Biomarkers For Major Depressive Disorder Research Articles

Discovery of potential female-specific biomarkers for major depressive disorder by LC-MS-based metabolomics.

The prevalence of major depressive disorder (MDD) is higher in females than males, emphasizing the need to identify gender-specific biomarkers to improve diagnosis accuracy. In this study, a cross-sectional investigation with 258 samples was conducted to evaluate the discriminative power of potential gender-specific biomarkers for MDD. Eighteen MDD-related differential metabolites have been identified, involving pathways of phospholipids, glycerolipids, fatty acids, sphingolipids, cholesterol, vitamin E, and heme. A potential biomarker combination consisting of palmitelaidic acid, gamma carboxyethyl hydroxychroman (gamma-CEHC), and lysoPE(16:0) was confirmed for predicting depression in women using binary logistic regression analysis. To evaluate the panel's specificity, nine generalized anxiety disorder (GAD) samples, which share highly similar clinical symptoms with MDD, were included in the validation set. The discovery and validation sets yielded an area under the receiver operating characteristic curve of 0.86 and 0.83, respectively. All nine female GAD samples were correctly predicted as non-MDD, demonstrating the panel's specificity in diagnosing female MDD. Remarkably, this composite panel achieved a 75 % prediction accuracy in female samples in both the discovery and validation sets, but it did not reach 60 % prediction accuracy in male samples in either set. Our findings highlight the importance of gender-specific molecular diagnostics in developing practical and accurate diagnostic methods for MDD.

Correlation between polygenic risk scores of depression and cortical morphology networks.

Cortical morphometry is an intermediate phenotype that is closely related to the genetics and onset of major depressive disorder (MDD), and cortical morphometric networks are considered more relevant to disease mechanisms than brain regions. We sought to investigate changes in cortical morphometric networks in MDD and their relationship with genetic risk in healthy controls. We recruited healthy controls and patients with MDD of Han Chinese descent. Participants underwent DNA extraction and magnetic resonance imaging, including T 1-weighted and diffusion tensor imaging. We calculated polygenic risk scores (PRS) based on previous summary statistics from a genome-wide association study of the Chinese Han population. We used a novel method based on Kullback-Leibler divergence to construct the morphometric inverse divergence (MIND) network, and we included the classic morphometric similarity network (MSN) as a complementary approach. Considering the relationship between cortical and white matter networks, we also constructed a streamlined density network. We conducted group comparison and PRS correlation analyses at both the regional and network level. We included 130 healthy controls and 195 patients with MDD. The results indicated enhanced connectivity in the MIND network among patients with MDD and people with high genetic risk, particularly in the somatomotor (SMN) and default mode networks (DMN). We did not observe significant findings in the MSN. The white matter network showed disruption among people with high genetic risk, also primarily in the SMN and DMN. The MIND network outperformed the MSN network in distinguishing MDD status. Our study was cross-sectional and could not explore the causal relationships between cortical morphological changes, white matter connectivity, and disease states. Some patients had received antidepressant treatment, which may have influenced brain morphology and white matter network structure. The genetic mechanisms of depression may be related to white matter disintegration, which could also be associated with decoupling of the SMN and DMN. These findings provide new insights into the genetic mechanisms and potential biomarkers of MDD.

Meta analysis of resting frontal alpha asymmetry as a biomarker of depression

This meta-analysis investigated resting frontal alpha asymmetry (FAA) as a potential biomarker for major depressive disorder (MDD). Studies included articles utilizing FAA measure involving EEG electrodes (F3/F4, F7/F8, or Fp1/Fp2) and covering both MDD and controls. Hedges’ d was calculated from FAA means and standard deviations (SDs). A systematic search of PubMed through July 2023 identified 23 studies involving 1928 MDD participants and 2604 controls. The analysis revealed a small but significant grand mean effect size (ES) for FAA (F4 − F3), suggesting limited diagnostic value of FAA in MDD. Despite the presence of high heterogeneity across studies, subgroup analyses did not identify significant differences based on calculation formula, reference montage, age, or depression severity. The findings indicate that FAA may have limited standalone diagnostic utility but could complement existing clinical assessments for MDD, highlighting the need for a multifaceted approach to depression diagnosis and prognosis.

