Peer-led intervention for individuals with major depression: study protocol for a randomized controlled trial (SUPEERMood).

BACKGROUND: Major Depressive Disorder (MDD) is one of the most disabling mental health problems worldwide. The Recovery Model emphasizes peer support to empower individuals with MDD, improve self-management, and patients' quality of life. Despite the demonstrated efficacy of peer-led interventions, further research is needed due to methodological limitations and variability in interventions across studies. Therefore, the objective of this trial is to evaluate the effectiveness of an adjuvant peer-led intervention for the reduction of depressive symptoms in individuals diagnosed with MDD attended in primary care mental health units. METHODS: A controlled, parallel, randomized clinical trial will be conducted. The intervention group (n = 35) will receive 6 weeks of peer-led sessions based on a peer support program drive whilst supervised by nurses, while the control group (n = 35) will use a mobile Health (mHealth) application for emotional wellness based on CBT for 6 weeks. Measurements will be collected at baseline, at 6 weeks, at 6 and 12 months after the intervention to evaluate post-intervention effects. The primary outcome is the reduction of depressive symptoms through the Beck Depression Inventory (BDI-II) after the intervention. Secondary outcomes will involve measures such as adherence to psychiatric treatment, quality of life, adherence to mediterranean diet, alcohol consumption and physical activity. DISCUSSION: We hypothesize that this peer-led intervention, in contrast to the mHealth, will show improvement in BDI-II score reduction of 6 points after six weeks, 6 and 12 months. Standardized peer-led programs can benefit patients and professionals in terms of efficacy and feasibility of clinical treatment of depression, healthy habits, self-care and quality of life. In addition, they can provide recovery and relapse reduction, improved psychosocial support, minimization of intensive care use, and support for patient autonomy through self-management. TRIAL REGISTRATION: The trial protocol is prospectively registered with ClinicalTrials.gov under protocol registration number NCT06398561. Date of registration: May 01, 2024. Recruitment is ongoing.

Glymphatic function assessment with diffusion tensor imaging along the perivascular space in patients with major depressive disorder and its relation to cerebral white-matter alteration.

Background: The link between glymphatic system function in the brain and alterations in white-matter microstructure among individuals with major depressive disorder (MDD) remains unclear. This study aimed to examine the assessment of glymphatic system function in patients with MDD using the diffusion tensor imaging along the perivascular space (DTI-ALPS) index and to evaluate its association with cerebral-white-matter abnormalities and neuropsychological scores. Methods: From February 2023 to November 2023, this cross-sectional study recruited 35 patients with MDD from the Psychosomatic Diseases Department of the First Affiliated Hospital of Dalian Medical University. In this time period, 23 healthy controls (HCs) were enlisted from the community and matched with the MDD cohort in terms of years of education, gender, and age. All participants underwent magnetic resonance imaging, depression, anxiety, and cognitive assessments. The tract-based spatial statistics (TBSS) analyzed DTI parameters and identified significant clusters. Automated fiber quantification (AFQ) was used to automatically identify fiber bundles with statistical differences. Mann-Whitney tests or two-sample t-tests were used for comparisons. Interobserver consistency of the DTI-ALPS measurements was evaluated using the interclass correlation coefficient (ICC). Partial correlation analyses and linear regression analyses were used to examine relationships. A comparison of the DTI-ALPS index was made between the two groups. Correlations among diffusion characteristics, neuropsychological scores, and the DTI-ALPS index were analyzed. Results: Compared to HCs, patients with MDD exhibited a lower DTI-ALPS score (P=0.001). According to using linear regression analysis, the ALPS index was found to be an independent predictor of the Hamilton Depression Rating Scale [B=-25.32; P=0.001; 95% confidence interval (CI): -40.35 to -11.55], Hamilton Anxiety Rating Scale (B=-33.48; P=0.003; 95% CI: -55.38 to -11.24), and Montreal Cognitive Assessment total score (B=8.59; P=0.008; 95% CI: 2.38 to 14.79). According to the TBSS analysis, there were clusters of increased axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD) in patients with MDD as compared to HCs (all P values <0.05). A lower DTI-ALPS score was correlated with higher AD (r=-0.592; P<0.001), MD (cluster 1: r=-0.567, P=0.001; cluster 2: r=-0.581, P<0.001), and RD (r=-0.491; P=0.004) values. AFQ analysis identified the significantly different diffusion indicators in the left cingulum bundle (CB_L), left inferior longitudinal fasciculus (ILF_L), and left uncinate fasciculus (UF_L) between the two groups (all false discovery rate P values <0.05). DTI-ALPS score was negatively correlated with the AD value of CB_L (r=-0.304; P=0.024), ILF_L (r=-0.35; P=0.008), and UF_L (r=-0.354; P=0.008) in AFQ tract-level analysis. In point-wise analysis, the MD value of CB_L at nodes 33 to 36 was negatively correlated with DTI-ALPS score (r ranging from -0.504 to -0.535; P<0.01). Conclusions: Our results indicated a decrease in DTI-ALPS index score in patients with MDD. DTI-ALPS score was associated with depression, anxiety, declined cognitive ability, and white-matter microstructural abnormalities and may thus be a promising biomarker for the partial evaluation of glymphatic system function in patients with MDD.

