American Journal of Psychiatry

Objective: To develop and validate clinically relevant polygenic scores (PGS) for alcohol (AUD), cannabis (CanUD), opioid (OUD), tobacco (TUD), and polysubstance use disorders (polySUD) across African (AA), European (EA), and Latinx (LA) ancestry populations. Methods: Using multiple genome-wide association study summary statistics and PGS methods, substance use disorder PGS were developed and evaluated in Indiana Biobank samples (IB, N: 1,356-24,989), then top-performing PGS were validated in All of Us Research Program samples (AOU, N: 62,389-209,952). Case and controls were defined using ICD-9/10 codes. All participants were aged 18 years or older (>=21 years for AUD controls). Clinical relevance was defined as an odds ratio (OR) >=2 for individuals with the highest PGS determined based on disorder prevalence compared to everyone else. Results: In EA and LA, all PGS achieved clinically relevant performance in both IB and AOU (ORs: 2.00-9.10; P <= 3.87E-4). In AA, PGS met this threshold in IB (ORs: 2.02-2.71; P <= 2.20E-4) but not in AOU (ORs: 1.28-1.56; P <=0.03). Overall, OUD PGS showed the strongest associations in most analyses, followed by CanUD and polySUD. Generally, compared to female PGS, male PGS had higher or comparable ORs, but the differences were not significant except AUD PGS in AOU LA. Conclusions: PGS demonstrated clinically meaningful risk prediction for substance use disorders in EA and LA, supporting the feasibility of future clinical implementation for population-level screening. However, reduced performance in AA underscores the urgent need for more genetic studies in that population.

Background: Suicide attempts (SA) are elevated in individuals with autism spectrum disorder (ASD), but population-level data characterizing how SA prevalence varies across demographic and clinical subgroups - at the scale and granularity needed to inform evidence-based risk stratification - have been largely unavailable. This study examines SA prevalence across sex, age group, psychiatric comorbidity type, and substance use disorder subtype in the largest real-world ASD cohort to date. Methods: We conducted a retrospective cross-sectional analysis using Epic Cosmos electronic health record data from 2,311,171 individuals with ASD identified by International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes, spanning 2016-2025. SA prevalence was calculated with Wilson score 95% confidence intervals. Modified Poisson regression with robust variance estimation was used to estimate adjusted prevalence ratios (aPRs) for sex, age group, and psychiatric comorbidity. Unadjusted prevalence ratios (uPRs) were calculated separately for individual comorbidity types and substance use disorder subtypes. Results: Overall SA prevalence was 1.7% (38,160 individuals). Females showed higher SA prevalence than males (2.9% vs. 1.2%; aPR 1.61, 95% CI 1.35-1.92). SA prevalence peaked in the 15-24 age group overall (aPR 5.14, 95% CI 3.62-7.29), with sex-stratified analyses revealing that females peaked earlier (15-24 years; aPR 4.33, 95% CI 4.23-4.43) than males (25-34 years; aPR 6.08, 95% CI 5.05-7.31) - a sex-specific divergence in the timing of peak SA prevalence not previously documented in ASD. Having at least one psychiatric comorbidity was associated with a 30-fold higher SA prevalence (aPR 30.56, 95% CI 26.03-35.89), with the effect stronger in females (aPR 36.54, 95% CI 31.04-43.01) than males (aPR 27.77, 95% CI 22.65-34.06). Among comorbidity subtypes, substance-related disorders showed the highest crude SA prevalence (16.9%; uPR 134.20, 95% CI 67.68-266.10). Subtype-level characterization revealed SA prevalence ranging from 19.7% to 24.3% across all five substance use disorder subtypes examined, with stimulant use disorder showing the highest unadjusted prevalence ratio of any subtype (uPR 196.11, 95% CI 100.63-382.20). Conclusion: SA prevalence in ASD is markedly elevated relative to the general population and varies meaningfully by sex, age, and comorbidity profile in clinically important ways. Females carry a disproportionate SA burden relative to males, with peak vulnerability arriving earlier in adolescence; males peak later in young adulthood and remain at elevated risk into midlife. Psychiatric comorbidity - particularly substance use disorders - is associated with the largest relative elevations in SA prevalence. These population-level estimates are directly applicable to EHR-based risk stratification models and can inform the development of ASD-specific clinical decision support tools that concentrate surveillance and intervention on those at demonstrably elevated risk, rather than applying uniform approaches across a heterogeneous population.

