JAMA Psychiatry

BackgroundThe Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial was designed to give guidance in selecting the best next-step treatment for depressed patients who did not remit during their first, and/or subsequent, antidepressant trial, with up to four trials per patient. Our prior research documented protocol violations which inflated STAR*Ds reported cumulative remission rate by 91.4%. A similar reanalysis of the step-2 drug-switch trial has not been done until now. MethodsWe reanalyzed the patient-level dataset of STAR*Ds drug-switch treatment therapies--with fidelity to the original research protocol and related publications--to determine whether there were clinically-relevant differences in results compared to the original publication. ResultsWhile our reanalysis largely comported with STAR*Ds published findings of no significant differences between drug-switch treatments, we found the following discrepancies: Lower than reported step-2 remission rates ranging from 16.2 to 19.3% (versus 17.6 to 24.8%); A significant increase in treatment-emergent suicidal ideation during the step-2 drug-switch therapies ranging from 11.2 to 15.0% compared to step-1 citalopram treatment (9.0%); A four times greater number of severe suicidal behaviors reported by the treating clinicians compared to the published suicide-related Serious Adverse Events (16 versus 4); and A sustained remission rate of only 3.1 to 8.4%. ConclusionCompared to the original publication, our reanalysis found lower remission rates and more suicidal risk than reported. This adds to the discrepancies found in our prior reanalysis and also to the finding that switching antidepressants is not well supported by the evidence.

BackgroundThe STAR*D trial is the most influential study of sequential antidepressant treatment strategies. However, major STAR*D publications deviated from the protocol-defined analytic plan. Prior re-analyses found lower cumulative remission rates than STAR*D publications reported, sustained remission rates of only 3.1 to 8.4% at 12 months, and high rates of treatment-emergent suicidal ideation (TESI) during medication-switch therapy. A similar reanalysis is warranted for STAR*Ds augmentation study in which citalopram was augmented with sustained-release bupropion or buspirone. MethodsWe reanalyzed STAR*Ds patient-level augmentation dataset with fidelity to the original protocol or relevant STAR*D publications where the protocol did not prespecify an analytic plan. ResultsThis reanalysis identified 124 patients (21.9% of enrolled subjects) who were inappropriately included in the original STAR*D analysis, including 54 who were in protocol-defined remission before starting augmentation therapy. Remission rates as defined in the protocol were lower than reported in the original publication for bupropion SR (25.0% vs 29.7%) and buspirone (25.8% vs. 30.1%). Using a secondary definition of remission, bupropion SRs rate was significantly lower than reported in original publications (29.2% vs. 39.0%). Sustained remission through 12 months was low (4.9-12.5%). TESI rates were significantly higher for buspirone (13.9%) than bupropion SR (3.6%) augmentation. ConclusionCompared with the original STAR*D publication, our reanalysis identified inflated remission rates, low sustained remission, and marked differences in TESI risk between augmentation strategies. These findings suggest that both treatments offer lower acute and sustained benefit than is widely understood, with buspirone associated with more TESI.

STRUCTURED ABSTRACTO_ST_ABSBackground and HypothesisC_ST_ABSCannabis use has been linked to psychotic-like experiences (PLEs). Amid increasing legalization, we examined the extent to which cannabis use is associated with PLEs after adjusting for other risk factors in a contemporary United States sample. Study DesignWe performed a cross-sectional analysis of self-reported cannabis use and four types of self-reported PLEs (auditory and visual perceptual distortions, referential ideation, and persecutory ideation) in the population-based biobank, the All of Us Research Program release 8 (maximum analytic N = 62,153). Study ResultsCannabis ever-use (ORs = 1.21 - 1.44, p-values < 2.7e-6) and more frequent past 3-month cannabis use (within lifetime ever-users) were associated with all four PLEs ({chi}2(4) = 21.06 - 70.09, p-values = 3.08e-4 to 2.17e-14), and these associations remained when adjusting for personal and family history of schizophrenia and polygenic liability for schizophrenia. The schizophrenia polygenic score, but not cannabis use frequency, was correlated with greater likelihood of being prescribed medication for the PLEs. When adjusting for lifetime ever-use of other substances, cannabis ever-use was no longer associated with PLEs, while methamphetamine use, cigarette use, and opioid use were associated with PLEs (ORs = 1.22 to 1.65, p-values < 1.68e-05). ConclusionsPrior associations between cannabis use and PLEs may have been confounded by comorbid use of other substances. Future studies that distinguish cannabis use from other substance use in the etiology of PLEs could provide insight into this transdiagnostic construct.