Electroencephalographic markers in Major Depressive Disorder: insights from absolute, relative power, and asymmetry analyses.

Major Depressive Disorder (MDD) leads to dysfunction and impairment in neurological structures and cognitive functions. Despite extensive research, the pathophysiological mechanisms and effects of MDD on the brain remain unclear. This study aims to assess the impact of MDD on brain activity using EEG power spectral analysis and asymmetry metrics. EEG recordings were obtained from 48 patients with MDD and 78 healthy controls. The data were segmented into 2-second windows (1024 data points) and analyzed using the Welch method, an advanced variant of the Fast Fourier Transform (FFT). A Hanning time window with 50% overlap was applied to compute the modified periodogram. Absolute and relative power, along with asymmetry values in the theta, alpha, and beta frequency bands, were calculated. Patients with MDD exhibited significantly higher absolute and relative power in the theta and beta bands and decreased power in the alpha band compared to healthy controls. Asymmetry analysis revealed significant differences between symmetric channels in the theta band (F7-F8, C3-C4, T3-T4, T5-T6), alpha band (F7-F8, C3-C4, T3-T4, T5-T6, O1-O2), and beta band (C3-C4, T3-T4, T5-T6, P3-P4). The findings suggest that MDD affects brain mechanisms and cognitive functions, as evidenced by altered power values in the theta and alpha bands. Additionally, asymmetry values in theta, alpha, and beta bands may serve as potential biomarkers for MDD. This study highlights that beyond the commonly used alpha asymmetry, theta and beta asymmetry can also provide valuable insights into the neurophysiological effects of MDD, aligning with previous neuroimaging studies that indicate impairments in memory, attention, and neuroanatomical connectivity in MDD.

Emerging biomarkers in major depressive disorder: Diagnostic, prognostic, and therapeutic implications

Recent advancements in biomarker research for the diagnosis, prognosis, and treatment strategies of major depressive disorder (MDD) have yielded significant findings that warrant documentation. The clinical demand for biomarkers persists due to the limited accuracy and efficiency of subjective diagnostic approaches. This review scrutinized research papers related to MDD biomarkers published between January 2011 and till July 2024, focusing exclusively on human studies with statistically significant results. The compiled biomarkers encompass cellular membrane receptors, cytoplasmic organelles, and genomic and epigenomic intranuclear markers. Cell surface molecular receptors implicated in MDD pathogenesis include brain-derived neurotrophic factor (BDNF) receptors, N-methyl D-aspartate receptors (NMDAR), and interleukin (IL) receptors. Endogenous compounds with diagnostic and prognostic potential, such as L-carnitine and alpha-L-carnitine, have also been identified. Transcriptomic biomarkers, including mRNA expression levels of the BDNF, IL-1β, macrophage migration inhibitory factor, and tumor necrosis factor-alpha (TNF-α), have demonstrated utility in MDD management. MicroRNAs (miRNAs), the endogenous molecules that alter the structure of mRNA, show potential for diagnosis and treatment outcome prediction, with miR-221-3p, miR-129-5p, miR-134, and miR-184 emerging as key candidates for MDD monitoring. Long non-coding RNAs (lncRNAs), such as GSK3βAS1, GSK3βAS2, and GSK3βAS3 have been investigated for the evaluation of disease severity and treatment response. Most recently, the pathological role of circular (circRNA) and DNA methylation in MDD has also been documented. The rs155979 polymorphism in the lncRNA NOTHSAT102891 was significantly associated with depression and risk of suicide. The data compiled in this review aim to guide further research in the quest for biomarkers that will improve the management of MDD.

Neurobiological mechanisms of antidepressant properties of psilocybin: A systematic review of blood biomarkers.