MDD brain network analysis based on EEG functional connectivity and graph theory.

Background: Existing studies have shown that the brain network of major depression disorder (MDD) has abnormal topologies. However, constructing reliable MDD brain networks is still an open problem. New method: This paper proposed a reliable MDD brain network construction method. First, seven connectivity methods are used to calculate the correlation between channels and obtain the functional connectivity matrix. Then, the matrix is binarized using four binarization methods to obtain the EEG brain network. Besides, we proposed an improved binarization method based on the criterion of maximizing differences between groups: the adaptive threshold (AT) method. The AT can automatically set the optimal binarization threshold and overcome the artificial influence of traditional methods. After that, several network metrics are extracted from the brain network to analyze inter-group differences. Finally, we used statistical analysis and Fscore values to compare the performance of different methods and establish the most reliable method for brain network construction. Results: In theta, alpha, and total frequency bands, the clustering coefficient, global efficiency, local efficiency, and degree of the MDD brain network decrease, and the path length of the MDD brain network increases. Comparison with existing methods: The results show that AT outperforms the existing binarization methods. Compared with other methods, the brain network construction method based on phase-locked value (PLV) and AT has better reliability. Conclusions: MDD has brain dysfunction, particularly in the frontal and temporal lobes.

Changed ventral striatum structural covariance and grey matter volume in depression during a one-year follow-up.

Empirical findings suggest reduced cortico-striatal structural connectivity in patients with major depressive disorder (MDD). However, the relationship between the abnormal structural covariance and one-year outcome of first-episode drug-naive patients has not been evaluated. This longitudinal study aimed to identify specific changes of ventral striatum-related brain structural covariance and grey matter volume in forty-two first-episode patients with major depression disorder compared with thirty-seven healthy controls at the baseline and the one-year follow-up conditions. At the baseline, patients showed decreased structural covariance between the left ventral striatum and the bilateral superior frontal gyrus (SFG), bilateral middle frontal gyrus (MFG), right supplementary motor area (SMA) and left precentral gyrus and increased grey matter volume at the left fusiform and left parahippocampus. At the one-year follow-up, patients showed decreased structural covariance between the left ventral striatum and the right SFG, right MFG, left precentral gyrus and left postcentral gyrus, and increased structural covariance between the right ventral striatum and the right amygdala, right hippocampus, right parahippocampus, right superior temporal pole, right insula and right olfactory bulb and decreased volume at the left SMA compared with controls. These findings suggest that specific ventral striatum connectivity changes contribute to the early brain development of the MDD.

Transcutaneous Auricular Vagus Nerve Stimulation Modulating the Brain Topological Architecture of Functional Network in Major Depressive Disorder: An fMRI Study.

