European Psychiatry

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.

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.

Individuals with psychiatric disorders have significantly higher mortality rates than the general population. Despite identifying risk factors, few attempts have been made to systematically use this information to stratify mortality risk. To address this gap, we developed and externally validated a risk prediction model using national healthcare and social register data from two countries. Nationwide register data were used to create a Swedish cohort (n=530,201) for model development and a Finnish cohort (n=254,691) for external validation. Participants, aged 15-60 years at assessment, had diagnoses of common mental illnesses (schizophrenia-spectrum disorder, bipolar disorder, depression, or anxiety disorders) made in specialist care. A multivariable logistic regression model assessed predictors of 5-year mortality risk. Model performance was evaluated using discrimination (area under the curve [AUC]) and calibration metrics, including intercept, slope, and visual plots. Internal validation employed bootstrapping. A total of 18,619 (3.5%) Swedish and 11,206 (4.4%) Finnish patients died within 5 years of assessment in secondary care. The model incorporated 25 predictors across four domains: sociodemographic factors, clinical characteristics, somatic comorbidities, and psychotropic medication use. External validation demonstrated excellent discrimination (AUC = 0.803; 95% CI: 0.799-0.807). The higher mortality rate in the Finnish cohort required recalibration of the intercept, and post-adjustment calibration was good (intercept: 0.00; 95% CI: -0.02; 0.02; slope: 0.99; 95% CI: 0.98-1.01). Model findings were translated into a web-based calculator (MortOx) for research, training, and potential clinical use. A transdiagnostic prognostic model based on 25 predictors accurately predicts 5-year mortality risk in mental illness. Linkage to interventions is needed to evaluate clinical impact.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Understanding what is predictive of early adulthood depression could help inform resource targeting and direction of approaches aiming to alleviate the personal, cultural, and economic burden of depression and similar disorders. This work uses multivariate longitudinal data (n=3487) measured from conception to adulthood from a UK based birth cohort of young adults (Avon Longitudinal Study of Parents and Children (ALSPAC)) and a machine learning approach to a) investigate whether episodes of early adulthood depression can be predicted from various risk factors across early life and adolescence, and b) interpret which factors are most important for predicting episodes of early adulthood depression. Here, we build four models to predict participants having an episode of early adulthood depression and show that the highest performing model can predict if people experienced symptoms of depression with an F1-score of 0.66, using a range of biological, behavioural, and early life experience related risk factors.

ObjectiveThe Eating Disorders Genetics Initiative United Kingdom (EDGI UK), part of the National Institute for Health and Care Research (NIHR) Mental Health BioResource, aims to deepen our understanding of the environmental and genetic aetiology of eating disorders. EDGI UK launched in February 2020 and is partnered with the UK eating disorders charity, Beat. There are multiple EDGI branches worldwide. MethodEDGI UK recruits via media and clinical services. Anyone living in England, at least 16 years old, with a lifetime probable or clinical eating disorder is eligible to sign up online: edgiuk.org. Participants complete online questionnaires, donate a saliva sample for genetic analysis, and consent to medical record linkage and recontact for future studies. ResultsAs of September 2022, EDGI UK has recruited 8,397 survey participants: 98% female, 93% white, 97.7% cisgender, 67% heterosexual, and 52% have a university degree. Half (51.7%) of participants have returned their saliva kit. The most common diagnoses are anorexia nervosa (42.7%), atypical anorexia nervosa (31.4%), bulimia nervosa (33.2%), binge-eating disorder (14.6%), and purging disorder (33.5%). ConclusionEDGI UK is the largest UK eating disorders study but needs to increase its diversity, and efforts are underway to do so. It also offers a unique opportunity to accelerate eating disorder research, and collaboration between researchers and participants with lived experience, with unparalleled sample size.

ObjectiveTo identify risk factors for first episodes of suicidal ideation and suicide attempt in people with functional/dissociative seizures (FDS) or epilepsy. MethodsRetrospective cohort study from the UKs largest tertiary mental health care provider, with linked national admission data from the Hospital Episode Statistics (HES). Participants were 2383 people with a primary or secondary diagnosis of FDS or epilepsy attending between 01/01/2007 and 18/06/2021. Outcomes were a first report of suicidal ideation and a first hospital admission for suicide attempt (ICD-10 X60-X84). Demographic and clinical risk factors were assessed using multivariable bias-reduced binomial-response generalised linear models. ResultsIn both groups, ethnic minorities had significantly reduced odds of hospitalisations following a suicide attempt (OR: 0.45 - 0.49). Disorder-specific risk factors were gender, age, and comorbidity profile. In FDS, both genders had similar risk of suicidality; younger age was a risk factor for both outcomes (OR: 0.16 - 1.91) and a diagnosis of Depression or Personality Disorders was associated with higher odds of reporting suicidal ideation (OR: 1.91 and 3.01 respectively). In epilepsy, females had higher odds of being hospitalised following suicide attempt (OR: 1.64). Age had a quadratic association with both outcomes (OR: 0.88 - 1.06). A Substance Abuse Disorder was associated to higher suicidal ideation (OR: 2.67) whilst Developmental Disorders lowered the risk (OR: 0.16 - 0.24). ConclusionsThis is the first study systematically reporting risk factors for suicidality in people with FDS. Results for the large epilepsy cohort complement previous studies and will be useful in future meta-analyses. KEY MESSAGE BOXWhat is already known on this topic O_LIPeople with epilepsy and people with functional/dissociative seizures (FDS) are at elevated risk of suicide C_LI Identification of risk factors for suicidal ideation and suicide attempt in high-risk groups is essential to inform risk prevention strategies What this study adds O_LISeveral factors are associated with suicidality in FDS, including age, ethnicity, comorbid Depression and Personality Disorders. C_LIO_LIIn epilepsy, suicidality is associated with age, gender, ethnicity, comorbid Substance Misuse Disorder and Developmental Disorders. C_LI How this study might affect research, practice or policy O_LIWhilst disorder-specific factors will be useful to identify groups at higher risk in clinical settings, general risk factors can be target of population-based preventive strategies. C_LI

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.

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.

During recent decades, parenthood has declined in many Western countries. Simultaneously, mental disorders have become more prevalent. We investigated the link between parenthood and mental health in the entire Norwegian-born population aged 31 to 80 from 2006 to 2019 (n=2,234,087). We used logistic regression models on national register data and included sibling- and twin-matched analyses to address unobserved confounders. Parenthood was associated with a lower risk of mental disorders, including depressive and anxiety disorders. For symptoms related to mental disorders, fathers had a reduced risk, while mothers had a slightly elevated risk. Mental health disparities between parents and non-parents were greater among men than among women and persisted across adulthood, before reducing at older ages. Our main findings were largely consistent in sibling- and twin-matched designs. The disparity between parents and non-parents increased over the study period, suggesting stronger selection into parenthood. Our findings highlight parenthood as a significant indicator of mental health inequalities, with its importance growing over time.