ObjectivePersons with substance use disorders (SUD) often suffer from additional comorbidities. Researchers have explored this overlap via phenome wide association studies (PheWAS). However, PheWAS are largely cross-sectional, limiting our understanding of whether diagnoses predate development of an SUD. We characterize whether polygenic scores (PGS) are associated with time to comorbid diagnoses in electronic health records (EHR) after the first documented SUD diagnosis. MethodsUsing data from All of Us (N = 393,596), we explored: 1) whether social determinants of health (SDoH) are associated with lifetime risk of SUD (N cases = 42,568) and 2) within a subset those with a diagnosed SUD and available genetic data SUD (N = 21,357), whether PGS for alcohol use disorders, cannabis use disorders, depression, externalizing, post-traumatic stress disorder, and schizophrenia were associated with subsequent diagnoses via a phenome-wide survival analysis. ResultsMultiple SDoH were associated with lifetime SUD diagnosis, with annual household income having the largest overall associations (e.g., <$10K annually vs $100K-$150K annually: OR = 3.89, 95% CI = 3.66, 4.13). There were 101 phenome-wide significant PGS associations with subsequent diagnoses across various bodily systems. PGSs for alcohol use disorders, post-traumatic stress disorder, and schizophrenia were each associated with time to their respective diagnoses. ConclusionsSocial determinants, especially those related to income, have profound associations with lifetime SUD risk. Additionally, PGS for psychiatric conditions are associated with multiple post-SUD diagnoses within those with a SUD, suggesting PGS may capture information beyond lifetime risk, including timing and severity of comorbidities related to SUD.

BackgroundExecutive function consists of several cognitive control processes that are able to regulate lower level processes. Poorer performance in tasks designed to test executive function is associated with a range of psychopathologies such as schizophrenia, major depressive disorder (MDD) and anxiety, as well as with smoking and alcohol consumption. Despite these well-documented associations, whether they reflect causal relationships, and if so in what direction, remains unclear. We aimed to establish whether there is a causal relationship between a latent factor for performance on multiple executive function tasks - which we refer to as common executive function (cEF) - and liability to schizophrenia, MDD, anxiety, smoking initiation, alcohol consumption, alcohol dependence and cannabis use disorder (CUD), and the directionality of any relationship observed. MethodsWe used a two-sample bidirectional Mendelian randomisation (MR) approach using genome-wide association study (GWAS) summary data from large cohorts (N=17,310 to 848,460) to examine whether causal relationships exist, and if so in which direction. ResultsWe found evidence of a causal effect of increased cEF on reduced schizophrenia liability (IVW: OR=0.10; 95% CI 0.05 to 0.19; p-value=3.43x10-12), reduced MDD liability (IVW: OR=0.52; 95% CI 0.38 to 0.72; p-value=5.23x10-05), decreased drinks per week (IVW: {beta}=-0.06; 95% CI -0.10 to -0.02; p-value=0.003), and reduced CUD liability (IVW: OR=0.27; 95% CI 0.12 to 0.61; p-value=1.58x10-03). We also found evidence of a causal effect of increased schizophrenia liability on decreased cEF (IVW: {beta}=-0.04; 95% CI -0.04 to -0.03; p-value=3.25x10-27), as well as smoking initiation on decreased cEF (IVW: {beta}=-0.06; 95%CI -0.09 to -0.03; p-value=6.11x10-05). ConclusionOur results indicate a potential bidirectional causal relationship between a latent factor measure of executive function (cEF) and schizophrenia liability, a possible causal effect of increased cEF on reduced MDD liability, CUD liability, and alcohol consumption, and a possible causal effect of smoking initiation on decreased cEF. These results suggest that executive function should be considered as a potential risk factor for some mental health and substance use outcomes, and may also be impacted by mental health (particularly schizophrenia). Further studies are required to improve our understanding of the underlying mechanisms of these effects, but our results suggest that executive function may be a promising intervention target. These results may therefore inform the prioritisation of experimental medicine studies (e.g., of executive function interventions), for both mental health and substance use outcomes, to improve the likelihood of successful translation.