Psilocybin represents a novel therapeutic approach for individuals with major depressive disorder (MDD) who do not respond to conventional antidepressant treatment. Investigating the influence of psilocybin on the pathophysiological processes involved in MDD could enhance our neurobiological understanding of the presumed antidepressant action mechanism. This systematic review aims to summarize the results of human studies investigating changes in blood-based biomarkers of MDD to guide future research on potentially relevant analytes that could be monitored in clinical trials. A systematic search was performed in MEDLINE, Embase, and Web of Science to retrieve studies investigating changes in serum and plasma levels of neurotrophic, immunologic, neuroendocrine, and metabolic markers. Nine studies were included, describing findings on 15 biomarkers, exclusively in healthy participants. Studies consistently reported a decrease in interleukin-6, C-reactive protein, and eosinophils, and an increase in cortisol, prolactin, oxytocin, thyroid-stimulating hormone, adrenocorticotropic hormone, brain-derived neurotrophic factor, and free fatty acids following psilocybin administration. Less consistent effects were observed on interleukin-1β, interleukin-8, tumour necrosis factor-alpha, soluble urokinase plasminogen activator receptor, and growth hormone. The results are in line with preclinical studies and provide initial support from human studies that psilocybin potentially leads to beneficial effects on biomarkers of MDD. However, given the limited number of studies, findings should be approached with caution prior to replication. Further research should include larger samples, clinical populations, longer-term assessment, rigorous experimental designs, and account for the potential confounding of psychological stress related to the psychedelic experience.

Individualized Spectral Features in First-episode and Drug-naïve Major Depressive Disorder: Insights from Periodic and Aperiodic EEG Analysis.

The detection of abnormal brain activity plays an important role in the early diagnosis and treatment of major depressive disorder (MDD). Recent studies have shown that the decomposition of the electroencephalography (EEG) spectrum into periodic and aperiodic components is useful for identifying the drivers of electrophysiologic abnormalities and avoiding individual differences. This study aimed to elucidate the pathologic changes in individualized periodic and aperiodic activities and their relationships with the symptoms of MDD. EEG data in the eyes-closed resting state were continuously recorded from 97 first-episode and drug-naïve patients with MDD and 90 healthy control (HC) participants. Both periodic oscillations and aperiodic components were obtained via the "fitting oscillations and one-over f" (FOOOF) algorithm and then used to compute individualized spectral features. MDD patients presented higher canonical alpha and beta band power but lower aperiodic-adjusted alpha and beta power. Furthermore, we found that alpha power was strongly correlated with the age of patients but not with disease symptoms. The aperiodic intercept was lower in the parietal‒occipital region and was positively correlated with the Hamilton Depression Rating Scale (HAMD) score after accounting for age and sex. In the asymmetry analysis, alpha activity appeared asymmetrical only in the HC group, whereas aperiodic activity was symmetrical in both groups. The findings of this study provide insights into the role of abnormal neural spiking activity and impaired neuroplasticity in MDD progression and suggest that the aperiodic intercept in resting-state EEG may be a potential biomarker of MDD.

When biomarkers for major depressive disorder remain elusive

When biomarkers for major depressive disorder remain elusive

Microglia‐Derived Vitamin D Binding Protein Mediates Synaptic Damage and Induces Depression by Binding to the Neuronal Receptor Megalin

AbstractVitamin D binding protein (VDBP) is a potential biomarker of major depressive disorder (MDD). This study demonstrates for the first time that VDBP is highly expressed in core emotion‐related brain regions of mice susceptible to chronic unpredictable mild stress (CUMS). Specifically, the overexpression of microglia (MG)‐derived VDBP in the prelimbic leads to depression‐like behavior and aggravates CUMS‐induced depressive phenotypes in mice, whereas conditional knockout of MG‐derived VDBP can reverse both neuronal damage and depression‐like behaviors. Mechanistically, the binding of MG‐derived VDBP with the neuronal receptor megalin mediates the downstream SRC signaling pathway, leading to neuronal and synaptic damage and depression‐like behaviors. These events may be caused by biased activation of inhibitory neurons and excitatory–inhibitory imbalance. Importantly, this study has effectively identified MG‐derived VDBP as a pivotal mediator in the interplay between microglia and neurons via its interaction with the neuronal receptor megalin and intricate downstream impacts on neuronal functions, thus offering a promising therapeutic target for MDD.

