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BackgroundThe etiopathogenesis of major depressive disorder （MDD） is strongly associated with neuroinflammation. MDD is a highly heterogeneous psychiatric disorder， and the disease subtyping is an essential step for the identification of biological markers. The presence of psychomotor retardation seriously affects the prognosis of MDD， whereas the underlying mechanism is not yet completely clear. A potential involvement of granulocyte colony-stimulating factor （G-CSF） and macrophage colony-stimulating factor （M-CSF） in the pathogenesis of MDD with psychomotor retardation has been suggested in previous studies， but little detailed research has been completed.ObjectiveTo analyze the correlation of plasma G-CSF and M-CSF levels with psychomotor retardation in patients with MDD， and to explore the potential biological underpinnings of psychomotor retardation in MDD.MethodsA total of 50 MDD patients who met the diagnostic criteria of the Diagnostic and Statistical Manual of Mental Disorders， fifth edition （DSM-5） and attended the outpatient clinics of Shanghai Mental Health Center from April 2018 to April 2019 were included. The severity of symptoms was assessed using the Hamilton Depression Scale-17 item （HAMD-17）. According to the retardation factor in HAMD-17， patients with a score of ≥8 were included in retardation group （n=22）， and those with a score below 8 were included in non-retardation group （n=28）. Another 22 age- and sex-matched healthy controls were concurrently recruited. Plasma G-CSF and M-CSF levels were measured in all subjects using Luminex liquid suspension chip technology. Spearman correlation analysis was adopted to verify the correlation of retardation factor score in HAMD-17 with plasma G-CSF and M-CSF levels in MDD patients.ResultsPlasma G-CSF levels were decreased in MDD patients compared with healthy controls ［57.34（39.24， 83.15）pg/mL vs. 71.47（61.20， 79.99）pg/mL， Z=-2.098， P

People with an evening chronotype have an increased risk of experiencing a major depressive disorder (MDD). It is unclear if this effect is predominantly related to the initial development of MDD or also present in recurrent episodes. The current study aimed to investigate if the association between chronotype and depressive severity in MDD patients is comparable in MDD patients with first and recurrent episodes. 386 MDD patients, 70.7% females and aged between 16 and 64, participated in the study. The Morningness – Eveningness Questionnaire (MEQ), Pittsburgh Sleep Quality Index (PSQI), Multidimensional Fatigue Inventory (MFI20), and Quick Inventory of Depressive Symptomatology (QIDS-SR16) were administered to participants to determine chronotype, sleep quality, fatigue level, and depressive severity, respectively. Multivariate regression models were utilized to analyze how chronotype influences depressive severity. The study showed that chronotype, sleep quality, and fatigue level were all associated with depressive severity. Eveningness significantly predicted an increase in depressive severity independently of sleep quality and fatigue level only in patients with the first episode (−0.068, p = 0.010), but not in patients with recurrent episodes (0.013, p = 0.594). Circadian-focused treatment should be considered in first-episode depression only. [ABSTRACT FROM AUTHOR]

Objective and quantitative neuroimaging biomarkers are crucial for early diagnosis of major depressive disorder (MDD). However, previous studies using machine learning (ML) to distinguish MDD have often used small sample sizes and overlooked MDD's neural connectome and mechanism. To address these gaps, we applied Dynamic Graph Convolutional Nets (DGCNs) to a large multi-site dataset of 2317 resting state functional MRI (RS-fMRI) scans from 1081 MDD patients and 1236 healthy controls from 16 Rest-meta-MDD consortium sites. Our DGCN model combined with the personal whole brain functional connectivity (FC) network achieved an accuracy of 82.5 % (95 % CI:81.6-83.4 %, AUC:0.869), outperforming other universal ML classifiers. The most prominent domains for classification were mainly in the default mode network, fronto-parietal and cingulo-opercular network. Our study supports the stability and efficacy of using DGCN to characterize MDD and demonstrates its potential in enhancing neurobiological comprehension of MDD by detecting clinically related disorders in FC network topologies., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Copyright © 2024. Published by Elsevier B.V.)