JAMA Psychiatry

Psychosis prevention relies on early detection of individuals at clinical high risk for psychosis (CHR-P) remains limited, constraining preventive care. The effectiveness of the CHR-P state is constrained, in part due to clinical assessments requiring specialist interpretation of narrative interviews, limiting scalability. Here, we evaluate whether large language models (LLMs; deep learning models trained on large text corpora to process and generate language) can extract clinically meaningful information from such interviews to support psychosis risk assessment. We assessed 11 open-weight LLMs on 678 PSYCHS interview transcripts from 373 participants (77.7% CHR-P). Models inferred CHR-P status and estimated severity and frequency across 15 symptom domains, benchmarked against researcher-rated scores. Larger models achieved the strongest classification performance (Llama-3.3-70B: accuracy = 0.80, sensitivity = 0.93, specificity = 0.58). LLM-generated symptom scores showed good correlations with researcher-rated scores (ICCsev = 0.74, ICCfreq = 0.75). Performance disparities were minimal across most demographic groups but varied across sites. Generated summaries were largely faithful to source transcripts, with low rates of clinically relevant confabulation (3%). Errors primarily reflected over-pathologisation of non-clinical experiences. While accuracy scaled with model size, smaller models achieved competitive performance with substantially lower computational cost. These findings demonstrate that open-weight LLMs can assess psychosis risk from clinical interview transcripts, supporting scalable, human-in-the-loop approaches to early detection.

Extended duration of under-treated psychosis (DUP) is among the strongest predictors of poor outcome, yet diagnostic heterogeneity impedes treatment matching, with approximately 50% of patients failing to respond to first-line antipsychotics. Negative symptoms and cognitive impairment are particularly refractory, lacking effective pharmacological treatments. Identifying neurotransmitter systems associated with specific symptom dimensions could accelerate targeted therapeutic development and reduce DUP. We applied Partial Least Squares correlation (PLSc) to derive whole-brain resting-state functional connectivity (RSFC) and anatomical (cortical thickness and subcortical volume) signatures associated with five psychopathology dimensions (positive symptoms, negative symptoms, general psychopathology, mania, and cognition) in a transdiagnostic sample from the Human Connectome Project-Early Psychosis (HCP-EP; n=124). We tested associations with potential confounds including antipsychotic medication dosage and substance use. Signatures were spatially correlated with 21 Positron Emission Tomography (PET)-derived receptor and transporter maps across 9 neurotransmitter systems using the neuromaps toolbox. Significant RSFC signatures emerged for positive symptoms, negative symptoms, general psychopathology, and cognition, but not mania. The negative symptom RSFC signature correlated with norepinephrine transporter (NET; {rho}=.40, q=.030) and vesicular acetylcholine transporter (VAChT; {rho}=.38, q=.048) distributions. The cognition signature similarly correlated with VAChT ({rho}=.48, q=.025). Anatomical signatures were associated with positive symptoms, general psychopathology, and cognition, but were more susceptible to confounding by medication and substance use. No significant receptor associations were detected for anatomical signatures. These findings implicate cholinergic and noradrenergic systems as molecular targets for negative symptoms and cognitive impairment, supporting prioritization of these systems in pharmacotherapy development in early psychosis.