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.

In the past two decades, the focus on genome-wide association studies in large samples of unrelated patients has overshadowed family genetic studies. Therefore, little is still known about the levels and effects of the transmission of polygenic risk scores (PRS) among familial cases of schizophrenia (SZ) or bipolar disorder (BD) and their unaffected relatives. Prior research has shown that PRS are elevated in both patients and young individuals at familial risk for BD and SZ. We sought to study the transmission of PRS in affected multigenerational families and non-affected adult relatives (NAARs) with or without other non-mood nonpsychotic DSM-IV diagnoses and unrelated non-affected individuals from the same population. We genotyped 1,117 participants divided in 48 families from the Eastern Quebec Schizophrenia and Bipolar Disorder Kindreds. PRSs for both SZ and BD were computed using Multivariate Lassosum. For both SZ PRS and BD PRS, SZ and BD cases present higher PRS compared to controls, replicating previous findings. Regardless of a diagnosis of other non-psychotic and non-mood conditions, NAARs presented higher PRS than the unrelated cohort. Crucially, a subset of families presented consistently low PRS transmission profiles across generations, falling below expectations from our polygenic inheritance model. When the effect of individual PRs is accounted for, we observed sex-specific associations between familial PRS and patients' symptom dimensions. Our results clearly demonstrate that polygenic inheritance alone does not adequately explain disease transmission in families. Such an approach may also clarify why some families exhibit dense clustering of cases despite minimal polygenic burden.

ImportanceCallous Unemotional (CU) traits are associated with significant clinical and neurophysiological heterogeneity that may affect treatment effectiveness. ObjectiveTo uncover neuroanatomical subtypes of CU traits using weakly-supervised machine learning and assess whether emerging subtypes differ on relevant clinical, temperamental, and environmental constructs. Design, Setting, and ParticipantsImaging data was from the longitudinal Adolescent, Brain, Cognitive Development (ABCD) Study. Participants were 9-10 years old at baseline (M=9.925, 69.9% male) and included 222 children with CU traits and 234 typically developing controls matched on age, sex and income. Main Outcomes and MeasuresThe weakly-supervised heterogeneity through discriminative analysis (HYDRA) model was trained on grey matter (GM) volumes from 84 regions of interest (ROIs) and tested for reproducibility using cross-validation and permutation testing. Derived subtypes were compared cross-sectionally and prospectively on relevant clinical, temperamental, and environmental measures and subsequent GM volume at 2-year follow up. ResultsHYDRA revealed an optimal 2-subtype solution within children with CU traits. Subtypes showed comparable levels of aggression that were significantly higher than typically developing controls. At the same time, subtype 1 had larger GM volume, fewer internalizing symptoms, and less adversity exposure, while subtype 2 was characterized by smaller GM volume, more internalizing symptoms, and more adversity exposure. Conclusions and RelevanceThis study provides evidence of neuroanatomically distinct subtypes of CU traits characterized by different clinical and etiological profiles, with implications for diagnosis and treatment.

Genetic instrumental variable studies can provide stronger insights into the causal relationships between smoking and mental illness than conventional observational studies because they are less susceptible to confounding and reverse causation. However, they still rely on genetic instruments that partly capture genetic influences shared with other substance use and socioeconomic status (SES), potentially biasing estimates of smoking-specific effects. We therefore aim to (1) develop a more specific genetic instrument for smoking that minimizes these shared influences and (2) use this instrument to examine the causal effects of smoking on psychiatric disorders. We applied Genomic Structural Equation Modelling to 19 European-ancestry GWAS summary statistics (7 smoking, 8 substance use and 4 SES phenotypes), deriving a novel smoking-specific genetic factor representing liability to smoking independent of shared substance-use and SES influences. We used this factor as an instrument in Mendelian Randomization analyses to test causal effects of smoking on eight psychiatric disorders. The smoking-specific factor was associated with 52 independent genome-wide significant loci. Genetically predicted smoking-specific liability was significantly causally associated with seven psychiatric disorders. These findings support a causal role of smoking in increasing the risk of multiple psychiatric disorders beyond influences shared with other substance use and SES, providing stronger evidence for smoking-specific effects and informing targeted smoking prevention and intervention strategies. More broadly, our findings highlight that the validity of Mendelian Randomization depends on the specificity of its genetic instruments. Future studies should strive to develop instruments that better isolate the exposure of interest from shared genetic influences, enabling more accurate identification of causal mechanisms.

