Early-diagnosis of major depressive disorder: From biomarkers to point-of-care testing

Alterations of serum metabolic profile in major depressive disorder: A case-control study in the Chinese population

BACKGROUND

Major depressive disorder (MDD) is characterized by persistent depressed mood and cognitive symptoms. This study aimed to discover biomarkers for MDD, explore its pathological mechanisms, and examine the associations of the identified biomarkers with clinical and psychological variables.

AIM

To discover candidate biomarkers for MDD identification and provide insight into the pathological mechanism of MDD.

METHODS

The current study adopted a single-center cross-sectional case-control design. Serum samples were obtained from 100 individuals diagnosed with MDD and 97 healthy controls (HCs) aged between 18 to 60 years. Metabolomics was performed on an Ultimate 3000 UHPLC system coupled with Q-Exactive MS (Thermo Scientific). The online software Metaboanalyst 6.0 was used to process and analyze the acquired raw data of peak intensities from the instrument.

RESULTS

The study included 100 MDD patients and 97 HCs. Metabolomic profiling identified 35 significantly different metabolites (e.g., cortisol, sebacic acid, and L-glutamic acid). Receiver operating characteristic curve analysis highlighted 8-HETE, 10-HDoHE, cortisol, 12-HHTrE, and 10-hydroxydecanoic acid as top diagnostic biomarkers for MDD. Significant correlations were found between metabolites (e.g., some lipids, steroids, and amino acids) and clinical and psychological variables.

CONCLUSION

Our study reported metabolites (some lipids, steroids, amino acids, carnitines, and alkaloids) responsible for discriminating MDD patients and HCs. This metabolite profile may enable the development of a laboratory-based diagnostic test for MDD. The mechanisms underlying the association between psychological or clinical variables and differential metabolites deserve further exploration.

Haematological markers of inflammation in major depressive disorder (MDD) patients with suicidal behaviour: A systematic review and meta-analysis

INTRODUCTION

Suicide is a significant global health issue linked to neuroinflammation. This study assessed neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR) as systemic inflammation indicators in suicidal behaviour of major depressive disorder (MDD) patients.

METHODS

The search included databases like Scopus, PubMed, PsycINFO, Web of Science, and CINAHL Complete, focusing on English-language observational studies up to March 2025. It examined the NLR, PLR, and MLR in individuals with MDD exhibiting suicidal behaviour, comparing them to MDD patients without such behaviors or healthy controls. Study quality was assessed using the Newcastle-Ottawa Scale (NOS), and data analysis utilized Stata software version 17.

RESULTS

From an initial 131 articles, ten studies were included, analyzing 2017 participants. MDD patients with suicidal behaviour had significantly elevated NLR levels compared to non-suicidal MDD patients (SMD: 0.34 [0.12, 0.56]; I2: 67.68 %) and healthy controls (SMD: 1.02 [0.28, 1.76]; I2: 96.15 %). Additionally, no differences in PLR levels were found in suicidal MDD patients compared to their non-suicidal counterparts (SMD: 0.11 [-0.04, 0.27]; I2: 38.39 %), but PLR levels were higher in comparison to healthy controls (SMD: 0.38 [0.17, 0.59]; I2: 38.02 %). Four studies showed higher MLR levels in suicidal MDD patients compared to the non-suicidal group (SMD: 0.33 [0.04, 0.62]; I2: 71.60 %) and healthy controls (SMD: 0.32 [0.06, 0.59]; I2: 44.37 %).

CONCLUSION

Increased inflammatory biomarkers in MDD patients with suicidal tendencies highlight the need for further research on their potential as suicide vulnerability indicators and the utility of NLR, PLR and MLR in clinical practice.

