European Psychiatry

Background:Previous self-harm elevates the risk of repeat self-harm and suicide, but the prognostic value of events and clinician observations around the index event is unclear. We evaluated established and exploratory risk factors for suicide and repeat self-harm among patients presenting to emergency psychiatric units after a suicide attempt or nonsuicidal self-injury (NSSI). Methods: Multicentre cohort study in Sweden (n = 804). Outcomes were suicide and repeat self-harm at 1-year and 5-year follow-up, ascertained through linked national registers. Established risk factors included psychiatric diagnoses, prior suicidal behaviour, and sociodemographic characteristics; exploratory factors comprised past-week self-reported symptom changes and clinician observations. LASSO-regularised Cox regression models were fitted for established (n=21) and exploratory (n=11) risk factors. Results: During five-year follow-up, 285 (35%) individuals had a new episode of self-harm and 41 (5%) died by suicide. No risk factors reached statistical significance for suicide, although male sex was retained after regularisation (1-year hazard ratio [HR] = 3.57 [95% CI 0-8.33]; 5-year HR = 2.5 [0.03-4.55]). Three established risk factors were significantly associated with repeat self-harm: psychiatric inpatient care in the three months before the index event (1-year HR = 1.85 [1.3-2.6]; 5-year HR = 1.72 [1.23-2.65]), previous suicide attempt (1-year HR = 2.01 [0.79-2.4]; 5-year HR = 2.19 [1.27-2.6]), and borderline personality disorder (1-year HR = 1.82 [1.13-3]; 5-year HR = 1.67 [0.14-2.75]). Among exploratory risk factors, clinician-observed hopelessness (1-year HR = 1.72 [1.1-2.3]; 5-year HR = 1.51 [1.03-1.91]) and personality disorder features (1-year HR = 1.48 [0.96-2.05]; 5-year HR = 1.47 [1.04-1.95]) were associated with repeat self-harm. Conclusions: Risk factor profiles for repeat self-harm were consistent at 1 and 5 years. Beyond established risk factors, clinician-observed hopelessness and personality disorder features emerged as markers of risk, suggesting that qualitative clinician assessments may yield prognostic information not available from medical records alone.

Background. UK clinical guidance recommends that structured risk prediction tools and risk stratification should not be used in self-harm, to predict suicide or determine who is offered treatment. Underpinning this position is the premise that routinely collected health data contain no useful predictive signal, which has received little direct scrutiny. Objective. To test whether routinely collected electronic health record data can distinguish groups at higher and lower risk of severe outcomes following paracetamol overdose. Methods. We analysed 4,095 adults presenting to NHS Lothian emergency departments with paracetamol overdose (2017-2023). Elastic-net logistic regression was fitted to 37 routinely collected electronic health record features to predict a composite of death or mental health inpatient admission at 0-7, 8-30 and 31-365 days following attendance, evaluated on a held-out 20% test set with bootstrapping. Findings. Events occurred in 5.5% of patients at 0-7 days, 2.0% at 8-30 days and 7.9% at 31-365 days, dominated by mental health admission. Bootstrap AUROC 95% confidence intervals lay above 0.5 in every window (0.65-0.82, 0.63-0.90, 0.71-0.85): models ranked patients better than chance. Calibration slopes (1.04, 1.14, 1.07) were close to one. Ranking drew primarily on mental health-related features. Conclusions. Routinely collected health data carried predictive signal for severe outcomes after paracetamol overdose, although discrimination fell short of what is needed for individual-level clinical use. Clinical implications. These models are not proposed for clinical deployment; however, treating risk prediction as a settled question will redirect research efforts, potentially excluding this patient population from machine learning advances driving improvements in care in other medical specialties.