BackgroundLongitudinal measurement of depression severity in outpatient psychiatric care is limited by infrequent standardized assessments. Although psychiatric clinical notes capture illness burden and functional impairment, this information is rarely quantified for analysis. ObjectiveTo evaluate whether large language models (LLMs) can infer clinically meaningful measures of depression severity from outpatient psychiatry notes. MethodsWe sampled 91,651 outpatient psychiatry notes from 8,287 adult patients across 58 clinics within a large academic medical center between 2015 and 2021. A HIPAA-compliant LLM (OpenAI GPT-5.2) was prompted to independently estimate three depression severity scores (Patient Health Questionnaire-9 [PHQ-9], Hamilton Depression Rating Scale [HAM-D], and depression-specific Clinical Global Impression-Severity [CGI-S]) from notes, with patient-reported PHQ-9 content within notes redacted to prevent biasing. Convergent validity was assessed against patient-reported PHQ-9 (n=3,757), study-clinician chart review (n=125), and treating-clinician suicide risk assessments (SRA; n=2,985). Predictive validity was evaluated using survival models of antidepressant switching and psychiatric emergency visits. Discriminant validity across diagnoses and consistency across demographic groups and clinics were also evaluated. Results10.8% of eligible visits had a PHQ-9 recorded within 7 days before the encounter. LLM-inferred PHQ-9 scores showed moderate agreement with patient-reported PHQ-9 (Cohens {kappa}=0.64, 95%CI:0.62-0.66; Pearson r=0.67, 95%CI: 0.65-0.68). Stronger agreement was found between LLM CGI-S and study-clinician chart review ({kappa}rater1=0.79, 95%CI: 0.70-0.85; {kappa}rater2=0.67, 95%CI: 0.58-0.77; r=0.86 with mean rating, 95%CI: 0.80-0.90). In prospective analyses, LLM CGI-S predicted antidepressant switching (C-index=0.60; CI95%: 0.58-0.62) and psychiatric emergency visits (C-index=0.63; 95%CI: 0.57-0.68), which was comparable to the predictive performance of patient-reported PHQ-9 and treating-clinician SRA. Correlations between LLM CGI-S and patient-reported PHQ-9 were consistent across clinics (I2<0.1) but significantly lower among Black (r=0.48, 95%CI: 0.38-0.57) and Hispanic (r=0.43, 95%CI: 0.27-0.56) patients. ConclusionsLLM-inferred depression severity scores from psychiatric outpatient notes support longitudinal, standardized phenotyping of depression severity, such as for routine outcome monitoring. These results have implications for facilitating genetic, pharmacoepidemiologic, and antidepressant treatment effectiveness studies using real-world evidence.

ImportanceAs the percentage of young people in the United States identifying as transgender and gender diverse increases, more youth face identity-linked social and structural stigma and discrimination. Little is known about the impact of stigma on psychotic-like experiences in gender diverse youth. ObjectiveTo assess the impact of bullying victimization and state-level policies on psychotic-like experiences among gender diverse youth. DesignIn this prospective cohort study, cross-sectional and longitudinal analyses were conducted using data collected between 2017-2022 across 17 states. SettingThe Adolescent Brain Cognitive Development (ABCD) Study is a U.S. population-based longitudinal study that follows and deeply phenotypes adolescents from the age of 9 to 18. ParticipantsCross-sectional analyses included data from 9,112 participants (mean age=13 {+/-} 0.6) collected between 2019 and 2022. Longitudinal analyses comprised 4,529 participants with data collected across 5 waves between 2017 and 2022. ExposuresSelf-reported frequency of bullying victimization and data on annual state-level policies related to gender identity. Main OutcomesSelf-reported psychotic-like experiences and associated distress, measured by the Prodromal Questionnaire - Brief Child Version. ResultsBased on a dimensional measure of gender, 689 adolescents were identified as most gender diverse (i.e., least congruent with birth-assigned sex) and 8,240 as least gender diverse (i.e., most congruent with birth-assigned sex). Rates of bullying victimization and psychotic-like experiences were significantly elevated in the most vs. least gender diverse group, with bullying partially mediating the difference in psychotic-like experiences (indirect effect = 0.11, p < 2x10-16; direct effect = 0.52, p < 1x10-16). Gender diverse adolescents exhibited greater sensitivity to the effects of bullying on psychotic-like experiences (interaction {beta} = 0.14, 95% CI [0.09, 0.19], p = 8.5x10-08). Moreover, the persistence of unsupportive legislation across 4 years was associated with significantly greater increases in psychotic-like experiences over time in gender diverse youth (interaction {beta} = 0.30, 95% CI [0.20, 0.40], p = 2.2x10-8). ConclusionsThese findings indicate that bullying victimization and unsupportive legislation may explain greater and increasing rates of psychotic-like experiences in gender diverse youth. KEY POINTSO_ST_ABSQuestionC_ST_ABSDo bullying and state-level policy related to gender identity contribute to mental health problems in gender diverse youth in the United States? FindingIn this large U.S.-based sample of adolescents (ages 9-13), gender diverse adolescents reported more frequent experiences of bullying, which partially accounted for increased rates of subclinical psychotic-like experiences (PLEs). Between 2017 and 2022, gender diversity was associated with increasing PLEs only in states with consistently unsupportive policies; in all other states, PLE scores remained stable over time or decreased. MeaningResults suggest that PLEs in the context of gender diversity are partially attributable to the sociopolitical environment and that policy decisions at the state-level have far-reaching impacts on the mental health of youth in the United States.