BACKGROUND: Transcutaneous auricular vagus nerve stimulation (taVNS) is effective in regulating mood and high-level cognition in patients with major depressive disorder (MDD). This study aimed to investigate the efficacy of taVNS treatment in patients with MDD and an altered brain topological organization of functional networks. METHODS: Nineteen patients with MDD were enrolled in this study. Patients with MDD underwent 4 weeks of taVNS treatments; resting-state functional magnetic resonance imaging (rs-fMRI) data of the patients were collected before and after taVNS treatment. The graph theory method and network-based statistics (NBS) analysis were used to detect abnormal topological organizations of functional networks in patients with MDD before and after taVNS treatment. A correlation analysis was performed to characterize the relationship between altered network properties and neuropsychological scores. RESULTS: After 4 weeks of taVNS treatment, patients with MDD had increased global efficiency and decreased characteristic path length (Lp). Additionally, patients with MDD exhibited increased nodal efficiency (NE) and degree centrality (DC) in the left angular gyrus. NBS results showed that patients with MDD exhibited reduced connectivity between default mode network (DMN)-frontoparietal network (FPN), DMN-cingulo-opercular network (CON), and FPN-CON. Furthermore, changes in Lp and DC were correlated with changes in Hamilton depression scores. CONCLUSIONS: These findings demonstrated that taVNS may be an effective method for reducing the severity of depressive symptoms in patients with MDD, mainly through modulating the brain's topological organization. Our study may offer insights into the underlying neural mechanism of taVNS treatment in patients with MDD.

Gene expression and brain imaging association study reveals gene signatures in major depressive disorder.

Major depressive disorder is often characterized by changes in the structure and function of the brain, which are influenced by modifications in gene expression profiles. How the depression-related genes work together within the scope of time and space to cause pathological changes remains unclear. By integrating the brain-wide gene expression data and imaging data in major depressive disorder, we identified gene signatures of major depressive disorder and explored their temporal-spatial expression specificity, network properties, function annotations and sex differences systematically. Based on correlation analysis with permutation testing, we found 345 depression-related genes significantly correlated with functional and structural alteration of brain images in major depressive disorder and separated them by directional effects. The genes with negative effect for grey matter density and positive effect for functional indices are enriched in downregulated genes in the post-mortem brain samples of patients with depression and risk genes identified by genome-wide association studies than genes with positive effect for grey matter density and negative effect for functional indices and control genes, confirming their potential association with major depressive disorder. By introducing a parameter of dispersion measure on the gene expression data of developing human brains, we revealed higher spatial specificity and lower temporal specificity of depression-related genes than control genes. Meanwhile, we found depression-related genes tend to be more highly expressed in females than males, which may contribute to the difference in incidence rate between male and female patients. In general, we found the genes with negative effect have lower network degree, more specialized function, higher spatial specificity, lower temporal specificity and more sex differences than genes with positive effect, indicating they may play different roles in the occurrence and development of major depressive disorder. These findings can enhance the understanding of molecular mechanisms underlying major depressive disorder and help develop tailored diagnostic and treatment strategies for patients of depression of different sex.

Major depressive disorder: point prevalence, suicidal ideation, and risk factors among Sudanese children and adolescents during Sudan army conflict: a cross-sectional study.

BACKGROUND: Tiredness, poor concentration, disturbed sleep and poor appetite can all be caused by depression, which is a common mental disorder and a leading cause of disability worldwide. This study aimed to assess the prevalence of major depressive disorder, suicidal ideation, and risk factors in Sudanese children and adolescents during the Sudanese army conflict. METHODS: A descriptive cross-sectional community-based study was carried out among Sudanese children between 11 and 17 years old who living in Sudan at the start of the conflict by using a self-administered questionnaire under the guidance of parents, if necessary. The questionnaire was adapted from the Patients Health Questionnaire-9 (PHQ-9) checklist for the assessment of major depression disorder symptoms according to the Diagnostic and Statistical Manual Edition 5th Edition (DSM-5). The questionnaire was translated into Arabic by two expert translators, and its validity and reliability were confirmed. Data analysis was performed using Statistical Package for the Social Sciences version 25 software, and descriptive analysis and any appropriate statistical tests were performed. RESULTS: Among the 963 participants, the mean age was 15.18 ± 2.1 years, 65.5% were female, and 67.7% had major depressive disorder. There was a significant relationship between MDD score, age, sex, current residency status, and traumatic event exposure, with P values less than 0.001 for all variables. CONCLUSION: Major depressive disorder was highly prevalent among Sudanese children and adolescents included in the present study. Additionally, suicidal ideation, which requires immediate intervention, was reported to be very high. The findings will help the government to provide proper mental health interventions for affected people.

Butyrate- and Beta-Hydroxybutyrate-Mediated Effects of Interventions with Pro- and Prebiotics, Fasting, and Caloric Restrictions on Depression: A Systematic Review and Meta-Analysis.