Advancing Depression Management Through Biomarker Discovery with a Focus on Genetic and Epigenetic Aspects: A Comprehensive Study on Neurobiological, Neuroendocrine, Metabolic, and Inflammatory Pathways

Introduction: Depression is a pervasive global health issue, affecting millions worldwide and causing significant disability. Despite its prevalence, current diagnostic and treatment approaches often yield suboptimal outcomes. The complexity of depression, characterized by diverse causes and symptoms, highlights the urgent need for advanced diagnostic tools and personalized therapies. Biomarkers, particularly genetic and epigenetic depression biomarkers, offer promise in uncovering the biological mechanisms underlying depression, potentially revolutionizing its management. Aim: Primary aim: To identify biomarkers associated with depressive disorders, with a focus on genetic and epigenetic biomarkers. Secondary aim: To optimize the current classification of biomarkers associated with different types of depressive disorders, with a focus on genetic and epigenetic biomarkers. Methods: We integrated findings with strategic keywords extracted from relevant studies, conducting a thorough literature review across the Google Scholar, PubMed, and Web of Science databases. Lastly, final reference inclusion had stringent criteria: recent, diverse peer-reviewed articles in English, all study designs, ensuring up-to-date coverage of genetic and epigenetic depression biomarker research. Results: The review reveals the classification of genetic and epigenetic biomarkers in regard to the type of biomarker, the system of the human body it derives from, and the sampling entity. All of the findings show promise in diagnosing depression, with the potential of predicting treatment outcomes and guiding personalized therapeutic approaches. We defined the significant correlations between genetic and epigenetic biomarker profiles and clinical parameters such as symptom severity and treatment response, thereby enhancing diagnostic accuracy and guiding treatment strategies tailored to individual patient needs across diverse depressive subtypes and treatment responses. Conclusion: Identifying biomarkers associated with depressive disorders, with a focus on genetic and epigenetic biomarkers, represents a critical step toward improving diagnostic precision and treatment efficacy. By elucidating the complex biological underpinnings of depression, this study contributes to the development of targeted therapies that address the diverse needs of individuals affected by this debilitating group of disorders. Future research should focus on validating these genetic and epigenetic biomarkers in larger cohorts and clinical trials to facilitate their clinical implementation and enhance patient outcomes.

Association of optic neuritis with incident depressive disorder risk in a Korean nationwide cohort

Studies have highlighted complex bidirectional relationships between autoimmune diseases and depressive disorders. Given that early mental health interventions have substantial public health implications, this study investigated association between optic neuritis, an autoimmune inflammatory disorder of the optic nerve, and risk of developing depressive disorders. Utilizing extensive national health insurance data encompassing almost the entire Korean population, this cohort study included 11,745 patients with optic neuritis and 58,725 age- and sex- matched controls between 2010 and 2017. The diagnosis of optic neuritis was confirmed using ICD-10 code H46 and patient medical records. The association with depression risk identified by ICD-10 codes F32 and F33 was assessed using Cox proportional hazards regression models after adjusting for demographics, lifestyle variables, and other comorbidities. Newly diagnosed optic neuritis was associated with an increased risk of depression (hazard ratio = 1.349, 95% confidence interval: 1.277-1.426), independent of potential confounding factors. Subgroup analysis revealed a stronger association for individuals under 50 years, males, current smokers, and those without hypertension. This association suggests that autoimmune neuroinflammatory responses impact mental health differently across demographics. These findings underscore the importance of implementing routine depression screening and developing targeted early intervention strategies for patients with optic neuritis, particularly for those with a high-risk of depression.

Miniaturized 3D-printed ratiometric electrochemical immunosensing platform for ultrasensitive detection of depression biomarker Apo-A4

The lack of standardized objective approaches hinders the accurate diagnosis and treatment of depression. Herein, a novel electrochemical platform was created utilizing cost-effective and rapid 3D printing technology to overcome the constraints of conventional diagnostic methods. This method allows for highly sensitive detection of Apolipoprotein A4 (Apo-A4), an important biomarker for depression, using dual-signal outputs. The electrode material utilized in this setup consisted of a combination of carbon black/polylactic acid (CB/PLA) and ferrocene-chitosan-gold nanoparticles (Fc-CS-AuNPs). On the other hand, the signal label was composed of gold nanoparticles-thionine-secondary antibody (AuNPs-Thi-Ab2). This setup yielded distinct electrochemical signals originating from ferrocene (IFc) and thionine (IThi), which exhibited an inverse relationship with fluctuations in Apo-A4 levels. Moreover, the ratio of IThi to IFc (IThi/IFc) demonstrated a direct association with the concentration of Apo-A4. The immunosensor exhibited a wide linear response range, covering from 10-5 to 102 ng mL-1, and achieved an exceptionally low detection limit (LOD) of 1.194 fg mL-1. The platform's performance was confirmed by reliable outcomes obtained directly from merely 500 μL spiked rat serum. This approach provides a valuable instrument for the objective evaluation and diagnosis of depression in a clinical setting.