Purpose: Suicide mortality is underestimated due to misclassification under undetermined and accidental deaths. This study examined national trends in suicide and related external causes of death in Portugal from 2002 to 2023, by sex and age group, assessing potential shifts suggesting masked suicide and quantifying the relationship between undetermined, suicide, and accident death rates through ratio indices. Methods: Using official mortality data from Portugal's Statistics Institute (INE) for 2002-2023, we calculated age-standardised (SDR) and age-specific death rates (ASDR) for suicide (X60-X84), undetermined intent deaths (Y10-Y34), and unintentional deaths (V01-X59), disaggregated by sex and four age groups (15-24, 25-44, 45-64, 65+). We estimated undetermined-to-suicide (UnD:Suic) and undetermined-to-accidents (UnD:Accs) rate ratios for SDRs and ASDRs. Trends were analysed using joinpoint regression (APC/AAPC) and structural breakpoint analysis (Chow test, BIC). Results: Suicide SDRs declined across the period for males (AAPC: -2.25%) and females (AAPC: -1.32%), with the sharpest reductions among males aged 25-44 (AAPC: -2.56%) and females aged 65+ (AAPC: -2.44%). Deaths of undetermined intent rose steeply from 2002 to 2005-2006 and declined thereafter. Unintentional deaths declined in most age groups, except females aged 65+ (AAPC: +1.41%). Both ratio series peaked around 2005-2009, declined progressively through the 2010s, and reached their lowest values in 2021-2022. Age-specific analyses revealed a significant and sustained increase in both ratios among females aged 45-64. Structural breakpoints clustered around 2004, 2013-2015, and 2019-2020. Conclusion: Suicide mortality declined in Portugal from 2002 to 2023, but divergent trends in undetermined and accidental deaths across sex and age subgroups highlight ongoing misclassification. Age- and sex-specific ratio analyses identify the population subgroups where misclassification is most concentrated, providing a foundation for future imputation-based estimates of probable suicide burden.

Background: Suicide prediction models in psychiatry often rely on purely data-driven feature selection, which can produce unstable and clinically opaque predictor sets in modest-sized samples. We developed Evidence-Based AI LASSO (EBAL), an evidence-guided regularization framework that incorporates curated clinical evidence into feature-specific penalty factors for interpretable prediction. Methods: Baseline data from 136 youth with confirmed bipolar spectrum disorder in the Greater Houston Area Bipolar Registry were analyzed using 20 candidate clinical predictors. Forty higher-level evidence documents on suicidality and related predictor domains were curated through a structured evidence synthesis workflow and indexed as an auditable evidence corpus. An open-weight large language model assigned feature-specific penalty factors using a prespecified scoring rubric, and these penalties were used to fit a weighted LASSO model. EBAL was compared with a standard evidence-agnostic LASSO using nested leave-one-out cross-validation. Results: For suicidal ideation, EBAL achieved an AUROC of 0.768, balanced accuracy of 0.757, sensitivity of 0.758, and specificity of 0.757. The standard LASSO achieved an AUROC of 0.760 and balanced accuracy of 0.715. EBAL improved balanced accuracy (+0.042, p=0.010) and Matthews correlation coefficient (+0.079, p=0.010), while retaining fewer stable predictors than standard LASSO (11/20 vs 18/20). The strongest positive predictors were current depressed mood, duration of mood disorder illness, and comorbid generalized anxiety disorder. For suicidal behavior, both models performed near chance and retained all candidate predictors. Limitations: The study was cross-sectional, single-site, and modest in sample size, with no external validation cohort. Conclusions: EBAL produced a sparser and more clinically coherent model for suicidal ideation in pediatric bipolar disorder, but did not improve prediction of suicidal behavior. These findings support evidence-guided regularization as a transparent strategy for aligning psychiatric prediction models with prior clinical knowledge while preserving interpretability.

Importance Childhood sexual abuse (CSA) is linked to adverse psychiatric outcomes in adulthood, but evidence on its association with cardiovascular disease and mortality from large, diagnostically ascertained cohorts remains limited. Objective To assess the 10-year risk of all-cause mortality, suicide or self-harm, drug overdose or poisoning, and cardiovascular disease among patients with a diagnosed history of CSA compared with a matched unexposed cohort. Methods In this retrospective cohort study, we used deidentified electronic health record data from 68 health care organizations in the TriNetX US Collaborative Network. Patients diagnosed with confirmed or suspected childhood sexual abuse (CSA) before age 18 between January 1, 2003, and December 31, 2015, who had a subsequent adult encounter, were propensity score matched 1:1 with unexposed patients on age, sex, race and ethnicity, and baseline psychiatric and medical comorbidities (n = 9,083 per cohort). Outcomes--all-cause mortality, suicide or self-harm, drug overdose or poisoning, and cardiovascular disease--were assessed over 10 years from the index adult encounter using risk and time-to-event analyses to estimate risks, risk ratios, and hazard ratios. Results Among 18,166 matched patients (mean [SD] age, 19.0 [2.0] years; 14,813 [81.6%] female), CSA was associated with significantly elevated risk of suicide or self-harm (5.1% vs 2.8%; risk ratio [RR], 1.84; 95% CI, 1.57-2.16), drug overdose or poisoning (5.5% vs 3.7%; RR, 1.47; 95% CI, 1.28-1.69), and cardiovascular disease (12.3% vs 9.3%; RR, 1.31; 95% CI, 1.20-1.44), with concordant hazard ratios (all P < .001). All-cause mortality was numerically higher but not statistically significant (0.5% vs 0.4%; RR, 1.16; 95% CI, 0.75-1.79; P = .51). Conclusions and Relevance A diagnostically confirmed history of CSA was associated with substantially elevated 10-year risk of self-harm, overdose, and cardiovascular disease, independent of baseline demographic and psychiatric comorbidity. These findings support integrated psychiatric and cardiovascular screening for adult survivors of CSA and trauma-informed care extending beyond mental health services alone.