Food addiction (FA) has been proposed as a phenotype sharing features with substance use disorders. Despite increasing recognition of food addiction as a behavioral phenotype with features overlapping substance use disorders, little is known about its prevalence or clinical significance among individuals with alcohol use disorder (AUD). Objective: To examine the prevalence of FA and to evaluate demographic, psychological, and alcohol-related correlates in individuals with AUD. Design, Setting, and Participants: This cross-sectional analysis included 743 adults with AUD who were either treatment seeking (Tx) (n = 534) for AUD and were enrolled in an inpatient program at the National Institutes of Health Clinical Center or not treatment-seeking (non Tx) (n = 209). Main Outcomes and Measures: FA symptoms were assessed using the Yale Food Addiction Scale, with >=2 symptoms categorized as FA in this report. Multivariable logistic regression models adjusted for age, education, and income were conducted separately within each cohort. Results: Among 743 adults with AUD, 238 (32.1%) met criteria for FA symptoms, with similar prevalence among Tx (32.6%) and nonTx (30.6%) participants despite marked differences in clinical characteristics. Across both cohorts, FA was independently associated with higher body mass index, greater psychological distress, and greater alcohol dependence severity. Childhood trauma and poorer sleep quality were additionally associated with FA among treatment-seeking participants, whereas alcohol-related measures differed according to treatment status. Conclusions and Relevance: FA was common among adults with AUD and was associated with greater psychological, behavioral, and metabolic burden regardless of treatment-seeking status. These findings suggest that FA identifies a clinically meaningful subgroup of individuals with AUD who may benefit from more comprehensive assessment and integrated treatment approaches.

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.

Psychiatric disorders are complex conditions characterized by substantial overlap in genetic risk and shared biological mechanisms. However, how individual risk genes contribute to shared disease mechanisms and disorder-specific phenotypes remains poorly understood. BRD1 has emerged as a chromatin-associated regulator with transdiagnostic relevance across psychiatric disorders and a central role in gene regulatory networks enriched for psychiatric risk genes. To investigate the biological consequences of reduced BRD1 function in a translationally relevant system, we generated a minipig model harboring a monoallelic deletion in BRD1 and performed longitudinal neuroimaging together with behavioral and multi-omics profiling. BRD1 haploinsufficient minipigs displayed normal growth and exploratory behavior but exhibited subtle age-dependent differences in motivational behavior. Despite the absence of overt developmental abnormalities, longitudinal neuroimaging revealed genotype-associated structural differences primarily involving the cerebral cortex and caudate nucleus, suggestive of altered neurodevelopmental trajectories. Integrated multi-omics analyses revealed striking convergence across transcriptomic and metabolomic datasets, identifying coordinated perturbations of mitochondrial function, redox regulation, and phospholipid metabolism across multiple brain regions. Notably, these molecular alterations were not restricted to the central nervous system, as peripheral multi-omics profiling revealed systemic metabolic alterations, including altered phospholipid composition and glucose metabolism. Together, these findings indicate that BRD1 haploinsufficiency is associated with coordinated neurodevelopmental and metabolic alterations across brain and peripheral tissues. More broadly, this study provides systems-level insight into how a psychiatric risk gene influences interconnected neurodevelopmental and metabolic processes across multiple levels of biological organization and highlights the value of large-animal multi-omics models for translational neuropsychiatric research.

