AUTHOR=Liu Xiao , Lai Shunkai , Ma Shisi , Yang Hong , Liu Lian , Yu Guocheng , Zhong Shuming , Jia Yanbin , Zhong Jingxiang 

TITLE=Development of a Novel Retina−Based Diagnostic Score for Early Detection of Major Depressive Disorder: An Interdisciplinary View

JOURNAL=Frontiers in Psychiatry

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2022.897759

DOI=10.3389/fpsyt.2022.897759

ISSN=1664-0640

ABSTRACT=Background: Clinically, effective markers for the diagnosis of major depressive disorder (MDD) are lacking. Alterations in retinal indicators are closely related to the pathophysiological mechanisms of MDD. However, the reliable retina-related diagnostic model for MDD remains to be developed. Thus, our study aimed to quantitatively evaluate retinal vascular and structural changes in MDD patients and to develop a reliable diagnostic model of MDD based on retinal parameters.
Methods: Seventy-eight patients with MDD and 47 healthy controls (HCs) performed retinal vessel density and structure examination using optical coherence tomography angiography (OCTA) and visual field examination using perimetry. Independent-sample t test was used to assess the differences in retinal parameters between the groups. Meanwhile, we constructed the corresponding retina-based diagnostic models by LASSO logistic regression. Finally, we evaluated their diagnostic ability by receiver operating characteristic curves and nomogram with calibration plot.
Results: MDD patients showed lower retinal vessel density (including radial peripapillary capillary vessel density (RPC-VD), superficial and deep capillary plexus vessel density (SCP/DCP-VD)), thinner subfoveal choroidal thickness (SFCT), and poorer visual fields compared to HCs (all p<0.05). Furthermore, we have constructed a retina-based diagnostic model, which shows a strong diagnostic capability for MDD (AUC=0.9015, p<0.001).
Conclusion: Patients with MDD showed distinct retinal features compared to HCs. The retina-based diagnostic model is expected to be a necessary complement to the diagnosis of MDD.