Background Individuals with severe mental illness (SMI), including schizophrenia spectrum disorders (SSD) and bipolar disorder (BD), have been shown to have an elevated risk of violent perpetration. However, no population-wide study has systematically examined how this risk varies across psychiatric comorbidity patterns and specific violent crime types. Methods Using the first nationwide Norwegian registry linkage comprising mental health and crime data, we included 3,612,215 individuals aged 15-79 years living in Norway on Jan 1, 2008, and followed them until Dec 31, 2022. We estimated absolute and relative risks (RRs) of violent offending overall and by specific violent crimes among individuals with SSD and BD. To capture clinically relevant comorbidity patterns, we included substance use disorders (SUD), common personality disorders (PD), and hyperkinetic disorders (ADHD). RR models were adjusted first for sex and age, and subsequently for co-occurring mental disorders. Findings At the population level, individuals with SMI accounted for a minority of violent offenders (SSD: 8.7%; BD: 4.6%), whereas SUD was present among a substantially larger proportion (36.8%). Absolute risk of violent offending increased markedly with psychiatric comorbidity, from e.g., 5.0% among individuals with SSD alone to 43.9% for SSD combined with SUD and PD. Compared with the remaining general population, the RR of violent offending for SSD decreased from 6.58 (95% CI 6.4-6.8, adjusted for sex and age), to 2.0 (2.0-2.1) after further adjustment for other mental disorders. Similar attenuation patterns were observed across specific violent crime types, although varying in magnitude. In contrast to SMI, elevated risks associated with SUD remained substantial after full adjustment across most crime categories. Interpretation The association between SMI and violent offending is strongly influenced by psychiatric comorbidity, particularly SUD, and varies across crime types. Our findings underscore the importance of identifying and treating co-occurring mental disorders and substance use, both in the clinical management of SMI and in population-level violence prevention strategies.

Background. The Perinatal Grief Scale (PGS) is a widely used instrument for assessing grief following pregnancy loss, yet no study has validated it specifically in men despite documented use in several studies. This gap is critical given fathers' persistent underrepresentation in perinatal bereavement research and the absence of empirically supported screening thresholds for this population. Methods. This cross-sectional validation study used data from the OPALE project (Observatory on PerinatAL hEalth) conducted by the CiaoLapo Foundation in Italy. Among 276 fathers who experienced stillbirth or miscarriage, we examined criterion validity by testing the association between PGS scores and trauma-related symptomatology assessed via three validated instruments: the Revised Impact of Event Scale (RIES, n=103), National Stressful Events Survey Short Scale (NSESSS, n=95), and SCL-90 (n=173). We systematically tested multiple threshold combinations to identify optimal discriminative performance. Results. The PGS demonstrated excellent criterion validity. The optimal threshold (PGS >=92) showed sensitivity 81.0%, specificity 81.8%, and Youden's J index 0.628. Fathers scoring >=92 had 19.12 times the odds of high trauma symptoms (95% CI: 9.35 to 39.14, p<0.001). ROC analysis yielded AUC=0.829 (95% CI: 0.778 to 0.880). Associations remained robust across all three trauma instruments in stratified analyses and after adjusting for time since loss, father's age, living children, and loss type. Conclusion. This is the first men-specific validation of the PGS, demonstrating strong criterion validity and establishing a clinically meaningful screening threshold (>=92) for identifying fathers at elevated risk following perinatal loss.

