World Journal of Biological Psychiatry最新文献

Objective: To assess the association between sleep irregularity, anxiety, and depression while controlling for other sleep dimensions and using a longitudinal design.

Methods: Longitudinal cohort study which started in April 2020 during the first French lockdown in the general population. Follow-up questionnaires were completed in June 2020, a period without lockdown measures. Participants were asked about their sleep (regularity, duration, timing, complaints) and their anxiety (General Anxiety Disorder-7) and depressive (Patient Health Questionnaire-9) symptoms.

Results: A total of 3745 participants were included (mean age: 28.9 years) with 2945 women (78.6%). At baseline, 38.1% (1428) of participants reported irregular sleep timing, 23.8% (891) anxiety and 28.9% (1081) depressive symptoms. In cross-sectional analyses, irregular sleep timing was associated with a 2.5-fold higher likelihood of anxiety and a 4-fold higher likelihood of depressive symptoms compared to regular sleepers. Associations were not explained by the other sleep dimensions and persisted in a longitudinal analysis, with irregular sleep timing at baseline being associated with anxiety (OR = 3.27[1.58-6.76]) and depressive symptoms (OR = 3.45[1.66-7.19]) during follow-up.

Conclusion: The results show a strong association between sleep irregularity and mental health. Furthers studies are needed to explore how sleep regularity could promote good mental health in non-clinical populations.

Objectives: Despite the clinical importance of bipolar depression (BDE), effective treatment options are still limited. Transcranial magnetic stimulation (rTMS) has proven of moderate efficacy in major depression, but the evidence remains inconclusive for BDE.

Methods: A 4-week, double-blind, randomised, parallel-group, sham-controlled study (trial ID ISRCTN77188420) explored the benefits of 10 Hz MRI-guided right ventrolateral (RVL) rTMS and left dorsolateral (LDL) rTMS as add-on treatments for BDE. Outcome measures included changes in the Montgomery-Åsberg Depression Rating Scale (MADRS) score, self-assessment, response and remission rates, and side effects.

Results: Sixty patients were randomly assigned to study groups, and forty-six completed the double-blind phase. The mean change from baseline to Week 4 in MADRS was greater in both active groups compared to the sham, yet differences did not achieve significance (RVL vs sham: -4.50, 95%CI -10.63 to 1.64, p = 0.3; LDL vs sham: -4.07, 95%CI -10.24 to 2.10, p = 0.4). None of the other outcome measures yielded significant results.

Conclusions: While not demonstrating the superiority of either 10 Hz rTMS over sham, with the limited sample size, we can not rule out a moderate yet clinically meaningful effect. Further well-powered studies are essential to elucidate the role of rTMS in managing BDE.

Objectives: Event-related potential measures have been extensively studied in mental disorders. Among them, P300 amplitude and latency reflect impaired cognitive abilities in major depressive disorder (MDD). The present systematic review and meta-analysis was conducted to investigate whether patients with MDD differ from healthy controls (HCs) with respect to P300 amplitude and latency.

Methods: PubMed and Web of Science databases were searched from inception to 15 January 2023 for case-control studies comparing P300 amplitude and latency in patients with MDD and HCs. The primary outcome was the standard mean difference. A total of 13 articles on P300 amplitude and latency were included in the meta-analysis.

Results: Random effect models indicated that MDD patients had decreased P300 amplitude, but similar latency compared to healthy controls. According to regression analysis, the effect size increased with the severity of depression and decreased with the proportion of women in the MDD samples. Funnel plot asymmetry was not significant for publication bias.

Conclusions: Decreased P300 amplitude may be a candidate diagnostic biomarker for MDD. However, prospective studies testing P300 amplitude as a monitoring biomarker for MDD are needed.

Objectives: 22q11.2 deletion is the most prominent risk factor for schizophrenia (SZ). The aim of the present study was to identify unique transcriptome profile for 22q11.2 deletion syndrome (DS)-related SZ-spectrum disorder (SZ-SD).

Methods: We performed RNA-Seq screening in lymphoblasts collected from 20 individuals with 22q11.2DS (10 men and 10 women, four of each sex with SZ-SD and six with no psychotic disorders (Np)).

Results: Sex effect in RNA-Seq descriptive analysis led to separating the analyses between men and women. In women, only one differentially expressed gene (DEG), HLA-DQA2, was associated with SZ-SD. In men, 48 DEGs (adjp < 0.05) were found to be associated with SZ-SD. Ingenuity pathway analysis of top 85 DEGs (p < 4.66E - 04) indicated significant enrichment for immune-inflammatory response (IIR) and neuro-inflammatory signalling pathways. Additionally, NFATC2, IFNG, IFN-alpha, STAT1 and IL-4 were identified as upstream regulators. Co-expression network analysis revealed the contribution of endoplasmic reticulum protein processing and N-Glycan biosynthesis. These findings indicate dysregulation of IIR and post-translational protein modification processes in individuals with 22q11.2DS-related SZ-SD.

Conclusions: Candidate pathways and upstream regulators may serve as novel biomarkers and treatment targets for SZ. Future transcriptome studies, including larger samples and proteomic analysis, are needed to substantiate our findings.

Objectives: Previous results demonstrated that CYP2D6 and CYP2C19 gene variants affect serum concentrations of antidepressants. We implemented a PGx service determining gene variants in CYP2D6 and CYP2C19 in our clinical routine care and report on our first patient cohort.

Methods: We analysed CYP2D6 and CYP2C19 allele, genotype, and phenotype frequencies, and actionable pharmacogenetic variants in this German psychiatric inpatient cohort. Two-tailed z-test was used to investigate for differences in CYP2D6 and CYP2C19 phenotypes and actionable/non-actionable genetic variant frequencies between our cohort and reference cohorts.

