The Eye as a Window to Neuroinflammation in Psychiatric Disorders?: A Meta-Analysis of Retinal Structural and Vascular Biomarkers
Bibliographic record
Abstract
Introduction: Psychiatric disorders like schizophrenia, bipolar disorder (BD), and major depressive disorder (MDD) represent major global health challenges with complex pathophysiology, potentially involving neuroinflammation. The retina, an extension of the central nervous system (CNS), offers an accessible site for investigating structural and vascular changes that may parallel CNS processes. Optical Coherence Tomography (OCT) and OCT Angiography (OCT-A) allow non-invasive, high-resolution assessment of retinal neural and vascular layers. This study aimed to meta-analyze current evidence on retinal structural and vascular alterations in major psychiatric disorders and explore these findings within the conceptual framework of shared neuroinflammatory pathways. Methods: A systematic literature search was conducted in PubMed, Scopus, and Web of Science databases for studies published between January 1st, 2013, and December 31st, 2024. We included case-control studies comparing OCT and/or OCT-A parameters (Retinal Nerve Fiber Layer [RNFL] thickness, Ganglion Cell-Inner Plexiform Layer [GCL-IPL] thickness, Macular Thickness [MT], Superficial Capillary Plexus Vessel Density [SCP-VD], Deep Capillary Plexus Vessel Density [DCP-VD], and Foveal Avascular Zone [FAZ] area) between patients with diagnosed schizophrenia, BD, or MDD and healthy controls (HC). Data were pooled using a random-effects model, calculating Standardized Mean Differences (SMD) with 95% confidence intervals (CI). Heterogeneity was assessed using I² statistics. The risk of bias was evaluated using the Newcastle-Ottawa Scale (NOS). Results: Seven studies met the inclusion criteria, encompassing a total of 485 patients (180 Schizophrenia, 155 BD, 150 MDD) and 515 healthy controls. Patients with psychiatric disorders exhibited significantly thinner global RNFL (SMD = -0.68; 95% CI [-0.95, -0.41]; p < 0.00001; I²=75%), GCL-IPL (SMD = -0.75; 95% CI [-1.08, -0.42]; p < 0.0001; I²=80%), and reduced macular SCP-VD (SMD = -0.55; 95% CI [-0.88, -0.22]; p = 0.001; I²=72%) compared to HC. DCP-VD also showed a trend towards reduction (SMD = -0.40; 95% CI [-0.85, 0.05]; p = 0.08; I²=79%). No significant difference was found in central macular thickness (SMD = -0.15; 95% CI [-0.45, 0.15]; p = 0.33; I²=60%) or FAZ area (SMD = 0.20; 95% CI [-0.10, 0.50]; p = 0.19; I²=55%). High heterogeneity was observed across most analyses. Study quality varied, with NOS scores ranging from 6 to 8. Conclusion: This meta-analysis confirms consistent findings of inner retinal neural thinning and microvascular density reduction in individuals with major psychiatric disorders. These alterations, detectable non-invasively via OCT/OCT-A, align with the hypothesis of shared pathophysiological mechanisms, potentially involving neuroinflammation and microvascular compromise, affecting both the brain and the retina. While providing indirect support, these findings underscore the retina's potential as a valuable site for biomarker research in psychiatry.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.033 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".