European Archives of Psychiatry and Clinical Neuroscience最新文献

Data on the comparative usefulness of medications commonly prescribed to individuals with alcohol use disorder (AUD) are scarce. This study compared the association between antidepressants, relapse-preventive AUD medication, both, and neither on the risk of subsequent alcohol-related hospitalization in individuals with severe AUD. This retrospective analysis of Swedish nationwide register data used Cox (primary analysis) and logistic (sensitivity analysis) regression models to assess the associations between medication exposure (antidepressants, AUD medication, both, neither) and risk of subsequent alcohol-related hospitalization. The analysis included data on 14,026 individuals who were admitted to the hospital for severe AUD between 2009 and 2020. Antidepressants were not significantly associated with a lower risk of subsequent alcohol-related hospitalization (hazard ratio [HR] = 0.94, 95% confidence interval [CI] = 0.82-1.08), but AUD medication was (HR = 0.61, 95% CI = 0.54-0.69), as were antidepressants plus AUD medication (HR = 0.63, 95% CI = 0.45-0.87) (reference: exposure to neither). Pairwise comparisons showed that AUD medication was associated with a lower risk of hospitalization than antidepressants (HR = 0.65, 95% CI = 0.54-0.78). Antidepressants alone were associated with a higher risk than antidepressants plus AUD medication (HR = 1.50, 95% CI = 1.05-2.15). The sensitivity analysis confirmed the association between AUD medication and lower hospitalization risk. Antidepressant monotherapy was not associated with a lower risk of subsequent alcohol-related hospitalization, but relapse-preventive medication for AUD was, both alone and in combination with antidepressants. These findings support the use of relapse-preventive medication to reduce hospitalization risk in individuals with severe AUD and raise questions about the benefit of antidepressant monotherapy for this purpose.

The familial relevance of depression is a critical aspect, with evidence suggesting a substantial hereditary component. Investigating the inheritance patterns within families can provide valuable insights into the genetic underpinnings of depression. To gain a new perspective on the genetics underpinning of depression, we conducted a novel analysis of the Genetics of Recurrent Early-onset Depression (GenRED) dataset including 683 U.S. Caucasian families, each with one proband with recurrent early-onset major depression and at least one sibling with depression. Using the MERLIN tool, we identified 37 genomic markers with nominal significance linkage to early-onset recurrent depression. The findings differ from prior analyses of GenRED using a different analytical tool. Our findings highlight the variability and potential discrepancies that can arise from using different analytical tools on the same dataset. Given the chance of microsatellites undergoing duplication, mutations, and amplification errors, our two-point analysis can be more robust compared to the previous multipoint analysis. Also, one limitation is genetic admixture; studies with more homogenous ethnic groups are warranted.

Background: Sleep disorders have a significant impact on physical health, mental health, and daily functioning, exacerbated by the accelerating pace of life and rising work-related stress in contemporary society. The incidence of sleep disorders continues to rise annually, becoming a significant global public health issue. The white blood cell-to-HDL-cholesterol ratio (WHR), lymphocyte-to-HDL-cholesterol ratio (LHR), monocyte-to-HDL-cholesterol ratio (MHR), neutrophil-to-HDL-cholesterol ratio (NHR), and platelet-to-HDL-cholesterol ratio (PHR) are emerging, convenient, and cost-effective biomarkers that reflect systemic inflammation and immunometabolic status. Given the growing evidence linking chronic inflammation and dysregulated immune responses to prevalence of sleep disorders, exploring the association between these hematologic-lipid ratios and prevalence of sleep disorders may provide novel insights into underlying pathophysiological mechanisms.

Methods: The study included a diverse and extensive sample of 43,273 participants, selected from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and March 2020. The association between HDL- associated inflammatory biomarkers and prevalence of sleep disorders was explored using a multivariate logistic regression model with weighted analysis. In addition, the mediating role of depression levels (PHQ-9) between HDL-associated inflammatory markers and prevalence of sleep disorders was examined.

Results: This study suggests that higher levels of MHR and PHR were associated with an increased risk of sleep disorders, with a more pronounced association observed for MHR among male smokers and individuals with comorbidities, and for PHR among males and those with comorbidities. Mediation analysis demonstrates that depression level (PHQ-9) plays a significant mediating role between prevalence of sleep disorders and MHR or PHR.

Conclusion: Elevated MHR and PHR levels were significantly associated with an increased risk of sleep disorders. The association of MHR was particularly pronounced among male smokers and individuals with comorbidities, while PHR showed stronger associations in males and those with comorbidities. Depression levels partially mediated these associations. MHR and PHR may serve as clinically useful biomarkers for identifying high-risk individuals, supporting the need for early screening and intervention. Future longitudinal studies are warranted to confirm causality and assess whether targeting these indices can improve sleep outcomes.

