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.

BackgroundExternal validations of existing risk models is an efficient step towards potential implementation, obviating the need to develop new models. However, validation in new clinical settings poses several challenges. ObjectiveTo externally validate the OxSATS tool using data from the Oxford Monitoring System for Self-harm in England. OxSATS is a validated tool to predict suicide after self-harm developed using Swedish population registers. MethodsWe selected episodes of self-harm (ICD-10 codes X60-84; Y10-34) by individuals aged 10-64 years who presented to a large regional hospital between 1 January 2000 and 31 December 2018, and were followed up until 31 December 2019. We applied the OxSATS tool to estimate each individuals suicide risk within 12 months after their index self-harm. We assessed model performance using discrimination (Harrells c-index) and calibration measures (calibration plot and the observed-to-expected events ratio, O:E). We assessed the effects of missing predictors on calibration and subsequently recalibrated the model. FindingsWe identified 16,120 individuals who presented to hospital with self-harm, of whom 101 (0.6%) died by suicide in the 12-month follow-up period. The OxSATS model showed good discrimination in external validation (c-index=0.72, 95% CI=0.67, 0.77). Recalibration was required because initial calibration reflected a lower outcome rate in the new data. After recalibration, calibration performance was excellent (O:E=1.00, 95% CI=0.80, 1.20). ConclusionsDespite differences in clinical services and outcome ascertainment, suicide risk models can maintain good predictive performance in new settings. However, recalibration should be considered when applying prediction models in new settings, and the impact of missing predictors should be assessed using sensitivity analyses. KEY MESSAGESO_ST_ABSWhat is already known on this topicC_ST_ABSSuicide risk is substantially elevated after hospital presentation for self-harm, but most existing risk assessment tools rely on rating scales or binary cut-offs, show limited predictive accuracy, and rarely report calibration. OxSATS is a prognostic model developed using Swedish register data that provides continuous risk estimates and demonstrated good discrimination and calibration in its original setting. External validation in new healthcare systems is essential before implementation, but is often complicated by differences in predictor definitions, missing variables, and outcome prevalence. What this study addsThis study provides the first external validation of OxSATS in an English clinical setting using routinely collected hospital data. The model retained good discrimination but initially overpredicted suicide risk due to a lower baseline event rate and one missing predictor, highlighting the importance of calibration assessment. How this study might affect research, practice or policyFuture research and implementation strategies should routinely incorporate external validation, sensitivity analyses for missing predictors, and local recalibration before clinical or policy adoption.

ImportanceSuicide deaths and suicidality are considered a public health emergency, yet their brain underpinnings remain elusive. ObjectiveTo examine individual, environmental, and clinical characteristics, as well as multimodal brain imaging correlates of suicidality in a US population-based sample of school-aged children. DesignCross-sectional analysis of the first wave of data from the Adolescent Brain Cognitive Development study SettingMulticenter population-based study ParticipantsChildren aged 9-10 years from unreferred, community samples with suicidality data available (n=7,994). Following quality control, we examined structural magnetic resonance imaging (sMRI) (n=6,238), resting state functional MRI (rs-fMRI) (n=4,134), and task-based fMRI (range n=4,075 to 4,608). ExposureLifetime suicidality, defined as suicidal ideation, plans and attempts reported by children or/and caregivers. Main Outcomes and MeasuresMultimodal neuroimaging analyses examined differences with Welchs t-test and Equivalence Tests, with observed effect sizes (ES, Cohens d) and their 90% confidence interval (CI) < |0.15|. Predictive values were examined using the area under precision-recall curves (AUPRC). Measures included, cortical volume and thickness, large-scale network connectivity and task-based MRI of reward processing, inhibitory control and working memory. ResultsAmong the 7,994 unrelated children (3,757 females [47.0%]), those will lifetime suicidality based on children (n=684 [8.6%]; 276 females [40.4%]), caregiver (n=654 [8.2%]; 233 females [35.6%]) or concordant reports (n=198 [2.5%]; 67 females [33.8%]), presented higher levels of social adversity and psychopathology on themselves and their caregivers compared to never-suicidal children (n=6,854 [85.7%]; 3,315 females [48.3%]). A wide range of brain areas was associated with suicidality, but only one test (0.06%) survived statistical correction: children with caregiver-reported suicidality had a thinner left bank of the superior temporal sulcus compared to never-suicidal children (ES=-0.17, 95%CI -0.26, -0.08, pFDR=0.019). Based on the prespecified bounds of |0.15|, [~]48% of the group mean differences for child-reported suicidality comparisons and a [~]22% for parent-reported suicidality comparisons were considered equivalent. All observed ES were relatively small (d[&le;]|0.20|) and with low predictive value (AUPRC[&le;]0.10). Conclusion and RelevanceUsing commonly-applied neuroimaging measures, we were unable to find a discrete brain signature related to suicidality in youth. There is a great need for improved approaches to the neurobiology of suicide.