Importance: Avoidant/restrictive food intake disorder (ARFID) is a feeding and eating disorder characterized by extremely restricted dietary variety and/or quantity resulting in significant physical health impairment and psychosocial dysfunction. ARFID frequently co-occurs with neurodevelopmental conditions, yet the extent to which this co-occurrence reflects shared genetic or environmental influences remains largely unknown, as few twin or genetic studies of ARFID have been conducted. Objective: To examine the extent to which genetic and environmental influences contribute to the association between a broad ARFID phenotype and neurodevelopmental traits. Design, Setting, and Participants: Population-based twin study using data from the Child and Adolescent Twin Study in Sweden, including 30,374 twins born 1992-2008. Main Outcomes and Measures: A broad ARFID phenotype was identified using a composite measure derived from parent reports and national health registers between ages 6 and 12 years. Parents completed measures of neurodevelopmental traits at age 9 or 12 years, including autism (subdomains: social communication problems and restricted/repetitive behaviors), attention-deficit/hyperactivity disorder (ADHD, subdomains: inattention and impulsivity/hyperactivity), tic disorders, learning disorders, oppositional defiant disorder, conduct disorder, obsessive-compulsive disorder (OCD), sensory perception problems, and sleep problems. Phenotypic associations were estimated using polyserial correlations. Bivariate twin models decomposed variance and covariance into genetic and environmental components. Results: Phenotypic correlations with the broad ARFID phenotype ranged from 0.18 (95% CI: 0.15-0.21) for OCD to 0.36 (95% CI: 0.33-0.38) for autism. Broad genetic correlations (rH; additive plus dominant genetic influences) ranged from 0.27 (95% CI: 0.21-0.33) for conduct disorder to 0.52 (95% CI: 0.44-0.60) for autism-restricted/repetitive behaviors. Genetic factors explained 77% to 95% of all phenotypic correlations. Non-shared environmental correlations were minimal to small, with the largest observed for autism (0.17; 95% CI: 0.08-0.26). Conclusions and Relevance: The broad ARFID phenotype shares substantial genetic influences with a number of neurodevelopmental traits. These findings suggest that the frequent co-occurrence of ARFID with neurodevelopmental traits largely reflects shared genetic influences rather than overlapping environmental influences, supporting the conceptualization of ARFID within a broader neurodevelopmental framework.

Background: Children and young people (CYP) with intellectual disability (ID) frequently have co-occurring neurodevelopmental (ND) and mental health (MH) difficulties. While copy number variants (CNVs) are identified as an important aetiology of ID, it is unclear whether and how CNV risk scores predict ND and MH characteristics within the CNV-associated ID population. Methods: We analysed data from the UK-based IMAGINE-ID cohort of CYP (aged 4-19 years) with ID and clinically-reported CNVs (N = 1,640). CNVs were annotated with Gencode 19 in ENSEMBL to calculate CNV risk scores, including summed probability of loss-of-function intolerance (pLI) and dosage sensitivity. Multivariate regression models examined the prediction of CNV variables and inheritance on ND and MH characteristics, assessed via the Development and Well-Being Assessment (DAWBA). Post-hoc analyses explored CNV variable stratification (lower vs. higher range pLI). Results: Higher summed pLI scores (indexing CNV genes' intolerance to loss of function) unexpectedly predicted fewer MH difficulties and a lower likelihood of ND diagnoses, even after accounting for demographic factors and CNV inheritance. Post-hoc analyses identified a threshold effect. Within the lower pLI range, higher pLI scores were associated with greater MH difficulties, consistent with findings from population-based samples. In contrast, within the higher pLI range, higher pLI scores were associated with fewer MH difficulties (among individuals more likely to have severe ID). Conclusion: These findings challenge the assumption that CNV genomic "risk scores" universally predict ND and MH difficulties. Instead, within CNV-associated ID, complex relationships exist between CNV risk scores, inheritance and phenotypes. These insights emphasise the necessity of integrating genomic results with familial and developmental context to understand individual vulnerabilities and support needs.

Background: Opioid use disorder (OUD) is heritable, yet most genome-wide association studies (GWAS) have focused on European populations, leaving the genetic architecture of OUD in non-European populations underexplored. Methods: We conducted GWAS of OUD across three ancestries using electronic health records and genomic data from 52,357 All of Us Research Program participants (8,912 cases; 43,445 matched opioid-exposed controls; 48.5% female). Participants were stratified into European (EUR), African (AFR), and Admixed American (AMR) ancestry groups for logistic regression GWAS, with independent replication in the Million Veteran Program. We then applied the deep-learning model AlphaGenome to predict the tissue-specific transcriptomic and splicing consequences of top risk variants across 13 reward-pathway brain regions. Results: We identified and replicated a novel DDX6 risk locus, alongside established OPRM1 and FURIN signals. AlphaGenome predicted the DDX6 regulatory allele downregulates the stress-resistance gene FOXR1 in the nucleus accumbens, while the protective OPRM1 variant (rs1799971) upregulates OPRM1 expression across reward networks. Other signals of interest included IL6R and SHISA9 (EUR); GHR (AFR); and ASTN2 (AMR). Conclusions: This study identifies DDX6 as a novel OUD risk locus, replicates associations with OPRM1 and FURIN, and highlights biologically plausible ancestry-specific signals in AFR and AMR populations. We also replicated top variants in an independent population. Finally, integrating GWAS with deep-learning annotations provides specific, localized biological hypotheses to guide future experimental validation and targeted therapeutics.

