Scn1a haploinsufficiency in the prefrontal cortex engages to cognitive impairment and depressive phenotype.

Altered development and function of the prefrontal cortex (PFC) during adolescence is implicated in the origin of mental disorders. Deficits in the GABAergic system prominently contribute to these alterations. Nav1.1 is a voltage-gated Na+ channel critical for normal GABAergic activity. Here, we studied the role of Nav1.1 in PFC function and its potential relationship with the aetiology of mental disorders. Dysfunction of Nav1.1 activity in the medial PFC (mPFC) of adolescent mice enhanced the local excitation/inhibition ratio, resulting in epileptic activity, cognitive deficits and depressive-like behaviour in adulthood, along with a gene expression profile linked to major depressive disorder (MDD). Additionally, it reduced extracellular serotonin concentration in the dorsal raphe nucleus and brain-derived neurotrophic factor expression in the hippocampus, two MDD-related brain areas beyond the PFC. We also observed alterations in oscillatory activity and impaired hippocampal-mPFC coherence during sleep. Finally, we found reduced expression levels of SCN1A, the gene encoding Nav1.1, in post-mortem PFC samples from human MDD subjects. Collectively, our results provide a novel mechanistic framework linking adolescence-specific alterations in Nav1.1 function in the PFC to the pathogenesis of epilepsy and comorbidities such as cognitive impairment and depressive disorders.

Multimodal neuroimaging network associated with executive function in adolescent major depressive disorder patients via cognition-guided magnetic resonance imaging fusion.

Adolescents are high-risk population for major depressive disorder. Executive dysfunction emerges as a common feature of depression and exerts a significant influence on the social functionality of adolescents. This study aimed to identify the multimodal co-varying brain network related to executive function in adolescent with major depressive disorder. A total of 24 adolescent major depressive disorder patients and 43 healthy controls were included and completed the Intra-Extra Dimensional Set Shift Task. Multimodal neuroimaging data, including the amplitude of low-frequency fluctuations from resting-state functional magnetic resonance imaging and gray matter volume from structural magnetic resonance imaging, were combined with executive function using a supervised fusion method named multimodal canonical correlation analysis with reference plus joint independent component analysis. The major depressive disorder showed more total errors than the healthy controls in the Intra-Extra Dimensional Set Shift task. Their performance on the Intra-Extra Dimensional Set Shift Task was negatively related to the 14-item Hamilton Rating Scale for Anxiety score. We discovered an executive function-related multimodal fronto-occipito-temporal network with lower amplitude of low-frequency fluctuation and gray matter volume loadings in major depressive disorder. The gray matter component of the identified network was negatively related to errors made in Intra-Extra Dimensional Set Shift while positively related to stages completed. These findings may help to deepen our understanding of the pathophysiological mechanisms of cognitive dysfunction in adolescent depression.

Multi-feature concatenation and multi-classifier stacking: An interpretable and generalizable machine learning method for MDD discrimination with rsfMRI.

Major depressive disorder (MDD) is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspectives on brain structure, function, and connectivity, is increasingly applied in the diagnosis and pathological research of MDD. Different machine learning algorithms are then developed to exploit the rich information in rsfMRI and discriminate MDD patients from normal controls. Despite recent advances reported, the MDD discrimination accuracy has room for further improvement. The generalizability and interpretability of the discrimination method are not sufficiently addressed either. Here, we propose a machine learning method (MFMC) for MDD discrimination by concatenating multiple features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD data set that contains 2428 subjects collected from 25 different sites. MFMC yields 96.9% MDD discrimination accuracy, demonstrating a significant improvement over existing methods. In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites. The use of XGBoost as the meta classifier allows us to probe the decision process of MFMC. We identify 13 feature values related to 9 brain regions including the posterior cingulate gyrus, superior frontal gyrus orbital part, and angular gyrus, which contribute most to the classification and also demonstrate significant differences at the group level. The use of these 13 feature values alone can reach 87% of MFMC's full performance when taking all feature values. These features may serve as clinically useful diagnostic and prognostic biomarkers for MDD in the future.