Despite previous evidence from retrospective cohorts suggest that survivors of COVID-19 may be at increased risk of psychiatric sequelae, questions remain on the incidence and absolute risk of psychiatric outcomes, and on the potential protective effect of vaccination. Addressing these knowledge gaps will help public health and clinical service planning during the ongoing pandemic. Based on UK Biobank prospective data, we constructed a SARS-CoV-2 infection cohort including participants with a positive PCR test for SARS-CoV-2 between March 1, 2020 and September 30, 2021; a contemporary control cohort with no evidence of SARS-CoV-2, and a historical control cohort predating the COVID-19 pandemic. Additional control cohorts were constructed for benchmarking, including participants diagnosed with other respiratory tract infection, or with a negative SARS-CoV-2 test. We used propensity score weighting using predefined (clinically informed) and data-driven covariates to minimize confounding. We then estimated incidence rates and risk of first psychiatric disorders diagnosed by ICD-10 codes and psychotropic prescriptions after SARS-CoV-2 infection using cause-specific Cox models. In this prospective cohort including 406,579 adults (224,681 women, 181,898 men; mean [SD] age 66.1 [8.4] years), 26,181 had a SARS-CoV-2 infection. Compared with contemporary controls (n=380,398), COVID-19 survivors had increased risks of subsequent psychiatric diagnoses (HR: 2.02, 95% CI 1.85-2.21; difference in incidence rate: 24.85, 95 CI 20.69-29.39 per 1000 person-years) and psychotropic prescriptions (HR: 1.61, 95% CI 1.48-1.75; difference in incidence rate: 21.77, 95% CI 16.59-27.54 per 1000 person-years). Regarding individual mental health related outcomes, the SARS-CoV-2 infection cohort showed an increased risk of psychotic disorders (2.26, 1.28-3.98), mood disorders (2.19, 1.92-2.50), anxiety disorders (2.08, 1.82-2.38), substance use disorders (1.59, 1.34-1.90), sleep disorders (1.95, 1.60-2.39); and prescriptions for antipsychotics (3.78, 2.74-5.21), antidepressants (1.55, 1.29-1.87), benzodiazepines (1.82, 1.58-2.11), and opioids (1.40, 1.26-1.55). Overall, the risk of any mental health outcome was increased with a HR of 1.58, 95% CI 1.47-1.70; and difference in incidence rate of 32.04, 25.76-38.81 per 1000 person-years. These results were consistent when comparing to a historical control cohort. Additionally, mental health risks were increased even further in participants who tested positive in hospital settings. Finally, participants who were fully vaccinated had a lower risk of mental health outcomes compared to those infected when unvaccinated or partially vaccinated. All observed risks of mental health outcomes were attenuated or even lower after SARS-CoV-2 infection compared with those with other respiratory infections, or with participants in the test-negative control cohort. In this prospective cohort study, people who survived COVID-19 were at increased risk of psychiatric outcomes and related psychotropic medications. These risks were higher in those with more severe disease, treated in hospital settings, and were significantly reduced in fully vaccinated people. Of note, compared to participants with other respiratory infections or with only negative testing results, those infected with SARS-CoV-2 had an even lower risk of mental health outcomes, warranting further research into causation. The early identification and treatment of psychiatric disorders among survivors of COVID-19 should be a priority in the long-term management of COVID-19. Particular attention might be needed for those with severe (hospitalized) disease and those who were not fully vaccinated at the time of infection.

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.