Evaluating Apelin as a Potential Biomarker in Major Depressive Disorder: Its Correlation with Clinical Symptomatology

To date, only a limited number of studies have investigated the potential effects of apelin on mood regulation and emotional behavior. Therefore, this study investigated apelin’s role in major depressive disorder (MDD) by comparing the serum and plasma apelin concentrations between 30 patients with MDD and 30 healthy controls (HCs), and the correlated serum and plasma apelin levels and the severity of depressive symptoms using the Montgomery–Åsberg Depression Rating Scale (MADRS). Blood samples were collected following 12 h of fasting, and the apelin levels were measured using an ELISA kit. The serum apelin concentrations showed no significant difference between the MDD and HC groups, while the plasma apelin levels were significantly lower in the MDD group (p = 0.002). Among the patients with MDD, a positive moderate correlation was observed between the total MADRS scores and plasma apelin levels (r = 0.439), with statistical significance (p < 0.05). Additionally, significant positive correlations (p < 0.05) were found between both the serum and plasma apelin levels and the MADRS subscales 5 (reduced appetite) and 6 (concentration difficulties). These preliminary findings, although not definitive, suggest that apelin profiles may help to identify distinct subgroups within MDD patients, warranting further investigation into the different apelin isoforms and their associations in different populations of MDD patients.

Immune-related gene characterization and biological mechanisms in major depressive disorder revealed based on transcriptomics and network pharmacology.

Major depressive disorder (MDD) is a severe psychiatric disorder characterized by complex etiology, with genetic determinants that are not fully understood. The objective of this study was to investigate the pathogenesis of MDD and to explore its association with the immune system by identifying hub biomarkers using bioinformatics analyses and examining immune infiltrates in human autopsy samples. Gene microarray data were obtained from the Gene Expression Omnibus (GEO) datasets GSE32280, GSE76826, GSE98793, and GSE39653. Our approach included differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis to identify hub genes associated with MDD. Subsequently, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cytoscape plugin CluGO, and Gene Set Enrichment Analysis (GSEA) were utilized to identify immune-related genes. The final selection of immune-related hub genes was determined through the least absolute shrinkage and selection operator (Lasso) regression analysis and PPI analysis. Immune cell infiltration in MDD patients was analyzed using CIBERSORT, and correlation analysis was performed between key immune cells and genes. The diagnostic accuracy of the identified hub genes was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, we conducted a study involving 10 MDD patients and 10 healthy controls (HCs) meeting specific criteria to assess the expression levels of these hub genes in their peripheral blood mononuclear cells (PBMCs). The Herbal Ingredient Target Database (HIT) was employed to screen for herbal components that target these genes, potentially identifying novel therapeutic agents. A total of 159 down-regulated and 51 up-regulated genes were identified for further analysis. WGCNA revealed 12 co-expression modules, with modules "darked", "darkurquoise" and "light yellow" showing significant positive associations with MDD. Functional enrichment pathway analysis indicated that these differential genes were associated with immune functions. Integration of differential and immune-related gene analysis identified 21 common genes. The Lasso algorithm confirmed 4 hub genes as potential biomarkers for MDD. GSEA analysis suggested that these genes may be involved in biological processes such as protein export, RNA degradation, and fc gamma r mediated cytotoxis. Pathway enrichment analysis identified three highly enriched immune-related pathways associated with the 4 hub genes. ROC curve analysis indicated that these hub genes possess good diagnostic value. Quantitative reverse transcription-polymerase chain reaction (RT-qPCR) demonstrated significant expression differences of these hub genes in PBMCs between MDD patients and HCs. Immune infiltration analysis revealed significant correlations between immune cells, including Mast cells resting, T cells CD8, NK cells resting, and Neutrophils, which were significantly correlated with the hub genes expression. HIT identified one herb target related to IL7R and 14 targets related to TLR2. The study identified four immune-related hub genes (TLR2, RETN, HP, and IL7R) in MDD that may impact the diagnosis and treatment of the disorder. By leveraging the GEO database, our findings contribute to the understanding of the relationship between MDD and immunity, presenting potential therapeutic targets.

