European Psychiatry最新文献

Background: Music listening has been used as a sleep intervention among different populations. This systematic review and meta-analysis aimed to explore whether music is an effective sleep aid in adults with mental health problems.

Methods: We searched for studies investigating music interventions for sleep in adults with mental health problems. The primary outcome was subjective sleep quality; secondary outcomes were objective sleep outcomes, quality of life, and other mental health symptoms. Risk of bias assessment (RoB1) and random-effect model were used for the systematic review and meta-analyses.

Results: The initial screening (n = 1492) resulted in 15 studies in the systematic review. Further qualified studies led to the meta-analysis of sleep quality (n = 7), depression (n = 5), and anxiety (n = 5). We found that the music listening intervention showed a potential effect on subjective sleep quality improvement compared to treatment-as-usual or no-intervention groups. When excluding an outlier study with an extreme effect, the meta-analysis showed a moderate effect on sleep quality (Hedges' g = -0.66, 95% CI [-1.19, -0.13], t = -3.21, p = 0.0236). The highest risk of bias was the blinding of participants and researchers due to the nature of the music intervention.

Conclusions: Our results suggest that music interventions could have the potential to improve sleep quality among individuals with mental health problems, even though more high-quality studies are needed to establish the effect fully.

Background: Stressors across the lifespan are associated with the onset of major depressive disorder (MDD) and increased severity of depressive symptoms. However, it is unclear how lifetime stressors are related to specific MDD subtypes. The present study aims to examine the relationships between MDD subtypes and stressors experienced across the lifespan while considering potential confounders.

Methods: Data analyzed were from the Zone d'Épidémiologie Psychiatrique du Sud-Ouest de Montréal (N = 1351). Lifetime stressors included childhood maltreatment, child-parent bonding, and stressful life events. Person-centered analyses were used to identify the clusters/profiles of the studied variables and multinomial logistic regression analyses were performed to examine the relationships between stressors and identified MDD subtypes. Intersectional analysis was applied to further examine how distal stressors interact with proximal stressors to impact the development of MDD subtypes.

Results: There was a significant association between proximal stressors and melancholic depression, whereas severe atypical depression and moderate depression were only associated with some domains of stressful life events. Additionally, those with severe atypical depression and melancholic depression were more likely to be exposed to distal stressors such as childhood maltreatment. The combinations of distal and proximal stressors predicted a greater risk of all MDD subtypes except for moderate atypical depression.

Conclusions: MDD was characterized into four subtypes based on depressive symptoms and severity. Different stressor profiles were linked with various MDD subtypes. More specific interventions and clinical management are called to provide precision treatment for MDD patients with unique stressor profiles and MDD subtypes.

Background: While several risk factors for schizophrenia have been identified, their individual impacts are rather small. The relative independent and cumulative impacts of multiple risk factors on disease risk and age of onset warrant further investigation.

Study design: We conducted a register-based case-control study including all individuals receiving a schizophrenia spectrum disorder in Denmark from 1973 to 2018 (N = 29,142), and a healthy control sample matched 5:1 on age, sex, and parental socioeconomic status (N = 136,387). Register data included parental history of psychiatric illness, birth weight, gestational age, season of birth, population density of birthplace, immigration, paternal age, and Apgar scores. Data were analysed using logistic regression and machine learning.

Results: Parental history of psychiatric illness (OR = 2.32 [95%CI 2.21-2.43]), high paternal age (OR = 1.30 [1.16-1.45]), and low birth weight (OR = 1.28 [1.16-1.41]) increased the odds of belonging to the patient group. In contrast, being a second-generation immigrant (OR = 0.65 [0.61-0.69]) and high population density of the birthplace (OR = 0.92 [0.89-0.96]) decreased the odds. The findings were supported by a decision tree analysis where parental history, paternal age, and birth weight contributed most to diagnostic classification (ACC_test = 0.69, AUC_test = 0.59, p < 0.001). Twenty percent of patients were child-onset cases. Here, female sex (OR = 1.82 [1.69-1.97]) and parental psychiatric illness (OR = 1.62 [1.49-1.77]) increased the odds of receiving the diagnosis <18 years.

Conclusion: Multiple early factors contribute independently to a higher psychosis risk, suggesting cumulative effects leading to symptom onset. Routine assessments of the most influential risk factors could be incorporated into clinical practise. Being female increased the risk of diagnosis during childhood, suggesting sex differences in the developmental trajectories of the disorder.

Background: Major depressive disorder (MDD) is a leading cause of disability and premature mortality. This study compared the overall survival (OS) between patients with MDD and non-MDD controls stratified by gender, age, and comorbidities.

