Search Results (200)

Background: Psychedelic-assisted therapy is gaining renewed attention as a potential treatment for various mental disorders. Despite increasing numbers of randomized controlled trials (RCTs) and meta-analyses, a comprehensive synthesis of the evidence across different substances and indications is lacking. This umbrella review aims to evaluate the effectiveness and safety of psychedelic-assisted therapy—primarily psilocybin, MDMA, and LSD—across major psychiatric disorders, including depression, post-traumatic stress disorder (PTSD), and substance use disorders. Methods: We systematically identified and synthesized data from 23 meta-analyses encompassing over 100 primary studies. Outcomes were standardized and re-expressed as Hedges’ g to enable cross-study comparisons. Study quality was assessed using AMSTAR2, and certainty of evidence was evaluated via the GRADE framework. Results: The number of identified meta-analyses differed markedly depending on the substance and clinical indication: psilocybin for depression (n = 9) and MDMA for PTSD (n = 10) had the strongest evidence base, while fewer meta-analyses were available for LSD in alcohol use disorder (n = 2) and depression (n = 2), ayahuasca in depression (n = 2), and MDMA in autism spectrum disorder (n = 2). Psilocybin demonstrated large effect sizes in major depression (Hedges’ g ≈ 1.05), with some evidence of sustained benefits up to six months. MDMA showed very large effects in reducing PTSD symptoms (Hedges’ g ≈ 1.24), often after 2–3 sessions. LSD yielded short-term benefits for alcohol use disorder (OR ≈ 2.0), though effects declined over time. Across studies, adverse events were generally mild and transient, with no consistent signal for serious harm. Considerable methodological variability was observed, including small and sometimes overlapping samples, heterogeneity, risk of bias, and limited long-term data. These constraints should be taken into account when interpreting the overall findings. Conclusions: Current evidence supports the short-term efficacy and safety of psychedelic-assisted therapy for selected psychiatric disorders, particularly depression and PTSD. However, the low methodological quality of studies and most meta-analyses, as well gaps in long-term safety data highlight the need for high-quality studies. Full article

Major Depressive Disorder (MDD) poses a significant global health burden, characterized by a complex and heterogeneous pathophysiology insufficiently targeted by conventional single-treatment approaches. This review presents an integrative framework incorporating network pharmacology, artificial intelligence (AI), and multi-omics technologies to advance a systems-level understanding and management of MDD. Its central contribution lies in moving beyond reductionist methods by embracing a holistic perspective that accounts for dynamic interactions within biological networks. The primary objective is to demonstrate how AI-powered integration of multi-omics data—spanning genomics, proteomics, and metabolomics—can enable the construction of predictive network models. These models are designed to uncover fundamental disease mechanisms, identify clinically relevant biotypes, and reveal novel therapeutic targets tailored to specific pathological contexts. Methodologically, the review examines the microbiota–gut–brain (MGB) axis as an illustrative case study, detailing its pathogenic roles through neuroimmune alterations, metabolic dysfunction, and disrupted neuro-plasticity. Furthermore, we propose a translational roadmap that includes AI-assisted biomarker discovery, computational drug repurposing, and patient-specific “digital twin” models to advance precision psychiatry. Our analysis confirms that this integrated framework offers a coherent route toward mechanism-based personalized therapies and helps bridge the gap between computational biology and clinical practice. Nevertheless, important challenges remain, particularly pertaining to data heterogeneity, model interpretability, and clinical implementation. In conclusion, we stress that future success will require integrating prospective longitudinal multi-omics cohorts, high-resolution digital phenotyping, and ethically aligned, explainable AI (XAI) systems. These concerted efforts are essential to realize the full potential of precision psychiatry for MDD. Full article

