Npj mental health research最新文献

Zinc regulates dopaminergic signaling, and reduced serum zinc levels have been reported in individuals with ADHD. However, genetic associations between zinc and ADHD remain unclear. We examined this link using large-scale GWAS and molecular analyses across three cohorts: iPSYCH (14,584 ADHD cases and 22,492 controls), FAMHES (n = 1798), and the Hamamatsu Birth Cohort (n = 726). Two-sample Mendelian randomization revealed bidirectional associations between low serum zinc levels and ADHD diagnosis. Genetic correlation and polygenic risk score analyses supported this association. In the birth cohort, lower cord blood zinc were associated with higher ADHD symptom scores at ages 8-9. Zinc levels negatively correlated with IL-6 and maternal depressive symptoms. Directed acyclic graph analysis indicated that maternal stress increased IL-6, which reduced fetal zinc levels, linking to ADHD symptoms. These findings suggest low prenatal zinc may contribute to ADHD pathophysiology in genetically vulnerable children, potentially mediated by maternal stress and inflammation.

Investigating long-term posttraumatic stress disorder (PTSD) course and its predictors may guide prevention and early intervention strategies following trauma exposure, potentially reducing the long-lasting impact of trauma. N = 155 emergency-admitted adults with (suspected) serious injury were repeatedly assessed until one-year post-trauma and completed a 12-15 year follow-up including a clinical PTSD interview. Adverse one-year PTSD trajectories; more exposure to additional potentially traumatic events and recent life stressors; and early post-trauma predictors (younger age, greater perceived impact of prior potentially traumatic events, higher heart rate) were significantly associated with higher PTSD symptom severity 12-15 years post-trauma. This study showed high consistency between one-year PTSD and its early post-trauma predictors with long-term PTSD outcomes. Early post-trauma predictors had predictive value up to 12-15 years. This suggests that early risk identification of one-year PTSD and subsequent effective early interventions also hold long-term beneficial effects for PTSD outcome.

The integration of artificial intelligence (AI) and pervasive computing offers new opportunities to sense mental health symptoms and deliver just-in-time adaptive interventions via mobile devices. This pilot study tested personalized versus generalized machine learning models for detecting individual and family mental health symptoms as a foundational step toward JITAI development, using data collected through the Colliga app on smart devices. Over a 60-day period, data from 35 families resulted in approximately 14 million data points across 52 data streams. Findings showed that personalized models consistently outperformed generalized models. Model performance varied significantly based on individual factors and symptom profiles, underscoring the need for tailored approaches. These preliminary findings suggest that successful implementation of passive sensing technologies for mental health will require accounting for users' unique characteristics. Further research with larger samples is needed to refine the models, address data heterogeneity, and develop scalable systems for personalized mental health interventions.

Depression is highly clustered among people with low socioeconomic status (SES). Improved environments are known to be potentially beneficial, but the extent to which environments alleviate socioeconomic inequalities in depression remains unclear. Based on 334,536 UK Biobank participants, we quantified mediating roles of green space and air pollution in association between SES and depression, and examined interactive and joint relationships between SES and environments on depression. Co-improvements in green space and air quality significantly mediated 2.7% of this association. Interaction analysis indicated stronger environmental benefits for low-SES populations. Joint analysis revealed that low-SES adults in favorable environments had a 14.6% lower depression risk than medium-SES individuals in unfavorable conditions, with more pronounced effects among females (16.4%) and older adults (9.8%). Our findings emphasize mitigating role of upstream environmental factors involving green space and air quality in tackling socioeconomic inequalities in depression, particularly for vulnerable populations like the elderly and females.

This study explores the link between moral injury and suicidal thoughts and behaviors among US military veterans, healthcare workers, and first responders (N = 1232). Specifically, it investigates the risk associated with moral injury that is not attributable to common mental health issues. Among the participants, 12.1% reported experiencing suicidal ideation in the past two weeks, and 7.4% had attempted suicide in their lifetime. Individuals who screened positive for probable moral injury (6.0% of the sample) had significantly higher odds of current suicidal ideation (AOR = 3.38, 95% CI = 1.65, 6.96) and lifetime attempt (AOR = 6.20, 95% CI = 2.87, 13.40), even after accounting for demographic, occupational, and mental health factors. The findings highlight the need to address moral injury alongside other mental health issues in comprehensive suicide prevention programs for high-stress, service-oriented professions.

