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.

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.

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.

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.

Importance Prenatal alcohol exposure (PAE) is associated with lasting cognitive and neurodevelopmental deficits and can quadruple risk for depression later in life. However, it remains unknown whether there are specific trimesters when PAE is more strongly associated with longitudinal trajectories of internalizing symptoms - an indicator of depression risk - across childhood and adolescence. Objective To investigate how PAE timing and dosage are associated with internalizing symptom trajectories from ages 4 to 16.5 years. Design, Setting and Participants We analyzed prospective data from the Avon Longitudinal Study of Parents and Children (ALSPAC), an ongoing longitudinal birth cohort from the United Kingdom. Internalizing symptom trajectories were estimated for 6,409 participants. Primary analyses were conducted on 2,254 participants with complete data on PAE in all three trimesters, covariates, and trajectories. Main Outcomes and Measures We used growth mixture modelling to identify latent trajectories of depressive symptoms measured using the internalizing symptom scale from the Strengths and Difficulties Questionnaire (SDQ) at seven occasions between ages 4 to 16.5 years. Prospective alcohol consumption during each trimester were categorized into three PAE dosages: unexposed (0 drinks/week), low (1-7 drinks/week) and high (7+ drinks/week). Results We identified five distinct depressive symptom trajectories: stable low (75.9% of participants), moderate childhood peak (11.2%), progressive increase (5.57%), high early childhood (4.73%), and early adolescent peak (2.61%). PAE in the second (relative risk [RR]=2.08, 95% CI=1.15-3.76) and third trimesters (RR=1.83, 95% CI=1.05-3.21), as well as total PAE burden across pregnancy (RR=1.33, 95% CI=1.06-1.68) increased risk for the progressive increase trajectory, versus the stable low trajectory. High PAE in the second (RR=2.71, 95% CI=1.41-5.21) and third (RR=2.27, 95% CI=1.27-4.05) trimesters drove elevated risk for this trajectory. PAE in the first trimester or at low dosages showed no associations with depressive symptom trajectories. Negative control analyses of paternal drinking also found no associations. Conclusions and Relevance Our results highlight the second and third trimesters as potential sensitive periods for the impact of PAE on rising depressive symptoms from childhood to adolescence. Ultimately, these findings could inform the design of prevention programs, and facilitate targeted interventions to youth at elevated risk for depression.

Importance Anxiety and depression frequently co occur and show developmentally patterned co-development from childhood to adolescence. Adult psychiatric outcomes vary according to the timing, sequencing, and persistence of early symptoms, yet it remains unclear whether patterns of co development are comparable across high income and low and middle income country contexts. Objective Examine joint developmental trajectories of anxiety and depression from childhood to adolescence and their associations with anxiety and depression diagnoses in young adulthood. Design, Setting and Participants Population based prospective cohort studies in the UK (Avon Longitudinal Study of Parents and Children [ALSPAC], N=9,586) and Brazil (Pelotas 2004 Birth Cohort, N=3,815). Main Outcomes and Measures Trajectories were derived using parallel process latent growth models and latent class growth analyses of anxiety and depression using the Development and Well Being Assessment at early childhood (6-7 years), middle childhood (10-11 years), and adolescence (13-15 years). Diagnoses of anxiety and depression at 18 years were assessed via the Clinical Interview Schedule (ALSPAC) and the Mini International Neuropsychiatric Interview (Pelotas). Results Prevalence of anxiety and depression from early childhood to adolescence was similar across cohorts. Co-development was stronger in ALSPAC, with modest increases in both conditions, whereas in Pelotas, anxiety increased rapidly while depression showed little average change. In both cohorts, four trajectory classes were identified: stable-low (ALSPAC, 41%; Pelotas, 54%), increasing (31%; 28%), decreasing (23%; 15%), and persistent-high anxiety/increasing depression (5%; 3%). Compared with the stable-low class, youth in the increasing and persistent-high classes had elevated odds of depression (ALSPAC: OR=2.0 [95% CI, 1.4-2.8] and 4.2 [2.6-6.7]; Pelotas: 2.2 [1.5-3.3] and 2.9 [1.4-6.0]) and anxiety in young adulthood (ALSPAC: 1.6 [1.2-2.2] and 4.8 [3.2-7.0]; Pelotas: 1.7 [1.2-2.6] and 2.9 [1.5-5.8]). No increased risk was observed in the decreasing class. Conclusions and Relevance Patterns of anxiety and depression co development were comparable across the UK and Brazil, suggesting shared developmental pathways. However, more rapid increases in anxiety among Brazilian youth may reflect context specific risk factors. Persistence or emergence beyond early childhood was critical for identifying later diagnostic risk in both settings, highlighting the importance of early monitoring and intervention.