Electrochemical biosensors for depression: Diagnosis and therapeutic monitoring

Electrochemical biosensors have revolutionized the detection of biomarkers related to depression and the quantification of antidepressant drugs. These biosensors leverage nanomaterials and advanced assay designs to achieve high sensitivity and selectivity for clinically relevant analytes. Key neurotransmitters implicated in depression, such as serotonin, dopamine, and glutamate, can be accurately measured via biosensors, providing insights into the effects of antidepressant treatments on neurotransmission. Biosensors can also detect biomarkers of inflammation, oxidative stress, and neuronal health that are altered in depression. Real-time biosensing techniques such as fast-scan cyclic voltammetry enable monitoring of dynamic neurotransmitter changes during depressive episodes and pharmacological interventions. Advancements incorporating graphene, gold nanoparticles, and other nanomaterials have enhanced biosensor performance, enabling the detection of low biomarker concentrations. Closed-loop biosensing systems hold promise for precision medicine by automating antidepressant dosage adjustments on the basis of neurotransmitter levels. A wide range of depression biomarkers, including apolipoprotein A4, heat shock protein 70, brain-derived neurotrophic factor, microRNAs, proteins, and combinatorial biomarker panels, have been detected via sophisticated biosensor platforms. Emerging biosensors show selectivity for antidepressant drugs such as serotonin-norepinephrine reuptake inhibitors, tricyclic antidepressants, and selective serotonin reuptake inhibitors in biological samples. This review emphasizes the transformative potential of electrochemical biosensors in combating depression. By facilitating earlier and more accurate diagnoses, these biosensors can revolutionize patient care and enhance treatment outcomes.

Functional Near-Infrared Spectroscopy-Based Computer-Aided Diagnosis of Major Depressive Disorder Using Convolutional Neural Network with a New Channel Embedding Layer Considering Inter-Hemispheric Asymmetry in Prefrontal Hemodynamic Responses

Background

Functional near-infrared spectroscopy (fNIRS) is being extensively explored as a potential primary screening tool for major depressive disorder (MDD) because of its portability, cost-effectiveness, and low susceptibility to motion artifacts. However, the fNIRS-based computer-aided diagnosis (CAD) of MDD using deep learning methods has rarely been studied. In this study, we propose a novel deep learning framework based on a convolutional neural network (CNN) for the fNIRS-based CAD of MDD with high accuracy.

Materials and Methods

The fNIRS data of participants-48 patients with MDD and 68 healthy controls (HCs)-were obtained while they performed a Stroop task. The hemodynamic responses calculated from the preprocessed fNIRS data were used as inputs to the proposed CNN model with an ensemble CNN architecture, comprising three 1D depth-wise convolutional layers specifically designed to reflect interhemispheric asymmetry in hemodynamic responses between patients with MDD and HCs, which is known to be a distinct characteristic in previous MDD studies. The performance of the proposed model was evaluated using a leave-one-subject-out cross-validation strategy and compared with those of conventional machine learning and CNN models.

Results

The proposed model exhibited a high accuracy, sensitivity, and specificity of 84.48%, 83.33%, and 85.29%, respectively. The accuracies of conventional machine learning algorithms-shrinkage linear discriminator analysis, regularized support vector machine, EEGNet, and ShallowConvNet-were 73.28%, 74.14%, 62.93%, and 62.07%, respectively.

Conclusions

In conclusion, the proposed deep learning model can differentiate between the patients with MDD and HCs more accurately than the conventional models, demonstrating its applicability in fNIRS-based CAD systems.