BackgroundThe absence of systematic screening for psychosis within general psychiatric services contribute to substantial treatment delays and poor long-term outcomes. We conducted a meta-analysis to estimate rates of psychotic experiences, clinical high-risk for psychosis syndrome (CHR-P), and psychotic disorders identified by screening treatment-seeking individuals to inform implementation recommendations for routine psychosis screening in general psychiatric settings. MethodsPubMed and Web of Science databases were searched to identify empirical studies that contained information on the point prevalence of psychotic experiences, CHR-P, or psychotic disorders identified by screening inpatient and outpatient samples aged 12-64 receiving general psychiatric care. Psychotic experiences were identified by meeting threshold scores on validated self-reported questionnaires, and psychotic disorders and CHR-P by gold-standard structured interview assessments. A meta-analysis of each outcome was conducted using the Restricted Maximum Likelihood Estimator method of estimating effect sizes in a random effects model. Results41 independent samples (k=36 outpatient) involving n=25,751 patients (58% female, mean age: 24.1 years) were included. Among a general psychiatric population, prevalence of psychotic experiences was 44.3% (95% CI: 35.8-52.8%; 28 samples, n=21,957); CHR-P was 26.4% (95% CI: 20.0-32.7%; 28 samples, n=14,395); and psychotic disorders was 6.6% (95% CI: 3.3-9.8%; 32 samples, n=20,371). ConclusionsHigh rates of psychotic spectrum illness in general psychiatric settings underscore need for secondary prevention with psychosis screening. These base rates can be used to plan training and resources required to conduct assessments for early detection, as well as build capacity in interventions for CHR-P and early psychosis in non-specialty mental health settings.

ImportanceAntidepressant treatment remains largely trial-and-error, with approximately one-third of patients with major depressive disorder (MDD) reporting inefficacy of first-line medications. Identifying predictors of treatment acceptability is needed to improve prescribing precision and patient outcomes. ObjectiveTo identify phenotypic and genetic signatures associated with antidepressant acceptability and treatment complexity. Design, Setting, and ParticipantsRetrospective cohort study of Australian Genetics of Depression Study participants dispensing [&ge;]1 prescription of the 10 most commonly used antidepressants, based on linked 4.5-year (2013-2017) prescription data. ExposuresTreatment complexity was assessed as number of antidepressant classes dispensed. Antidepressant acceptability was defined as sustained single-antidepressant use ([&ge;]360 cumulative days). Participants with genotyping data were classified into mutually exclusive groups based on sustained single-medication use. Main Outcomes and MeasuresAssociations with 40 phenotypes and polygenic scores (PGS) for 15 traits. Genome-wide association studies (GWAS) were conducted for SSRI and SSRI/SNRI antidepressant acceptability. ResultsOf 13,763 participants with [&ge;]1 antidepressant prescription, 9,844 had genotyping data (mean [SD] age 44.5 [15.0] years; 26% male; 89% lifetime MDD). Treatment complexity was significantly associated with 27 of 40 phenotypes (e.g., smoking, recurrent MDD, suicidal ideation, chronic pain, BMI) and higher PGSs for psychiatric traits (MDD, ADHD, bipolar disorder, neuroticism; {beta} = 0.028- 0.045 per PGS SD unit, p = 9.9 x 10-6 to 2.1 x 10-10). Sixty percent met criteria for an exclusive antidepressant acceptability group. These groups had distinct phenotypic profiles, including associations with BMI, suicidal ideation, and comorbidities. In particular, only SNRI users had higher BMI than SSRI users, but this association was explained by the BMI PGS, indicating genetic rather than treatment-induced differences. GWAS identified novel loci including an immune-related gene LY9, for which the G allele of rs6427545 was associated with reduced SSRI acceptability (frequency = 0.31; OR = 0.82; p = 2.8 x 10-8) Conclusions and RelevanceAntidepressant acceptability and complex treatment patterns correspond to distinct phenotypic and genetic signatures. PGS demonstrate potential for enabling precision psychiatry, and immune-related pathways warrant therapeutic investigation. Key PointsO_ST_ABSQuestionC_ST_ABSCan phenotypic and genetic factors help identify individuals likely to show antidepressant-specific acceptability and treatment complexity, improving treatment precision in major depressive disorder (MDD)? FindingsIn this cohort study of 13,763 individuals with antidepressant prescriptions, treatment complexity was associated with 27 self-reported phenotypic traits and higher polygenic scores (PGS) for psychiatric conditions. Among 9,844 genotyped participants, 60% met criteria for single-antidepressant acceptability, with distinct phenotypic and genetic profiles. Notably, higher BMI among sustained SNRI users was explained by genetic predisposition. A genome-wide association study identified novel loci, including an immune-related gene (LY9), associated with reduced SSRI acceptability. MeaningPhenotypic and genetic factors, including PGS, are associated with both antidepressant acceptability and treatment complexity. These findings support the use of such markers to guide treatment selection and identify patients at risk for more complex courses, informing precision psychiatry and early intervention in MDD.