Background Escalating mental health service demands have created a need to better identify young people most likely to require continued support from mental health services at the transition between childhood and adulthood. Anxiety is the most common adolescent mental health condition, yet its clinical significance and prognosis are not well understood. We aimed to examine the risk of young adult-onset psychiatric disorders in individuals with an adolescent anxiety disorder, and identify stratifiers of risk of subsequent psychiatric disorders in this group. Methods Individuals from the Norwegian Mother, Father, and Child Cohort Study (MoBa) with linked health records and aged 18 or over as of the 31st December 2023 were included. Those diagnosed with any ICD-10 anxiety disorder when aged 10-17 years were defined as having an adolescent anxiety disorder (n=2107, controls n=47,582). Polygenic scores (PGS) for psychiatric and neurodevelopmental conditions were calculated using LDpred2. Anxiety, comorbidities, and parental psychiatric history were defined through linked ICD-10 diagnoses. Sex was defined through linked records. Individuals were defined as having a young adult-onset psychiatric disorder if they first received any new psychiatric diagnosis aged 18-24. Results Adolescent anxiety diagnosis was associated with increased risk of all adult-onset psychiatric disorders (HR= 2.33-8.65). Post-traumatic stress disorder PGS, parental history of severe mental illness, and female sex were associated with increased risk of transition to a young adult-onset psychiatric disorder in people with an adolescent anxiety disorder. Conclusions Adolescent anxiety greatly increases the risk of a psychiatric disorder during the transition to adult life. Clinicians should consider female sex and parental psychiatric history when prioritising young people with anxiety for adult mental health service support. Future research needs to further consider whether polygenic scores would aid risk stratification in clinical practice.

Importance: Patients who achieve remission from major depressive disorder (MDD) often face a preference-sensitive decision between continued antidepressant maintenance and discontinuation with active monitoring. Quantifying the tradeoff between depression burden and long-term medication exposure may support more individualized shared decision-making. Objective: To quantify tradeoffs between continuous antidepressant maintenance and active monitoring after MDD remission, and to identify preference thresholds favoring each strategy across relapse-risk strata. Design: Individual-level decision-analytic health-state transition model calibrated to randomized maintenance-discontinuation trials and a longitudinal first depressive episode cohort, with a 5-year time horizon. Setting: Outpatient clinical decision after completion of an 8-month continuation phase following remission from MDD. Participants: Adults in remission from MDD, represented across 4 clinically anchored relapse-risk strata ranging from very low risk after a first mild episode to high risk after highly recurrent depression. Exposures: Continuous antidepressant maintenance vs discontinuation with active monitoring and antidepressant restart after detected relapse. Main Outcomes and Measures: Severity-weighted depression-months, antidepressant medication-years, medication-years per depression-month averted, and net benefit across preference thresholds defined as the maximum additional medication-years a patient would be willing to accept to avert 1 depression-month. Results: Continuous maintenance reduced depression burden but required substantially more medication exposure, with efficiency strongly dependent on relapse risk. Medication-years per depression-month averted ranged from 11.8 (95% uncertainty interval [UI], 7.8-19.6) in the very low-risk group to 1.5 (95% UI, 0.8-3.0) in the high-risk group. At a preference threshold of 3 medication-years per depression-month averted, maintenance was preferred for moderate- and high-risk patients; at a threshold of 2, only for high-risk patients; and at a threshold of 1, for no risk group. Conclusions and Relevance: In this decision-analytic model, the value of continuous antidepressant maintenance depended strongly on baseline relapse risk and patient preferences regarding long-term medication exposure. These findings provide a quantitative framework for shared decision-making about antidepressant maintenance after remission from MDD.