Purpose: Involuntary psychiatric hospitalization under court orders requires careful balancing of legal obligations and clinical needs. Identifying factors that influence the length of these hospital stays helps clarify the relationship between legal frameworks and psychiatric treatment. This study aims to describe the socio-demographic, clinical, and legal profiles of individuals hospitalized under court warrants and to identify factors independently associated with the duration of forensic hospitalization. Methods: A retrospective study was conducted on 119 patients discharged between 2018 and 2023. Data were collected from medical and legal records, including socio-demographic details, psychiatric diagnoses, offense types, hospital stay lengths, and legal proceedings. Results: Most patients were men (91.6%) diagnosed with schizophrenia or schizoaffective disorder (97.5%), with high rates of comorbid substance use disorder (79.0%) and unemployment (85.7%). The median hospital stay was 19.0 months, representing 40% of the maximum statutory sentence. Patients with low-severity offenses served a larger share of their maximum sentence (47%) than those with high-severity offenses (24%). Time to first discretionary leave was the strongest predictor of total stay duration in univariable analysis. Conclusion: The finding that patients with minor offenses have longer hospital stays than those with serious offenses confirms that clinical factors, rather than offense severity, primarily influence discharge decisions. These findings support moving toward personalized, clinically focused, and family-inclusive forensic discharge planning while maintaining public safety.

Neurobiological mechanisms underlying child maltreatment perpetration remain poorly understood, and the role of immune dysregulation has rarely been examined. Here, we tested whether peripheral inflammatory signatures are linked to brain structural alterations in mothers who have perpetrated maltreatment, and whether such alterations mediate this link to perpetration. In this cross-sectional study integrating structural MRI and inflammatory proteomics, 16 mothers with histories of maltreatment perpetration and 145 age-matched control mothers underwent brain imaging; a subgroup (n = 52; 11 maltreatment, 41 control) also completed plasma proteomic profiling using the Olink Target 96 Inflammation panel. Whole-brain voxel-based morphometry revealed significantly reduced gray matter volume (GMV) in the right middle/inferior temporal gyri, a region implicated in social cognition and contextual interpretation, in the maltreatment group. Proteomic analysis identified 16 inflammation-related proteins differentially expressed between groups; among these, nine were significantly associated with GMV in this temporal region. Lower GMV was associated with higher levels of pro-inflammatory proteins (CCL20, IL-17C) and with lower levels of immune-regulatory and metabolic proteins (CXCL1, CXCL6, SIRT2, STAMBP, MCP-2, MCP-4, 4E-BP1). Mediation analyses revealed that both protein sets were indirectly associated with perpetration through this regional GMV, with opposing patterns of direct association. These findings suggest that peripheral immune imbalance, characterized by elevated inflammatory signaling and diminished immune-regulatory capacity, is linked to structural vulnerability in a temporal cortical region involved in social cognition, specifically in perpetrating mothers. This neuroimmune pathway may contribute to maladaptive interpretation of child signals during caregiving and represents a potential target for biomarker-informed preventive intervention.