Internet gaming disorder (IGD) is recognized as a mental health issue. Traditional interventions have limitations, but mindfulness meditation (MM) shows promise due to its flexibility and social acceptance. Study 1, fMRI data from 61 IGD patients and 60 healthy controls (HCs) were compared to assess functional connectivity (FC). Study 2- a randomized clinical trial, 80 IGD patients underwent either an MM intervention (twice-weekly for 8 sessions) or progressive muscle relaxation (PMR) as a control (pre-registered-Chinese clinical trial registry, ChiCTR2300075869, September 18, 2023). Study 1 revealed abnormal FC within the executive control network (ECN) and between the ECN and reward network in IGD patients. Study 2 showed that MM enhanced FC within the ECN and frontostriatal pathway. MM refining the coupling between brain regions involved in executive control and reward processing. This enhancement improves top-down control over game craving. These findings suggest that MM can effectively treat IGD.

Linguistic features within individuals' text data may indicate their mental health. This trial examined the linguistic markers of depressive and anxiety symptoms in adults. Using a randomised cross over trial design, 218 adults provided eight different types of text data of varying frequencies and emotional valance. Linguistic features were extracted using LIWC-22 and correlated with self-reported symptoms. Machine learning was used to determine associations. No linguistic features were consistently associated with depressive or anxiety symptoms within or across all tasks. Features associated with depressive symptoms were different for each task and there was only some degree of reliability of these features within tasks. In all machine learning models, predicted values were weakly associated with actual values. Some text tasks had lower levels of engagement and negative impacts on mood. Overall, the linguistic markers of depression and anxiety shifted in response to contextual factors and the nature of the text analysed. This trial was prospectively registered with the Australian New Zealand Clinical Trials Registry (date registered: 15 September 2021, ACTRN12621001248853).

Although patients with criminal legal system involvement have among the highest rates of suicide, the model that identifies patients at high risk of suicide at the United States Veterans Health Administration (VHA) does not include predictors specific to criminal legal system involvement. We explored whether the model's predictive ability would be improved (1) by retraining the model for legal-involved veterans and (2) by adding additional predictors associated with legal-involvement. For a combined outcome of suicide attempt or suicide death, the retrained models showed a positive predictive value (PPV) of 0.124 and false negative rate (FNR) of 0.527. Adding additional predictors associated with being legal-involved did not improve predictive accuracy. Retraining the VHA suicide risk prediction model for legal-involved patients improves the model's predictive ability for this group of high-risk patients, more so than adding predictors associated with being legal-involved. A similar approach for other high-risk patients is worth exploring.

Self-harm-encompassing suicidality and nonsuicidal self-injury (NSSI)-presents a critical public health concern, particularly as it is a major risk factor of death by suicide. Understanding the psychosocial dynamics of self-harm is imperative. Accordingly, in a large-scale, naturalistic study, we leveraged modern language analysis methods to provide a comprehensive perspective on suicidality and NSSI, specifically in the context of borderline personality disorder (BPD), where self-harm is particularly prevalent. We utilised natural language processing techniques to analyse Reddit data (i.e., BPD forum posts) of 992 users with self-identified BPD (combined N posts = 66,786). The present findings generated further insight into the psychosocial dynamics of suicidality and NSSI, while also uncovering meaningful interactions between the online BPD community and these behaviours. By integrating advanced computational methods with psychological theory, our findings provide a nuanced understanding of self-harm, with implications for clinical practice, clinical and personality theory, and computational social science.

This study assessed the preliminary effectiveness of a game-based digital therapeutics (DTx) intervention for depression and anxiety using a randomized controlled trial (RCT) design to examine the role of reinforcement learning (RL) personalization. This RCT included 223 individuals with depressive symptoms, aged 18-50, divided into three groups: an RL Algorithm group (personalized treatment), an active control group (fixed treatment), and a no-intervention control group. The intervention combined cognitive bias modification and cognitive behavioral therapy, with outcomes measured by the Patient Health Questionnaire-9 and the Generalized Anxiety Disorder-7. Results showed significantly higher treatment response and recovery rates in the RL Algorithm group compared to the no-intervention group. The game-based DTx intervention, enhanced by RL personalization, effectively reduced depression and anxiety symptoms, supporting its potential for mental health treatment. The study was registered at clinicaltrials.gov (NCT06301555).