Autophagy dysfunction contributes to NLRP1 inflammasome-linked depressive-like behaviors in mice.

BACKGROUND: Major depressive disorder (MDD) is a common but severe psychiatric illness characterized by depressive mood and diminished interest. Both nucleotide-binding oligomerization domain, leucine-rich repeat and pyrin domain-containing 1 (NLRP1) inflammasome and autophagy have been reported to implicate in the pathological processes of depression. However, the mechanistic interplay between NLRP1 inflammasome, autophagy, and depression is still poorly known. METHODS: Animal model of depression was established by chronic social defeat stress (CSDS). Depressive-like behaviors were determined by social interaction test (SIT), sucrose preference test (SPT), open field test (OFT), forced swim test (FST), and tail-suspension test (TST). The protein expression levels of NLRP1 inflammasome complexes, pro-inflammatory cytokines, phosphorylated-phosphatidylinositol 3-kinase (p-PI3K)/PI3K, phosphorylated-AKT (p-AKT)/AKT, phosphorylated-mechanistic target of rapamycin (p-mTOR)/mTOR, brain-derived neurotrophic factor (BDNF), phosphorylated-tyrosine kinase receptor B (p-TrkB)/TrkB, Bcl-2-associated X protein (Bax)/B-cell lymphoma-2 (Bcl2) and cleaved cysteinyl aspartate-specific proteinase-3 (caspase-3) were examined by western blotting. The mRNA expression levels of pro-inflammatory cytokines were tested by quantitative real-time PCR. The interaction between proteins was detected by immunofluorescence and coimmunoprecipitation. Neuronal injury was assessed by Nissl staining. The autophagosomes were visualized by transmission electron microscopy. Nlrp1a knockdown was performed using an adeno-associated virus (AAV) vector containing Nlrp1a-shRNA-eGFP infusion. RESULTS: CSDS exposure caused a bidirectional change in hippocampal autophagy function, which was activated in the initial period but impaired at the later stage. In addition, CSDS exposure increased the expression levels of hippocampal NLRP1 inflammasome complexes, pro-inflammatory cytokines, p-PI3K, p-AKT and p-mTOR in a time-dependent manner. Interestingly, NLRP1 is immunoprecipitated with mTOR but not PI3K/AKT and CSDS exposure facilitated the immunoprecipitation between them. Hippocampal Nlrp1a knockdown inhibited the activity of PI3K/AKT/mTOR signaling, rescued the impaired autophagy and ameliorated depressive-like behavior induced by CSDS. In addition, rapamycin, an autophagy inducer, abolished NLRP1 inflammasome-driven inflammatory reactions, alleviated depressive-like behavior and exerted a neuroprotective effect. CONCLUSIONS: Autophagy dysfunction contributes to NLRP1 inflammasome-linked depressive-like behavior in mice and the regulation of autophagy could be a valuable therapeutic strategy for the management of depression.

The role of amygdala-ventral pallidum pathway in depression-like behaviors in male mice.

The basolateral amygdala (BLA) appears to serve an important function in the pathophysiology of depression. Depressive symptoms, such as anhedonia are largely caused by dysfunction in the brain's reward system, in which the ventral pallidum (VP) participates in by controlling dopamine release. However, the role of the BLA-VP pathway in the development of depression remains poorly understood. To investigate this pathway, we employed the Chronic Unpredictable Mild Stress (CUMS) mouse model, in which we injected retroAAV expressing GFP-Cre into the VP and AAV expressing hM4Di-mCherry into the BLA. We then used CNO to activate the Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) for all behavioral tests. The CUMS procedure resulted in significant depression symptoms such as decreased sucrose preference, limited weight gain, decreased immobile latency, and increased immobile time in the forced swim and tail suspension tests. Inhibition of the BLA-VP glutamatergic projections reversed these depression-like behaviors. We found that suppressing the BLA-VP circuitry had beneficial effects on CUMS-induced depression-like behaviors such as anorexia, anhedonia, and despair. Specifically, upon suppression of glutamatergic projections in the BLA-VP circuitry, these depression-like behaviors were significantly alleviated, which highlights the vital role of this circuitry in the development of depression. Furthermore, the beneficial effects of suppressing this circuitry seem to be associated with the brain's reward system, warranting further investigation.