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 Evidence on factors associated with cannabis for medical purposes (CMP) authorizations among Veterans Affairs Canada (VAC) clients remains limited and inconsistent, particularly concerning mental health and posttraumatic stress disorder (PTSD), a leading indication for use. We investigated demographic, clinical and service characteristics associated with VAC authorizations for CMP reimbursement. Method We linked VAC administrative CMP program data with responses from the 2019 Life After Services Studies cross-sectional survey of Regular Force veterans released between 1998 and 2018. Multivariable logistic regressions examined associations between CMP reimbursement (yes/no) and demographic, clinical and well-being factors, with analyses stratified by PTSD status. Results Among 1,289 respondents (weighted n=33,131), 18.4% were authorized for CMP reimbursement. Younger age (<40 vs. [&ge;]60 years: OR 4.78, 95% CI: 2.24-10.21), unemployment with inability to work vs. employed (OR 3.10, 95% CI: 1.78-5.40), land service vs. air (OR 2.07, 95% CI: 1.22-3.50), PTSD (OR 2.81, 95% CI: 1.69-4.66), anxiety (OR 2.32, 95% CI: 1.45-3.70), and severe pain vs. no pain (OR 3.61, 95% CI: 1.97-6.60) were independently associated with authorization. Unemployment and severe pain were consistent correlates across PTSD strata. Among those without PTSD, younger age, multiple physical conditions, and frequent mental health visits were significant; among those with PTSD, shorter service, witnessing destruction, and suicidal ideation were additional factors. Conclusions CMP authorization patterns among Canadian veterans reflect the intersection of mental health, pain, and functional impairment, with variation by PTSD status. These findings underscore the need for longitudinal research on CMP mechanisms, effectiveness and safety.

Genomic insights into psychiatric disorders remain heavily skewed toward European populations. In European-ancestry studies, educational attainment is typically negatively genetically correlated with major depression but paradoxically positively correlated with schizophrenia, raising the question of whether these relationships generalize across ancestries. We investigated whether this cross-trait architecture extends to East Asian ancestry (EAS). Using EAS GWAS summary statistics for major depressive disorder (MDD), schizophrenia (SZ), and educational attainment (EDU), we applied multi-trait (MTAG) and pleiotropy-informed (PLEIO) analyses to characterize shared genetic architecture across these traits. Across MTAG and PLEIO analyses, we identified 32 unique genome-wide significant loci (p < 5 x 10-8), including seven novel loci revealed in depression analysis that overlapped schizophrenia-associated signals, consistent with shared cross-trait architecture. Results reinforce a convergent risk architecture for affective and psychotic disorders in this population. Fine-mapping analyses prioritized variants mapping to candidate genes, including serine/threonine kinase VRK2, nominating targets for future follow-up. Cross-trait analyses supported a positive genetic relationship between EDU and MDD (rg = 0.308, p = 9.63 x 10-17) in East Asian data, contrasting to the negative correlation typically observed in European ancestry. These findings suggest that the genetic relationship between educational attainment and psychiatric risk may not be fully transferable across ancestries. In an independent cohort of individuals at ultra-high risk for psychosis, MTAG-derived polygenic risk scores improved case-control discrimination relative to single-trait GWAS-based scores. These results underscore the importance of ancestry-specific genomic frameworks for interpreting cross-trait psychiatric architecture and improving polygenic prediction.

BackgroundThe severity of positive psychotic symptoms largely defines emerging psychosis syndromes. However, depressive and negative symptoms are strongly psychologically and biologically interlinked. A transdiagnostic exploration of symptom severity across early illness syndromes could enhance the understanding of shared common factors and future trajectories of mental illness. We aimed to identify subgroups based on the severity of positive, negative, and depressive symptoms and assess relationships with: 1) premorbid functioning, 2) longitudinal illness course, 3) genetic risk, and 4) brain volume differences. MethodsWe analysed 749 participants from a multisite, naturalistic, longitudinal (18 months) cohort study of: clinical high risk for psychosis (n=147), recent onset psychosis (n=161), and healthy controls (n=286), and recent onset depression (n=155). Participants were stratified into subgroups based on severity of baseline positive, negative, and depression symptoms. Baseline and longitudinal differences between groups for clinical, functioning, and polygenic risk scores (schizophrenia, depression, cross-disorder) were assessed with ANOVAs and linear mixed models. Voxel-based morphometry was used to examine whole-brain grey matter volume differences. Discovery findings were replicated in a held-out sample (n=610). ResultsParticipants were stratified into no (n=241), mild (n=50), moderate (n=182), and severe symptom (n=254) subgroups. The mean (SD) age was 25.3 (6.0) and 344 (47.3%) were male. Symptom severity was associated with poorer premorbid functioning and illness trajectory, greater genetic risk, and lower brain volume. Findings were not confounded by the original study groups or symptoms and were largely replicated. Conclusions and relevanceTransdiagnostic symptom severity is linked to shared aetiologies, prognoses, and biological markers across diagnoses and illness stages. Such commonalities could guide therapeutic selection and future research aiming to detect unique contributions to specific psychopathologies.