A Bibliometric Analysis of Clinical Trials on Salivary Biomarkers for Mental Health (2003-2024)

Mental health conditions, such as depression, anxiety, and stress-related disorders, are often difficult to diagnose and monitor using traditional methods. Salivary biomarkers offer a promising alternative due to their non-invasive nature, ease of collection, and the potential to reflect real-time physiological changes associated with mental health. This bibliometric analysis examines 95 clinical trials on stress biomarkers for mental health, published between 2003 and 2024. The field is characterized by extensive collaboration and global participation, involving 593 authors and publications across 73 journals. Despite a consistent annual publication rate, notable increases in 2011, 2014, and 2018 indicate growing research interest. The United States leads in research output, followed by Australia, Germany, and Japan, with Psychoneuroendocrinology being the most prominent journal. Co-occurrence analysis identified nine research clusters, suggesting diverse directions such as the impact of stress-related hormones, circadian rhythms, mindfulness, various therapies, aging, psychological adaptation mechanisms, exercise therapy, anxiety disorders, and the autonomic nervous system on salivary biomarkers. Key terms such as "biomarkers/metabolism," AND "hydrocortisone/metabolism," AND "saliva/metabolism" were central, with significant activity from 2012 to 2018. This analysis highlights a growing focus on the metabolic processes and therapeutic applications of salivary biomarkers in mental health. This bibliometric analysis calls attention to the promising potential of salivary biomarkers to revolutionize mental health diagnostics and treatment through non-invasive methods, fostering interdisciplinary research, technological advancements, and global health improvements.

Vacancy-Driven High-Performance Metabolic Assay for Diagnosis and Therapeutic Evaluation of Depression

Depression is one of the most common mental illnesses and is a well-known risk factor for suicide, characterized by low overall efficacy (<50%) and high relapse rate (40%). A rapid and objective approach for screening and prognosis of depression is highly desirable but still awaits further development. Herein, a high-performance metabolite-based assay to aid the diagnosis and therapeutic evaluation of depression by developing a vacancy-engineered cobalt oxide (Vo-Co3O4) assisted laser desorption/ionization mass spectrometer platform is presented. The easy-prepared nanoparticles with optimal vacancy achieve a considerable signal enhancement, characterized by favorable charge transfer and increased photothermal conversion. The optimized Vo-Co3O4 allows for a direct and robust record of plasma metabolic fingerprints (PMFs). Through machine learning of PMFs, high-performance depression diagnosis is achieved, with the areas under the curve (AUC) of 0.941-0.980 and an accuracy of over 92%. Furthermore, a simplified diagnostic panel for depression is established, with a desirable AUC value of 0.933. Finally, proline levels are quantified in a follow-up cohort of depressive patients, highlighting the potential of metabolite quantification in the therapeutic evaluation of depression. This work promotes the progression of advanced matrixes and brings insights into the management of depression.

High-throughput single biomarker identification using droplet nanopore

Biomarkers are present in various metabolism processes, demanding precise and meticulous analysis at the single-molecule level for accurate clinical diagnosis. Given the need for high sensitivity, biological nanopore have been applied for single biomarker sensing. However, the detection of low-volume biomarkers poses challenges due to their low concentrations in dilute buffer solutions, as well as difficulty in parallel detection. Here, a droplet nanopore technique is developed for low-volume and high-throughput single biomarker detection at the sub-microliter scale, which shows a 2000-fold volume reduction compared to conventional setups. To prove the concept, this nanopore sensing platform not only enables multichannel recording but also significantly lowers the detection limit for various types of biomarkers such as angiotensin II, to 42 pg. This advancement enables direct biomarker detection at the picogram level. Such a leap forward in detection capability positions this nanopore sensing platform as a promising candidate for point-of-care testing of biomarker at single-molecule level, while substantially minimizing the need for sample dilution.

Combining Glial Fibrillary Acidic Protein and Neurofilament Light Chain for the Diagnosis of Major Depressive Disorder