Epigenome-Wide DNA Methylation in Unipolar Depression - Predictive Biomarker of Antidepressant Treatment Response?

BACKGROUND: Despite the well-documented efficacy of antidepressant agents for the treatment of major depressive disorder (MDD), initial treatment non-response rates are high. Recent years have seen an increase in research into predictive biomarkers towards improving diagnosis and individualized treatment. Among those, epigenetic mechanisms such as DNA methylation constitute promising candidate markers in predicting antidepressant treatment response in MDD. The present study sought to address epigenome-wide DNA methylation as a predictor of antidepressant treatment response in the so far largest sample of patients with MDD. METHODS: Epigenome-wide DNA methylation was analyzed using the Infinium MethylationEPIC BeadChip in peripheral blood of N=230 Caucasian patients with MDD receiving six-week antidepressant treatment in a naturalistic in-patient setting as well as in a subsample of N=107 patients primarily receiving continuous treatment with SSRIs or SNRIs. Treatment response was assessed by means of the Hamilton Depression Scale (HAM-D). RESULTS: No genome-wide significant hits were observed. Suggestive (p<1E-5) epigenome-wide evidence was discerned for altered DNA methylation at six CpG sites (LOC102724467, LOC100506023, RSPO2, SAG, IL16, PRKCI) to predict response to naturalistic antidepressant treatment. In patients treated with SSRIs or SNRIs, differential DNA methylation at 11 CpGs, e.g. mapping to the TIMP2, VDAC1 or SORL1 genes, was suggestively associated with treatment response. CONCLUSIONS: The present results provide preliminary evidence for altered DNA methylation patterns to be associated with antidepressant treatment response in MDD. Provided significant replication in independent and larger samples, the present findings might in the future aid in clinical decision making towards more individualized and thus more efficacious treatments of MDD.

A comprehensive hierarchical comparison of structural connectomes in Major Depressive Disorder cases v. controls in two large population samples.

BACKGROUND: The brain can be represented as a network, with nodes as brain regions and edges as region-to-region connections. Nodes with the most connections (hubs) are central to efficient brain function. Current findings on structural differences in Major Depressive Disorder (MDD) identified using network approaches remain inconsistent, potentially due to small sample sizes. It is still uncertain at what level of the connectome hierarchy differences may exist, and whether they are concentrated in hubs, disrupting fundamental brain connectivity. METHODS: We utilized two large cohorts, UK Biobank (UKB, N = 5104) and Generation Scotland (GS, N = 725), to investigate MDD case-control differences in brain network properties. Network analysis was done across four hierarchical levels: (1) global, (2) tier (nodes grouped into four tiers based on degree) and rich club (between-hub connections), (3) nodal, and (4) connection. RESULTS: In UKB, reductions in network efficiency were observed in MDD cases globally (d = -0.076, pFDR = 0.033), across all tiers (d = -0.069 to -0.079, pFDR = 0.020), and in hubs (d = -0.080 to -0.113, pFDR = 0.013-0.035). No differences in rich club organization and region-to-region connections were identified. The effect sizes and direction for these associations were generally consistent in GS, albeit not significant in our lower-N replication sample. CONCLUSION: Our results suggest that the brain's fundamental rich club structure is similar in MDD cases and controls, but subtle topological differences exist across the brain. Consistent with recent large-scale neuroimaging findings, our findings offer a connectomic perspective on a similar scale and support the idea that minimal differences exist between MDD cases and controls.

Assessing expectancy and suggestibility in a trial of escitalopram v. psilocybin for depression.