IntroductionPatients recently discharged from psychiatric hospitalization are at increased risk of intentional self-harm, including suicide. Using linked population-based registry data from Catalonia, Spain, we developed machine learning-based prediction models for post-discharge intentional self-harm across different follow-up horizons, sex, and age groups, and evaluated their generalizability and robustness with multiple validation strategies. MethodsRetrospective cohort study including 41,827 individuals accounting for 71,865 psychiatric hospitalizations with discharge at age [&ge;]10 years, between January 1, 2015, and December 31, 2018, in Catalonia, Spain, with follow-up until December 31, 2019. Primary outcome was intentional self-harm (fatal or non-fatal) within 7, 30, 90, 180, and 365 days post-discharge. Models incorporated 247 predictors from electronic health records, including sociodemographic characteristics, mental and physical disorder categories, categories of dispensed psychotropic medication, and history of self-harm and psychiatric hospitalization. Model performance was evaluated using the area under the receiver operating characteristic curve (AUCROC) and the area under the precision-recall curve (AUCPR). Predictor importance was assessed using Shapley Additive Explanations (SHAP). ResultsWithin 365 days, 4,901 hospitalizations (6.8%) were followed by intentional self-harm. The 365-day model trained on the full cohort achieved a AUCROC of 0.819, in the test sample with adjusted AUCPR indicating a median 5.4-fold improvement over baseline prevalence. This model generalized well across event horizons and sex-age strata, outperforming subgroup-specific models when data sparsity limited performance. Separate models trained by event horizons, and stratified by sex, and sex-age groups achieved a median AUCROC of 0.775 (IQR 0.764-0.808), with adjusted AUCPR indicating a median 5.4-fold improvement over baseline prevalence (IQR 4.5-6.2). Key predictors included the recency of the last registered diagnosis of depressive episodes, recurrent depression, adjustment disorders, and schizophrenia, as well as recent SSRI dispensation and the number of childhood-onset disorder and musculoskeletal disease diagnoses in the previous five years. Predictor importance varied considerably across sex-age strata, with smaller differences across horizons. Subject-level and temporal split validation strategies reduced performance (AUCROC 0.711-0.746), though estimates remained clinically informative (2.8-3.1-fold improvement over baseline prevalence). ConclusionsMachine learning models using routinely collected health records predicted intentional self-harm after psychiatric hospitalization with good discrimination and clinically meaningful precision-recall performance. A single 365-day model generalized well across horizons and demographic groups, suggesting that one broadly trained model may provide a pragmatic and scalable approach for clinical implementation.