Major depressive disorder (MDD) is a prevalent brain disorder affecting more than 2% of the world's population. Due to the lack of well-specific biomarkers, it is difficult to distinguish MDD from other diseases with similar clinical symptoms (such as Alzheimer's disease and cerebral thrombosis). In this work, we provided a strategy to address this issue by constructing a combinatorial biomarker of serum glial fibrillary acidic protein (GFAP) and neurofilament light chain (NFL). To achieve the convenient and sensitive detection of two proteins, we developed an electrochemical immunosandwich sensor using two metal-ion-doped carbon dots (Pb-CDs and Cu-CDs) as probes for signal output. Each probe contains approximately 300 Pb2+ or 200 Cu2+, providing excellent signal amplification. This method achieved detection limits of 0.3 pg mL-1 for GFAP and 0.2 pg mL-1 for NFL, lower than most of the reported detection limits. Analysis of real serum samples showed that the concentration ratio of GFAP to NFL, which is associated with the relative degree of brain inflammation and neurodegeneration, is suitable for not only distinguishing MDD from healthy individuals but also specifically distinguishing MDD from Alzheimer's disease and cerebral thrombosis. The good specificity gives the combinatorial GFAP/NFL biomarker broad application prospects in the screening, diagnosis, and treatment of MDD.

Evaluation of a Nondepleted Plasma Multiprotein-Based Model for Discriminating Psychiatric Disorders Using Multiple Reaction Monitoring-Mass Spectrometry: Proof-of-Concept Study

Psychiatric evaluation relies on subjective symptoms and behavioral observation, which sometimes leads to misdiagnosis. Despite previous efforts to utilize plasma proteins as objective markers, the depletion method is time-consuming. Therefore, this study aimed to enhance previous quantification methods and construct objective discriminative models for major psychiatric disorders using nondepleted plasma. Multiple reaction monitoring-mass spectrometry (MRM-MS) assays for quantifying 453 peptides in nondepleted plasma from 132 individuals [35 major depressive disorder (MDD), 47 bipolar disorder (BD), 23 schizophrenia (SCZ) patients, and 27 healthy controls (HC)] were developed. Pairwise discriminative models for MDD, BD, and SCZ, and a discriminative model between patients and HC were constructed by machine learning approaches. In addition, the proteins from nondepleted plasma-based discriminative models were compared with previously developed depleted plasma-based discriminative models. Discriminative models for MDD versus BD, BD versus SCZ, MDD versus SCZ, and patients versus HC were constructed with 11 to 13 proteins and showed reasonable performances (AUROC = 0.890-0.955). Most of the shared proteins between nondepleted and depleted plasma models had consistent directions of expression levels and were associated with neural signaling, inflammatory, and lipid metabolism pathways. These results suggest that multiprotein markers from nondepleted plasma have a potential role in psychiatric evaluation.

Generation of synthetic EEG data for training algorithms supporting the diagnosis of major depressive disorder

Introduction

Major depressive disorder (MDD) is the most common mental disorder worldwide, leading to impairment in quality and independence of life. Electroencephalography (EEG) biomarkers processed with machine learning (ML) algorithms have been explored for objective diagnoses with promising results. However, the generalizability of those models, a prerequisite for clinical application, is restricted by small datasets. One approach to train ML models with good generalizability is complementing the original with synthetic data produced by generative algorithms. Another advantage of synthetic data is the possibility of publishing the data for other researchers without risking patient data privacy. Synthetic EEG time-series have not yet been generated for two clinical populations like MDD patients and healthy controls.

Methods

We first reviewed 27 studies presenting EEG data augmentation with generative algorithms for classification tasks, like diagnosis, for the possibilities and shortcomings of recent methods. The subsequent empirical study generated EEG time-series based on two public datasets with 30/28 and 24/29 subjects (MDD/controls). To obtain baseline diagnostic accuracies, convolutional neural networks (CNN) were trained with time-series from each dataset. The data were synthesized with generative adversarial networks (GAN) consisting of CNNs. We evaluated the synthetic data qualitatively and quantitatively and finally used it for re-training the diagnostic model.

Results

The reviewed studies improved their classification accuracies by between 1 and 40% with the synthetic data. Our own diagnostic accuracy improved up to 10% for one dataset but not significantly for the other. We found a rich repertoire of generative models in the reviewed literature, solving various technical issues. A major shortcoming in the field is the lack of meaningful evaluation metrics for synthetic data. The few studies analyzing the data in the frequency domain, including our own, show that only some features can be produced truthfully.

Discussion

The systematic review combined with our own investigation provides an overview of the available methods for generating EEG data for a classification task, their possibilities, and shortcomings. The approach is promising and the technical basis is set. For a broad application of these techniques in neuroscience research or clinical application, the methods need fine-tuning facilitated by domain expertise in (clinical) EEG research.