BACKGROUND: To investigate the association between pre-trial expectancy, suggestibility, and response to treatment in a trial of escitalopram and investigational drug, COMP360, psilocybin, in the treatment of major depressive disorder (ClinicalTrials.gov registration: NCT03429075). METHODS: We used data (n = 55) from our recent double-blind, parallel-group, randomized head-to-head comparison trial of escitalopram and investigational drug, COMP360, psilocybin. Mixed linear models were used to investigate the association between pre-treatment efficacy-related expectations, as well as baseline trait suggestibility and absorption, and therapeutic response to both escitalopram and COMP360 psilocybin. RESULTS: Patients had significantly higher expectancy for psilocybin relative to escitalopram; however, expectancy for escitalopram was associated with improved therapeutic outcomes to escitalopram, expectancy for psilocybin was not predictive of response to psilocybin. Separately, we found that pre-treatment trait suggestibility was associated with therapeutic response in the psilocybin arm, but not in the escitalopram arm. CONCLUSIONS: Overall, our results suggest that psychedelic therapy may be less vulnerable to expectancy biases than previously suspected. The relationship between baseline trait suggestibility and response to psilocybin therapy implies that highly suggestible individuals may be primed for response to this treatment.

Using Alternative Definitions of Controls to Increase Statistical Power in GWAS.

Genome-wide association studies (GWAS) are often underpowered due to small effect sizes of common single nucleotide polymorphisms (SNPs) on phenotypes and extreme multiple testing thresholds. The most common approach for increasing statistical power is to increase sample size. We propose an alternative strategy of redefining case-control outcomes into ordinal case-subthreshold-asymptomatic variables. While maintaining the clinical case threshold, we subdivide controls into two groups: individuals who are symptomatic but do not meet the clinical criteria for diagnosis (subthreshold) and individuals who are effectively asymptomatic. We conducted a simulation study to examine the impact of effect size, minor allele frequency, population prevalence, and the prevalence of the subthreshold group on statistical power to detect genetic associations in three scenarios: a standard case-control, an ordinal, and a case-asymptomatic control analysis. Our results suggest the ordinal model consistently provides the greatest statistical power while the case-control model the least. Power in the case-asymptomatic control model reflects the case-control or ordinal model depending on the population prevalence and size of the subthreshold category. We then analyzed a major depression phenotype from the UK Biobank to corroborate our simulation results. Overall, the ordinal model improves statistical power in GWAS consistent with increasing the sample size by approximately 10%.

Converged avenues: depression and Alzheimer's disease- shared pathophysiology and novel therapeutics.

Depression, a highly prevalent disorder affecting over 280 million people worldwide, is comorbid with many neurological disorders, particularly Alzheimer's disease (AD). Depression and AD share overlapping pathophysiology, and the search for accountable biological substrates made it an essential and intriguing field of research. The paper outlines the neurobiological pathways coinciding with depression and AD, including neurotrophin signalling, the hypothalamic-pituitary-adrenal axis (HPA), cellular apoptosis, neuroinflammation, and other aetiological factors. Understanding overlapping pathways is crucial in identifying common pathophysiological substrates that can be targeted for effective management of disease state. Antidepressants, particularly monoaminergic drugs (first-line therapy), are shown to have modest or no clinical benefits. Regardless of the ineffectiveness of conventional antidepressants, these drugs remain the mainstay for treating depressive symptoms in AD. To overcome the ineffectiveness of traditional pharmacological agents in treating comorbid conditions, a novel therapeutic class has been discussed in the paper. This includes neurotransmitter modulators, glutamatergic system modulators, mitochondrial modulators, antioxidant agents, HPA axis targeted therapy, inflammatory system targeted therapy, neurogenesis targeted therapy, repurposed anti-diabetic agents, and others. The primary clinical challenge is the development of therapeutic agents and the effective diagnosis of the comorbid condition for which no specific diagnosable scale is present. Hence, introducing Artificial Intelligence (AI) into the healthcare system is revolutionary. AI implemented with interdisciplinary strategies (neuroimaging, EEG, molecular biomarkers) bound to have accurate clinical interpretation of symptoms. Moreover, AI has the potential to forecast neurodegenerative and psychiatric illness much in advance before visible/observable clinical symptoms get precipitated.

Recent Advances in the Treatment of Treatment-Resistant Depression: A Narrative Review of Literature Published from 2018 to 2023.