To examine the butyrate- and beta-hydroxybutyrate (BHB)-modulated effects of pre- and probiotic interventions, fasting, and caloric restriction interventions, a systematic literature review was carried out with a subsequent meta-analysis. Three pre-and probiotic intervention randomized control trials (RCTs) were included in the meta-analysis. A significant increase in butyrate (standardized mean difference (SMD) [confidence interval (CI)] 0.34; [0.02-0.67]) and an improvement in depression scores (SMD [CI] 0.15, [-0.35-0.70]) through pre- and probiotic interventions were shown in the meta-analysis. The intervention duration of the included studies ranged from three days to four weeks, with the examined population being healthy adults. Butyrate was measured in either plasma or feces, and the depression score was obtained under the Swedish core affect scale, the hospital anxiety and depression scale (HADS), or the depression, anxiety, and stress scale-21 items (DASS-21). In addition to butyrate, the total SCFA concentration also seems to be positively associated with pre- and probiotic administration (SMD [CI] 0.55 [0.15-0.95]). Despite the significant short-chain fatty acid (SCFA) and butyrate concentration changes, no significant correlation between butyrate and depression or between SCFAs and depression could be shown through linear regression models. Nevertheless, the regression coefficient b1 = 1.57 (p = 0.17) for butyrate suggests a strong, positive connection between butyrate and depression. Additionally, three studies were qualitatively analyzed, examining fasting as an intervention and revealing a connection between fasting, BHB, and depression. The association between fasting, BHB, and depression or mood elevation appeared to be related to BHB concentrations, which may be due to the similar biochemical properties of BHB and butyrate. Furthermore, caloric restrictions as alternatives to fasting were proposed as potential long-term interventions.

Longitudinal Association between Stressful Life Events and Suicidal Ideation in Adults with Major Depression Disorder: The Mediating Effects of Insomnia Symptoms.

Stressful life events (SLEs) and suicidal ideation (SI) are prevalent in persons with major depression disorder (MDD). Less is known about the underlying role of insomnia symptoms in the association between SLEs and SI. This three-wave prospective cohort study sought to investigate the longitudinal association among SLEs, insomnia symptoms, and SI in persons with MDD. The study population included 511 persons with MDD (mean [SD] age, 28.7 [6.7] years; 67.1% were females). Generalized estimated equations (GEEs) were utilized to explore prospective association among exposure of SLEs, insomnia symptoms, and SI. Additionally, a structural equation model (SEM) was employed to estimate the longitudinal mediating effect of insomnia symptoms in the relationship between SLEs and SI. Our study demonstrated that cumulative SLEs were determined to be longitudinally associated with SI in persons with MDD. We further observed that the association between SLEs and SI was significantly mediated by insomnia symptoms. Clinicians assessing persons with MDD, especially those with the history of SLE, could carefully evaluate and promptly treat insomnia symptoms as part of personalized assessment of their depressive illness, thereby achieving early prevention and intervention for suicidal behaviors in persons with MDD.

TanhReLU -based convolutional neural networks for MDD classification.

Major Depression Disorder (MDD), a complex mental health disorder, poses significant challenges in accurate diagnosis. In addressing the issue of gradient vanishing in the classification of MDD using current data-driven electroencephalogram (EEG) data, this study introduces a TanhReLU-based Convolutional Neural Network (CNN). By integrating the TanhReLU activation function, which combines the characteristics of the hyperbolic tangent (Tanh) and rectified linear unit (ReLU) activations, the model aims to improve performance in identifying patterns associated with MDD while alleviating the issue of model overfitting and gradient vanishing. Experimental results demonstrate promising outcomes in the task of MDD classification upon the publicly available EEG data, suggesting potential clinical applications.

Technical Report: Efficacy and Safety of Low-Intensity Transcranial Magnetic Stimulation in the Remission of Depressive Symptoms in Patients With Treatment-Resistant Depression in Mexico.

Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique that induces action potentials in the stimulated cortical area and has been approved by the Food and Drug Administration (FDA) for the treatment of major depressive disorder (MDD). The prevalence of MDD in Mexico almost tripled after the COVID-19 pandemic. In this study, we evaluated the safety and therapeutic effects of low-intensity TMS (Li-TMS) - characterized by inducing electric currents below the action potential threshold on the cerebral cortex - in 41 subjects diagnosed with treatment-resistant depression (TRD). A Li-TMS device dispensed repetitive magnetic pulses at 30 mT for 60 minutes during 20 sessions (once daily from Monday to Saturday) with the theta burst pattern. Our results suggest that Li-TMS is a safe therapy with antidepressant effects, demonstrated by the decrease in Beck Depression Inventory (BDI) scores and lessening of depressive symptoms.