Background: Bipolar disorder (BIP) and obsessive-compulsive disorder (OCD) frequently co-occur and show evidence of genetic overlap, yet the specific pleiotropic loci and their functional mechanisms remain unclear. Methods: We conducted large-scale genetic analyses using GWAS summary statistics for BIP and OCD, excluding 23andMe data. We applied conjunctional FDR analysis to identify pleiotropic variants jointly associated with BIP and OCD, followed by integrative annotation through transcriptomic (eQTL, sQTL), epigenomic (mQTL, haQTL), and proteomic (pQTL, histone PTM) data. SMR analysis was used to prioritize putative regulatory effects, while AlphaGenome predictions and targeted histone proteomics were employed to evaluate allele-specific chromatin changes. Results: We observed a significant genetic correlation (rg = 0.38, P = 3.8 x 10-29) and extensive polygenic overlap between BIP and OCD. Bidirectional MR supported causal effects in both directions, with stronger evidence for BIP influencing OCD risk. ConjFDR analysis revealed 2,143 pleiotropic SNPs jointly associated with BIP and OCD, with convergent signals at the ITIH3/ITIH4 locus. Summary-data-based Mendelian randomization (SMR) and colocalization with multi-omic QTLs (eQTL, pQTL, mQTL, and haQTL) further prioritized the ITIH3/4 locus, where multiple SNPs (e.g., rs3774364) colocalized with H3K27ac histone acetylation QTLs in the prefrontal cortex (PP_H4 > 0.5). Integrated PBMC RNA-seq and complementary histone mass spectrometry linked immune--ECM transcriptional activity to exploratory global histone acetylation changes in BIP and OCS-BIP, with suggestive alterations in H3K27ac-containing peptides. Conclusions: Our multi-omic analysis highlights ITIH3/ITIH4 as a prioritized pleiotropic locus for BIP and OCD. Epigenetic regulation, particularly through histone acetylation, may underlie shared susceptibility and offers a novel mechanistic link between these psychiatric disorders.

Background. Cord blood DNA methylation profile scores (MPSs) based on genetic and pre-/perinatal risk factors for neurodevelopmental conditions (NDCs) may capture downstream biological effects and help understand how combined exposure signals contribute to NDC risk. Methods. Using data from two longitudinal birth cohorts, Generation R (N-train = 1856, N-test = 476) and ALSPAC (N-validation= 832), we developed cord blood MPSs based on genetic and pre-/perinatal NDC risk factors. We assessed individual and combined predictive performance of risk factors and MPSs for eight childhood psychiatric outcomes (four broad, four specific), measured between ages 5 and 14 years. We also evaluated if the MPSs could be combined into a composite "transmission load" MPS. Results. We validated four novel MPSs: maternal age, birthweight, and genetic liability for ADHD and schizophrenia (r range = 0.08 to 0.29) and included two previously validated MPSs: maternal smoking and gestational age (r range = 0.42 to 0.63). Jointly modeling the six MPSs with their corresponding risk factors explained on average 3.3% of variance in outcomes, higher than that explained by risk factors (1.8%) or MPSs alone (1.6%), indicating complementary sources of risk. The "transmission load" MPS did not replicate due to heterogeneous contributions of the predictors across cohorts. Conclusions. The four novel MPSs based on genetic and pre-/perinatal risk factors can serve as valuable tools for future research. Integrating genetic and prenatal risk factors with DNA methylation at birth can provide insights into their individual and joint contributions to early psychiatric risk and may improve prediction.