PURPOSE OF REVIEW: We review recent advances in the treatment of treatment-resistant depression (TRD), a disorder with very limited treatment options until recently. We examine advances in psychotherapeutic, psychopharmacologic, and interventional psychiatry approaches to treatment of TRD. We also highlight various definitions of TRD in recent scientific literature. RECENT FINDINGS: Recent evidence suggests some forms of psychotherapy can be effective as adjunctive treatments for TRD, but not as monotherapies alone. Little recent evidence supports the use of adjunctive non-antidepressant pharmacotherapies such as buprenorphine and antipsychotics for the treatment of TRD; side effects and increased medication discontinuation rates may outweigh the benefits of these adjunctive pharmacotherapies. Finally, a wealth of recent evidence supports the use of interventional approaches such as electroconvulsive therapy, ketamine/esketamine, and transcranial magnetic stimulation for TRD. Recent advances in our understanding of how to treat TRD have largely expanded our knowledge of best practices in, and efficacy of, interventional psychiatric approaches. Recent research has used a variety of TRD definitions for study inclusion criteria; research on TRD should adhere to inclusion criteria based on internationally defined guidelines for more meaningfully generalizable results.

Evaluating the prevalence and risk factors for depression in patients with temporal lobe epilepsy with hippocampal sclerosis: A cross-sectional multicenter study.

BACKGROUND: Epilepsy frequently accompanies Major Depressive Disorder (MDD). Notably, people with temporal lobe epilepsy and hippocampal sclerosis may face an increased susceptibility to MDD, as evidence indicates the involvement of the limbic system in the development of emotional symptoms. OBJECTIVES: To determine the prevalence and predictors of depression in temporal lobe epilepsy with hippocampal sclerosis (TLE-HS) and compare them to those of other epilepsy types. METHODS: A sample of 293 epilepsy patients, including 159 non-TLE-HS and 134 TLE-HS, were recruited from three hospitals. Of these, 215 completed a two-section electronic survey. The first section collected demographic and epilepsy data, while the second used the Arabic version of the Neurological Disorders Depression Inventory for Epilepsy (NDDI-E). RESULTS: Of 215 patients, 104 (48%) had TLE-HS-38 with right TLE-HS (37%), 56 with left TLE-HS (54%), and 10 with bilateral TLE-HS (10%). The prevalence and severity of depression was assessed with an NDDI-E score of 15 or higher identified 35 patients (16%) with MDD. Valproic acid and lamotrigine were associated with higher NDDI-E scores. No such associations were found for levetiracetam or carbamazepine. Polytherapy in TLE-HS showed a significant correlation with daily poor concentration. CONCLUSION: We explored the differences in depression prevalence between TLE-HS and other epilepsy types and concluded they are minimal but slightly higher in TLE-HS. Predictors of depression such as seizure frequency and disease duration influenced MDD prevalence in TLE-HS. Lamotrigine and valproate were linked to higher NDDI-E scores.

A comprehensive review of the intersection between asthma and depression.

OBJECTIVE: To emphasize the necessity for increased research in this field, incorporating depression into the preventative, diagnostic, and therapeutic considerations for asthma. Additionally, we seek to highlight upcoming advancements that can be applied to simultaneously address these comorbidities, ultimately improving the overall well-being and quality of life for individuals coping with these conditions. METHODS: A rigorous search in PubMed using the MeSH terms "asthma" and "depression" was performed, and papers were screened by the authors in view of their eligibility to contribute to the study. RESULTS: There exists a correlation between these two conditions, with specific biological mechanisms and genetic factors playing a crucial role in their concurrent occurrence. In this review, we present preclinical and clinical research data, shed light on the possible mechanisms contributing to the co-occurrence of symptoms associated with both asthma and depression, and explore the intricate relationship between both conditions. CONCLUSION: The evidence presented here supports the existence of a correlation between asthma and depression. By acknowledging these shared biological mechanisms, genetic factors, and epidemiological trends, we can formulate more efficacious strategies for addressing the dual impact of asthma and depression.

Advancing past ketamine: emerging glutamatergic compounds for the treatment of depression.