Unlocking new avenues for neuropsychiatric disease therapy: the emerging potential of Peroxisome proliferator-activated receptors as promising therapeutic targets.

RATIONALE: Peroxisome proliferator-activated receptors (PPARs) are transcription factors that regulate various physiological processes such as inflammation, lipid metabolism, and glucose homeostasis. Recent studies suggest that targeting PPARs could be beneficial in treating neuropsychiatric disorders by modulating neuronal function and signaling pathways in the brain. PPAR-α, PPAR-δ, and PPAR-γ have been found to play important roles in cognitive function, neuroinflammation, and neuroprotection. Dysregulation of PPARs has been associated with neuropsychiatric disorders like bipolar disorder, schizophrenia, major depression disorder, and autism spectrum disorder. The limitations and side effects of current treatments have prompted research to target PPARs as a promising novel therapeutic strategy. Preclinical and clinical studies have shown the potential of PPAR agonists and antagonists to improve symptoms associated with these disorders. OBJECTIVE: This review aims to provide an overview of the current understanding of PPARs in neuropsychiatric disorders, their potential as therapeutic targets, and the challenges and future directions for developing PPAR-based therapies. METHODS: An extensive literature review of various search engines like PubMed, Medline, Bentham, Scopus, and EMBASE (Elsevier) databases was carried out with the keywords "PPAR, Neuropsychiatric disorders, Oxidative stress, Inflammation, Bipolar Disorder, Schizophrenia, Major depression disorder, Autism spectrum disorder, molecular pathway". RESULT & CONCLUSION: Although PPARs present a hopeful direction for innovative therapeutic approaches in neuropsychiatric conditions, additional research is required to address obstacles and convert this potential into clinically viable and individualized treatments.

Advances in biosensors for major depressive disorder diagnostic biomarkers.

Depression is one of the most common mental disorders and is mainly characterized by low mood or lack of interest and pleasure. It can be accompanied by varying degrees of cognitive and behavioral changes and may lead to suicide risk in severe cases. Due to the subjectivity of diagnostic methods and the complexity of patients' conditions, the diagnosis of major depressive disorder (MDD) has always been a difficult problem in psychiatry. With the discovery of more diagnostic biomarkers associated with MDD in recent years, especially emerging non-coding RNAs (ncRNAs), it is possible to quantify the condition of patients with mental illness based on biomarker levels. Point-of-care biosensors have emerged due to their advantages of convenient sampling, rapid detection, miniaturization, and portability. After summarizing the pathogenesis of MDD, representative biomarkers, including proteins, hormones, and RNAs, are discussed. Furthermore, we analyzed recent advances in biosensors for detecting various types of biomarkers of MDD, highlighting representative electrochemical sensors. Future trends in terms of new biomarkers, new sample processing methods, and new detection modalities are expected to provide a complete reference for psychiatrists and biomedical engineers.

Coding of Childhood Psychiatric and Neurodevelopmental Disorders in Electronic Health Records of a Large Integrated Health Care System: Validation Study.