Methods: This nationwide population-based cohort study utilized longitudinal patient data (01/01/2010 - 12/31/2020) from the Hungarian National Health Insurance Fund database, which contains healthcare service data for the Hungarian population. Patients with MDD were selected and matched 1:1 to those without MDD using exact matching. The rates of conversion from MDD to bipolar disorder (BD) or schizophrenia were also investigated.

Results: Overall, 471,773 patients were included in each of the matched MDD and non-MDD groups. Patients with MDD had significantly worse OS than non-MDD controls (hazard ratio [HR] = 1.50; 95% CI: 1.48-1.51; males HR = 1.69, 95% CI: 1.66-1.72; females HR = 1.40, 95% CI: 1.38-1.42). The estimated life expectancy of patients with MDD was 7.8 and 6.0 years less than that of controls aged 20 and 45 years, respectively. Adjusted analyses based on the presence of baseline comorbidities also showed that patients with MDD had worse survival than non-MDD controls (adjusted HR = 1.29, 95% CI: 1.28-1.31). After 11 years of follow-up, the cumulative conversions from MDD to BD and schizophrenia were 6.8 and 3.4%, respectively. Converted patients had significantly worse OS than non-converted patients.

Conclusions: Compared with the non-MDD controls, a higher mortality rate in patients with MDD, especially in those with comorbidities and/or who have converted to BD or schizophrenia, suggests that early detection and personalized treatment of MDD may reduce the mortality in patients diagnosed with MDD.

Background: Cognitive impairment (CI) is one of the most prevalent and burdensome consequences of COVID-19 infection, which can persist up to months or even years after remission of the infection. Current guidelines on post-COVID CI are based on available knowledge on treatments used for improving CI in other conditions. The current review aims to provide an updated overview of the existing evidence on the efficacy of treatments for post-COVID CI.

Methods: A systematic literature search was conducted for studies published up to December 2023 using three databases (PubMed-Scopus-ProQuest). Controlled and noncontrolled trials, cohort studies, case series, and reports testing interventions on subjects with CI following COVID-19 infection were included.

Results: After screening 7790 articles, 29 studies were included. Multidisciplinary approaches, particularly those combining cognitive remediation interventions, physical exercise, and dietary and sleep support, may improve CI and address the different needs of individuals with post-COVID-19 condition. Cognitive remediation interventions can provide a safe, cost-effective option and may be tailored to deficits in specific cognitive domains. Noninvasive brain stimulation techniques and hyperbaric oxygen therapy showed mixed and preliminary results. Evidence for other interventions, including pharmacological ones, remains sparse. Challenges in interpreting existing evidence include heterogeneity in study designs, assessment tools, and recruitment criteria; lack of long-term follow-up; and under-characterization of samples in relation to confounding factors.

Conclusions: Further research, grounded on shared definitions of the post-COVID condition and on the accurate assessment of COVID-related CI, in well-defined study samples and with longer follow-ups, is crucial to address this significant unmet need.

Background: Major depressive disorder (MDD) is one of the most prevalent medical conditions worldwide. Different factors were found to play a role in its etiology, including environmental ones (e.g., air pollution). The aim of this study was to evaluate the association between air pollution exposure and MDD severity.

Methods: Four hundred sixteen MDD subjects were recruited. Severity of MDD and functioning were evaluated through five rating scales: Montgomery-Asberg Depression Rating Scale (MADRS), Hamilton Depression Rating Scale (HAMD), Clinical Global Impression (CGI), Global Assessment of Functioning (GAF), and Sheehan Disability Scale (SDS). Daily mean estimates of particulate matter with diameter ≤10 (PM10) and 2.5 μm (PM2.5), nitrogen dioxide (NO₂), and apparent temperature (AT) were estimated based on subjects' residential addresses. Daily estimates of the 2 weeks preceding recruitment were averaged to obtain cumulative exposure. Multivariate linear and ordinal regression models were applied to assess the associations between air pollutants and MDD severity, overall and stratifying by hypersusceptibility and AT.

Results: Two-thirds of subjects were women and one-third had a family history of depression. Most women had depression with symptoms of anxiety, while men had predominantly melancholic depression. NO₂ exposure was associated with worsening of MDD severity (HAMD: β = 1.94, 95% confidence interval [CI], [0.41-3.47]; GAF: β = -1.93, 95% CI [-3.89 to 0.02]), especially when temperatures were low or among hypersusceptible subjects. PM exposure showed an association with MDD severity only in these subgroups.

Conclusions: Exposure to air pollution worsens MDD severity, with hypersusceptibility and lower temperatures being exacerbating factors.

Background: We need to better understand the risk factors and predictors of medication-related weight gain to improve metabolic health of individuals with schizophrenia. This study explores how trajectories of antipsychotic medication (AP) use impact body weight early in the course of schizophrenia.