Changes in glutamatergic neuroplasticity has been proposed as one of the core mechanisms underlying the pathophysiology of depression. In consequence components of the glutamatergic synapse have been explored as potential targets for antidepressant treatment. The rapid antidepressant effect of the NMDA receptor antagonist ketamine and subsequent approval of its S-enantiomer (i.e. esketamine), have set the precedent for investigation into other glutamatergic rapid acting antidepressants (RAADs). In this review, we discuss the potential of the different glutamatergic targets for antidepressant treatment. We describe important clinical outcomes of several key molecules targeting components of the glutamatergic synapse and their applicability as RAADs. Specifically, here we focus on substances beyond (es)ketamine, for which meaningful data from clinical trials are available, including arketamine, esmethadone, nitrous oxide and other glutamate receptor modulators. Molecules only successful in preclinical settings and case reports/series are only marginally discussed. With this review, we aim underscore the critical role of glutamatergic modulation in advancing antidepressant therapy, thereby possibly enhancing clinical outcomes but also to reducing the burden of depression through faster therapeutic effects.

Change in brain activation after transcranial pulsed electromagnetic fields in treatment-resistant depression.

BACKGROUND: Preliminary evidence suggests antidepressant effects of transcranial pulsed electromagnetic fields (tPEMF). However, the precise mechanism of action in the brain is still unknown. The aim of this study was to investigate the influence of tPEMF on brain activation in patients with treatment-resistant depression (TRD) by studying two processes that might be of particular interest in relation to the symptoms of depression: emotional processing and reward processing. METHODS: Eligible participants (n = 50) with TRD in this sham-controlled double-blind multicenter trial [registered at the Dutch Trial Register ( http://www.trialregister.nl ), NTR3702] were randomly assigned to five weeks daily active or sham tPEMF. Pre- and post-treatment functional MR-scans were made during which participants performed a social-emotional task and a reward task. RESULTS: Participants in the active treatment group showed a stronger decrease in activation post-treatment compared to sham during reward-outcome processing in the left inferior frontal gyrus and in a cluster comprising the right lingual gyrus and the posterior part of the middle temporal gyrus. No effect of tPEMF was found on activation during the social-emotional task. Neurostimulation with tPEMF did also not affect behavioral performance for both tasks. CONCLUSIONS: We found a decrease in reward-related activation as a result of tPEMF stimulation, while no effect of tPEMF on social-emotional processing was found. The treatment-related reduction in activation of regulatory regions may reflect normalization and may have implications for anhedonia. These findings suggest that there is an effect of tPEMF on brain activation of relevant circuits, albeit in the absence of a clinical antidepressant effect.

Novel synergistic treatment for depression: involvement of GSK3ß-regulated AMPA receptors in the prefrontal cortex of mice.

Previous evidence has suggested a vital role of glycogen synthase kinase 3ß-mediated α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors trafficking in depression. Considering the antidepressant effect of α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors activation in the prefrontal cortex, we hypothesized that glycogen synthase kinase 3ß-induced alterations in α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors function in the prefrontal cortex participate in depression. Herein, we confirmed that the levels of phosphorylated glycogen synthase kinase 3ß and GluA1, the latter being a subunit of α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors, were decreased in the prefrontal cortex of the chronic social defeat stress model mice presenting with depressive-like behaviors. We then found that a glycogen synthase kinase 3ß (p.S9A) point mutation downregulated GluA1 and induced depressive-like behaviors in mice, whereas an agonist of α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors, PF-4778574 (2 mg/kg) did not reversed the molecular changes. On the other hand, the antidepressant effect of PF-4778574 was dose dependent, and the single administration of PF-4778574 at a lower dose (0.5 mg/kg) or of the glycogen synthase kinase 3ß inhibitor SB216763 (5 and 10 mg/kg) did not evoke an antidepressant effect. In contrast, co-treatment with PF-4778574 (0.5 mg/kg) and SB216763 (10 mg/kg) led to antidepressant effects similar to those of PF-4778574 (2 mg/kg). Our results suggest that glycogen synthase kinase 3ß-induced α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptors dysfunction in the prefrontal cortex is one of the key mechanisms of depression, and the combination of a lower dose of PF-4778574 with SB216763 shows potential as a novel synergistic treatment for depression.