Stimulant use disorder (StimUD) is a significant public health problem, but genetic studies have been limited by small sample sizes. We conducted genome-wide association studies (GWAS) of StimUD in the Million Veteran Program (MVP) and All of Us (AOU), followed by meta-analysis with FinnGen and 10 additional datasets, for a total of 709,369 individuals (Ncases=33,977, Ncontrols=675,392) in four broad ancestry groups: European (EUR) (Ncases=22,564, Ncontrols=624,672), African (AFR) (Ncases=7,574, Ncontrols=34,189), Admixed American (AMR) (Ncases=3,657, Ncontrols=15,698), and East Asian (EAS) (Ncases=182, Ncontrols=833). Population-specific SNP heritability was 6.1% in EUR and 2.4% in AFR. We discovered a total of 19 genome-wide-significant loci, six in EUR, including DRD2*rs5794864, P=7.32E-10, one in AFR, five in a multi-ancestry meta-analysis, including CHRNA5*rs55781567, P=3.27E-9, two in a male-only meta-analysis, including FTO*rs8057044, P=9.50E10-9, and five in a meta-analysis of sex-stratified results. In a hold-out AOU subsample (NEUR=18,841, NAFR=12,263, NAMR=9,739), ancestry-specific polygenic risk scores were significantly associated with StimUD in EUR (OR=3.28, 95% confidence interval (CI)=2.89-3.71) and AMR (OR=2.01, 95% CI=1.71-2.37). Transcriptome-wide association studies, fine-mapping, and colocalization analyses prioritized additional genes (e.g., GPX1, BSN). Genetic correlation, Mendelian randomization, and causal mixture analyses revealed relationships with other substance use and use disorder phenotypes, including cannabis use disorder (rg=0.94, P=5.43E-237) and opioid use disorder (rg=1.01, P=4.40E-107), and other psychiatric traits, including anxiety, depression, neuroticism, and attention-deficit/hyperactivity disorder. This is the first well-powered GWAS of StimUD, and it offers significant insights into disease biology.

Cultural engagement is associated longitudinally with better mental health and reduced depression incidence, but evidence has largely relied on self-reported symptoms and diagnoses, leaving uncertainty about clinically recorded disorders, and residual confounding remains a concern. Here, we examined whether cultural engagement (including going to cinemas, museums, galleries, exhibitions, theatre, concerts, or opera) predicts hospital-treated mental disorders in 8,274 adults aged 50 years or older from the English Longitudinal Study of Ageing. Participant records were linked to ICD-10 diagnoses in Hospital Episode Statistics and mortality records with follow-up of up to 20 years. In fully adjusted Cox models accounting for sociodemographic, lifestyle, and social factors and multiple testing, frequent cultural engagement was associated with lower risk of any mental disorders (HR 0.71, 95% CI 0.62-0.82, FDR adjusted P value<0.001), dementia (0.71, 0.56-0.89, FDR adjusted P value=0.010), substance misuse (0.75, 0.59-0.95,FDR adjusted P value=0.040), and mood disorders (0.73, 0.56-0.95, FDR adjusted P value=0.044), but not neurotic disorders. Associations persisted after excluding early incident cases and adjusting for baseline depressive symptoms and cognition, and showed robustness to unmeasured confounders. To further probe causality, eye disease, ear disease, and traumatic brain injury, which share similar socio-demographic profiles to mental disorders, were prespecified as negative control outcomes. Cultural engagement was not associated with any negative control outcomes. These findings provide triangulated statistical data to suggest that cultural engagement is associated with reduced risk of several clinically recorded mental disorders and support further testing of cultural engagement as a population mental health strategy.