ImportanceUnderstanding antidepressant mechanisms could help design more effective and tolerated treatments. ObjectiveIdentify DNA methylation (DNAm) changes associated with antidepressant exposure. DesignCase-control methylome-wide association studies (MWAS) of antidepressant exposure were performed from blood samples collected between 2006-2011 in Generation Scotland (GS). The summary statistics were tested for enrichment in specific tissues, gene ontologies and an independent MWAS in the Netherlands Study of Depression and Anxiety (NESDA). A methylation profile score (MPS) was derived and tested for its association with antidepressant exposure in eight independent cohorts, alongside prospective data from GS. SettingCohorts; GS, NESDA, FTC, SHIP-Trend, FOR2107, LBC1936, MARS-UniDep, ALSPAC, E-Risk, and NTR. ParticipantsParticipants with DNAm data and self-report/prescription derived antidepressant exposure. Main Outcome(s) and Measure(s)Whole-blood DNAm levels were assayed by the EPIC/450K Illumina array (9 studies, Nexposed = 661, Nunexposed= 9,575) alongside MBD-Seq in NESDA (Nexposed= 398, Nunexposed= 414). Antidepressant exposure was measured by self- report and/or antidepressant prescriptions. ResultsThe self-report MWAS (N = 16,536, Nexposed = 1,508, mean age = 48, 59% female) and the prescription-derived MWAS (N = 7,951, Nexposed = 861, mean age = 47, 59% female), found hypermethylation at seven and four DNAm sites (p < 9.42x10-8), respectively. The top locus was cg26277237 (KANK1, pself-report= 9.3x10-13, pprescription = 6.1x10-3). The self-report MWAS found a differentially methylated region, mapping to DGUOK-AS1 (padj = 5.0x10-3) alongside significant enrichment for genes expressed in the amygdala, the "synaptic vesicle membrane" gene ontology and the top 1% of CpGs from the NESDA MWAS (OR = 1.39, p < 0.042). The MPS was associated with antidepressant exposure in meta-analysed data from external cohorts (Nstudies= 9, N = 10,236, Nexposed = 661, f3 = 0.196, p < 1x10-4). Conclusions and RelevanceAntidepressant exposure is associated with changes in DNAm across different cohorts. Further investigation into these changes could inform on new targets for antidepressant treatments. 3 Key PointsO_ST_ABSQuestionC_ST_ABSIs antidepressant exposure associated with differential whole blood DNA methylation? FindingsIn this methylome-wide association study of 16,536 adults across Scotland, antidepressant exposure was significantly associated with hypermethylation at CpGs mapping to KANK1 and DGUOK-AS1. A methylation profile score trained on this sample was significantly associated with antidepressant exposure (pooled f3 [95%CI]=0.196 [0.105, 0.288], p < 1x10-4) in a meta-analysis of external datasets. MeaningAntidepressant exposure is associated with hypermethylation at KANK1 and DGUOK-AS1, which have roles in mitochondrial metabolism and neurite outgrowth. If replicated in future studies, targeting these genes could inform the design of more effective and better tolerated treatments for depression.

Cannabis and tobacco use are highly prevalent among people with psychosis and are associated with medical comorbidities and poor prognosis. Concurrent use of cannabis and tobacco ("co-use") is rising in the general population but has not been studied in psychosis. Given the devastating consequences of cannabis and tobacco use, it is critical to understand how their co-use affects psychiatric symptoms and the development of psychosis. We used the North American Prodrome Longitudinal Study 2, a multi-site prospective study of individuals at clinical high risk for psychosis (CHR) and healthy controls, to examine baseline differences in psychiatric symptoms and conversion to psychosis across substance groups: 1) CHR tobacco use, 2) CHR cannabis use, 3) CHR co-use, 4) CHR non-tobacco or cannabis substance use, 5) CHR without substance use, and 6) healthy controls. Among 1,014 participants (734 CHR, 280 controls), more frequent cannabis and tobacco use was linked to greater psychiatric symptom severity, including psychosis, anxiety, and depression. In survival analyses, co-use (HR = 2.53, 95% CI [1.44-4.45], p =.001), especially heavy co-use (HR = 3.63, 95% CI: 1.53-8.63, p = 0.003), was associated with increased risk of conversion to psychosis. Co-use of tobacco and cannabis was not associated with psychiatric symptom severity but did predict higher risk of conversion to psychosis. The combination of cannabis and tobacco use may exert a synergistic effect, amplifying conversion risk more than either substance alone, or may be a marker of an elevated underlying psychosis risk. These results highlight the need for early intervention strategies that address co-use in CHR populations to mitigate potential long-term psychiatric consequences.