Background: Personality change is a debilitating consequence of traumatic brain injury (TBI), yet its prevalence, characteristics, and treatment remain poorly understood. Methods: We completed a pre-registered (CRD42023440990) systematic review and meta-analysis searching four databases (MEDLINE, PsycINFO, EMBASE and CINAHL) for primary studies assessing personality change after TBI. We synthesised conceptualisation, prevalence, longitudinal outcome, lesion location and treatment. Prevalence was estimated using a random effect meta-analysis using the Paule-Mandel estimator, with subgroup, meta-regression and robustness analyses. Results: 101 studies were included in this review, seventeen of which were suitable for meta-analysis. Personality change was defined inconsistently although common symptoms involved the emergence or increase of affective, behavioural, and social disturbances, including irritability, depression, emotional instability, anger outbursts, social withdrawal, anxiety, impulsivity, restlessness, aberrant motor behaviours, and aggression. The prevalence of secondary personality disorder was estimated as 29.1% (CIs 22.5% - 36.2%) and prevalence of broad personality change was 68.1% (CIs 53.4% - 81.2%). Robustness analyses showed that the estimate for broad personality change should be treated with caution as it was unstable when adjusted for risk of bias and potential publication bias. Follow-up studies, although of varying quality, consistently showed personality change remained stable over long follow-up periods. The relationship between personality change and specific lesion locations in TBI remains unclear, likely due to the poor methodological quality of studies examining this association. Perhaps most concerning, there is limited evidence and very few systematic studies addressing treatment. Conclusion: Personality change is a common and persistent consequence of TBI. Varying definitions, and the lack of high-quality lesion mapping studies and systematic investigations into treatment highlights critical gaps in understanding and management.

Background: Major depressive disorder is a severe, recurrent and disabling condition. Although diagnosis and clinical monitoring are based on medical interviews and validated rating scales, speech and discourse analysis may provide complementary digital biomarkers reflecting depressive severity and clinical evolution. However, current evidence remains limited by methodological heterogeneity, predominantly cross-sectional designs, limited longitudinal data and underrepresentation of non-English-speaking clinical populations. Objective: The aim of the VOICE-DEP study is to develop and formalize a standardized, reproducible and clinically grounded protocol for the multimodal analysis of voice and discourse during medical interviews as a tool to support the diagnosis of depressive disorder and to assess whether speech-derived biomarkers change over time in parallel with clinical severity measures. Methods: VOICE-DEP is an observational, prospective, longitudinal pilot study of patients with major depressive disorder with a healthy control group, conducted in a hospital-based clinical setting in Spain. The study will include 25 adult patients with moderate or severe unipolar depression, with or without psychotic symptoms, and 50 healthy controls without a personal history of psychiatric disorders. Patients will be assessed at five time points: baseline (V0) and four monthly follow-up visits at 30, 60, 90 and 120 days. Healthy controls will be assessed once at baseline. The planned dataset comprises 175 voice recordings: 125 from patients and 50 from controls. At each assessment, the Montgomery-Asberg Depression Rating Scale related part of the medical interview, lasting approximately 10-30 minutes and including an initial free-speech segment, will be recorded using a standardized audio protocol. Acoustic, paralinguistic and linguistic features will be extracted and analyzed in relation to clinician-rated severity measures and self-reported symptoms. Ethics: This protocol has been reviewed and approved by the local Research Ethics Committee, which complies with the international standards of GCP CPMP/ICH/135/95 (Comunidad Foral de Navarra Research Ethics Committee; reference code: 2026.110). Written informed consent will be obtained from all participants before any study procedure. Voice recordings and clinical data will be pseudonymized, stored securely and processed in accordance with applicable Spanish and European data protection regulations. Expected outcomes: This protocol is expected to generate a clinically grounded Spanish-language longitudinal speech corpus and a transparent analytical framework for evaluating voice- and discourse-derived biomarkers as complementary tools for depression assessment and monitoring