Low-carbohydrate diet as a nutritional intervention in a major depression disorder: focus on relapse prevention.

OBJECTIVES: Mood disorders are trending to be among the leading causes of years lived with disability. Despite multiple treatment options, around 30% patients with major depressive disorder (MDD) develop treatment resistant depression (TRD) and fail to respond to current pharmacological therapies. This study aimed to explore the potential benefits of nutritional treatment strategies, along with their molecular mechanisms of action, focusing especially on low-carbohydrate diet (LCHD), ketogenic diet (KD) and other strategies based on carbohydrates intake reduction. METHODS: A comprehensive literature review was conducted to determine the impact of LCHD on alleviating depressive symptoms in patients with MDD, along with an explanation of its mode of action. RESULTS: The study revealed significant impact of nutritional interventions based on restriction in carbohydrate intake such as LCHD, KD or sugar-sweetened beverages (SSB) exclusion on anxiety or depression symptoms reduction, mood improvement and lower risk of cognitive impairment or depression. The efficacy of these approaches is further substantiated by their underlying molecular mechanisms, mainly brain-derived neurotrophic factor (BDNF) which is a potential key target of sugar restriction diets in terms of neuroplasticity. DISCUSSION: Healthcare professionals may consider implementing LCHD strategies for MDD and TRD patients to modify the disease process, maintain euthymia, and prevent depressive episode relapses. Ranging from the exclusion of SSB to the adherence to rigorous LCHD regimens, these nutritional approaches are safe, straightforward to implement, and may confer benefits for well-being and relapse prevention in this specific patient population.

Prevalence of major depressive disorder and its associated factors among adult patients with neurolathyrism in Dawunt District, Ethiopia; 2022: community-based cross-sectional study.

INTRODUCTION: Major Depressive Disorder (MDD) is one of the commonest mental disorders affecting more than 250 million people globally. Patients with chronic illnesses had higher risks for developing MDD than the general population. Neurolathyrism is a chronic illness characterized by lifelong incurable spastic paralysis of lower extremities; causing permanent disability. It is highly prevalent in Dawunt district, Ethiopia; with a point prevalence of 2.4%. Despite this, there were no previous studies assessing the prevalence of MDD among patients with neurolathyrism in Ethiopia. OBJECTIVE: To assess the prevalence of MDD and to identify its associated factors among patients with neurolathyrism in Dawunt district, Ethiopia. METHODS: A community based cross-sectional study was conducted on 260 samples in Dawunt district from February 01 to March 30/ 2021. Multistage sampling technique was used to select study participants. The patient Health Questionnaire-9 (PHQ-9) depression screening tool was used to diagnose MDD. PHQ-9 is a standardized depression screening tool and a PHQ-9 score of ≥ 10 has a sensitivity and specificity of 88.0% [95% CI (83.0-92.0%)] and 85.0% [95% CI (82.0-88.0%)] for screening MDD. Data were collected by interview; entered to EpiData version 4.2.0; exported to SPSS version 25.0 for analysis; descriptive statistics and binary logistic regression model were used; AOR with 95% CI was used to interpret the associations; and finally results were presented by texts, charts, graphs, and tables. RESULTS: A total of 256 adult patients with neurolathyrism were participated; and the prevalence of MDD was found to be 38.7%. Being female [AOR = 3.00; 95% CI (1.15, 7.84)], living alone [AOR = 2.77; 95% CI (1.02-7.53)], being on neurolathyrism stage-3 [AOR = 3.22; 95% CI (1.09, 9.54)] or stage-4 [AOR = 4.00; 95% CI (1.28, 12.48)], stigma [AOR = 2.69; 95% CI (1.34, 5.39)], and lack of social/ family support [AOR = 3.61; 95% CI (1.80, 7.24)] were found to have statistically significant association with an increased odds of MDD; while regular exercise and ever formal counselling were found to have statistically significant association with a decreased odds of MDD. CONCLUSION: The prevalence of MDD among neurolathyrism patients in Dawunt district was high. Lack of social support, stigma, not getting formal counselling, and not involving in regular exercise were modifiable risk factors. Therefore, social support, reducing stigma, formal counselling, and encouraging regular exercise might help to reduce the burden of MDD among neurolathyrism patients.