Brain-derived neurotrophic factor (BDNF) is crucial for the growth, differentiation and maintenance of neuronal systems, which is closely associated with major depressive disorder (MDD). The objective of this study was to investigate the BDNF levels and their associations with psychopathology and lipid metabolism parameters in adolescents with MDD. From January to December 2021, the study included 141 adolescents with MDD and 90 healthy controls (HCs). The Center for Epidemiological Studies Depression Scale (CES-D), the Insomnia Severity Index Scale (ISI), the Epworth Sleepiness Scale (ESS) and the Positive and Negative Suicidal Ideation Scale (PANSI) were used to assess depressive symptoms, insomnia, excessive daytime sleepiness, and suicidal ideation, respectively. BDNF levels and lipid metabolism parameters were also measured. Compared to HCs, adolescents with MDD had significantly lower BDNF levels (p < 0.001). In patients, BDNF levels were positively correlated with age, BMI, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C); and negatively correlated with the scores of CES-D and ISI (all p < 0.05). The results of the multivariate linear regression analyses indicated that BDNF levels were positively associated with age (β = 0.198, t = 2.447, p = 0.016), first-episode MDD (β = 0.176, t = 2.234, p = 0.027) and TC level (β = 0.240, t = 3.048, p = 0.003), and negatively associated with the scores of ESS (β = -0.171, t = -2.203, p = 0.029) and ISI (β = -0.231, t = -2.996, p = 0.003). Of note, the associations between BDNF and psychopathology were observed only in female and first-episode patients. BDNF levels were decreased in adolescents with MDD. Patients with low BDNF levels were in a more severe psychiatric state and had changes in lipid metabolism parameters. This study provided preliminary evidence that BDNF may play a role in the onset and progression of MDD.

Background: High dosage accelerated intermittent theta-burst stimulation (aiTBS) protocols (10 sessions per day for 5 days) combined with precision targeting and depth adjusted iTBS intensity yield high response and remission rates in depression. However, disentangling their efficacy components to develop pragmatic mental health solutions is challenging. This pilot study applied such a high dosage aiTBS protocol without using any precision features.

Methods: Eight patients with treatment-resistant depression (TRD) underwent open-label aiTBS targeting the left dorsolateral prefrontal cortex (DLPFC) using the Beam F3 algorithm. Over 5 days, patients received 50 aiTBS sessions, each delivering 1800 pulses at 90% resting motor threshold with 50-min inter-session intervals. All patients underwent a 4 weeks follow-up without stimulation, were offered tDCS for 4 weeks thereafter and had a final follow-up after 6 months. Treatment effects were assessed by clinical and cognitive measures.

Results: Patients received 46 aiTBS sessions on average. At one-month follow-up, mean MADRS scores decreased by -12.50 ± 9.81 (Cohen's d = 2.83; 95% CI, 2.34-3.32; p < 0.001), with response and remission rates of 50% and 12.5%, respectively. After tDCS, 28.6% and 14.3% sustained response and remission, which declined to 16.7% and 0% at six months.

Conclusion: This pilot trial evidenced the antidepressant effect of a high dosage aiTBS protocol comparable with the Stanford Neuromodulation Therapy (SNT) approach but without individualized precision components. Its effectiveness appeared lower than previously reported for SNT. Randomized controlled trials should systematically investigate the contribution of precision components to the overall effectiveness of aiTBS in depression. This trial is a part of a real-world clinical study of non-invasive brain stimulation treatments conducted at our department (preregistered at DRKS-ID: DRKS00024776, drks.de).

Background: Antenatal depression, which is prevalent during pregnancy, frequently continues into the postpartum period. We aimed to investigate the potential neurophysiological brain changes in women exhibiting antenatal depressive symptoms, using resting-state quantitative electroencephalography (qEEG) patterns as an objective indicator.

Methods: Pregnant women with high-risk conditions were included and evaluated for antenatal depressive symptoms using the Edinburgh Postnatal Depression Scale (EPDS), then divided into groups based on an EPDS score of 10. Resting-state qEEG recordings were then obtained to assess relative power topography within classical frequency bands, comparing these measures across the two groups and examining their correlation with EPDS scores.

Results: Among 36 participants, 12 scored ≥ 10 on the EPDS, indicative of significant depressive symptoms, while 24 scored < 10. Those with scores ≥ 10 exhibited heightened beta power in frontal areas (Fz and F4; p < 0.05), along with significant alpha and theta band asymmetry at the T3/T4 (r = 0.383, p = 0.021) and P3/P4 (r = 0.369, p = 0.027) sites respectively, positively correlating with EPDS scores.

Limitations: Depressive symptoms were solely evaluated according to the EPDS, which is a screening tool. Additional limitations include the cross-sectional study design and the relatively small sample size, necessitating cautious interpretation of the results.

Conclusion: The distinct qEEG patterns observed in women with EPDS scores ≥ 10 highlight the potential of qEEG as an objective indicator for assessing antenatal depression.