Major depressive disorder (MDD) continues to be a primary cause of disability globally, with a significant number of patients exhibiting resistance to standard pharmacological and psychotherapeutic interventions. In recent years, non-invasive brain stimulation techniques, especially transcranial magnetic stimulation (TMS) and transcranial direct current stimulation (tDCS), have emerged as promising therapies for treatment-resistant MDD. A comprehensive search was performed in PubMed, which included all studies published over the last ten years. Eligible studies encompassed both animal models and clinical investigations. This review provides a comparative overview of transcranial electrical stimulation modalities, with a focus on their mechanisms of action, clinical efficacy, and underlying neurobiological mechanisms. We pay particular attention to the role of the neurotrophin system, specifically brain-derived neurotrophic factor (BDNF), in mediating the treatment effects of transcranial stimulation. Recent findings indicate that neuromodulation could improve neuroplasticity by increasing BDNF levels and associated signaling pathways, which may help stabilize mood and enhance the improvement of individuals with MDD. A more profound understanding of these mechanisms could lead to more precise, biomarker-driven interventions. Further research is essential to elucidating the long-term effects of brain stimulation on neurotrophin levels and to creating more individualized treatment strategies. Full article

Background: Psychosocial oncology research has largely focused on DSM Axis I disorders such as depression and anxiety, while broader psychological dimensions—particularly personality functioning—have received limited attention. In the context of long-term survivorship and chronic cancer care, understanding personality disorders (PDs) is increasingly important. Objective: This systematic review aimed to estimate the prevalence of PDs in adults with cancer based on studies using DSM or ICD diagnostic frameworks. Methods: Following PRISMA guidelines, we systematically searched major electronic databases (PubMed, Scopus, Web of Science) for studies published over the past 45 years that reported PD prevalence in individuals with cancer. Eligible studies employed DSM or ICD criteria for diagnosing PDs. Screening, data extraction, and quality assessments were conducted independently by multiple reviewers. Extracted data included study design, sample characteristics, diagnostic tools, and PD prevalence. Results: Seven studies met the inclusion criteria. Reported prevalence estimates varied widely, ranging from 1.4% to 100%. Most studies were characterized by small sample sizes, cross-sectional designs, and a primary focus on breast cancer populations; only one study included a broader cancer cohort. Study quality was generally low to moderate. A major limitation across studies was the use of unvalidated or non-standardized diagnostic instruments, along with limited methodological detail and heterogeneity in assessment approaches, preventing meaningful pooling of results. Conclusions: The findings reveal a substantial lack of high-quality research examining personality disorders in oncology settings. Robust, methodologically rigorous studies using validated diagnostic tools are urgently needed to clarify the prevalence and clinical relevance of PDs in cancer populations and to inform the development of comprehensive, patient-centered psychosocial care models. Full article

Anxiety symptoms are highly prevalent in major depressive disorder (MDD) and are associated with greater severity, functional impairment, and poorer treatment outcomes. Bupropion is widely used in clinical practice and is generally considered to have a favorable tolerability profile, but its effects on comorbid anxiety remain uncertain. We conducted a PRISMA-guided systematic review of randomized controlled trials, pooled analyses, and open-label comparative studies evaluating bupropion in adults with MDD and clinically significant anxiety symptoms. Searches of PubMed, Scopus and Web of Science were performed through August 2025. Outcomes included validated measures of anxiety and depressive symptoms and reported tolerability. Risk of bias was assessed using RoB 2 and ROBINS-I, and certainty of evidence was evaluated using GRADE. Six studies (n ≈ 3700) met inclusion criteria. Anxiety was a predefined secondary outcome in some trials and a post hoc or exploratory measure in others. Across designs, bupropion was generally associated with improvements in anxiety and depressive symptoms on secondary or exploratory anxiety measures. In pooled patient-level analyses, SSRIs showed a modest advantage over bupropion in patients with high baseline anxiety, whereas individual randomized and open-label studies found no significant between-group differences. None of the included studies reported a clear signal of anxiety worsening with bupropion on the anxiety measures used. Tolerability findings indicated a lower risk of sexual dysfunction with bupropion compared with SSRIs, while insomnia occurred more frequently but was generally manageable. Low-certainty evidence suggests that bupropion may provide clinically relevant improvement in anxiety symptoms in adults with MDD, with generally comparable efficacy to SSRIs in most presentations but a modest SSRI advantage in highly anxious subgroups. Interpretation should consider that anxiety outcomes were often secondary or exploratory and that several studies were at risk of bias. Well-designed randomized trials with anxiety as a primary endpoint are needed. Full article