BackgroundSuicide is the second leading cause of death for patients aged 10 to 26 years old. Pediatric suicidality is underreported, which poses significant challenges for effective intervention and prevention strategies. Identifying populations at risk for suicidality can provide critical benefits in terms of study cohort selection, prevalence estimation and resource allocation. Objective(1) Measure prevalence of mental health comorbidities associated with suicidality; (2) propensity match diagnosed suicidality cohorts to select high-risk undiagnosed suicidality cohorts. MethodsICD-10 diagnosis codes were analyzed for patients aged 6-18 years old presenting to the emergency department at a large academic pediatric hospital between June 1, 2016, and June 1, 2022. Suicidality case definition included subtypes for severity: ideation, self-harm, and attempt. Comorbidities were measured as conditional probabilities of suicidality given a co-occurring ICD-10 diagnosis code. Propensity scores were used to match known suicidality cases to undiagnosed patients at risk of suicidality. ResultsIn total, 2.9% of ED encounters met an ICD-10-based case definition of suicidality during the study period. Comorbidities of suicidality were statistically significant for 55 frequently co-occurring diagnosis codes. Nearly half (26/55) were not present in the DSM-5 codeset and nearly a quarter (12/55) included ICD-10 codes for harm without documented self-harm intent. The probability of suicidality diagnosis was 44% for patients with personality disorder, gender dysphoria (43%), bipolar disorder (36%), depression (33%), and schizophrenia spectrum disorders (32%). Compared to ground truth comparison, 53.4% of propensity matched comparators were true positive suicidality cases. ConclusionsPropensity score matching is informative for selection of undiagnosed suicidality cases whose comorbidity profiles closely resemble known cases of suicidality.

BackgroundEating disorders are severe psychiatric conditions associated with high mortality rates, particularly among young people. These disorders often co-occur with self-harm and suicidal ideation, yet the temporal dynamics between these variables remain poorly understood. AimsThis study aims to elucidate the longitudinal associations between eating disorder symptoms, self- harm, and suicidal ideation using structural equation modelling. MethodRepeated measures of these phenotypes were used to construct a hypothetical model that includes cross-path analyses within and between the variables in two cohorts: the Twins Early Development Study (TEDS; ages 16, 21 and 26; N=5,196), representing a general population sample, and the Covid-19 Psychiatry and Neurological Genetics study (COPING; data collected between June 2020 and July 2021; N=490), which focused on individuals with a history of anxiety or depression. In the TEDS cohort, symptoms of eating disorders, self-harm, and suicidal ideation showed limited continuity across adolescence and young adulthood, with peak symptom severity at age 21. ResultsCross-domain associations revealed that both self-harm and suicidal ideation at age 21 were more strongly associated with eating disorders at 26 than the reverse. In contrast, the COPING cohort exhibited more stability in symptoms over time but showed minimal cross-domain effects. ConclusionsThe effects of self-harm and suicidal ideation on eating disorders in early adulthood are stronger than the influence of disordered eating on suicidality.

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.

INTRODUCTIONSince the start of the COVID-19 pandemic inequalities around child mortality are likely to have increased. Suicide in young people has risen in many countries over the last 10 years, and suicide in particular may have been expected to increase over the course of the lockdown, as rates of mental health needs increased. AIMThe aim of this work was to report any changes, and characteristics of children dying of suicide in England, before, and during the COVID pandemic. METHODSChild deaths from suicide, reported to the National Child Mortality Database, occurring between 1st April 2019 and 31st March 2023 were identified, and linked to demographic data, death-review data and routine Hospital Episodes Statistics (HES) data (preceding the death). Routine HES data was used to identify mental health disorders and self-harm events. Temporal trends across the time period were quantified, alongside any changes in sociodemographic characteristics. Using Case-Cross Over methodology, we investigated the relative risk of suicide, after recent HES-coded events. RESULTSIn total there were 498 deaths likely due to suicide, during the 4 year period. Overall risk of death by suicide was 14.31 (13.08-15.63) per 1,000,000 CYP per year. Overall, there was little evidence that risk (p=0.863) or method (p=0.199) changed over the period (p=0.863). There was evidence that the relationship between deprivation and suicide risk was different between ethnic groups (both p<0.001), with decreasing deprivation associated with increasing risk of suicide in white children (IRR 1.12 (1.03-1.21)), and decreasing risk in Asian (IRR 0.52 (0.41-0.65)), Black (IRR 0.31 (0.21-0.44)) and Mixed/Other ethnicity (IRR 0.73 (0.60-0.89) children. Only a recorded diagnosis of self-harm was more common before the death than in the preceding control periods (OR 8.99 (4.27-18.94)). CONCLUSIONIn England, suicide rates do not appear to be increasing, and the methods of suicide remain static. However, the role of deprivation and suicide risk appears to be different between children of different ethnic groups, and while hospital admission and a recorded diagnosis of mental health disorder does not appear to predict suicide in the subsequent month, there was a strong association with self-harm events.