Objective. Persistent, distressing psychotic-like experiences (PLEs) are associated with neurobiological alterations and increased psychosis risk. We combined individual-level neuroimaging measures with effect sizes from large neuroimaging studies to create a summary score ('Psychosis Neuroscore') reflecting neuroanatomic liability for psychosis, and examined its ability to predict PLE trajectories in young adolescents. Method. Using latent growth mixture models, we estimated PLE trajectories from four annual visits of the Adolescent Brain Cognitive Development Study (N=9584, ages 9-10 at baseline). Using baseline T1-weighted and diffusion-weighted imaging data, we calculated Psychosis Neuroscores, as well as Neuroscores for two psychiatric disorders with late adolescent/adult onset (Major Depressive Disorder, Bipolar Disorder). We compared Psychosis Neuroscores to i) other psychiatric Neuroscores, ii) modifiable risk factors, and iii) established risk factors in predicting trajectory membership. Results. We identified four trajectories of distressing PLEs: Persistent Elevated (N=1,968, 21%), Gradual Decreasing (N=3,424, 36%), Rapid Decreasing (N=1,593, 17%) and Low/No Distress (N=2,599, 27%). Adolescents with Persistent Elevated PLEs had significantly higher Multimodal (combined T1 and diffusion-weighted) and T1-weighted Psychosis Neuroscores than all other trajectories (Odds Ratios [ORs] 1.27-1.34,pFDR<.01). Bipolar Disorder Neuroscores showed a similar pattern (ORs 1.16-1.23,pFDR<.01). Psychosis Neuroscores showed comparable associations with established risk factors in predicting trajectory membership, but smaller associations than modifiable risk factors, including screen time, physical activity, and sleep disturbances. Conclusion. Psychosis Neuroscores differentiate youth with persistent PLEs from those with decreasing, remitting or low PLEs, demonstrating their potential utility for early risk stratification. Integration with established risk factors may enhance psychosis risk prediction in youth.

Background and hypothesesHospitalization is common during first-episode psychosis (FEP) and is linked to functional decline, stigma, and healthcare burden. Coordinated Specialty Care (CSC) programs aim to reduce hospitalization and improve outcomes through early, multidisciplinary intervention. This study examined hospitalization outcomes and predictors among participants in the Academic Community Early Psychosis Intervention Network (AC-EPINET), a multisite CSC hub in the United States. Study designParticipants with FEP (N = 701; mean age = 21.6 years, 64% male) were followed after CSC admission, with analyses restricted to the first 24 months. Primary outcomes included time to first hospitalization, number of hospitalizations, and length of stay (LOS). Kaplan-Meier survival and multivariable Cox regression examined predictors of time to first hospitalization, while negative binomial regression assessed hospitalization frequency and LOS. Study resultsHospitalization rates declined after CSC enrollment. Females had shorter time to first hospitalization (HR = 2.96, 95% CI [1.24-7.10]) and more frequent admissions (IRR = 1.38, 95% CI [1.06-1.79]) than males. Younger age also predicted earlier (HR = 0.80, 95% CI [0.67-0.95]) and more frequent hospitalizations (IRR = 0.70 per 5 years, 95% CI [0.58-0.84]). Prior hospitalization predicted more admissions (IRR = 4.83, p < .0001) and longer LOS (RR = 10.72, p < .0001). Black/African American participants had longer LOS than White participants (RR = 1.67, p = .01). ConclusionsWhile CSC reduces overall hospitalization risk, females, younger individuals, and those with prior admissions remain at elevated risk. These findings underscore the need for tailored strategies to mitigate disparities and optimize early psychosis care.

