A study on electroretinography as a biomarker for seasonal vulnerability in depression

Nov 19, 2025Translational psychiatry

Using eye response tests to identify seasonal risk in depression

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Circadian Biology on OpenScience ↗PubMed ↗DOI ↗OA ↗

Abstract

The model achieved good discriminative power with an AUC of 0.861.

Six predictors were identified that distinguish seasonal from non-seasonal Major Depressive Episodes (MDE).
These predictors include reduced bipolar cell amplitude, increased cone response amplitude, increased daytime sleepiness, higher depression severity, younger age, and female gender.
The model explains 30.5% of the variance in seasonal vulnerability among MDE patients.
Subjective sleep assessments, psychiatric evaluations, and measures were utilized in the analysis.
This approach may provide a more nuanced understanding of the underlying mechanisms of depression compared to traditional classifications.

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Depressive disorders involve disruptions in light signal processing. Seasonal Affective Disorder (SAD) has been linked to abnormalities in phototransduction, including impaired retinal responses and sleep disturbances. Similar retinal anomalies have been observed in Major Depressive Episode (MDE) patients without seasonal pattern. However, no study has directly compared light-signaling biomarkers between SAD and non-seasonal MDE using sleep assessments and (ERG) measures. This study aims to develop a model combining clinical and ERG markers to predict seasonality in MDE patients. Patients with MDE (N = 320) were classified based on their vulnerability to seasonality using the (GSS) from the Seasonal Pattern Assessment Questionnaire (SPAQ), with a threshold of ≥ 11 indicating seasonal vulnerability. This dimensional approach provides a more nuanced reflection of underlying pathophysiological mechanisms than the categorical DSM-5-TR classification of SAD. Subjective sleep, psychiatric, and ERG biomarkers were analyzed. Significant variables were entered into a Backward Stepwise Logistic Regression (N = 35, subset of participants who had all available data including ERG measures), and model performance was assessed using sensitivity, specificity, accuracy, AUC, ROC curve, and Youden's index. The model retained six predictors: reduced bipolar cell amplitude (rods), increased cone response amplitude at 7 cd·s⁻¹·m⁻², increased daytime sleepiness, higher depression severity, younger age, and female gender. It demonstrated good discriminative power (AUC = 0.861, sensitivity = 0.905, specificity = 0.714, Youden's index = 0.619). The model effectively distinguishes seasonal from non-seasonal MDE, explaining 30.5% of the variance. ERG is a promising tool for identifying biomarkers of Seasonal Vulnerability in depression. Enhancing predictive approaches with multimodal diagnostic and longitudinal data could further improve early detection and personalized interventions.

Key numbers

0.861

AUC

Area under the receiver operating characteristic curve for the model.

0.905

Sensitivity

Proportion of true positives correctly identified by the model.

0.714

Specificity

Proportion of true negatives correctly identified by the model.

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A study on electroretinography as a biomarker for seasonal vulnerability in depression

Abstract

Key numbers

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