Violent victimisation affects 1-4% of populations annually and constitutes a major risk factor for psychiatric morbidity and suicidal behaviours. However, the aetiological mechanisms underlying vulnerability to severe victimisation remain poorly understood. We examined genetic and environmental contributions to victimisation risk across development using nationwide family data from 4,458,368 individuals born in Sweden (1973-2004) and Finland (1970-2003). Violent victimisation was identified through hospital admissions and mortality records. Quantitative genetic models estimated additive genetic, shared environmental, and unique environmental influences across developmental periods, with sex-limitation analyses examining sex-specific effects. Among 154,209 individuals (2.9%) with documented victimisation, familial aggregation was proportional to genetic relatedness (adjusted hazard ratios: 6.0 [95% CI 4.0-9.0] for monozygotic twins; 1.4 [95% CI 1.4-1.5] for paternal half-siblings). Aetiological architecture varied substantially across development. Childhood-onset victimisation showed high heritability (h2=70%, 95% CI 44-95%) with notable shared environmental contributions (c2=22%, 95% CI 9-35%). Adolescent-onset and adult-onset victimisation demonstrated lower heritability (h2=40-44%) with predominant unique environmental effects (e2=56-60%) and negligible shared environmental influence. Sex-limitation models revealed comparable heritability between sexes but moderate cross-sex genetic correlations (rg=0.77-0.78), indicating partially distinct genetic pathways. Violent victimisation therefore exhibits a developmentally dynamic genetic architecture, with heritability decreasing and unique environmental contributions increasing from childhood to adulthood. Partially sex-specific genetic pathways underscore the need for age- and sex-stratified genomic investigations.

Sibling comparison designs are increasingly used to address unmeasured familial confounding in observational studies. We propose that three key interpretational fallacies - sibling characteristics, exposure correlation and non-shared confounding, and unmet life course model assumptions - can mislead causal conclusions. We demonstrate these fallacies by investigating childhood family income and mental health. A nationwide Danish cohort of individuals born between 1986 and 1996 (n = 643,814; 404,179 siblings) was followed-up from age 15 until onset of severe mental disorders. Population-wide and within-sibling adjusted hazard ratios (aHR) between childhood family income and offspring mental disorders were estimated, supplemented by descriptive statistics and pseudo-sibling analyses. A $15,000 increase in family income at age 14 was associated with a reduced rate of severe mental disorders (aHR = 0.78; 95% CI: 0.76-0.81), with comparable estimates across measurement ages 1-14 (range: 0.67-0.82). Null results were observed in both a pseudo-sibling cohort of unrelated individuals with the same income differences as the true sibling cohort (aHR = 0.93; 95% CI: 0.85-1.01) and the true sibling cohort (aHR = 1.02; 95% CI: 0.94-1.11). Siblings were typically born three years apart, with an average monthly income difference of $496 at age 14 (IQR;$150-$641). This study advocates for cautious causal interpretation of null results in sibling comparison studies because (1) it may not capture meaningful differences in family income across siblings due to minor income fluctuations; (2) the pseudo-sibling cohort showed evidence of unmeasured non-shared confounding; (3) sibling comparison designs test a critical periods life course model, but the results favour an accumulating/vulnerable model. We present guidelines and R syntax to assess these interpretational fallacies.

BackgroundForensic psychiatric services are expanding in many countries, and discharging patients from secure hospitals relies on accurate estimates of risk of adverse outcomes. Novel evidence-based tools for estimating one key risk, violent reoffending, have been developed in recent years. We aimed to externally validate one new tool, FoVOx, in forensic psychiatric patients sentenced to treatment, and to develop an updated model (FoVOx2), incorporating additional clinical predictors. MethodsUsing Swedish national registers, we conducted a temporal external validation of FoVOx by examining 767 patients discharged between 2014 and 2023. For the FoVOx2 cohort, 906 patients discharged between 2008 and 2023 were followed up, and additional predictors tested. The outcome was violent reconviction within 12 or 24 months. Model performance was evaluated using Harrells C-index, time-dependent AUCs, calibration, and classification metrics at predefined thresholds. ResultsIn temporal validation, FoVOx showed moderate discrimination (AUCs 0.69 and 0.71; C-index = 0.69) and acceptable overall accuracy (Brier <0.11). Calibration was generally good, with mild overestimation at the highest predicted risks (>20%) at 12 months and slight underprediction at 24 months. The updated FoVOx2 model newly incorporated clozapine treatment and additional diagnostic categories. It was associated with improved performance (AUCs 0.77; optimism-corrected C-index = 0.72; Brier 0.06 and 0.09) and achieved good calibration (intercept {approx} 0; slopes 1.03 and 1.05). ConclusionsUpdating risk assessment tools with additional clinical factors can lead to incremental improvement in model performance. Implementing tools should consider clinical utility and impact as next steps.