Methods: We recruited 92 participants with first-episode psychosis (FEP, n = 92) during their first psychiatric hospitalization. We prospectively collected weight, body mass index (BMI), metabolic markers, and exact daily medication exposure during 6-week hospitalization. We quantified the trajectory of AP medication changes and AP polypharmacy using a novel approach based on meta-analytical ranking of medications and tested it as a predictor of weight gain together with traditional risk factors.

Results: Most people started treatment with risperidone (n = 57), followed by olanzapine (n = 29). Then, 48% of individuals remained on their first prescribed medication, while 33% of people remained on monotherapy. Almost half of the individuals (39/92) experienced escalation of medications, mostly switch to AP polypharmacy (90%). Only baseline BMI was a predictor of BMI change. Individuals in the top tercile of weight gain, compared to those in the bottom tercile, showed lower follow-up symptoms, a trend for longer prehospitalization antipsychotic treatment, and greater exposure to metabolically problematic medications.

Conclusions: Early in the course of illness, during inpatient treatment, baseline BMI is the strongest and earliest predictor of weight gain on APs and is a better predictor than type of medication, polypharmacy, or medication switches. Baseline BMI predicted weight change over a period of weeks, when other traditional predictors demonstrated a much smaller effect.

The present commentary raises some concerns about the risk of iatrogenic harm arising out of the diagnosis of functional neurologic and somatic disorders. These concerns are supported by evidence from the history of hysteria and findings from contemporary brain imaging. We discuss their implications for practice.

Background: High rates of psychiatric comorbidities have been found in people with problem gambling (PBG), including substance use, anxiety, and mood disorders. Psychotic disorders have received less attention, although this comorbidity is expected to have a significant impact on the course, consequences, and treatment of PBG. This review aimed to estimate the prevalence of psychotic disorders in PBG.

Methods: Medline (Ovid), EMBASE, PsycINFO (Ovid), CINAHL, CENTRAL, Web of Science, and ProQuest were searched on November 1, 2023, without language restrictions. Studies involving people with PBG and reporting the prevalence of schizophrenia spectrum and other psychotic disorders were included. Risk of bias was assessed using the Joanna Briggs Institute critical appraisal checklist for systematic reviews of prevalence data. The pooled prevalence of psychotic disorders was calculated using a random effects generalized linear mixed model and presented with forest plots.

Results: Of 1,271 records screened, 22 studies (n = 19,131) were included. The overall prevalence of psychotic disorders was 4.9% (95% CI, 3.6-6.5%, I² = 88%). A lower prevalence was found in surveyed/recruited populations, compared with treatment-seeking individuals and register-based studies. No differences were found for factors such as treatment setting (inpatient/outpatient), diagnoses of psychotic disorders (schizophrenia only/other psychotic disorders), and assessment time frame (current/lifetime). The majority of included studies had a moderate risk of bias.

Conclusions: These findings highlight the relevance of screening problem gamblers for schizophrenia spectrum and other psychotic disorders, as well as any other comorbid mental health conditions, given the significant impact such comorbidities can have on the recovery process.

Background: Cardiometabolic diseases (CMDs) including heart disease, stroke, and type 2 diabetes have been individually linked to depression. However, their combined impact on depression risk is unclear. We aimed to examine the association between cardiometabolic multimorbidity and depression and explore the role of genetic background in this association.

Methods: Within the Swedish Twin Registry, 40,080 depression-free individuals (mean age 60 years) were followed for 18 years. Cardiometabolic multimorbidity was defined as having ≥2 CMDs. CMDs and depression were ascertained based on the National Patient Register. Cox regression was used to estimate the CMD-depression association in a classical cohort study design and a matched co-twin design involving 176 twin pairs. By comparing the associations between monozygotic and dizygotic co-twins, the contribution of genetic background was estimated.

Results: At baseline, 4809 (12.0%) participants had one CMD and 969 (2.4%) had ≥2 CMDs. Over the follow-up period, 1361 participants developed depression. In the classical cohort design, the multi-adjusted hazard ratios (95% confidence interval [CIs]) of depression were 1.52 (1.31-1.76) for those with one CMD and 1.83 (1.29-2.58) for those with ≥2 CMDs. CMDs had a greater risk effect on depression if they developed in mid-life (<60 years) as opposed to late life (≥60 years). In matched co-twin analysis, the CMD-depression association was significant among dizygotic twins (HR = 1.63, 95% CI, 1.02-2.59) but not monozygotic twins (HR = 0.90, 95% CI, 0.32-2.51).

Conclusions: Cardiometabolic multimorbidity is associated with an elevated risk of depression. Genetic factors may contribute to the association between CMDs and depression.