Processing electroencephalogram (EEG) data using neural networks is becoming increasingly important in modern medicine. This study introduces the development of a neural network method using a combination of psychological questionnaire data and spectral characteristics of resting-state EEG. The data were collected from 71 individuals: 42 healthy and 29 with major depressive disorder (MDD). We evaluated four classes of algorithms—traditional machine learning, deep learning (LSTM), ablation analysis, and feature importance analysis—for two primary tasks: binary classification (healthy vs. MDD) and regression for predicting Beck Depression Inventory scores (BDI). Our results demonstrate that the superiority of a given method is task-dependent. For regression, an LSTM network applied to delta-rhythm EEG data achieved a breakthrough performance of R² = 0.742 (MAE = 6.114), representing an 86% improvement over traditional Ridge regression. Ablation studies identified delta and alpha rhythms as the most informative neurophysiological biomarkers. Furthermore, feature importance analysis revealed a triad of dominant psychometric predictors: ruminative thinking (31.2%), age (27.9%), and hostility (18.5%), which collectively accounted for 75.2% of the feature importance in predicting severity. LSTM on spectral EEG data provides a superior quantitative assessment of depression severity, while Logistic Regression on psychometric or EEG data offers a highly reliable tool for screening and confirmatory diagnosis. This methodology provides a robust, objective framework for MDD diagnosis that is independent of a patient’s subjective self-assessment, thus facilitating enhanced clinical decision-making and personalized treatment monitoring. Full article

Gulf War Veterans (GWVs) presenting with irritable bowel syndrome-diarrhea (IBS-D) often exhibit concurrent post-traumatic stress disorder (PTSD) or traumatic brain injury (TBI). All Veterans’ Affair physicians are required to assess symptoms of depression, anxiety, and suicide ideation by maintaining yearly training. In a previous study for IBS-D (3), we identified significant vitamin D deficiency (VDD), with an average level of 19 ng/mL. This includes those with and without PTSD, TBI, showing depression and anxiety symptoms. Since VDD is associated with PTSD, and all veterans not on supplementation were found to be VDD (>90%) at our facility, we investigated a possible association between VDD and these neuropsychological conditions. While age and body mass index (BMI), seasons, and demographic locations are known to affect vitamin D levels, we found no correlation between these factors and VDD in the PTSD group and those with IBS-D. In the TBI group, VDD did correlate with BMI. Multiple deployments appeared to have a minor negative effect on vitamin D levels (a 11–13% contribution) in veterans with either PTSD or TBI. Although these veterans showed signs of inflammation with elevated minor C-reactive protein (CRP) levels (4.7 mg/L), there was a negative correlation between CRP and vitamin D to suggest that inflammation is not the primary cause of PTSD or TBI. Following daily vitamin D treatment, vitamin D levels returned to a normal average of 30 ng/mL (less than 30 ng/mL is abnormal). Treatment had no effect on serum calcium levels, but did lead to a resolution of depression, anxiety, TBI, and PTSD symptoms in the majority of patients. These findings suggest that correcting VDD in GWVs visiting GI clinics with co-occurring PTSD and TBI had reduced psychological symptoms. Replacing vitamin D is a simple strategy to implement, rather than increasing neurotrophic medications in some patients. Full article

Background/Objectives: Limited access to mental health services contributes to poorer outcomes among individuals with mental health conditions, including major depressive disorder (MDD). Nurse-led interventions serve as a strategic model of care to improve mental health service delivery and enhance patient outcomes. This project aims to co-design a nurse-led telehealth family psychoeducation (FPE) for MDD and primarily assess its feasibility by evaluating the recruitment and retention rates. Methods: A multi-methods study encompassing a co-design phase (Study Phase 1) and a feasibility study (Study Phase 2). Study Phase 1 will involve semi-structured interviews with individuals with MDD and their families or significant others, as well as surveys and focus groups with mental health professionals to develop telehealth FPE for MDD. Study Phase 2 will evaluate the feasibility and acceptability of the intervention, which comprises three biweekly FPE sessions and a six-week follow-up with patient–family dyads using a single-group pre-post design. The primary outcomes comprise the feasibility and acceptability of intervention. Exploratory secondary outcomes include personal recovery, medication necessity beliefs and concerns, antidepressant adherence, and depression severity, measured at baseline, immediately post-intervention, and at 6-week follow-up using validated measures. Data analysis will primarily involve descriptive statistics and thematic analysis. The TIDieR checklist will be followed in reporting the intervention development. Conclusions: Findings from the proposed study will inform the design and protocol for a future randomised trial of telehealth FPE for improving clinical and non-clinical outcomes in MDD. The feasibility study was prospectively registered with the ClinicalTrial.gov on 8 June 2025 (NCT07014241). Full article