BackgroundSuicidality, including suicidal ideation (SI), attempt (SA), and death (SD), represents complex and partially overlapping phenotypes. This complexity contributes to study population heterogeneity in suicidality research, impeding replication efforts and data consolidation by research consortia. The standardization of suicidality definitions would help but has been insufficiently addressed in existing literature. Here, the Suicide Workgroup of the Psychiatric Genomics Consortium (PGC) provides International Classification of Disease (ICD) definitions, a critical real-world data source, for SA and SI. MethodsThe PGC Suicide Workgroup used published definitions coupled with expert consensus to develop ICD lists to serve as suicidality phenotype definitions. One SI and two SA lists were produced and evaluated for performance against patient screening responses in two independent cohorts (N = 9,151 and 12,621) with differing ascertainment strategies. OutcomesICD list suicidality definitions were produced. Evaluation of generated ICD lists versus patient responses across two cohorts demonstrated varied sensitivity (15{middle dot}4% to 71{middle dot}1%), specificity (67{middle dot}6% to 96{middle dot}3%), and positive predictive values (0{middle dot}57-0{middle dot}92). SI ICD code performance also varied in sensitivity (29{middle dot}4%-86{middle dot}1%), specificity (64{middle dot}2% to 90{middle dot}6%), and positive predictive values (0{middle dot}67 to 0{middle dot}98). InterpretationGuidelines were developed to provide more consistent and comparable suicidality definitions. However, real-world application of ICD codes leads to a wide range of performance, dependent on cohort characteristics, that will need to be carefully considered in implementation. Future efforts would benefit from consistent training in use of ICD codes between sites to improve generalizability, and should include validation in diverse populations. FundingThis work was funded by NIMH R01MH132733 (Mullins), R01MH132733 (Ruderfer), R01MH123619 (Docherty), R01MH123489 (Coon), R01MH124839 (PGC4), R01MH118233 and MH117599 (Smoller), Brain and Behavior Research Foundation No. 31248 (Monson), the Huntsman Mental Health Institute, National Science Foundation Graduate Research Fellowship Program Grant #1842169, and by grant # I01BX005881 and #IK6BX006523 (Kimbrel) from the Department of Veterans Affairs.

Previous research links Adverse Childhood Experiences (ACEs) with problem gambling, but most studies rely on retrospective reporting and focus narrowly on maltreatment, overlooking adversities such as parental mental health issues. Using data on 3794 young adults in the Avon Longitudinal Study of Parents and Children, we examined longitudinal associations between 10 prospectively measured ACEs (individually and cumulatively), and moderate-risk/problem gambling (Problem Gambling Severity Index >=3) at ages 17, 20 and 24, adjusted for socioeconomic and other background factors. Population attributable fractions (PAFs) estimated proportions of cases potentially attributable to ACEs. Most ACEs were associated with higher odds of moderate-risk/problem gambling across ages (24/30 estimates) after adjustment, though effect sizes were generally small (median adjusted odds ratio [aOR] 1.31, interquartile range 1.24-1.59), and confidence intervals (CIs) wide. Sexual abuse showed the strongest association (aORs 2.4-4.2, CIs 0.5-10.5), while bullying and parental conviction were associated at ages 17 and 20 only, parental separation age 24 only. Evidence for a dose-response relationship was weak. PAFs suggested ACEs accounted for up to 12% of moderate-risk/problem gambling cases. These findings highlight potential impacts of ACEs on later gambling behaviour, but imprecise estimates suggest findings should be interpreted cautiously and strengthened through larger datasets and meta-analyses.