Circular RNAs (circRNAs) remain an underexplored layer of transcriptomic regulation in psychiatric disorders. We quantified circRNA expression from 1,022 [518 neurotypical, 365 schizophrenia (SCZ) and 139 bipolar disorder (BIP)] postmortem cortex samples from PsychENCODE consortium cohorts and integrated these profiles with matched linear RNA and genotype profiles. We identified 23 SCZ-associated and 3 BIP-associated differentially expressed circRNAs (FDR<0.05; FDR-circDEG). We trained genetically regulated circRNA expression (circGReX) models using neurotypicals and applied them to SCZ and BIP GWAS to perform Transcriptomic Wide association analysis (TWAS) which identified 22 and 4 circGReX trait associations (circGTAs), respectively. Pathway enrichment of circDEGs and circGTAs implicated neuronal and synaptic processes for both disorders. In UK Biobank, circGReX-imaging associations were predominantly negatively correlated with SCZ and BIP circGTAs, but positively correlated with Alzheimers disease circGTAs. circKLHL24 isoforms showed the most prominent imaging associations. Many co-expression modules containing our FDR-circDEGs were enriched for psychiatric and neurodegenerative risk genes, including our identified circGTAs, and these modules were enriched for cognitive and neurodevelopmental traits. To conclude, circRNAs represent a distinct regulatory layer in psychiatric disorders, linking genetic risk to synaptic biology, brain structure and cognition through disease-specific expression, TWAS prioritization, and imaging associations.

ObjectiveTobacco and cannabis are the most used substances among individuals at clinical high risk for psychosis (CHR-P), but it remains controversial whether substance use drives symptom exacerbation and psychosis transition, or vice versa. We investigated longitudinal dose-response relationships of tobacco and cannabis use with clinical presentation in a CHR-P population. MethodsData was obtained from the North American Prodrome Longitudinal Study (NAPLS2) CHR-P cohort (n=764). Participants were assessed every 6 months over two years. Substance use frequency, psychiatric symptoms (psychosis, depression, anxiety, and social anxiety), global social and role functioning, and neurocognitive performance were measured. Linear mixed effect models were used to model the relationship between substance use and clinical measurements across visits, and that between baseline use and trajectory of symptoms, functioning, and cognition. ResultsPsychiatric symptoms, functioning, and cognitive performance improved, while tobacco and cannabis use frequency did not change over two years for CHR-P individuals in NAPLS2. Heavier tobacco and cannabis use at current visit predicted worse anxiety at next visit (tobacco: {beta}=0.178, p=0.033; cannabis: {beta}=0.162, p=0.018). Better social functioning predicted heavier tobacco ({beta}=0.178, p<0.001) and cannabis: ({beta}=0.162, p<0.001) use at next visit. We observed a significant baseline cannabis-by-time interaction, where heavier baseline cannabis use predicted slower improvement of negative symptoms ({beta}=0.159, p=0.0017, FDRp=0.0067) and deterioration of role function ({beta}=-0.046, p=0.018). ConclusionsIn CHR-R, current tobacco and cannabis use predicted worse anxiety at future visits. Baseline cannabis use frequency predicts worse clinical trajectory, especially for negative symptoms.

Background: Schizophrenia, bipolar disorder, and depression share substantial genetic liability. However, the molecular mechanisms underlying this shared architecture remain poorly characterized. In particular, the role of circulating proteins as potential mediators and therapeutic targets is not well understood. Methods: Based on large-scale genome-wide association studies, we constructed a latent psychiatric common factor using genomic structural equation modeling. We then performed proteome-wide Mendelian randomization to estimate the associations between circulating proteins and this shared liability, based on four independent proteomic cohorts. Protein-psychiatric common factor associations were prioritized through comprehensive sensitivity analyses and colocalization. We additionally performed tissue- and single-cell expression enrichment analyses and a systematic druggability assessment. Results: We identified 36 circulating proteins with evidence of association with the psychiatric common factor that withstood multiple sensitivity analyses. Several proteins showed distinct tissue-specific expression patterns, with enrichment in brain, immune, or liver tissues, highlighting convergent neuroimmune and systemic pathways. For instance, genetically predicted higher levels of MAPK3, FES, MRE11A, HS6ST3, OLFM1, BTN3A1, BTN3A2 and BTN3A3 were associated with increased psychiatric risk, whereas higher levels of CD40, ITIH3, and ITIH4 were associated with decreased risk. Druggability assessment identified CD40, MAPK3, FES, MRE11A and BTN3A1 as established or potential therapeutic targets. Conclusions: By integrating genetic, proteomic, and transcriptomic data, this study identifies circulating proteins that associated with the shared genetic effects on three major psychiatric disorders. These findings provide biologically grounded candidates for therapeutic targeting and offer insights into shared disease mechanisms.