Treatment options for major depression are limited: only about one-third of patients achieve remission with first line treatments with no established predictive markers. Parameters associated with treatment refractory depression, including metabolic markers (increased BMI, increased triglyceride levels), inflammation markers (C-reactive protein, CRP), autonomic disturbances (reduced blood pressure, reduced heart rate variability), and brain morphology changes (increased volume of the choroid plexus and brain ventricle volumes), may serve such purpose. These features can be linked mechanistically to an increase in aldosterone plasma concentration due to a reduced mineralocorticoid receptor (MR) sensitivity. The primary CNS target of aldosterone is the nucleus of the solitary tract (NTS), which is also the entry point of the vagus nerve. This nucleus integrates signals from endocrine, inflammatory, chemoreceptive, and physiological parameters, including blood pressure. In search of a mechanism to overcome this pathology, we identified a molecule which is derived from the licorice plant glycyrrhiza glabra, namely glycyrrhizin and its biologically active metabolite enoxolone. These molecules potentially reverse the above-described pathology. They inhibit the enzyme 11beta hydroxysteroid-dehydrogenase type 2 (11betaHSD2) and the toll-like receptor 4 (TLR4). 11betaHSD2 regulates the activity of the mineralocorticoid receptor (MR) by degrading cortisol/corticosterone, which allows aldosterone to bind to the MR. TLR4 is the ligand for lipopolysaccharide (LPS, endotoxin) and trigger of innate immunity. Consequently, patients with increased inflammation markers, increased aldosterone, or low blood pressure may preferentially benefit from the treatment with glycyrrhizin/enoxolone. Importantly, these patients can be identified BEFORE treatment is initiated. Clinically, patients sharing these biological indicators are primarily young females or patients with a history of childhood trauma. A combination of enoxolone with standard antidepressants may therefore avoid a trial-and-error approach and allow to achieve recovery faster. Full article

Major depression (MD) is associated with chronic inflammation and impaired neuroplasticity; however, the cellular mechanisms underlying antidepressant action remain incompletely understood. We performed transcriptomic profiling and functional validation in human microglia treated with venlafaxine (VEN) and vortioxetine (VOR), or with stable ST3GAL5 overexpression (ST3GAL5OE). Differential expression analysis, enrichment studies, and functional assays using NF-κB-RE-NlucP and SIE-NlucP reporter lines were conducted to assess the impact on inflammatory signaling. Microarray analysis identified 41 genes consistently upregulated and 316 consistently downregulated across VEN, VOR, and ST3GAL5OE conditions. Upregulated genes were enriched for synaptic organization, whereas downregulated genes were associated with nitric oxide biosynthesis and pro-inflammatory pathways, including Rap1, MAPK, and PI3K-Akt signaling. Functional assays confirmed that VEN and VOR suppressed cytokine-induced NF-κB and STAT3 activation, effects that were recapitulated by exogenous GM3 treatment and ST3GAL5 overexpression. Chronic exposure to VEN or VOR produced more modest, pathway-specific suppression, supporting convergence on the ST3GAL5–GM3 axis. These findings extend the conventional monoaminergic model of antidepressant action by highlighting the ST3GAL5–GM3 lipid remodeling axis as a novel regulatory pathway that attenuates microglial inflammatory signaling. Although validation in primary microglia and in vivo models is required, our results suggest that this axis could serve as both a therapeutic target and a candidate biomarker for inflammation-associated MD. Full article