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.

ImportanceNo study with available data from birth into late childhood has explored how prenatal antidepressant exposure affects offspring body mass index (BMI) throughout childhood. ObjectiveTo determine the association between prenatal antidepressant exposure and longitudinal differences in child BMI up to age 8 years. Design, Setting, and ParticipantsWe used data from the Norwegian Mother, Father, and Child Cohort Study (MoBa) linked to the Medical Birth Registry of Norway and the MoBa Genetics. We included 6,084 pregnancy-child dyads (singleton, liveborn) with available parent-reported data on child BMI from birth up to 8 years of age, born to women with depression/anxiety prior to pregnancy. Analysis was performed between January 2023 and April 2024. ExposuresPrenatal antidepressant exposure was categorized as i) continued antidepressants in pregnancy (n=626); ii) discontinued antidepressants proximal to pregnancy (n=412); or iii) unexposed to antidepressants both before and during pregnancy (n=5,046). Main outcomes and measuresChild BMI up to 8 years of age. Mean BMI differences over time across antidepressant exposure groups were compared using multilevel mixed-effect linear models. ResultsChildren born to mothers who continued antidepressant into pregnancy had comparable childhood BMIs with those born to unexposed mothers or mothers who discontinued antidepressant proximal to pregnancy. Higher BMI was observed up to 3 years of age among male offspring born to antidepressant continuers compared to discontinuers, especially in those exposed to selective-serotonin-reuptake-inhibitor before pregnancy (mean difference in BMI, {beta}=0.334; 95% CI: 0.081 to 0.588 at baseline). Lower BMI was seen among female offspring born to continued vs. discontinued mothers and the gap became larger over time, especially between low-moderate use of antidepressant vs. discontinuation during pregnancy. Analyses integrating parental genetic liability for depression, BMI, and antidepressant response using polygenic risk scores in a sub-population (n=1,913) suggests potential influence of the genetic component on the differences in BMI across antidepressant trajectory groups in some strata. Conclusion and relevanceThe longitudinal childhood BMI of children born to mothers with pre-pregnancy depression/anxiety did not differ across prenatal antidepressant exposure trajectories. Exploratory analyses revealed differences at specific time frames which might be sex-specific and potentially influenced by genetic liability profiles. KEY POINTSO_ST_ABSQuestionC_ST_ABSDoes prenatal antidepressant exposure affect longitudinal childhood BMI? FindingsIn this cohort study of 6084 pregnancy-child dyads in mothers with pre-pregnancy depressive/anxiety disorders, no difference in longitudinal childhood BMI across prenatal antidepressant exposure groups were observed. Exploratory analyses revealed differences at specific time frames which might be sex-specific and potentially influenced by parental genetic liability profiles. MeaningLongitudinal BMI throughout childhood of children born to mothers with pre-pregnancy depression/anxiety did not differ across prenatal antidepressant exposure trajectories. Further research is needed to investigate the time-dependent, sex-specific, and genetic-related aspects of some strata of antidepressant exposure on BMI differences.

BackgroundInternalizing disorders are among the most common psychiatric conditions in adolescence, often associated with long-term adverse outcomes. Early identification of at-risk youth is important for effective intervention, though it remains challenging due to the multifactorial nature of risk. Machine learning (ML) offers opportunities to integrate multiple data sources and improve risk prediction for internalizing disorders. MethodsWe used data from 13,743 adolescents (mean age 14.45 years; 52.7% female) participating in the Norwegian Mother, Father and Child Cohort Study (MoBa), linked to national health registries. Logistic regression with elastic net regularization was applied to predict the risk of an internalizing disorder (mood, anxiety or stress-related) occurring within one to five years after assessment. Nested models of increasing complexity incorporated sociodemographic, clinical, lifestyle, mental health, psychosocial, and genetic predictors. Model performance was evaluated in a hold-out test set. Simplified models combining three questionnaire scales were also evaluated. ResultsTest-set performance increased with model complexity, reaching area under the receiver operating characteristic curve (AUC) of 0.732 for the full model. Mental health self-reported symptoms and psychosocial predictors contributed most to the discrimination. Simplified models using three questionnaire scales, alongside age and sex, achieved AUCs up to 0.715 and effectively stratified adolescents into high- and low-risk groups (OR80/20 ranged 6.39-10.60). ConclusionMultimodal ML models integrating registry information, mental health symptoms, psychosocial factors, and genetic data demonstrated moderate predictive performance. Simplified models with three questionnaire items reached comparable performance, highlighting their potential utility in the early identification of adolescents at elevated internalizing disorder risk.