Antidepressants are widely prescribed for major depressive disorder, yet only one-third of patients achieve remission after initial treatment. Previous genome-wide association studies (GWAS) of clinically assessed antidepressant response combined multiple antidepressant classes, potentially obscuring class-specific effects. This study focused on selective serotonin reuptake inhibitors (SSRIs), often first-line due to better tolerability. Data from 15 cohorts across four ancestries were integrated: European (N = 3887; 11 studies), East Asian (N = 1068; 4), African (N = 277; 1), and Admixed American (N = 250; 1). GWAS of non-remission and percentage improvement were conducted within cohorts, followed by ancestry-specific meta-analyses and trans-ancestry meta-regression. Single nucleotide polymorphism (SNP)-based heritability was estimated in European samples. Polygenic scores were used for leave-one-out prediction and to assess shared genetic architecture with psychiatric traits. Gene-level and gene-set enrichment analyses were also performed. No genome-wide significant variants were identified for either outcome in any ancestry-specific or trans-ancestry analyses. However, trans-ancestry meta-regression yielded eight independent loci with suggestive associations (p < 1 x 10-5) for non-remission and 17 for percentage improvement. Gene-set analyses revealed nominal enrichment of the serotonergic synapse pathway for non-remission. SNP-based heritability estimates were not significantly different from zero for either outcome. Better SSRI response was nominally associated with lower genetic predisposition to major depressive disorder, post-traumatic stress disorder, and schizophrenia. This study represents the largest trans-ancestry GWAS of SSRI response, highlighting emerging biological signals. Limited power emphasises the need for larger and ancestrally diverse cohorts to better characterise the genetic architecture of antidepressant response.

BackgroundDifferences in age of psychosis onset (AOO) in schizophrenia (SCZ) are associated with different illness trajectories. Determining whether AOO differences can be explained by genome-wide or pathway-partitioned polygenic risk for SCZ (SCZ-PRS) may elucidate mechanisms underlying clinical variability. This study examined relationships between AOO, genome-wide SCZ-PRS, and pathway-partitioned SCZ-PRS in a harmonized, multi-ancestry North American dataset (SCZ-NA) and in UK Biobank (SCZ-UKBB). MethodsFor each cohort, we computed one genome-wide SCZ-PRS and 18 mutually-exclusive pathway-based PRS derived from previous published and validated neurodevelopmental gene-sets. We evaluated 13 SNP-to-gene mapping strategies, including comparing non-coding SNP-to-gene mappings informed by functional annotations versus distance-based windows. SCZ case-control prediction and AOO associations were tested using logistic and linear mixed models, respectively, controlling for sex, ancestry principal components, and genetic relatedness. ResultsGenome-wide SCZ-PRS robustly predicted SCZ case-control status in both cohorts but not AOO. In contrast, pathway-based analyses identified AOO associations for a fetal angiogenesis and a postnatal synaptic signaling and plasticity gene-set across both cohorts (p < .05), alongside nominal cohort-specific associations in other gene-sets. Associations depended on SNP-to-gene mapping definitions; experimentally informed strategies, particularly those incorporating brain expression Quantitative Trait Locus (eQTL) annotations performed best. ConclusionFindings suggest that neurovascular and postnatal synaptic signaling and refinement mechanisms contribute to AOO variation in SCZ, and that pathway-informed PRS, especially with brain-specific non-coding SNP-to-gene mappings, can help identify mechanisms contributing to variability in AOO. Replication in larger, prospectively phenotyped cohorts with harmonized AOO definitions will further clarify genetic mechanisms underlying clinical variability in SCZ.