Aperiodic (1/f) neural activity robustly tracks symptom severity changes in treatment-resistant depression.

A reliable physiological biomarker for Major Depressive Disorder is essential for developing and optimizing neuromodulatory treatment paradigms. This study investigates a passive electrophysiologic biomarker that tracks changes in depressive symptom severity on the order of minutes to hours. We analyze brief recordings from intracranial electrodes implanted deep in the brain during a clinical trial of deep brain stimulation for treatment-resistant depression in 5 human subjects (nfemale= 3, nmale = 2). This surgical setting allows for precise temporal and spatial sensitivity in the ventromedial prefrontal cortex, a challenging area to measure. We focused on the aperiodic slope of the power spectral density, a metric reflecting the balance of activity across all frequency bands and serving as a proxy for excitatory/inhibitory balance in the brain. Our findings demonstrate that shifts in aperiodic slope correlate with depression severity, with flatter (less negative) slopes indicating reduced depression severity. This significant correlation was observed in all N=5 subjects, particularly in the ventromedial prefrontal cortex. This biomarker offers a new way to track patient responses to Major Depressive Disorder treatment, paving the way for individualized therapies in both intracranial and non-invasive monitoring contexts.

The voice of depression: speech features as biomarkers for major depressive disorder

BackgroundPsychiatry faces a challenge due to the lack of objective biomarkers, as current assessments are based on subjective evaluations. Automated speech analysis shows promise in detecting symptom severity in depressed patients. This project aimed to identify discriminating speech features between patients with major depressive disorder (MDD) and healthy controls (HCs) by examining associations with symptom severity measures.MethodsForty-four MDD patients from the Psychiatry Department, University Hospital Aachen, Germany and fifty-two HCs were recruited. Participants described positive and negative life events, which were recorded for analysis. The Beck Depression Inventory (BDI-II) and the Hamilton Rating Scale for Depression gauged depression severity. Transcribed audio recordings underwent feature extraction, including acoustics, speech rate, and content. Machine learning models including speech features and neuropsychological assessments, were used to differentiate between the MDD patients and HCs.ResultsAcoustic variables such as pitch and loudness differed significantly between the MDD patients and HCs (effect sizes \U0001d73c2 between 0.183 and 0.3, p < 0.001). Furthermore, variables pertaining to temporality, lexical richness, and speech sentiment displayed moderate to high effect sizes (\U0001d73c2 between 0.062 and 0.143, p < 0.02). A support vector machine (SVM) model based on 10 acoustic features showed a high performance (AUC = 0.93) in differentiating between HCs and patients with MDD, comparable to an SVM based on the BDI-II (AUC = 0.99, p = 0.01).ConclusionsThis study identified robust speech features associated with MDD. A machine learning model based on speech features yielded similar results to an established pen-and-paper depression assessment. In the future, these findings may shape voice-based biomarkers, enhancing clinical diagnosis and MDD monitoring.

Lower DTI-ALPS index in patients with major depressive disorder: Correlation with fatigue

Background and purposeDiffusion tensor imaging along perivascular spaces (DTI-ALPS) is an index that may provide insights into intracranial waste clearance processes. Glymphatic system dysfunction has been suggested to play a role in the development of major depressive disorder (MDD). Additionally, fatigue—a common precursor of MDD—is also closely connected to the waste clearance function of the central nervous system (CNS), further underscoring the significance of efficient waste removal in MDD. However, evidence linking altered DTI-ALPS index to MDD remains limited. This study aims to investigate the changes in the DTI-ALPS index in patients with MDD and explore the potential interplay between DTI-ALPS index alterations, fatigue, and the presence of MDD. Material and methodsA total of 46 patients with MDD and 55 healthy controls (HC) were included in the study. All participants underwent diffusion tensor imaging using the same 3-T MRI (3-Tesla Magnetic Resonance Imaging) scanner. The DTI-ALPS index was assessed, and the Chalder Fatigue Scale (CFS) was used to evaluate fatigue levels in both groups, and the 17-item Hamilton Depression Rating Scale (HAMD-17) was used to evaluate the severity of depression in the patients. We compared the DTI-ALPS index and clinical characteristics between the MDD and HC group, and explored the relationship among the DTI-ALPS index, CFS scores, and the presence of MDD through mediation analysis. ResultsThe DTI-ALPS index in the right hemisphere (DTI-ALPS-R) is significantly lower in patients with MDD (t = 2.41, P = 0.02). The MDD patients exhibited significantly higher scores on the CFS scales compared with HCs (t = 13.12, P <.001). Mediation analysis showed that the CFS score plays a significant mediating role between DTI-ALPS-R and the presence of MDD, acting as a full mediator (indirect effect β = −0.230, 95 % CI: [-0.388, −0.059]). ConclusionOur study found that patients with MDD have a reduced DTI-ALPS index. This reduction appears to contribute to the development of MDD by facilitating the accumulation of fatigue symptoms. These findings may provide a new perspective on the pathogenesis of MDD, suggest a potential new biomarker for MDD, and offer new insights for its treatment.