Importance: Individuals with psychiatric disorders face elevated cardiometabolic risk which is linked to increased mortality. The extent to which this reflects shared pathogenesis or the downstream effects of illness and treatment remains poorly understood. Objective: To characterize the direct pleiotropic effects of psychiatric genetic liability on circulating metabolites and aggregate cardiometabolic risk, independent of psychiatric diagnosis and psychotropic medication use. Design: Cohort study. Setting: Mass General Brigham Biobank (MGBB). Participants: MGBB participants with metabolomic profiling, genomic data, and linked electronic health records. Exposures: Genetic liability to nine psychiatric disorders quantified using polygenic risk scores (PRS): attention deficit/hyperactivity disorder (ADHD), anorexia nervosa (ANO), anxiety disorder (ANX), autism spectrum disorder (ASD), bipolar disorder (BD), major depressive disorder (MDD), PTSD, schizophrenia (SCZ), and substance use disorder (SUD). Main Outcomes and Measures: 249 circulating metabolites and four metabolomic risk scores (MRS) for type 2 diabetes, myocardial infarction, ischemic stroke, and vascular dementia. PRS-metabolite associations were estimated using nested models adjusting for lifetime psychiatric diagnosis and psychotropic medication use. Results: Across 25,290 participants, we identified 604 significant PRS-metabolite associations (Bonferroni p< 1.36 x 10-4), of which 89% persisted after adjustment for lifetime diagnosis and medication use, suggesting that the direct genetic effects on metabolism are largely independent of illness or treatment. PRS for MDD, PTSD, and ADHD showed the most extensive dysregulation, with a transdiagnostic pattern of elevated lipids and systemic inflammation, specifically triglycerides ({beta} = 0.04 to 0.05, all p< 4.4 x10-13) and glycoprotein acetyls ({beta} = 0.05, all p< 2.2 x10-16). Notably, PRS for SCZ and BD showed minimal metabolite dysregulation despite having the strongest association with their target diagnoses. PRS for MDD, PTSD, ADHD, and SUD were associated with increased MRS across cardiometabolic conditions ({beta} = 0.03 to 0.08, all p< 2.1 x10-4). Sensitivity analyses controlling for BMI or excluding participants without any psychiatric history (N: 21,305 and 11,150, respectively) showed a similar pattern. Conclusions and Relevance: Psychiatric genetic liability is associated with systemic metabolic dysregulation independent of illness onset or treatment, supporting a partially pleiotropic basis for psychiatric-cardiometabolic comorbidity.

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.

Addiction is a complex and heritable trait which progresses through several developmental stages, each of which is presumably influenced by multiple partially overlapping genetic factors. Cocaine initially produces rewarding effects, followed by aversive effects including anxiety, craving, anhedonia, and withdrawal. These aversive effects have been suggested to contribute to the etiology of cocaine use disorders (CUD), as repeated exposure is thought to desensitize the rewarding effects and sensitize the aversive effects through a process involving both aberrant reward-based learning and aberrant avoidance-based learning. We examined the genetic basis of aversion learning using both food-based and cocaine-based behavioral assays in outbred Heterogenous Stock (HS) rats. A total of 1,074 HS rats (35.3% male) underwent runway operant cocaine-seeking, food-based progressive ratio and punishment testing, and locomotion testing. These phenotypes were significantly heritable (with h2 estimates as high as 0.307) and identified significant (p < 0.05) genetic loci related to avoidance-based learning including from the punishment task on Chromosomes 2, 3, 5, and 6, and the cocaine-operant runway latency task on Chromosome X. 172 positional candidate genes were identified from significant and suggestive loci, including Cdh10, Cdh12, Cdh18 which have previously been associated with smoking initiation from human GWAS, Adcy3, Cfap206, and Drc1 which are associated with primary neuronal cilia, as well as SNPs associated with novelty-related and social interaction phenotypes in independent samples of HS rats. Our results suggest that these aversion learning phenotypes are themselves complex heritable traits influenced by multiple genetic loci, which may pleiotropically affect other aspects of addiction biology.

Background: Long COVID is a syndrome characterized by symptoms and conditions across all biological systems. This breadth of Long COVID phenotypes impedes efforts to identify the mechanistic pathways of Long COVID. Low serotonin may play a role in long-term sequelae of COVID-19, and selective serotonin reuptake inhibitors (SSRIs) may prevent these sequelae. Evaluation of the relationship between SSRIs and distinct categories of symptoms and conditions associated with Long COVID can highlight the mechanistic pathways that drive these relationships. Methods: We evaluated electronic health record data from a retrospective cohort of patients in the National Clinical Cohort Collaborative with comorbid depression and COVID-19 between October 2021 and February 2024. We estimated the relationship between SSRI prescription (versus no SSRI prescription) during acute COVID-19 and the one-year cumulative incidence of Long COVID-related conditions and symptoms across 14 human phenotype ontology categories. We applied Super Learner and targeted maximum likelihood estimation to estimate risk ratios while adjusting for confounders of interest and correcting for false discoveries from repeated testing. Results: We evaluated EHR data from 542,938 patients. We found that patients who were prescribed SSRIs during COVID-19 had a significantly lower risk of symptoms and conditions related to gastrointestinal factors (adjusted risk ratio (aRR) 0.95, 95% CI 0.92, 0.97), general health (aRR 0.91, 95% CI 0.88, 0.95), headaches (aRR 0.96, 95% CI 0.92, 0.99) and skin (aRR 0.92, 95% CI 0.87, 0.98). Discussion: We found that the prescription of SSRIs during acute COVID-19 was associated with a significantly lower risk of post-COVID sequelae related to gastrointestinal, headache-related, skin-related, and general symptoms and conditions, compared with no SSRI prescription. These findings highlight the role of serotonin in Long COVID and specific sequelae that may be reduced by SSRIs.