Background: Dissociative experiences encompass a variety of discontinuities in awareness and perception that are elevated in the dissociative disorders and associated with extensive comorbid symptomatology. Accumulating evidence points to developmental trauma and trait responsiveness to verbal suggestions (REVS) as factors that confer risk for severe dissociative symptoms, but they have typically been studied in isolation. This study integrated these measures using prediction modelling to better understand their predictive value for the risk of dissociative psychopathology. Method: 1,104 non-clinical participants completed measures of trauma, dissociation and trait REVS. The predictive model was developed using elastic net logistic regression, internally validated with 10-fold cross-validation, and assessed using receiver operating characteristic (ROC) curve and area under the ROC (AUROC). Variables entered into the model were components of REVS, trauma, age, and their interactions. Results: A dissociative psychopathology at-risk group (7%) was characterised by younger age, greater trauma and elevated REVS, particularly involuntariness during cognitive-perceptual suggestions. The prediction model retained nine of ten predictors, with an AUROC of .77 [95% CI: .73, .82], reflecting good discrimination with moderate sensitivity (78%) but modest specificity (67%). Conclusions: These findings reinforce trauma and trait REVS as risk factors for dissociative psychopathology and demonstrate that they can be integrated in a model that can identify at-risk individuals. Further validation and extension of the model is necessary to improve the identification of individuals at risk for severe dissociative symptomatology and the diagnosis of dissociative disorders with implications for outcome trajectories.

Background Multimorbidity is increasingly prevalent, and associated with worse clinical and psychosocial burdens. Interoception, the brain's ability to sense and interpret internal bodily signals, may contribute to multimorbidity, through its link with health behaviors, stress regulation, and mental health. This study examines whether self-reported interoceptive accuracy and attention is associated with multimorbidity, by identifying multimorbid subgroups and their interoceptive profiles. Methods Morbidity classes were identified through latent class analyses in two Dutch survey datasets, focusing on depression and alexithymia (DA-dataset; N = 671) and lifestyle factors (L-dataset; N = 1022). Linear regression analyses were used to assess interoceptive accuracy and attention (by the Interoceptive Accuracy Scale and Interoceptive Attention Scale respectively) among different subgroups. Results Multimorbid subgroups were characterized by older age, low socioeconomic position, and elevated physical, psychological, and behavioral problems. Multimorbid classes exhibited lower interoceptive accuracy (DA-dataset: B = -1.14, 95% CI = [-2.89, 0.62]; L-dataset: B = -2.36, 95% CI = [-3.83, -0.89]) and higher attention (DA-dataset: B = 3.62, 95% CI = [0.97, 6.27]; L-dataset: B = 1.07, 95% CI = [-1.42, 3.56]) compared to healthier classes. Conclusion Multimorbid populations demonstrated lower interoceptive accuracy and higher interoceptive attention. This highlights the psychosocial complexity of multimorbid populations which may impact their self-management and health behavior. These findings underscore the need to expand treatments to include psychosocial domains for multimorbid patients.

Importance: Psychiatric emergency department (ED) presentations are difficult to predict using general medical risk stratification tools. Health information exchange (HIE) data may improve prediction by capturing fragmented care across settings. Objective: To develop and temporally validate a machine learning model using HIE and geospatial data to predict 30-day psychiatric ED presentation among outpatients receiving psychiatric care and to compare its performance with standard clinical risk scores. Design, Setting, and Participants: This retrospective cohort study included patients seen at Frontier Psychiatry with records in the Big Sky Care Connect statewide HIE. Structured clinical data were linked to zip code-level sociodemographic measures. The analytic unit was the patient snapshot, defined as all structured data available up to a given point. Models were evaluated in temporally separated train and test sets. Exposures: Predictors derived from HIE structured data, including prior utilization, diagnoses, medications, laboratory data, and zip code-linked geospatial deprivation and vulnerability measures. Main Outcomes and Measures: The primary outcome was psychiatric ED presentation within 30 days, identified from structured encounter-type fields and primary diagnosis codes for psychiatric or substance use disorders. Model discrimination was compared with a parsimonious clinical baseline model and LACE and Elixhauser scores. Results: In the test set, 343 of 16,469 snapshots (2.1%) were followed by a qualifying psychiatric ED presentation within 30 days, corresponding to 102 ED visits among 68 patients. The machine learning model showed discrimination in temporally held-out testing and outperformed the clinical baseline model as well as LACE and Elixhauser scores. At a prespecified decision threshold, the model reduced the number needed to evaluate from more than 40 with universal screening to 3.4 to identify 1 true-positive case, while identifying over two fifths of 30-day psychiatric ED presentations. Conclusions and Relevance: In this retrospective cohort study, a locally developed machine learning model using statewide HIE data showed improved prediction of 30-day psychiatric ED presentation compared with selected general-purpose risk scores. The results support the feasibility of HIE-enabled local psychiatric risk modeling and suggest other practices could develop similarly tailored models. Prospective studies are needed to assess clinical utility and effects on outcomes.