Results: Out of the 154 patients included, 44.8% of patients were classified as CYP2D6 normal metabolizer, 38.3% as intermediate metabolizers, 8.4% as poor metabolizers, and 2.6% as ultrarapid metabolizers. As for CYP2C19, 40.9% of patients were classified as normal metabolizers, 19.5% as intermediate metabolizers, 2.6% as poor metabolizers, 31.2% as rapid metabolizers, and 5.8% as ultrarapid metabolizers. Approximately, 80% of patients had at least one actionable PGx variant.

Conclusion: There is a high prevalence of actionable PGx variants in psychiatric inpatients which may affect treatment response. Physicians should refer to PGx-informed dosing guidelines in carriers of these variants. Pre-emptive PGx testing in general may facilitate precision medicine also for other drugs metabolised by CYP2D6 and/or CYP2C19.

Background: The evidence for repetitive transcranial magnetic stimulation (rTMS) to treat negative symptoms in schizophrenia (SCZ) is increasing, although variable response rates remain a challenge. Subject´s sex critically influences rTMS´ treatment outcomes. Females with major depressive disorder are more likely to respond to rTMS, while SCZ data is scarce.

Methods: Using data from the 'rTMS for the Treatment of Negative Symptoms in Schizophrenia' (RESIS) trial we assessed the impact of sex on rTMS´ clinical response rate from screening up to 105 days after intervention among SCZ patients. The impact of resting motor threshold (RMT) on response rates was also assessed.

Results: 157 patients received either active or sham rTMS treatment. No significant group differences were observed. Linear mixed model showed no effects on response rates (all p > 0.519). Apart from a significant sex*time interaction for the positive subscale of the positive and negative syndrome scale (PANSS) scores (p = 0.032), no other significant effects of sex on continuous PANSS scores were observed. RMT had no effect on response rate.

Conclusion: In the largest rTMS trial on the treatment of SCZ negative symptoms we did not observe any significant effect of sex on treatment outcomes. Better assessments of sex-related differences could improve treatment individualisation.

Objectives: Schizophrenia genetics is intricate, with common and rare variants' contributions not fully understood. Certain copy number variations (CNVs) elevate risk, pivotal for understanding mental disorder models. Despite CNVs' genome-wide distribution and variable gene and protein effects, we must explore beyond affected genes to interaction partners and molecular pathways.

Methods: In this study, we developed machine-readable interactive pathways to enable analysis of functional effects of genes within CNV loci and identify ten common pathways across CNVs with high schizophrenia risk using the WikiPathways database, schizophrenia risk gene collections from GWAS studies, and a gene-disease association database.

Results: For CNVs that are pathogenic for schizophrenia, we found overlapping pathways, including BDNF signalling, cytoskeleton, and inflammation. Common schizophrenia risk genes identified by different studies are found in all CNV pathways, but not enriched.

Conclusions: Our findings suggest that specific pathways - BDNF signalling - are critical contributors to schizophrenia risk conferred by rare CNVs. Our approach highlights the importance of not only investigating deleted or duplicated genes within pathogenic CNV loci, but also study their direct interaction partners, which may explain pleiotropic effects of CNVs on schizophrenia risk and offer a broader field for interventions.

Objectives: This study aims to explore the potential interconnections among gut microbiota, COVID-19 infection, depression and anxiety disorder. Additionally, it tries to assess potential therapeutic interventions that may improve the dysbiosis of gut microbiota.

Methods: To achieve these objectives, we reviewed existing literature, encompassing studies and critical reviews that intersect the domains of gut microbiota, COVID-19, depression and anxiety disorders.

Results: The findings highlight a notable correlation between the dysbiosis of gut microbiota and psychiatric symptoms in the context of COVID-19. Specifically, there is a marked reduction in the populations of bacteria that generate anti-inflammatory short-chain fatty acids (SCFAs), alongside a rise in the prevalence of gut bacterial clusters linked to inflammatory processes. Furthermore, several potential treatment strategies were summarised for improving the dysbiosis.

Conclusions: Gut microbiota plays a significant role in psychiatric symptoms during COVID-19, which has significant implications for the study and prevention of psychiatric symptoms in major epidemic diseases.

Objectives: This study compared machine learning models using unimodal imaging measures and combined multi-modal imaging measures for deep brain stimulation (DBS) outcome prediction in treatment resistant depression (TRD).

Methods: Regional brain glucose metabolism (CMRGlu), cerebral blood flow (CBF), and grey matter volume (GMV) were measured at baseline using 18F-fluorodeoxy glucose (18F-FDG) positron emission tomography (PET), arterial spin labelling (ASL) magnetic resonance imaging (MRI), and T1-weighted MRI, respectively, in 19 patients with TRD receiving subcallosal cingulate (SCC)-DBS. Responders (n = 9) were defined by a 50% reduction in HAMD-17 at 6 months from the baseline. Using an atlas-based approach, values of each measure were determined for pre-selected brain regions. OneR feature selection algorithm and the naïve Bayes model was used for classification. Leave-out-one cross validation was used for classifier evaluation.

Results: The performance accuracy of the CMRGlu classification model (84%) was greater than CBF (74%) or GMV (74%) models. The classification model using the three image modalities together led to a similar accuracy (84%0 compared to the CMRGlu classification model.

Conclusions: CMRGlu imaging measures may be useful for the development of multivariate prediction models for SCC-DBS studies for TRD. The future of multivariate methods for multimodal imaging may rest on the selection of complementing features and the developing better models.Clinical Trial Registration: ClinicalTrials.gov (#NCT01983904).