Major depressive disorder (MDD) is a prevalent and disabling condition. Transcranial direct current stimulation (tDCS) may improve symptoms by modulating neuroplastic and inflammatory mechanisms. This randomized, double-blind, placebo-controlled trial will recruit adult outpatients with MDD showing residual symptoms despite at least four weeks of stable SSRI treatment. Participants will be randomized to active or sham add-on tDCS while continuing their antidepressant regimen. The intervention will consist of 15 sessions over 3 weeks, targeting the left dorsolateral prefrontal cortex (anode F3, cathode F4) at 2 mA for 30 min per session. The primary outcome is the reduction of depressive symptoms measured by the Hamilton Depression Rating Scale-17 (HDRS), with remission defined as HDRS-17 ≤ 7. Secondary outcomes include cognitive performance (attention, executive functioning, memory), serum biomarkers (BDNF, VEGF, NGF, NRG1, angiogenin, IGF1, IL-6, TNF-α), cortical excitability assessed by transcranial magnetic stimulation (motor threshold, silent period, intracortical inhibition/facilitation), and cerebral hemodynamics by transcranial Doppler sonography (blood flow velocity, pulsatility, resistivity). Assessments will occur at baseline, post-treatment, and 3- and 6-month follow-ups. This trial aims to evaluate the efficacy of adjunctive tDCS in MDD with residual symptoms and its biological correlates, bridging clinical improvement with electrophysiological and neurovascular mechanisms. Full article

Background/Objectives: We aimed to test whether home-based low-intensity interval training (LIIT) could be equally or more effective than traditional continuous walking advice (TWA) in a population hospitalized and healed from severe COVID-19. Methods: This pragmatic nonrandomized controlled trial (NCT04615390) enrolled patients admitted to intensive care units due to COVID-19 who at discharge from the hospital were given a choice between either a home-based LIIT program or TWA. The former received a structured LIIT walking (1:1 walk:rest ratio per 10 times) to be performed at a prescribed progressively increasing speed maintained with a metronome. The latter received TWA according to the guidelines (30 min or moderate intensity activity, 5 days/week). Outcome measures, collected at baseline, at the end of the 3-month training and at the 6-month follow-up, included 6 min walking distance (primary), lower limb strength, quality of life, depression and cognitive status. Results: From a total of 85 enrolled patients, 69 of them (LIIT n = 32; TWA n = 37) completed the study. Home exercise was safely executed with an 82% adherence for the LIIT group and 64% adherence for TWA. After the 3-month program, both groups significantly improved the 6MWD (LIIT: +87 m vs. TWA +42 m; p < 0.001) with a significant difference that was also maintained at follow-up (LIIT: +138 m vs. TWA +69 m; p < 0.001). No other significant between-group differences were noted. However, patients in the LIIT group significantly improved in the majority of the outcomes, while patients of TWA improved in only the primary outcome and the physical component of quality of life. Conclusions: Compared with TWA, LIIT walking was feasible, safe and associated with more favorable multidimensional recovery in COVID-19 survivors after hospitalization for severe pneumonitis. Full article

γ-aminobutyric acid (GABA), the primary inhibitory neurotransmitter in the central nervous system (CNS), regulates neuronal excitability, synaptic plasticity, and oscillatory activity essential for cognition, emotion, and behavior. Disruptions in GABAergic signaling are increasingly recognized as key contributors to a range of neurodevelopmental disorders (NDDs), including schizophrenia (SZ), autism spectrum disorder (ASD), major depressive disorder (MDD), bipolar disorder (BD), and intellectual disability (ID). In this review, we analyze the data available from the literature concerning the components of the GABA pathway. We describe the main steps of GABA metabolism, including GABA synthesis and release, GABA receptors neurotransmission, GABA reuptake and catabolism, and evaluate their involvement in the pathogenesis of neurodevelopmental disorders. We suggest the possibility of existence of so far undescribed mechanisms which maintain the concentrations of GABA at a relatively physiological level when the function of glutamic acid decarboxylases is compromised by mutations. Searching for these mechanisms could be important for better understanding neurodevelopment and could give a clue for future searches for new therapeutic approaches for treating or alleviating the symptoms of BD and SZ. We also argue that the metabolic stage of the GABA pathway has only a minor direct effect on GABA signaling and rather causes clinical effects due to accumulation of neurotoxic byproducts. Full article