Background: Major depressive disorder (MDD) is clinically heterogeneous, hindering identification of reproducible biomarkers. Using a semi-supervised machine learning approach, HYDRA, we previously identified two neuroanatomical dimensions from structural MRI in medication-free MDD from COORDINATE-MDD consortium. These dimensions (D1, D2) showed differential responses to selective serotonin reuptake inhibitor (SSRI) antidepressants and placebo. External replication in UK Biobank linked D2, characterized by widespread subtle neuroanatomical reductions, to an immuno-metabolic profile. Here, we examined whether these dimensions are detectable early in the course of illness. Methods: We applied the pre-trained model to structural MRI data from the multisite PRONIA cohort, comprising individuals with recent-onset depression (ROD; n = 377; mean age 25.8 years, SD 6.0; 51.3% female) and healthy controls (n = 267; mean age 25.5 years, SD 6.4; 61.0% female). Participants were assigned to clusters (C1, C2) corresponding to the previously identified dimensions (D1, D2). Clusters were compared on clinical symptom profiles, peripheral inflammatory markers, and in a subset (n = 107), proteomic ageing indices. Results: Two neuroanatomical clusters were identified in PRONIA. C1 (n = 265) showed higher negative symptom severity and elevated interleukin-2 levels. C2 (n = 140) was associated with higher residual proteomic age. Overall depressive symptom severity did not differ significantly between clusters. Conclusions: Neuroanatomical dimensions of MDD are reproducible and detectable at illness onset. Associations with negative symptom severity, inflammatory signalling, and proteomic ageing suggest these dimensions capture biologically meaningful heterogeneity early in depression. These findings support a biologically informed framework for stratified treatment approaches in MDD.

Depression is accompanied by considerable comorbidity and excess mortality. We examined multimorbidity data using the validated pharmacy-based Rx-Risk Comorbidity Index and examined healthcare costs associated with chronic illness burden in the Australian Genetics of Depression Study (AGDS). Australian Pharmaceutical Benefits Scheme (PBS) record linkage for 15,890 AGDS participants was available from 01/07/2013-31/12/2017. Forty-six health morbidities were inferred by mapping the prescription data using Anatomical Therapeutic Chemical Classification System codes and PBS Item Codes. Morbidity prevalence rates were then compared with an unselected 10% Australian representative population sample (10PCT) with PBS claims data available from 01/07/2010-31/12/2014. The average number of inferred comorbidities was higher among AGDS participants (4.6 {+/-} 2.9) than 10PCT individuals (3.0 {+/-} 3.0). Excluding depression, 89.1% of AGDS participants had one or more inferred comorbidity, most commonly pain (51.0%), inflammation/pain (40.3%), and anxiety (32.3%). In the AGDS, the number of comorbidities was higher among women compared to men and positively correlated with participant age, BMI, number of depressive episodes experienced, and annual health care costs. Compared to participants with no inferred comorbidities, the median annual health care costs were ~65% higher among those with 2-3 comorbidities. This study highlights the patterns of health morbidities experienced by individuals living with depression and shows that this chronic disease burden is significantly associated with increased health costs to the individual and the health system.

Objective: Polygenic risk scores (PRS) for posttraumatic stress disorder (PTSD) often account for a low amount of variance. Ancestry-related differences in PRS scale and variance limit cross-group comparisons. This methodological challenge further complicates gene-by-environment (GxE) analyses, given that socioenvironmental exposures are inequitably distributed across ethnoracial groups. We constructed an ancestry-calibrated polygenic risk score (AC-PRS) for PTSD in the largest longitudinal study of trauma survivors to date and investigated GxE interactions. Method: Recent trauma survivors (N=1,801) provided a blood specimen for genotyping. Six PTSD trajectories were previously identified from PTSD Checklist for DSM-5 (PCL-5) scores at 2-weeks, 8-weeks, 3-months, and 6-months post-trauma. Greenspace (normalized difference vegetation index [NDVI) and socioeconomic disadvantage (area deprivation index [ADI]) were derived from residential addresses. Logistic regressions examined interactions between newly developed AC-PRS and neighborhood factors on trajectories after adjusting for sociodemographic and trauma-related covariates. Secondary linear models considered GxE interactions on 6-month PCL-5 scores. Results: AC-PRS performed well across ethnoracial groups, explaining significant variability in PTSD trajectories (R2=.053). ADI moderated the association between AC-PRS and the likelihood of assignment in a high nonremitting trajectory of PTSD symptoms and severity of symptoms at 6-months (ps < .05). There were no NDVI x AC-PRS interactions in any models. Conclusions: AC-PRS captures genetic risk for PTSD in admixed trauma survivors, demonstrating good discrimination between nonremitting and resilient courses of PTSD. However, neighborhood disadvantage may modify utility of PRS for PTSD, warranting careful consideration when applying these scores across contexts.