Background: Mental, emotional, and behavioral disorders are chronic pediatric conditions, and their prevalence has been on the rise over recent decades. Affected children have long-term health sequelae and a decline in health-related quality of life. Due to the lack of a validated database for pharmacoepidemiological research on selected mental, emotional, and behavioral disorders, there is uncertainty in their reported prevalence in the literature. objectives: We aimed to evaluate the accuracy of coding related to pediatric mental, emotional, and behavioral disorders in a large integrated health care system's electronic health records (EHRs) and compare the coding quality before and after the implementation of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding as well as before and after the COVID-19 pandemic. Methods: Medical records of 1200 member children aged 2-17 years with at least 1 clinical visit before the COVID-19 pandemic (January 1, 2012, to December 31, 2014, the ICD-9-CM coding period; and January 1, 2017, to December 31, 2019, the ICD-10-CM coding period) and after the COVID-19 pandemic (January 1, 2021, to December 31, 2022) were selected with stratified random sampling from EHRs for chart review. Two trained research associates reviewed the EHRs for all potential cases of autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), major depression disorder (MDD), anxiety disorder (AD), and disruptive behavior disorders (DBD) in children during the study period. Children were considered cases only if there was a mention of any one of the conditions (yes for diagnosis) in the electronic chart during the corresponding time period. The validity of diagnosis codes was evaluated by directly comparing them with the gold standard of chart abstraction using sensitivity, specificity, positive predictive value, negative predictive value, the summary statistics of the F-score, and Youden J statistic. κ statistic for interrater reliability among the 2 abstractors was calculated. Results: The overall agreement between the identification of mental, behavioral, and emotional conditions using diagnosis codes compared to medical record abstraction was strong and similar across the ICD-9-CM and ICD-10-CM coding periods as well as during the prepandemic and pandemic time periods. The performance of AD coding, while strong, was relatively lower compared to the other conditions. The weighted sensitivity, specificity, positive predictive value, and negative predictive value for each of the 5 conditions were as follows: 100%, 100%, 99.2%, and 100%, respectively, for ASD; 100%, 99.9%, 99.2%, and 100%, respectively, for ADHD; 100%, 100%, 100%, and 100%, respectively for DBD; 87.7%, 100%, 100%, and 99.2%, respectively, for AD; and 100%, 100%, 99.2%, and 100%, respectively, for MDD. The F-score and Youden J statistic ranged between 87.7% and 100%. The overall agreement between abstractors was almost perfect (κ=95%). Conclusions: Diagnostic codes are quite reliable for identifying selected childhood mental, behavioral, and emotional conditions. The findings remained similar during the pandemic and after the implementation of the ICD-10-CM coding in the EHR system.

Unraveling the molecular basis of cannabidiolic acid methyl Ester's anti-depressive effects in a rat model of treatment-resistant depression.

Major depressive disorder (MDD) stands as a significant cause of disability globally. Cannabidiolic Acid-Methyl Ester (CBDA-ME) (EPM-301, HU-580), a derivative of Cannabidiol, demonstrates immediate antidepressant-like effects, yet it has undergone only minimal evaluation in psychopharmacology. Our goal was to investigate the behavioral and potential molecular mechanisms associated with the chronic oral administration of this compound in the Wistar Kyoto (WKY) genetic model of treatment-resistant depression. Male WKY rats were subjected to behavioral assessments before and after receiving chronic (14-day) oral doses of CBDA-ME (0.5 mg/kg), 15 mg/kg of imipramine or vehicle. At the end of the study, plasma corticosterone levels and mRNA expression of various genes in the medial Prefrontal Cortex and Hippocampus were measured. Behavioral outcomes from CBDA-ME treatment indicated an antidepressant-like effect similar to imipramine, as oral ingestion reduced immobility and increased swimming duration in the Forced Swim Test. Neither treatment influenced locomotion in the Open Field Test nor preference in the Saccharin Preference Test. The behavioral impact in WKY rats coincided with reduced corticosterone serum levels, upregulated mRNA expression of Cannabinoid receptor 1, Fatty Acid Amide Hydrolase, and Corticotropin-Releasing Hormone Receptor 1, alongside downregulation of the Serotonin Transporter in the hippocampus. Additionally, there was an upregulation of CB1 mRNA expression and downregulation of Brain-Derived Neurotrophic Factor in the mPFC. These findings contribute to our limited understanding of the antidepressant effects of CBDA-ME and shed light on its potential psychopharmacological mechanisms. This discovery opens up possibilities for utilizing cannabinoids in the treatment of major depressive disorder and related conditions.

Analysis of functional connectivity changes in attention networks and default mode networks in patients with depression and insomnia.