Objective Psychosis spectrum symptoms (PSS) are common among children and youth. These symptoms may be clinically significant as studies indicate a heightened risk of mental health disorders, in general, as well as psychotic disorders, specifically, in youth that endorse PSS. This systematic review and meta-analysis investigates the longitudinal association between PSS in children and youth and subsequent mental health diagnosis. Methods A comprehensive search of Ovid Medline, PsycINFO, and EMBASE databases was conducted to identify longitudinal studies that: (i) assess PSS at a baseline timepoint, (ii) in individuals under 25 years, and (iii) assess mental health disorder diagnosis using a structured assessment at a later time point in the same sample. We conducted a meta-analysis and calculated pooled odds ratios (ORs) for mental health and psychotic disorders using random-effects models. Post-hoc meta-regressions were performed to examine the influence of a number of moderators on the relationship between earlier recorded PSS and subsequent mental health disorders or psychotic disorders. Results The search yielded 41 eligible studies of which 25 were included in the meta-analysis. Most included studies assessed PSS using brief self-report measures and recruited their samples from clinical or community settings. Among children and youth without an identified mental health diagnosis at baseline assessment, baseline PSS were associated with a 2-fold (OR = 2.07, CI = 1.61 - 2.66, I2 = 86.92%, p < 0.0001) increased risk of meeting diagnostic criteria for subsequent mental health disorder diagnosis and a 3-fold increased risk (OR = 3.11, CI = 2.11 - 4.58, (I2 = 60.93%, p < 0.0090) of meeting diagnostic criteria for a subsequent psychotic disorder diagnosis with a minimum 1 year follow-up time from baseline assessment. Meta-regression analysis indicated that study quality and sample size explained a substantial proportion of between-study heterogeneity for psychotic disorder outcomes. Conclusions Our results suggest that administration of simple self-report measures of PSS in both clinical and community settings may be helpful to identify children and youth at higher risk of subsequently meeting criteria for a mental disorder generally, and for a severe mental illness (i.e., psychotic disorder), specifically. Future longitudinal studies should focus on improving study design characteristics to increase confidence in identified longitudinal associations. The results of our work suggests that integration of self-report measures of PSS may be useful in a variety of settings to identify youth at increased risk of subsequent mental illness.

Background: Cannabis is one of the most widely used psychoactive substances worldwide. {Delta}-tetrahydrocannabinol ({Delta}-THC) is the main contributor to cannabis-induced effects such as euphoria, anxiety, and psychotomimetic effects, and is metabolized by several hepatic enzymes, including CYP3A4. There are interindividual differences in how cannabis affects users, which have substantial genetic contributors. Methods: We examined how real-time effects of {Delta}-THC on psychotomimetic measures and on subjective effects of "high", sadness and anxiety in 188 healthy volunteers in a laboratory infusion paradigm, relate to polygenic risk scores (PRS) for cannabis lifetime use (CanLU), cannabis use disorder (CanUD), and CYP3A4 expression. Results: CYP3A4 expression PRS was significantly associated with {Delta}-THC-induced psychotomimetic effects. Genetic liability to use and misuse cannabis is potentially associated with lower {Delta}-THC-induced psychotomimetic symptoms. CanLU PRS nominally predicted enhanced {Delta}-THC-induced "high", while CanUD PRS predicted it to be lower. Conclusions: Our findings suggest that genetic liability to produce more CYP3A4 enzyme may be associated with faster {Delta}-THC degradation and the consequential diminution of the latter's effects. Nominal effects suggest that aversive outcomes may reduce cannabis use and use disorder genetic liability, and that CanUD subjects may need higher {Delta}-THC doses to experience euphoria ("high"). In total, this study provides novel insights regarding some of the specific genetic factors that influence interindividual variability in {Delta}-THC effects, mainly via {Delta}-THC metabolism.