Background/Objectives: Dementia is a major public health challenge, with age as its primary non-modifiable risk factor. Several modifiable conditions, such as hypertension, diabetes, and depression, have been identified as potential targets for prevention. The aim is to describe the methodology and preliminary results of a study that will be conducted within the Italian National Health Service (INHS), designed to assess the impact of hypertension, diabetes, depression, and their interactions on the onset of dementia. Methods: This population-based cohort study, part of the PREV-ITA-DEM project, was conducted using a Common Data Model (CDM) approach across five Italian regions and cities participating in the NeuroEpiNet network. Individuals aged ≥ 50 years without prior diagnoses of dementia, depression, diabetes, or hypertension were followed from cohort entry (2011–2013) until dementia diagnosis, death, emigration, or study end (2019–2022). Exposures were time-dependent and defined using validated algorithms applied to Healthcare Utilization Databases (HUDs). Associations between chronic conditions and dementia risk will be estimated using competing risks regression models adjusted for confounders. Results: The final cohort comprised more than 3 million individuals, with a mean baseline age of 63–65 years and a female proportion of 52–55%. On 1 January 2011, the prevalence of individuals aged ≥ 50 years with dementia ranged from 8.7 to 14.7 per 1000 population. A harmonized methodological framework based on a CDM was developed and implemented across all sites, incorporating a shared protocol, standardized local databases, and uniform analytic scripts, and the results will be pooled using meta-analytic techniques. Conclusions: Preliminary findings confirm the feasibility of a standardized, multi-regional CDM approach and the potential for HUDs to support large-scale dementia prevention studies in real-world settings. Full article

Mood disorders, including major depressive disorder (MDD) and bipolar disorder (BD), are among the leading causes of disability worldwide and are frequently associated with treatment resistance, functional impairment, and high comorbidity with metabolic dysfunction. Increasing evidence implicates insulin resistance (IR) as a key pathophysiological factor linking metabolic and psychiatric illness. IR is associated with chronic low-grade inflammation, hypothalamic–pituitary–adrenal (HPA) axis dysregulation, impaired neuroplasticity, mitochondrial dysfunction, and altered reward processing mechanisms that may contribute to core depressive features such as anhedonia, cognitive slowing, and emotional dysregulation. These processes are further exacerbated by the metabolic side effects of many psychotropic medications, creating a self-perpetuating cycle that worsens both psychiatric and physical health outcomes. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), initially developed for type 2 diabetes and obesity, have emerged as promising candidates to address this metabolic–psychiatric interface. Beyond improving glycemic control and promoting weight loss, GLP-1 RAs exert central actions relevant to mood disorders, including modulation of dopaminergic reward pathways, enhancement of hippocampal neurogenesis, attenuation of neuroinflammation, and regulation of appetite and energy balance. Preclinical studies demonstrate that GLP-1 RAs reduce microglial activation, promote hippocampal neurogenesis, and normalize stress-induced behavioral changes. Early clinical trials in patients with metabolic disorders suggest improvements in depressive symptoms, quality of life, and cognitive function, with some effects independent of weight loss or glycemic outcomes. Observational evidence also indicates reduced antidepressant use and psychological distress in diabetic and obese populations receiving GLP-1 RAs. While these findings are promising, large randomized controlled trials in primary psychiatric populations are lacking. Key challenges include clarifying dose–response relationships, disentangling central from peripheral effects, and addressing safety and adherence concerns in individuals with comorbid psychiatric conditions. Future research should focus on biomarker-informed stratification, comparative trials with standard treatments, and integration of GLP-1 RAs into multimodal care frameworks. Overall, GLP-1 RAs represent a biologically plausible and clinically relevant approach to bridging metabolic and psychiatric care, with the potential to improve outcomes in patients with mood disorders who carry a high metabolic burden. Full article