BackgroundSchizophrenia genome-wide association studies (GWASes) have identified >250 significant loci and prioritized >100 disease-related genes. However, gene prioritization efforts have mostly been restricted to locus-based methods that ignore information from the rest of the genome. MethodsTo more accurately characterize genes involved in schizophrenia etiology, we applied a combination of highly-predictive tools to a published GWAS of 67,390 schizophrenia cases and 94,015 controls. We combined both locus-based methods (fine-mapped coding variants, distance to GWAS signals) and genome-wide methods (PoPS, MAGMA, ultra-rare coding variant burden tests). To validate our findings, we compared them with previous prioritization efforts, known neurodevelopmental genes, and results from the PsyOPS tool. ResultsWe prioritized 62 schizophrenia genes, 41 of which were also highlighted by our validation methods. In addition to DRD2, the principal target of antipsychotics, we prioritized 9 genes that are targeted by approved or investigational drugs. These included drugs targeting glutamatergic receptors (GRIN2A and GRM3), calcium channels (CACNA1C and CACNB2), and GABAB receptor (GABBR2). These also included genes in loci that are shared with an addiction GWAS (e.g. PDE4B and VRK2). ConclusionsWe curated a high-quality list of 62 genes that likely play a role in the development of schizophrenia. Developing or repurposing drugs that target these genes may lead to a new generation of schizophrenia therapies. Rodent models of addiction more closely resemble the human disorder than rodent models of schizophrenia. As such, genes prioritized for both disorders could be explored in rodent addiction models, potentially facilitating drug development.

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.

We report a pilot study of deep brain stimulation (DBS) in three individuals with treatment refractory schizophrenia (TRS). DBS target selection was supported by an inpatient brain network mapping paradigm using intracranial electroencephalography. In conjunction with assessing stimulation-dependent symptom improvement, we derived spatiotemporally resolved profiles of psychosis and healthy brain states and identified stimulation targets that best shifted brain networks towards healthy states. Therapeutic stimulation sites were personalized for each participant but converged on salience network nodes including anterior cingulate and anterior insula. No significant adverse events were noted across participants. Moreover, two participants with chronic stimulation and clinical follow-up of at least 4 months reported significant improvement in both positive and negative symptoms, and treatment optimization is underway for the third. These critical pilot data establish the feasibility of personalized DBS guided by concurrent stimulation mapping, behavioral assessment, and biomarker monitoring as a treatment for TRS.

Adolescence is a critical developmental window for the emergence of substance use and psychosis-spectrum symptoms, yet early risk for these outcomes remains poorly understood. Using longitudinal data from the Adolescent Brain Cognitive Development (ABCD) Study (n=10,134), we tested whether demographic, clinical, and structural and functional neuroimaging measures assessed in childhood (mean baseline age=9.96 years) predict later adolescent substance use, psychotic-like experiences, and/or their co-occurrence. Multivariate machine learning models reliably predicted later emergence of psychotic-like experiences (AUROC=0.780) and their co-occurrence with substance use (AUROC= 0.828), as well as substance use on its own (AUROC=0.626). Distinct patterns of functional brain connectivity, task-related brain activation, demographic, and clinical factors differentiated each outcome. Findings suggest that partially dissociable developmental risk profiles are detectable as early as childhood, and results underscore the importance of explicitly modeling comorbidity when interrogating risk factors for mental health outcomes.

Acute COVID-19 infection can be followed by diverse clinical manifestations referred to as Post Acute Sequelae of SARS-CoV2 Infection (PASC). Studies have shown an increased risk of being diagnosed with new-onset psychiatric disease following a diagnosis of acute COVID-19. However, it was unclear whether non-psychiatric PASC-associated manifestations (PASC-AMs) are associated with an increased risk of new-onset psychiatric disease following COVID-19. A retrospective EHR cohort study of 1,603,767 individuals with acute COVID-19 was performed to evaluate whether non-psychiatric PASC-AMs are associated with new-onset psychiatric disease. Data were obtained from the National COVID Cohort Collaborative (N3C), which has EHR data from 65 clinical organizations. EHR codes were mapped to 151 non-psychiatric PASC-AMs recorded 28-120 days following SARS-CoV-2 diagnosis and before diagnosis of new-onset psychiatric disease. Association of newly diagnosed psychiatric disease with age, sex, race, pre-existing comorbidities, and PASC-AMs in seven categories was assessed by logistic regression. There was a significant association between six categories and newly diagnosed anxiety, mood, and psychotic disorders, with odds ratios highest for cardiovascular (1.35, 1.27-1.42) PASC-AMs. Secondary analysis revealed that the proportions of 95 individual clinical features significantly differed between patients diagnosed with different psychiatric disorders. Our study provides evidence for association between non-psychiatric PASC-AMs and the incidence of newly diagnosed psychiatric disease. Significant associations were found for features related to multiple organ systems. This information could prove useful in understanding risk stratification for new-onset psychiatric disease following COVID-19. Prospective studies are needed to corroborate these findings. FundingNCATS U24 TR002306