Lower cognitive ability measured in childhood or late adolescence has been consistently associated with higher mortality risk across adulthood. However, this evidence largely relies on single assessments, leaving it unclear to what extent mortality risk reflects cognitive differences established early in life versus developmental divergence during adolescence - a period of substantial neurocognitive plasticity. Using two nationally representative Swedish cohorts comprising 9,412 males born in 1948 and 1953, we linked cognitive ability assessed in primary school at age 13 years and military conscription at age 18 years to all-cause and cause-specific mortality recorded in nationwide registers through 2025. We decomposed late-adolescent cognitive ability into childhood cognitive level and adolescent cognitive change and evaluated their independent associations with mortality. Childhood cognitive level (HR = 0.81; 95% CI, 0.78-0.85) and adolescent cognitive change (HR = 0.84; 95% CI, 0.79-0.89) independently predicted lower mortality risk, also after adjustment for parental education. Childhood cognitive level and adolescent cognitive change showed partially distinct cause-specific patterns. Childhood cognitive level was most strongly associated with mortality from intrinsic causes, whereas adolescent cognitive change showed relatively stronger associations with external causes, particularly accidental deaths. Although adolescent cognitive change was associated with psychosocial factors including education and psychiatric diagnosis at conscription, its association with mortality persisted after adjustment for these factors. These findings suggest that cognitive development during adolescence carries independent prognostic information regarding long-term survival beyond cognitive level established by late childhood, highlighting adolescence as a consequential period for lifelong health.

Background: Participants in the ReINVEST randomised placebo-controlled trial of sertraline, conducted among men with high trait impulsivity and histories of violent offending, received structured clinical contact throughout the trial, including psychiatric assessments, nursing consultations, crisis support, and referrals to mental health and external services. We estimated the effect of placebo trial participation, compared with non-participation after baseline and single-blind run-in, on violent and domestic-violence reoffending. Methods: This prespecified secondary analysis included men from the ReINVEST trial pathway who completed baseline assessment and entered the single-blind run-in phase but did not proceed to randomisation, to inform the counterfactual. Violent and domestic-violence offences were identified from linked administrative records over 12- and 24-month follow-up periods. The adjusted difference in offending was estimated using two independent analytical approaches accounting for baseline differences. Additional analyses examined whether the effect varied by baseline clinical and criminal-history characteristics, whether pre-randomisation external referrals explained selection into placebo participation, and whether post-randomisation external referrals accounted for any part of the estimated effect. Results: Placebo trial participation was associated with lower offending across both outcome domains and follow-up periods. Placebo-standardised mean count differences for violent offending were -0.19 (95% confidence interval [CI] -0.38, -0.04) at 12 months and -0.22 (95% CI -0.51, -0.05) at 24 months. Corresponding differences for domestic-violence offending were -0.37 (95% CI -0.81, -0.14) at 12 months and -0.49 (95% CI -0.92, -0.22) at 24 months. The association was more apparent among men with a documented psychiatric history and, for domestic-violence offending, among those with higher baseline anger, irritability and aggression. Pre-randomisation referrals did not explain selection into placebo participation or materially alter the estimates. Post-randomisation referrals were observed in both groups, remained more common in the placebo group, and did not account for the observed association. Conclusion: Placebo participation in this trial involved sustained clinical contact and psychosocial support beyond exposure to inactive medication, and these non-pharmacological components may have contributed to lower reoffending. In placebo-controlled trials involving populations with high psychiatric morbidity and limited continuity of coordinated care, the clinical content of placebo participation should be explicitly characterised in trial design and interpretation.