BackgroundGenetic liability of depressive disorder can be captured by psychopathology in relatives (family history). Various methods summarize family history in a single score, differing in included information as well as underlying model. We systematically compared the performance of family history indicators, including promising new indicators based on the liability threshold model, in predicting depressive disorder. MethodsWe calculated selected family history indicators for depression (dichotomous, proportion, novel genetically-informed method PAFGRS) in 1339 participants of the Netherlands Study of Depression and Anxiety (Ncase= 1086). Polygenic scores were computed from the most recent GWAS for major depression. We assessed correlations between genetic liability indicators, as well as their prediction of lifetime depressive disorder diagnosis. ResultsCorrelations of family history indicators with each other were high (r = 0.71 - 0.99), and much lower with the PGS (r = 0.15). There was a suggested increase in predictive accuracy for more elaborately computed scores, ranging from proportion (AUC = 0.66, OR = 2.26, 95%CI = 1.88-2.71) to PAFGRS (AUC = 0.70, OR =17.06, 95%CI = 9.46 - 30.77). The best-performing family history indicator and the PGS were independently associated with depressive disorder (PAFGRS: OR = 15.17, 95%CI = 8.36-27.51, p = 3.59x10-19; PGS: OR = 1.30, 95%CI = 1.12-1.50, p = 0.0004). ConclusionsOur analysis shows that more elaborate family history indicators, including family size, prevalence, heritability and based on genetic theory, would be preferrable over simpler methods. Family history and PGS were complementary in prediction, showing the added value of including both in future studies.

IMPORTANCEAvoidant restrictive food intake disorder (ARFID) is characterized by an extremely limited range and/or amount of food eaten, resulting in the persistent failure to meet nutritional and/or energy needs. Its etiology is poorly understood and knowledge of genetic and environmental contributions to ARFID is needed to guide future research. OBJECTIVETo determine the extent to which genetic and environmental factors contribute to the liability to the broad ARFID phenotype. DESIGN, SETTING, AND PARTICIPANTSA nationwide Swedish twin cohort including 16,951 twin pairs born 1992-2010 whose parents participated in the Child and Adolescent Twin Study in Sweden (CATSS) at twin age 9 or 12 years (49.4% female). CATSS was linked to the National Patient Register (NPR) and the Prescribed Drug Register (PDR). MAIN OUTCOME/MEASURESFrom CATSS, NPR, and PDR, we extracted all parent-reports, diagnoses, procedures, and prescribed drugs between age 6 and 12 that were relevant to the DSM-5 ARFID criteria and developed a composite measure for the ARFID phenotype (i.e., avoidant/restrictive eating with clinically significant impact such as low weight or nutritional deficiency, and with fear of weight gain as an exclusion). In sensitivity analyses, we controlled for autism and medical conditions that could account for the eating disturbance. We fitted univariate liability threshold models to estimate the relative contribution of genetic and environmental variation to the liability to the ARFID phenotype. RESULTSWe identified 667 children (2.0%, 38.2% female) with the ARFID phenotype between age 6 and 12. Variation in the liability to ARFID was largely explained by additive genetic factors (0.79, 95% confidence interval [CI] 0.71-0.86), with significant contributions from non-shared environmental factors (0.21, 95% CI 0.14-0.29). Heritability was very similar when excluding children with autism (0.77, 95% CI 0.67-0.84) or medical illnesses that could account for the eating disturbance (0.80, 95% CI 0.71-0.86). CONCLUSIONS AND RELEVANCEPrevalence and sex distribution of the broad ARFID phenotype were similar to previous studies, supporting the use of existing epidemiological data to identify ARFID. This first study of the genetic and environmental etiology of ARFID suggests that ARFID is highly heritable, encouraging future twin and molecular genetic studies.