May high mobility group box protein-1 be a biomarker for major depressive disorder?

High Mobility Group Box Protein-1 (HMGB1), which has proinflammatory properties, is known to be involved in psychiatric disorders as far as we know, there are only one clinical studies investigating the role of HMGB1 in major depressive disorder (MDD). In this study, we aimed to investigate the role of HMGB1 in the etiopathogenesis of MDD and whether HMGB1 can be used as a biomarker in MDD by measuring the serum HMGB1 levels of depressed patients in the episode and remission periods. This study included 30 patients diagnosed with MDD in episode, 30 patients in remission and 30 healthy controls. Each group comprised 20 female and 10 male participants. In this study, serum HMGB1 levels were found to be lower in the patient group in the episode compared to the patient group in the remission period and the healthy control group. There was no significant difference between the patient group in remission and the healthy control group in terms of serum HMGB1 levels. The fact that serum HMGB1 levels were lower in the patient group in the episode compared to the patient group in the remission period and the control group may be related to the neuroprotective effects of HMGB1. HMGB1 may be used as a biomarker for MDD.

Identification and experimental validation of hub genes underlying depressive-like behaviors induced by chronic social defeat stress.

Introduction: Major depressive disorder (MDD), characterized by severe neuropsychiatric symptoms and significant cognitive deficits, continues to present both etiological and therapeutic challenges. However, the specific underlying mechanisms and therapeutic targets remain unclear. Methods: We analyzed human postmortem dorsolateral prefrontal cortex (dlPFC) samples from MDD patients using datasets GSE53987 and GSE54568, identifying three key genes: AGA, FBXO38, and RGS5. To model depressive-like behavior, we employed chronic social defeat stress (CSDS) and subsequently measured the expression of AGA, FBXO38, and RGS5 in the dlPFC using qPCR and Western blot analysis following CSDS exposure. Results: CSDS significantly induced depressive-like behavior, and both the protein and transcriptional expression levels of AGA, FBXO38, and RGS5 in the dlPFC of mice were markedly reduced after stress, consistent with findings from datasets GSE53987 and GSE54568. Conclusion: Our research suggests that AGA, FBXO38, and RGS5 are potential biomarkers for MDD and could serve as valuable targets for MDD risk prediction.

Unraveling the Associations Between Voice Pitch and Major Depressive Disorder: A Multisite Genetic Study.

Major depressive disorder (MDD) often goes undiagnosed due to the absence of clear biomarkers. We sought to identify voice biomarkers for MDD and separate biomarkers indicative of MDD predisposition from biomarkers reflecting current depressive symptoms. Using a two-stage meta-analytic design to remove confounds, we tested the association between features representing vocal pitch and MDD in a multisite case-control cohort study of Chinese women with recurrent depression. Sixteen features were replicated in an independent cohort, with absolute association coefficients (beta values) from the combined analysis ranging from 0.24 to 1.07, indicating moderate to large effects. The statistical significance of these associations remained robust, with P-values ranging from 7.2 × 10-6 to 6.8 × 10-58. Eleven features were significantly associated with current depressive symptoms. Using genotype data, we found that this association was driven in part by a genetic correlation with MDD. Significant voice features, reflecting a slower pitch change and a lower pitch, achieved an AUC-ROC of 0.90 (sensitivity of 0.85 and specificity of 0.81) in MDD classification. Our results return vocal features to a more central position in clinical and research work on MDD.