PURPOSE: Major Depressive Disorder (MDD) and Insomnia Disorder (ID) are prevalent psychiatric conditions often occurring concurrently, leading to substantial impairment in daily functioning. Understanding the neurobiological underpinnings of these disorders and their comorbidity is crucial for developing effective interventions. This study aims to analyze changes in functional connectivity within attention networks and default mode networks in patients with depression and insomnia. METHODS: The functional connectivity alterations in individuals with MDD, ID, comorbid MDD and insomnia (iMDD), and healthy controls (HC) were assessed from a cohort of 174 participants. They underwent rs-fMRI scans, demographic assessments, and scale evaluations for depression and sleep quality. Functional connectivity analysis was conducted using region-of-interest (ROI) and whole-brain methods. RESULTS: The MDD and iMDD groups exhibited higher Hamilton Depression Scale (HAMD) scores compared to HC and ID groups (P < 0.001). Both ID and MDD groups displayed enhanced connectivity between the left and right orbital frontal cortex compared to HC (P < 0.05), while the iMDD group showed reduced connectivity compared to HC and ID groups (P < 0.05). In the left insula, reduced connectivity with the right medial superior frontal gyrus was observed across patient groups compared to HC (P < 0.05), with the iMDD group showing increased connectivity compared to MDD (P < 0.05). Moreover, alterations in functional connectivity between the left thalamus and left temporal pole were found in iMDD compared to HC and MDD (P < 0.05). Correlation analyses revealed associations between abnormal connectivity and symptom severity in MDD and ID groups. CONCLUSIONS: Our findings demonstrate distinct patterns of altered functional connectivity in individuals with MDD, ID, and iMDD compared to healthy controls. These findings contribute to a better understanding of the pathophysiology of depression and insomnia, which could be used as a reference for the diagnosis and treatments of these patients.

The causal relationship between major depression disorder and thyroid diseases: A Mendelian randomization study and mediation analysis.

BACKGROUND: Studies have been conducted on the relationship between depression and thyroid diseases and function, its causal relationship remains unclear. METHODS: Using summary statistics of genome-wide association studies of European and East Asian ancestry, we conducted 2-sample bidirectional Mendelian randomization to estimate the association between MDD and thyroid function (European: normal range TSH, T4, T3, fT4, TPOAb levels and TPOAb-positives; East Asian: T4) and thyroid diseases (hypothyroidism, hyperthyroidism, and Hashimoto's thyroiditis), and used Mediation analysis to evaluate potential mediators (alcohol intake, antidepressant) of the association and calculate the mediated proportions. RESULTS: It was observed a significant causal association between MDD on hypothyroidism (P = 8.94 × 10-5), hyperthyroidism (P = 8.68 × 10-3), and hashimoto's thyroiditis (P = 3.97 × 10-5) among European ancestry, which was mediated by Alcohol intake (alcohol intake versus 10 years previously for hypothyroidism (P = 0.026), hashimoto's thyroiditis (P = 0.042), and alcohol intake frequency for hypothyroidism (P = 0.015)) and antidepressant (for hypothyroidism (P = 0.008), hashimoto's thyroiditis (P = 0.010)), but not among East Asian ancestry (PMDD-hypothyroidism = 0.016, but ß direction was different; PMDD-hyperthyroidism = 0.438; PMDD-hashimoto's thyroiditis = 0.496). There was no evidence for bidirectional causal association between thyroid function mentioned above and MDD among both ancestry (all P > 0.05). CONCLUSION: We importantly observed a significant causal association between MDD on risk of hypothyroidism, hyperthyroidism, and hashimoto's thyroiditis among European ancestry, and Alcohol intake and antidepressant as mediators for prevention of hypothyroidism, hashimoto's thyroiditis attributable to MDD.

Global prevalence of major depressive disorder in LGBTQ+ samples: A systematic review and meta-analysis of epidemiological studies.

