Omics approaches open new horizons in major depressive disorder: from biomarkers to precision medicine

Predicted plasma proteomics from genetic scores and treatment outcomes in major depression: a meta-analysis

Proteomics has been scarcely explored for predicting treatment outcomes in major depressive disorder (MDD), due to methodological challenges and costs. Predicting protein levels from genetic scores provides opportunities for exploratory studies and the selection of targeted panels. In this study, we examined the association between genetically predicted plasma proteins and treatment outcomes - including non-response, non-remission, and treatment-resistant depression (TRD) - in 3559 patients with MDD from four clinical samples. Protein levels were predicted from individual-level genotypes using genetic scores from the publicly available OmicsPred database, which estimated genetic scores based on genome-wide genotypes and proteomic measurements from the Olink and SomaScan platforms. Associations between predicted protein levels and treatment outcomes were assessed using logistic regression models, adjusted for potential confounders including population stratification. Results were meta-analysed using a random-effects model. The Bonferroni correction was applied. We analysed 257 proteins for Olink and 1502 for SomaScan; 111 proteins overlapped between the two platforms. Despite no association was significant after multiple-testing correction, many top results were consistent across phenotypes, in particular seven proteins were nominally associated with all the analysed outcomes (CHL1, DUSP13, EVA1C, FCRL2, KITLG, SMAP1, and TIM3/HAVCR2). Additionally, three proteins (CXCL6, IL5RA, and RARRES2) showed consistent nominal associations across both the Olink and SomaScan platforms. The convergence of results across phenotypes is in line with the hypothesis of the involvement of immune-inflammatory mechanisms and neuroplasticity in treatment response. These results can provide hints for guiding the selection of protein panels in future proteomic studies.

Neuroinflammation and Natural Antidepressants: Balancing Fire with Flora

Background/Objectives: Major depressive disorder (MDD) is a major global health concern that is intimately linked to neuroinflammation, oxidative stress, mitochondrial dysfunction, and complicated metabolic abnormalities. Traditional antidepressants frequently fall short, highlighting the urgent need for new, safer, and more acceptable therapeutic techniques. Phytochemicals, i.e., natural antidepressants derived from plants, are emerging as powerful plant-based therapies capable of targeting many pathogenic pathways at the same time. Summary: This narrative review synthesizes evidence from preclinical and clinical studies on the efficacy of phytochemicals such as curcumin, polyphenols, flavonoids, and alkaloids in lowering depressed symptoms. Consistent data show that these substances have neuroprotective, anti-inflammatory, and antioxidant properties, altering neuroimmune interactions, reducing oxidative damage, and improving mitochondrial resilience. Particularly, polyphenols and flavonoids have great therapeutic potential because of their capacity to penetrate the blood-brain barrier, inhibit cytokine activity, and encourage neuroplasticity mediated by brain-derived neurotrophic factor (BDNF). Despite promising results, the heterogeneity in study designs, phytochemical formulations, and patient demographics highlights the importance of thorough, standardized clinical studies. Conclusions: This review identifies phytochemicals as compelling adjuvant or independent therapies in depression treatment, providing multimodal mechanisms and enhanced tolerability. Additional research into improved dosage, pharmacokinetics, long-term safety, and integrative therapy approaches is essential. Using phytotherapeutics could considerably improve holistic and customized depression care, encouraging new research routes in integrative neuroscience and clinical psychiatry.

Advancing Antidepressive Agents: Drug Discovery and Polymer-Based Drug Delivery Systems for Improved Treatment Outcome

Depressive disorder (a subclass of mental disorders) is characterized by persistent affective symptoms. Without timely therapeutic intervention, it leads to clinical deterioration manifested as reduced quality of life and may increase suicide risk in severe cases. Given its complex etiology, intertwined with intrinsic factors such as genetics and environment, and impacted by various issues such as first-pass effect and blood-brain barrier, the therapeutic efficacy of many antidepressant medications is limited for patients. Therefore, by delving into the exploration of novel antidepressant drugs and biomaterials, this review aims to offer fresh perspectives that may facilitate the discovery of innovative antidepressant medications and enhance their therapeutic outcomes. Notably, the review highlights polymers' crucial role in enhancing antidepressants' pharmacological efficacy and pharmacokinetic properties by optimizing their parameters, and they will undoubtedly become powerful tools in improving antidepressive outcomes in future research.

Deep immunophenotyping of circulating immune cells in major depressive disorder patients reveals immune correlates of clinical course and treatment response

Major Depressive Disorder (MDD) is a widespread psychiatric condition impacting social and occupational functioning, making it a leading cause of disability. The diagnosis of MDD remains clinical, based on the Diagnostic and Statistical Manual of Mental Disorders (DSM)-5 criteria, as biomarkers have not yet been validated for diagnostic purposes or as predictors of treatment response. Traditional treatment strategies often follow a one-size-fits-all approach obtaining suboptimal outcomes for many patients who fail to experience response or recovery. Several studies have reported an association between MDD and immune system dysregulation, but few have focused on the deep characterization of circulating cells, during the acute phase of MDD. This work aimed at immunophenotyping peripheral blood cells in the relapse phase of the disorder, to identify relevant cell populations for clinical monitoring of patients. Multiparametric analysis was performed on the peripheral blood of 60 MDD patients using flow cytometry to identify lymphocytes (naïve/effector, memory, regulatory) and myeloid cells (dendritic cells, monocytes). We studied the associations between immunophenotype and depressive symptoms, social and working functioning, and subjective quality of life during the acute phase and after three months of treatment. Multivariate analysis showed that CD4+ terminally differentiated effector memory (TEMRA) were associated with more depressive symptoms with a particular emphasis on anhedonic features and worse social and working functioning and quality of life. CD8+ TEMRA were associated with those depressive symptoms related to hopelessness. Conversely, ICOS + Tregs were associated with low-intensity suicidal ideation, suggestive of a protective role. Baseline T CD4+ effector memory (EM) was a negative predictor of reduction of depressive symptoms after three months of treatment, whilst plasmacytoid dendritic cells (pDC) were predicting reduction of hopelessness. These results confirm the involvement of the immune system in MDD and demonstrate the existence of immunological signatures associated with the severity of major depressive episodes and treatment response that could guide clinical monitoring and future personalized therapies.

RNA Editing Signatures Powered by Artificial Intelligence: A New Frontier in Differentiating Schizophrenia, Bipolar, and Schizoaffective Disorders

Mental health disorders are devastating illnesses, often misdiagnosed due to overlapping clinical symptoms. Among these conditions, bipolar disorder, schizophrenia, and schizoaffective disorder are particularly difficult to distinguish, as they share alternating positive and negative mood symptoms. Accurate and timely diagnosis of these diseases is crucial to ensure effective treatment and to tailor therapeutic management to each individual patient. In this context, it is essential to move beyond standard clinical assessment and employ innovative approaches to identify new biomarkers that can be reliably quantified. We previously identified a panel of RNA editing biomarkers capable of differentiating healthy controls from depressed patients and, among depressed patients, those with major depressive disorder and those with bipolar disorder. In this study, we integrated Adenosine-to-Inosine RNA editing blood biomarkers with clinical data through machine learning algorithms to establish specific signatures for bipolar disorder and schizophrenia spectrum disorders. This groundbreaking study paves the way for the application of RNA editing in other psychiatric disorders, such as schizophrenia and schizoaffective disorder. It represents a first proof-of-concept and provides compelling evidence for the establishment of an RNA editing signature for the diagnosis of these psychiatric conditions.