Background Childhood adversity can affect propensity to risk-taking behaviors. We aim to investigate the relation between childhood adversity and risk-taking behaviors in youth using emergency room (ER) admissions and survey data. Method Using the DANLIFE study, we included 1.2 million Danes. Individuals were assigned into five groups based on childhood adversity exposure from ages 0 to 15 years. We applied survival analyses on repeated outcomes to model ER-admissions due to substances, violence and unintentional injury in the full cohort between ages 16 and 24. We applied logistic regression models to weighted survey data on frequent binge drinking, cannabis use, drug use, and unsafe sex in a nested subsample of 34,064 18 year olds from the Danish National Birth Cohort. Results The high adversity group was at highest risk of ER-admissions due to substances (HR=3.27, 95% CI [3.10, 3.46]), violence (HR=2.67, 95% CI [2.58, 2.76]) and unintentional injuries (HR=1.30, 95% CI [1.28, 1.33]). In the nested subsample, the high adversity was at highest risk of cannabis use (OR=1.59, 95% CI [1.21, 2.09]), drug use (OR=2.44, 95% CI [1.71, 3.49]) and unsafe sex (OR=1.72, 95% CI [1.34, 2.22]), but at lower risk of frequent binge drinking (OR=0.57, 95% CI [0.37, 0.87]). Conclusion These findings highlight how childhood adversity is associated with increased engagement in and harm from risk-taking behaviors. To prevent inequalities in health in youth, there is a need for interventions and policies that promote child welfare, as well as targeted support for youth with harmful behavioral patterns.

Background: Depression and anxiety symptoms emerge early in life. We examined developmental trajectories of emotional symptoms, starting from early childhood, in three UK birth-cohorts spanning successive generations and diverse socio-ethnic contexts. Methods: Using data from three longitudinal, population-based UK birth-cohorts: Avon Longitudinal Study of Parents and Children (ALSPAC), Millenium Cohort Study (MCS), and Born in Bradford (BiB) we identified group-based trajectories of emotional symptoms using repeated Strengths and Difficulties Questionnaire, Emotional Subscale (SDQ-E) scores from ages 3-14y. Baseline samples comprised children with [&ge;]1 SDQ-E measure between age 3-14y (NALSPAC=11,025; NMCS=15,446; NBiB=6711). Participants were born three decades apart (ALSPAC: 1990-2, MCS: 2000-2, BiB: 2007-10) in distinct socioeconomic and ethnic contexts. We characterised group membership by: female sex, non-white ethnicity, maternal depression/anxiety and IMD quintile. In ALSPAC we modelled associations between trajectories and depression/anxiety diagnoses in early adulthood (24y and 30y). Results: In all cohorts 49% were female. ALSPAC had few non-white participants (4%) compared to MCS (17%) and BiB (66%). Each cohort had low-, mid- and high-level symptom trajectories. High-level trajectories comprised 6-7% of the population in each cohort. However, in younger cohorts, high-level symptom trajectories started high and persisted from age 3-5y but started low and increased in the oldest cohort. Female sex and maternal depression/anxiety were associated with higher odds of high-level or increasing symptom trajectories across all cohorts. Higher socioeconomic status and belonging to the ethnic majority was protective. Mid- and high-level symptom trajectories had higher odds of depression/anxiety diagnoses in early-adulthood in the older ALSPAC cohort. Conclusions: Developmental trajectories of emotional symptoms across childhood and adolescence are broadly similar across generations and diverse social contexts. However, children born more recently and in more diverse contexts may experience more persistent, severe emotional symptoms from a young age Key words: Longitudinal trajectories; emotional symptoms; SDQ, ALSPAC; MCS; Born in Bradford