A hierarchical random effects state-space model for modeling brain activities from electroencephalogram data.

Mental disorders present challenges in diagnosis and treatment due to their complex and heterogeneous nature. Electroencephalogram (EEG) has shown promise as a source of potential biomarkers for these disorders. However, existing methods for analyzing EEG signals have limitations in addressing heterogeneity and capturing complex brain activity patterns between regions. This paper proposes a novel random effects state-space model (RESSM) for analyzing large-scale multi-channel resting-state EEG signals, accounting for the heterogeneity of brain connectivities between groups and individual subjects. We incorporate multi-level random effects for temporal dynamical and spatial mapping matrices and address non-stationarity so that the brain connectivity patterns can vary over time. The model is fitted under a Bayesian hierarchical model framework coupled with a Gibbs sampler. Compared to previous mixed-effects state-space models, we directly model high-dimensional random effects matrices of interest without structural constraints and tackle the challenge of identifiability. Through extensive simulation studies, we demonstrate that our approach yields valid estimation and inference. We apply RESSM to a multi-site clinical trial of major depressive disorder (MDD). Our analysis uncovers significant differences in resting-state brain temporal dynamics among MDD patients compared to healthy individuals. In addition, we show the subject-level EEG features derived from RESSM exhibit a superior predictive value for the heterogeneous treatment effect compared to the EEG frequency band power, suggesting the potential of EEG as a valuable biomarker for MDD.

PRKCB methylation: a potential biomarker of MDD with childhood chronic stress, a cross-sectional study in drug-naive, first-episode adolescent MDD

ABSTRACT The purpose of this study was to investigate the relationship between childhood chronic stress(CCS), Protein kinase C beta (PRKCB) methylation and adolescent major depressive disorder (MDD). After recruiting 100 adolescents with MDD and 50 healthy controls (HCs), we evaluated the severity of CCS. PRKCB methylation was assessed by pyrosequencing using whole blood-derived DNA. To explore the relationship between CCS, PRKCB and adolescent MDD, we conducted correlation analysis and regression analysis, and constructed multiplicative interaction models and generalized linear models. PRKCB methylation and CCS were both found to be associated with MDD, and CCS was associated with PRKCB methylation. No significant CCS-PRKCB methylation interactions were observed. However, we found the interaction of CCS and MDD on PRKCB methylation. Our results found that PRKCB methylation was influenced by CCS and the disease itself, and PRKCB methylation was significantly positively associated with MDD severity, suggesting that PRKCB methylation may be a potential biomarker for adolescent MDD. This study is a cross-sectional observational study, which cannot draw the conclusion of causality. Prospective cohort studies are needed to further examine the relationship between CCS, adolescent MDD, and PRKCB methylation.

Exploring miRNA Biomarkers in Major Depressive Disorder: A Molecular Medicine Perspective.

Major depressive disorder (MDD) is a complex mental health condition with a multifaceted and incompletely elucidated pathophysiology. MicroRNAs (miRNAs) have emerged as potential biomarkers due to their role in gene regulation and the observed dysregulation in MDD. The aim of this study is to detect the presence of specific molecular diagnostic biomarkers in major depressive disorder. This cross-sectional study analyzed plasma miRNA expression in ten MDD patients and eight healthy controls using real-time PCR. Differentially expressed miRNAs were identified using independent t-tests, and their diagnostic potential was assessed with ROC curve analysis. Fifteen miRNAs exhibited significant dysregulation in MDD patients. Notably, hsa-miR-29c-3p, hsa-miR-376a-3p, hsa-miR-532-5p, and hsa-miR-339-5p showed excellent discriminatory power (AUC > 0.8). This study identifies differentially expressed plasma miRNAs in MDD, suggesting their potential for improved diagnosis and personalized treatment. However, further validation in larger cohorts and investigation into their functional roles are warranted.