent retinal abnormalities and three prevalent psychiatric disorders (schizophrenia, depression, bipolar disorder) as instrumental variables. The Two-sample Mendelian randomization analysis was mainly performed by inverse variance weighting and weighted median method. The Cochran Q test and leave-one-out sensitivity were used to ensure the robustness of the results. The Mendelian random polymorphism residuals and outliers were used to detect single nucleotide polymorphism outliers, and MR-Egger intercept test was used to test single nucleotide polymorphism horizontal pleiotropy.
Results:
IVW showed that retinal nerve fiber layer thickness was positively associated with schizophrenia (OR=1.057, 95%CI: 1.000-1.117, P<0.05), in the study of bipolar disorder, MR analysis also suggested a positive causal relationship between retinal nerve fiber layer thickness and bipolar disorder (OR=1.025, 95%CI: 1.005-1.046, P<0.05), which indicated possible causal relationships between retinal nerve fiber layer thickness and these two diseases. Depression (OR=1.000143, 95%CI: 0.9992631-1.001024, P=0.74) indicated no significant causal association. No reverse causal effects of psychiatric disorders on retinal nerve fiber layer thickness were found.
Conclusions:
A statistically significant causal relationship between retinal nerve fiber layer thickness and schizophrenia and bipolar disorder has been supported by genetic means, indicating that retinal nerve fiber layer thickness can be used as a potentially effective indicator for the prediction and diagnosis of these two diseases.