Background: Depression is clinically heterogeneous, and immune-metabolic disturbances may not map uniformly onto categorical diagnosis or symptom severity. In populations exposed to substantial structural adversity, it remains unclear whether inflammatory and metabolic biomarker differences reflect adversity exposure itself or cluster around specific depressive phenotypes. Methods: Data were drawn from the Sao Paulo Megacity Mental Health Survey, a population-based study in which 5,037 household residents underwent structured psychiatric interviews. Among 770 participants assessed as having clinically significant symptoms (SCID-I), individuals with chronic physical illnesses, hs-CRP >20 mg/L, or missing data were excluded, yielding an analytic sample of 653. Latent class analysis of 16 DSM-IV depressive symptoms was used to identify symptom-derived phenotypes. Multinomial logistic regression tested associations between latent classes and immune-metabolic biomarkers, including hs-CRP, lipid fractions, fasting glucose, and triglycerides, adjusting for age, sex, education, smoking, and BMI. Results: A four-class solution identified asymptomatic (44.56%), mild-moderate (19.14%), atypical-like (16.69%), and melancholic-like (19.60%) classes. The two highseverity classes diverged by neurovegetative features: atypical-like depression was characterised by weight gain, hypersomnia, and psychomotor retardation, whereas melancholic-like depression was characterised by weight loss, insomnia, and psychomotor agitation. hs-CRP was highest in the atypical-like class and lowest in the melancholic-like class despite similar symptom severity. After BMI adjustment, elevated hs-CRP in the atypical-like class attenuated, whereas lower hs-CRP in the melancholic-like class persisted. Other metabolic markers did not robustly differentiate classes after adjustment. Conclusions: Immune-metabolic differences in depression do not simply follow symptom severity. In this Sao Paulo cohort, higher and lower hs-CRP profiles clustered around distinct latent depressive phenotypes, supporting biologically heterogeneous depressive presentations within a socially exposed urban population.

The emotional content of suicide notes is typically examined using categorical coding, where each labeled passage is treated in isolation from its surrounding language. In contrast, dimensional models of psychopathology propose that affective content varies along continuous gradients. We evaluated this proposition directly. Excerpts from 884 annotated suicide notes were embedded in a semantic space defined solely by their linguistic properties, and we investigated whether human-assigned emotion labels changed smoothly across this space. They did: affective tone showed clear spatial autocorrelation (Moran's $I = 0.18$, $z = 19.68$, $p < 0.001$), an effect that replicated across three different encoders and remained after removing all within-note dependencies. Emotions occupied recognizable yet overlapping regions rather than forming distinct clusters and varied substantially in how tightly they were concentrated: love and hopelessness appeared with similar frequency, but love was far more localized ($z = 15.7$ versus $10.8$). Among all emotions, hopelessness was the most linguistically diffuse, implying that a single categorical label is capturing multiple, qualitatively different manifestations of suicidal distress.

Background: Childhood trauma exposure (TE) may heighten negative emotional responses, overwhelm cognitive control, and increase risk for anxiety disorders. Cognitive control is facilitated by glutamatergic (Glu) excitatory neurotransmission within the dorsal anterior cingulate cortex (dACC). Dynamic changes in dACC Glu levels were investigated using 1H functional magnetic resonance spectroscopy (1H fMRS) to assess the impact of negative emotional processing on neural mechanisms supporting cognitive control in TE-youth. Methods: Fifty adolescents were categorized into two TE-Groups: Higher (Mtrauma=6{+/-}1events) and Lower (Mtrauma=3{+/-}1events). 1H fMRS from the dACC was acquired during an inhibitory motor control task requiring tapping responses to stimuli under two Response Modes, NonSelective (100% response) and Selective (80% response, 20% inhibition), executed with two Stimuli Conditions, Squares (no emotion) and Faces (emotion). Glu modulation (relative to basal levels) was tested across TE-Group, Stimuli Condition, and their interaction. Within each Stimuli Condition, Glu modulation was tested across Response Modes by TE-Group. Results: We observed a 2-way interaction of TE-Group x Stimuli Condition ({chi}2=4.66, p=0.031). Post-hoc tests revealed significantly lower Glu modulation in Higher TE vs Lower TE (p=.023) during Faces but not Squares. This Glu modulation did not differ across Response Modes. Within the Higher TE-Group, Glu was significantly reduced during Faces compared to Squares (p<.001). Basal dACC Glu levels did not differ between groups. Conclusions: TE-Group differences in adolescent dACC Glu modulation were observed during cognitive control performed with emotional, but not non-emotional, stimuli, highlighting the value of 1H fMRS for detecting trauma-related differences in task-related excitatory neurochemical dynamics.