Objective: Biopsychosocial models recognize multiple determinants of post-trauma mental disorders, but their relative and interactive effects remain unclear. We quantified the independent contribution of traumatic event severity, preexisting vulnerability, social support, and coping capacity, and tested mediation pathways. Methods: In a Brazilian clinical sample reporting traumatic or stressful events (N = 612), constructs were operationalized as composite scores and a dichotomous clinical outcome was derived from intake assessments. Logistic regression (n = 594) and structural equation modeling evaluated prediction and mediation. Results: Vulnerability was the strongest risk factor (OR = 1.46, p < .001) and social support the main protective factor (OR = 0.60, p < .001). Traumatic event severity remained an independent predictor (OR = 1.39, p < .001), whereas coping capacity was not significant (OR = 0.94, p = .410). Discrimination was good (AUC = 0.80). Mediation indicated vulnerability reduced social support and coping capacity, with a significant indirect effect via social support. Conclusions: Findings support a multifactorial model centered on a triad of vulnerability, social support, and traumatic exposure. Risk is shaped primarily by preexisting vulnerability and relational context, alongside a direct trauma effect, providing a clinically relevant framework for assessment and intervention.

BackgroundA range of rare chromosomal micro-deletions or -duplications (Copy Number Variants - CNVs) are associated with high risk of neurodevelopmental and mental health conditions (ND-CNVs). There is great individual variability in outcomes, but we lack insights into the contributing social factors, including family functioning. MethodsCaregivers of 598 children and young people (CYP) with a range of 16 ND-CNVs and 222 siblings without ND-CNVs (controls) completed questionnaires on overall family climate (cohesion and conflict) as well as caregiver-CYP relationship warmth and hostility and took part in a research diagnostic interview about CYPs psychiatric symptoms. CYPs intelligence quotient (IQ) was also measured. ResultsComparisons with published data from neurotypical families indicated that families affected by ND-CNVs are characterised by higher family cohesion and conflict as well as lower caregiver-CYP warmth and hostility. Symptoms of oppositional defiant disorder reduced more steeply in CYP with ND-CNVs compared to controls with increasing family cohesion (interaction effect: {beta} = -0.14, p = 4.65 x 10-{superscript 2}). In contrast, they rose more steeply with increasing family conflict (interaction effect: {beta} = 0.18, p = 1.05 x 10-{superscript 2}). Furthermore, symptoms of mood disorder increased more steeply with increased caregiver-CYP hostility in CYP with ND-CNVs (interaction effect: {beta} = 0.15, p = 4.55 x 10-{superscript 2}). ConclusionsRaising a CYP with a rare genetic condition is challenging. Timely access to interventions that support caregivers in fostering a positive family environment may reduce behavioural difficulties in CYP, with subsequent benefits for family functioning.

Suicidal ideation in obsessive-compulsive disorder (OCD) is common and clinically significant, yet much of the existing literature conceptualizes suicide risk through the lens of comorbid depressive symptomatology. The present study examined whether other clinical features can identify clinically meaningful patterns associated with SI. Participants included 231 individuals with clinically significant OCD symptoms. SI was operationalized using Item 9 of the Beck Depression Inventory-II and binarized to reflect the presence or absence of suicidal thoughts. Depression severity scores were intentionally excluded from the predictive feature set, and three machine learning models (ElasticNet, Random Forest, and Explainable Boosting Machines) were evaluated using repeated nested cross-validation. All three algorithms showed comparable predictive performance. Given this overlap, the EBM was selected for interpretation due to its ability to model nonlinear relationships and interaction effects transparently. The model identified quality of life, obsessive-compulsive trait severity, somatic burden, and conscientiousness as prominent predictors of SI. Risk functions suggested nonlinear increases in estimated suicide risk at elevated levels of obsessive-compulsive traits and reduced quality of life. Additionally, interaction analyses indicated that severe obsessive-compulsive traits combined with elevated somatic burden were associated with higher estimated suicide risk than either factor alone. These findings suggest that interpretable machine learning can support clinically relevant phenotypic hypothesis generation. They also highlight somatic burden, functional impairment, obsessive-compulsive trait severity, and conscientiousness as potentially underappreciated targets for SI risk assessment in OCD, beyond the traditional focus on depressive comorbidity.