BACKGROUND: LGBTQ+ populations have been reported to have higher rates of depression compared with their heterosexual peers. Such data provided us the impetus to conduct a meta-analysis on the worldwide prevalence of major depressive disorder (MDD) in LGBTQ+ populations and moderating factors that contributed to differences in prevalence estimates between studies. METHODS: A systematic literature search was performed in major international (PubMed, PsycINFO, Web of Science, EMBASE) and Chinese (Chinese Nation Knowledge Infrastructure (CNKI) and WANFANG) databases from dates of inception to 10 December 2021. RESULTS: 48 articles comprising 4,616,903 individuals were included in the meta-analysis. The overall prevalence of MDD was 32.2 % (95%CI: 30.8-33.6 %, I2 = 99.6 %, τ2 = 0.284). MDD prevalence was higher in the LGBTQ+ samples from the United States than other countries, though the difference was not significant in moderator analyses. Moderator analyses indicated point and lifetime prevalence of MDD were significantly higher than estimates based on the past year (Q = 6.270, p = 0.043). Furthermore, studies that relied on convenience sampling had a higher prevalence of MDD than those based on other sampling methods (Q = 8.159, p = 0.017). In meta-regression analyses, mean age (B = 0.03, z = 9.54, p < 0.001) and study quality assessment score (B = 0.24, z = 67.64, p < 0.001) were positively associated with pooled prevalence of MDD while mediation data of year of study (B = -0.08, z = -72.55, p < 0.001) and sample size (B = -1.46, z = -37.83, p < 0.001) were negatively associated with pooled prevalence of MDD in LGBTQ+ samples. CONCLUSIONS: MDD is common among in LGBTQ+ individuals. Considering the negative consequences MDD has on daily life and well-being, appropriate prevention and treatment measures should be provided to vulnerable members of these populations. The findings of this meta-analysis could facilitate identifying at-risk subgroups, developing relevant health policy for LGBTQ+ individuals and allocating health resources from an intersectionality perspective.

Development and validation of a machine learning model for prediction of comorbid major depression disorder among narcolepsy type 1.

BACKGROUND: Major depression disorder (MDD) forms a common psychiatric comorbidity among patients with narcolepsy type 1 (NT1), yet its impact on patients with NT1 is often overlooked by neurologists. Currently, there is a lack of effective methods for accurately predicting MDD in patients with NT1. OBJECTIVE: This study utilized machine learning (ML) algorithms to identify critical variables and developed the prediction model for predicting MDD in patients with NT1. METHODS: The study included 267 NT1 patients from four sleep centers. The diagnosis of comorbid MDD was based on Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5). ML models, including six full models and six compact models, were developed using a training set. The performance of these models was compared in the testing set, and the optimal model was evaluated in the testing set. Various evaluation metrics, such as Area under the receiver operating curve (AUC), precision-recall (PR) curve and calibration curve were employed to assess and compare the performance of the ML models. Model interpretability was demonstrated using SHAP. RESULT: In the testing set, the logistic regression (LG) model demonstrated superior performance compared to other ML models based on evaluation metrics such as AUC, PR curve, and calibration curve. The top eight features used in the LG model, ranked by feature importance, included social impact scale (SIS) score, narcolepsy severity scale (NSS) score, total sleep time, body mass index (BMI), education years, age of onset, sleep efficiency, sleep latency. CONCLUSION: The study yielded a straightforward and practical ML model for the early identification of MDD in patients with NT1. A web-based tool for clinical applications was developed, which deserves further verification in diverse clinical settings.

Factors affecting major depression in Iran: a mixed-method study.

Depression is a major cause of disability and, if left untreated, can increase the risk of suicide. Evidence on the determinants of depression is incomplete, making it challenging to interpret results across studies. This study aims to identify the social, economic, environmental, political, and technological factors influencing the great recession in Iran. The study was conducted in two parts. The first step involved a literature review to identify the factors, using PubMed, Scopus, and Web of Science for the search. The reference lists of all identified articles were reviewed to find relevant studies, and the extracted information was summarized and reported descriptively. The second steps involved compiling and consulting 14 experts from different fields, using a framework analysis method. Twenty-four articles were used as primary sources of information, and a total of 28 factors were found to exist. After removing duplicates and related factors, 19 of these were subsequently declared as factors, resulting in a total of 36 determinants being identified. Most of these factors belong to the social category. The health policies implemented have a significant impact on disease risk factors and ultimately their occurrence. Political decisions and policy-making processes play a crucial role in all areas, particularly in addressing disease risk factors. Severe depression can disrupt all aspects of the healthcare system, underscoring the importance of access to care. Policies concerning physical education, transportation, nutrition, employment, green spaces, recreational facilities, and tobacco are vital in this context. The influence of health policies on disease risk factors and disease occurrence is profound. Severe depression can have far-reaching effects on the healthcare system, emphasizing the critical need for access to care. The formulation of policies to combat depression must be thoroughly evaluated in terms of economic, political, social, technological, and environmental factors. The findings suggest that addressing social inequalities and emphasizing the role of political action, as highlighted by the social determinants of health, should be top priorities in addressing depression. Efforts to prevent depression should incorporate ecological approaches that consider the impact of the socioeconomic environment on depressive symptoms.