Utilization and outcomes of transcranial magnetic stimulation and usual care for MDD in a large group psychiatric practice.

BACKGROUND: General psychiatrists' practice standards vary regarding when to implement transcranial magnetic stimulation (TMS) for care of patients with major depressive disorder (MDD). Furthermore, few studies have examined real-world utilization and clinical outcomes of TMS. This study analyzed data from a large, multi-site psychiatric practice to evaluate utilization and outcomes of TMS as well as usual care (UC) for patients with MDD. METHODS: Depression outcomes for TMS and UC among adult patients at a multi-site psychiatric group practice were examined in this retrospective cohort analysis. Patients with a primary diagnosis of MDD, PHQ-9 ≥ 10, and a visit in November 2020 with 6-month follow-up were included and categorized into the TMS or UC cohorts. RESULTS: Of 1,011 patients with qualifying PHQ-9 at the baseline visit, 9% (89) received a full course of TMS, and 583 patients receiving UC met study inclusion criteria (339 patients were excluded due to lacking a 6-month follow-up visit or receiving esketamine during the study period). The TMS cohort had higher baseline PHQ-9 than UC (17.9 vs. 15.5, p < .001) and had failed more medication trials (≥ 4 vs. 3.1, p < .001). Mean PHQ-9 decreased by 5.7 points (SD = 6.7, p < .001) in the TMS cohort and by 4.2 points (SD = 6.4, p < .001) in the UC cohort over the study period. Among patients who had failed four or more antidepressant medications, PHQ-9 decreased by 5.8 points in the TMS cohort (SD = 6.7, p < .001) and by 3.2 points in the UC cohort (SD = 6.3, p < .001). CONCLUSIONS: TMS utilization was low, despite TMS showing significant real-world clinical benefits. Future research should examine and address barriers to wider adoption of TMS into routine patient care for patients with treatment-resistant MDD. Wider adoption including routine use of TMS in less treatment-resistant patients will allow statistical comparisons of outcomes between TMS and UC populations that are difficult to do when TMS is underutilized.

An association study of m6A methylation with major depressive disorder.

OBJECTIVE: To find the relationship between N6-methyladenosine (m6A) genes and Major Depressive Disorder (MDD). METHODS: Differential expression of m6A associated genes between normal and MDD samples was initially identified. Subsequent analysis was conducted on the functions of these genes and the pathways they may affect. A diagnostic model was constructed using the expression matrix of these differential genes, and visualized using a nomogram. Simultaneously, an unsupervised classification method was employed to classify all patients based on the expression of these m6A associated genes. Following this, common differential genes among different clusters were computed. By analyzing the functions of the common differential expressed genes among clusters, the role of m6A-related genes in the pathogenesis of MDD patients was elucidated. RESULTS: Differential expression was observed in ELAVL1 and YTHDC2 between the MDD group and the control group. ELAVL1 was associated with comorbid anxiety in MDD patients. A linear regression model based on these two genes could accurately predict whether patients in the GSE98793 dataset had MDD and could provide a net benefit for clinical decision-making. Based on the expression matrix of ELAVL1 and YTHDC2, MDD patients were classified into three clusters. Among these clusters, there were 937 common differential genes. Enrichment analysis was also performed on these genes. The ssGSEA method was applied to predict the content of 23 immune cells in the GSE98793 dataset samples. The relationship between these immune cells and ELAVL1, YTHDC2, and different clusters was analyzed. CONCLUSION: Among all the m6A genes, ELAVL1 and YTHDC2 are closely associated with MDD, ELAVL1 is related to comorbid anxiety in MDD. ELAVL1 and YTHDC2 have opposite associations with immune cells in MDD.