Background: Despite the proven efficacy of antipsychotics in relapse prevention in schizophrenia and schizoaffective disorder, every third patient experiences a relapse within less than one year. Relapses can worsen psychosocial and treatment related outcomes and lead to substantial economic costs, primarily due to frequent and prolonged hospitalizations. The aim of this project is to evaluate a smartphone- and web-based digital solution for detecting early warning signs of schizophrenia and schizoaffective disorder to reduce relapses and subsequent hospitalizations.

Methods: This randomized controlled trial compares the add-on use of a smartphone-based app for monitoring relapse warning signs in patients with schizophrenia and schizoaffective disorders (ICD-10 F20/F25) used within the routine psychiatric outpatient treatment against treatment as usual (TAU) without any further study-related intervention. Patients in the intervention group use the app for one year, fill in the weekly ten-item Early Warning Signs Questionnaire (EWSQ-10P) and obtain in-app feedback. Clinicians can access the symptom trajectory via a browser-accessible dashboard. If a threshold is exceeded in the inbuilt automatic algorithm, an alert is sent to both, the clinician and patient, enabling timely contact and, as part of a shared decision-making process, an optional adjustment of treatment decision. A total of 110 outpatients are recruited across eight study sites.

Discussion: Continuous monitoring of early warning signs is expected to lead to behavioral changes and to decrease the necessity and duration of psychiatric hospital stays, thereby lowering healthcare costs. Additionally, the intervention could reduce symptom severity, alleviate medication adherence, shared decision-making, patient activation or quality of life. Qualitative data is collected to better understand patient needs and preferences regarding app usage and relapses. Insights gained from this study can be integrated into routine psychiatric care, improving the long-term treatment of patients with schizophrenia or schizoaffective disorder.

Trial registration: German Clinical Trials Register (ID: DRKS00034991; registration date: 30.08.2024).

Bipolar disorder (BPD) affects approximately 2% of the global population. Its clinical course is highly variable and current treatments are not always effective for all patients. Genetic factors play a significant role in BPD and its treatment, although the genetic background appear to be highly heterogeneous. Polygenic risk scores (PRS) are a powerful tool for risk assessment, yet using all genomic data may introduce confounding factors. Focusing on specific genetic clusters PRS (gcPRS) may mitigate this issue. This study aims to assess a neural network model's efficacy in predicting response to treatment (RtT) in BPD individuals using PRS calculated from specific gcPRS and other variables. 1538 individuals from STEP-BD (age 41.39 ± 12.66, 59.17% female) were analyzed. gcPRS were calculated from a Genome-wide association study (GWAS) with clinical covariates and a molecular pathway analysis (MPA) based on drugs interaction networks. A neural network was trained using gcPRS and clinical variables to predict RtT. Ten biological networks were identified through MPA, with gcPRS derived from risk variants within corresponding gene groups. However, the model did not show significant accuracy in predicting RtT in BPD individuals. RtT in BPD is influenced by multiple factors. This study attempted a comprehensive approach integrating clinical and biological data to predict RtT. However, the model did not achieve significant accuracy, possibly due to limitations such as sample size, disorder complexity, and population heterogeneity. This data highlights the challenge of developing personalized treatments for BPD and the necessity for further research in this area.

Background: We aimed to investigate the extent of cognitive impairments in early-onset bipolar disorder (EBD) during manic episode in comparison to remission period.

Method: 30 healthy controls (HC) and 95 patients with EBD, with manic episode (n = 55) and remission period (n = 40) were included. Additionally, 31 (%56.4) of 55 patients with manic episode were re-evaluated during remission. A comprehensive cognitive battery was implemented to asses verbal and visual learning/memory, attention, inhibition, problem-solving, working memory, processing speed, and verbal fluency skills and global cognitive factor was calculated to estimate overall cognitive ability. Theory of mind (ToM) was evaluated using the Reading the Mind in the Eyes and Faux-Pas tests.

Findings: Individuals in patient groups and HC were matched for gender and education. Patients in remission had a significantly older mean age than the other groups. Antipsychotic dosage was also higher in cases with mania. Patients with manic episode had moderate impairments in processing speed (Cohen's d: 0.51-0.78), attention (d: 0.57), inhibition (d: 0.56-0.63) and global cognitive function (d: 0.54) compared to patients in remission period. Individuals in remission period had poorer performance in verbal memory (d: 1.03-1.32), working memory (d: 0.88-1.13), ToM (d: 0.60-0.87), processing speed (d: 1.21-1.27), problem solving (d: 0.56-0.67), attention (d: 0.58), inhibition (d: 0.89-1.00) and visual memory (d: 1.28-1.37) in comparison with HC.

Conclusion: Our findings indicated that impairments in social cognition, processing speed, inhibition, and attention were more prominent in the manic episode. Future studies should focus on pharmaco- and psychotherapeutic interventions aimed to treat neurocognitive impairments.