ImportanceSuicide is a leading cause of death in the United States with risk strongly influenced by Interpersonal trauma, contributing to treatment resistance and clinical complexity. ObjectiveTo assess clinical and genetic factors in individuals who died from suicide, with and without interpersonal trauma exposure. DesignIndividuals who died from suicide with and without trauma were compared in a retrospective case-case design. Prevalence of 19 broad clinical categories was assessed between groups. Results directed selection of 42 clinical subcategories, and 40 polygenic scores (PGS) for further assessment. Multivariable logistic regression models, adjusted for critical covariates and multiple tests, were formulated. Models were also stratified by age group (<26yo and [&ge;]26yo), sex, and age/sex. SettingA population-based evaluation of comorbidity and polygenic scoring in two suicide death subgroups. ParticipantsA total of 8 738 Utah Suicide Mortality Research Study individuals (23.9% female, average age = 42.6 yo) who died from suicide were evaluated, divided into trauma (N = 1 091) and non-trauma exposed (N = 7 647) individuals. A subset of unrelated European genotyped individuals was also assessed in PGS analyses (Trauma N = 491; Non-trauma N = 3 233). Exposures"Trauma" is here defined as interpersonal trauma exposure, including abuse, assault, and neglect from International Classification of Disease coding. Main Outcomes and MeasuresPrevalence of comorbid clinical sub/categories and PGS enrichment in trauma versus non-trauma exposed suicide deaths. ResultsOverall, trauma-exposed individuals died from suicide earlier (mean age of 38.1 yo versus 43.3 yo; P <0.0001) and were disproportionately female (38% versus 21%, OR = 3.3, CI = 2.9-3.8). Prevalence of asphyxiation and overdose methods, prior suicidality, psychiatric diagnoses, and substance use (OR range = 1.3-3.7) were elevated in trauma exposed individuals who died from suicide. Genetic PGS were also elevated in trauma-exposed individuals who died from suicide for depression, bipolar disorder, cannabis use, PTSD, insomnia, and schizophrenia (OR range = 1.1-1.4) with ADHD and opioid use showing uniquely elevated PGS in trauma exposed males (OR range = 1.2-1.4). Conclusions and RelevanceResults demonstrated multiple convergent lines of age- and sex-specific evidence differentiating trauma-exposed from non-trauma exposed suicide death. Such findings suggest unique biological backgrounds and may refine identification and treatment of this high-risk group.

BackgroundAnorexia nervosa (AN) is a severe psychiatric disorder associated with profound malnutrition, multisystem medical complications, and one of the highest mortality rates among mental illnesses. Despite decades of research into its biological and neurocognitive mechanisms, effective pharmacological treatments remain limited. While systematic reviews synthesize results from published studies, clinical trial registries offer a complementary perspective by capturing ongoing research efforts, discontinued studies, and emerging therapeutic strategies that may not yet be reflected in the published literature. ObjectiveThis study aimed to characterize the landscape of clinical research in AN by systematically analyzing studies registered on ClinicalTrials.gov. MethodsWe conducted a structured analysis of studies registered on ClinicalTrials.gov related to AN. Trial characteristics, including study design, intervention type, phase classification, geographic distribution, and recruitment status, were extracted and analyzed using an automated text-based classification pipeline. ResultsNearly 400 studies investigating AN were identified over the past 25 years. Approximately 71% were classified as interventional studies; however, a large proportion were not associated with conventional clinical trial phases, suggesting that many registered trials correspond to mechanistic or exploratory investigations rather than therapeutic development programs. The geographic distribution of studies revealed a strong predominance of North America and Western Europe. A substantial proportion of trials were terminated or discontinued, highlighting the significant challenges associated with conducting interventional studies in this population. Observational studies generally included larger sample sizes than interventional trials. ConclusionsRegistry-based analyses provide valuable insights into the evolving landscape of clinical research in AN. Despite considerable scientific activity, important gaps remain between mechanistic knowledge and the development of therapeutic interventions. Understanding these gaps may help inform future translational research strategies aimed at improving treatment options for this severe disorder.