Psychological Medicine最新文献

Background: Depression is strongly associated with risk for suicidal behaviors. However, depression is a highly heterogeneous condition (i.e. there are more than 200 combinations of DSM-5-TR depressive symptoms to correspond to a depression diagnosis). Limited research to date has taken an empirical approach to see how people cluster together based on their classification of depressive symptoms and whether people in certain classes are more likely to report suicide outcomes than other classes. This analysis leverages the National Survey on Drug Use and Health and examines classes of depressive symptoms to explore differences in suicide-related outcomes by class among adults endorsing depressive symptoms (n = 41 969).

Methods: We used latent class analysis (LCA) to identify classes of individuals' DSM-5 depressive symptoms presentation and then explored differences in suicide-related outcomes (i.e. suicide plans, suicide attempts) by the resulting classes.

Results: A four-class model was determined to optimize the fit criteria. Class 3 (high depressive symptoms) had significantly greater rates of suicide-related outcomes, followed by class 1 (high depressed mood and moderate worthlessness), with classes 4 and 2 having significantly lower rates of suicide-related outcomes.

Conclusions: The use of LCA provided valuable findings on the importance of leveraging both a multi-faceted assessment of depressive symptoms to identify cases where a high number of depressive symptoms are endorsed, and review of the specific symptoms endorsed. Worthlessness, in particular, may be of particular value to focus on within the context of suicide prevention.

The social defeat hypothesis posits that low status and repeated humiliation increase the risk for psychotic disorders (PDs) and psychotic experiences (PEs). The purpose of this paper was to provide a systematic review of studies on risk of PDs and PEs among lesbian, gay, or bisexual (LGB) people and a quantitative synthesis of any difference in risk. PubMed, PsycINFO, Embase, and Web of Science were searched from database inception until January 30, 2024. Two independent reviewers assessed the eligibility and quality of studies, extracted effect sizes, and noted the results of mediation analyses. Using a random effects model we computed pooled odds ratios (ORs). Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed. The search identified seven studies of PDs and six of PEs. As for PDs, the unadjusted (2.13; 95% confidence interval 0.72-6.34) and covariate-adjusted pooled OR (2.24; 1.72-3.53) were not significantly increased for LGB individuals. After exclusion of a study of limited quality, both the unadjusted pooled OR (2.77; 1.21-6.32) and the covariate-adjusted pooled OR (2.67; 1.53-4.66) were significantly increased. The pooled ORs were increased for PEs: unadjusted, pooled OR = 1.97 (1.47-2.63), covariate-adjusted, pooled OR = 1.85 (1.50-2.28). Studies of PE that examined the mediating role of several variables reported that the contribution of drug abuse was small compared to that of psychosocial stressors. The results of a study in adolescents suggested a protective effect of parental support. These findings suggest an increased psychosis risk for LGB people and support the social defeat hypothesis.

We examined the efficacy of cognitive and behavioral interventions for improving symptoms of depression and anxiety in adults with neurological disorders. A pre-registered systematic search of Cochrane Central Register of Controlled Trials, MEDLINE, PsycINFO, Embase, and Neurobite was performed from inception to May 2024. Randomized controlled trials (RCTs) which examined the efficacy of cognitive and behavioral interventions in treating depression and/or anxiety among adults with neurological disorders were included. Estimates were pooled using a random-effects meta-analysis. Subgroup analyses and meta-regression were performed on categorical and continuous moderators, respectively. Main outcomes were pre- and post-intervention depression and anxiety symptom scores, as reported using standardized measures. Fifty-four RCTs involving 5372 participants with 11 neurological disorders (including multiple sclerosis, epilepsy, stroke) were included. The overall effect of interventions yielded significant improvements in both depression (57 arms, Hedges' g = 0.45, 95% confidence interval [CI] 0.35-0.54) and anxiety symptoms (29 arms, g = 0.38, 95% CI 0.29-0.48), compared to controls. Efficacy was greater in studies which employed a minimum baseline symptom severity inclusion criterion for both outcomes, and greater in trials using inactive controls for depression only. There was also evidence of differential efficacy of interventions across the neurological disorder types and the outcome measure used. Risk of bias, intervention delivery mode, intervention tailoring for neurological disorders, sample size, and study year did not moderate effects. Cognitive and behavioral interventions yield small-to-moderate improvements in symptoms of both depression and anxiety in adults with a range of neurological disorders.

Major depressive disorder (MDD) is the leading cause of disability globally, with moderate heritability and well-established socio-environmental risk factors. Genetic studies have been mostly restricted to European settings, with polygenic scores (PGS) demonstrating low portability across diverse global populations.

This study examines genetic architecture, polygenic prediction, and socio-environmental correlates of MDD in a family-based sample of 10 032 individuals from Nepal with array genotyping data. We used genome-based restricted maximum likelihood to estimate heritability, applied S-LDXR to estimate the cross-ancestry genetic correlation between Nepalese and European samples, and modeled PGS trained on a GWAS meta-analysis of European and East Asian ancestry samples.

We estimated the narrow-sense heritability of lifetime MDD in Nepal to be 0.26 (95% CI 0.18–0.34, p = 8.5 × 10−6). Our analysis was underpowered to estimate the cross-ancestry genetic correlation (rg = 0.26, 95% CI −0.29 to 0.81). MDD risk was associated with higher age (beta = 0.071, 95% CI 0.06–0.08), female sex (beta = 0.160, 95% CI 0.15–0.17), and childhood exposure to potentially traumatic events (beta = 0.050, 95% CI 0.03–0.07), while neither the depression PGS (beta = 0.004, 95% CI −0.004 to 0.01) or its interaction with childhood trauma (beta = 0.007, 95% CI −0.01 to 0.03) were strongly associated with MDD.

Estimates of lifetime MDD heritability in this Nepalese sample were similar to previous European ancestry samples, but PGS trained on European data did not predict MDD in this sample. This may be due to differences in ancestry-linked causal variants, differences in depression phenotyping between the training and target data, or setting-specific environmental factors that modulate genetic effects. Additional research among under-represented global populations will ensure equitable translation of genomic findings.

Although the Department of Veterans Affairs (VA) has made important suicide prevention advances, efforts primarily target high-risk patients with documented suicide risk, such as suicidal ideation, prior suicide attempts, and recent psychiatric hospitalization. Approximately 90% of VA patients that go on to die by suicide do not meet these high-risk criteria and therefore do not receive targeted suicide prevention services. In this study, we used national VA data to focus on patients that were not classified as high-risk, but died by suicide.

Our sample included all VA patients who died by suicide in 2017 or 2018. We determined whether patients were classified as high-risk using the VA's machine learning risk prediction algorithm. After excluding these patients, we used principal component analysis to identify moderate-risk and low-risk patients and investigated demographics, service-usage, diagnoses, and social determinants of health differences across high-, moderate-, and low-risk subgroups.

High-risk (n = 452) patients tended to be younger, White, unmarried, homeless, and have more mental health diagnoses compared to moderate- (n = 2149) and low-risk (n = 2209) patients. Moderate- and low-risk patients tended to be older, married, Black, and Native American or Pacific Islander, and have more physical health diagnoses compared to high-risk patients. Low-risk patients had more missing data than higher-risk patients.

Study expands epidemiological understanding about non-high-risk suicide decedents, historically understudied and underserved populations. Findings raise concerns about reliance on machine learning risk prediction models that may be biased by relative underrepresentation of racial/ethnic minorities within health system.