Schizophrenia spectrum disorders (SSDs) are clinically and neurobiologically heterogeneous. Normative modeling addresses heterogeneity of structural brain alterations by focusing on individual-level deviations, but their clinical relevance in SSDs remains controversial. We mapped the relationship between individual gray matter volume (GMV) deviations and schizophrenia diagnosis and symptoms. Normative models of GMV were established using cross-sectional, T1-weighted magnetic resonance imaging data from a large, multi-site, healthy reference cohort (N = 7957). Deviations were derived for SSD patients (n = 379) and healthy controls (n =149). Patients showed a significantly more negative average deviation compared to controls and regional deviations predicted diagnostic status with adequate performance (AUC = 0.79). A more negative deviation was associated with higher symptom severity and lower cognitive functioning in SSD. Negative deviations were scattered across the brain, with the largest alterations in the salience network. Our findings strengthen the potential of normative modeling to disentangle the heterogeneous underpinnings of SSD and provide further evidence for individualized structural deviations, particularly in the salience network, as promising markers of illness severity in SSDs.

Schizophrenias substantial heterogeneity poses a major challenge for understanding its neurobiological mechanisms and predicting treatment response. Moving toward precision psychiatry, we identified clinically meaningful subtypes and characterised their neural and pharmacological profiles. Clustering of multidimensional clinical feature space revealed two distinct patient subtypes, primarily differentiated by degree of illness insight. In parallel, three symptom-severity groups defined by positive and negative psychopathology dimensions provided a complementary stratification framework. Resting-state fMRI analyses revealed that higher-insight patients exhibited greater dynamic reconfiguration of regional functional connectivity, emerging as the primary neuroimaging feature differentiating subtypes. Multivariate classification and feature importance analysis confirmed the discriminative value of neuroimaging metrics. Across both subtyping approaches, regional flexibility was spatially associated with cortical receptor density maps in a subtype-specific manner, particularly for D2 and 5-HT2A when accounting for estimated antipsychotic receptor occupancies. Additionally, pharmacological-clinical associations were stronger and more spatially widespread in specific subtypes, indicating subtype-dependent pharmacodynamic relationships. Furthermore, structural equation modelling demonstrated that neuroimaging measures mediate receptor pharmacologys influence on clinical outcomes. These findings together show that integrating clinical, neuroimaging, and pharmacological data can uncover biologically grounded schizophrenia subtypes, identify functional biomarkers, and inform personalised therapeutic strategies.

Aims and SettingIn the U.S., the emergence of new adulterants and novel psychoactive substances continues to complicate approaches to overdose, treatment, and public safety. Information about this changing drug supply is often gleaned from police drug seizures, but community drug checking services, which test the contents of a persons drug supply and share that data, provide another means to understand local drug supplies. However, it is unclear how seized drugs differ from those collected in the community, whether one approach is potentially more instructive, and what can be learned about local drug supplies from each source. We therefore compared drug samples tested from police departments (PDs) and community partner (CP) drug checking programs to examine what, if any, differences existed in sample content, form, submitter characteristics, and emerging substance presence. DesignWe conducted a retrospective cohort analysis of drug samples collected and tested between April 2018 and December 2025 by the Massachusetts Drug Supply DataStream derived from CPs and PDs operating in the same geographic area across eight locations. Bivariate analyses (Chi-square, Fishers exact) tested for differences in sample and submitter characteristics by source. FindingsThere were 2,430 unique samples submitted by CPs (68.1%) and PDs (31.9%) from the same location. Compared to CP samples, proportionally more PD samples showed fentanyl as primary substance (74.2% PD vs. 64% CP, p<.001) and less often contained additives (xylazine 15.0% PD vs. 27.4% CP; medetomidine 0.6% PD vs. 2.2% CP, both p<.001). PD samples were typically powders (73.2% vs. 37.9%) and pills (13.6% vs. 3.6%) while CP samples were more often residue (51.9% vs. 2.1%, p<.001). Submitter characteristics, when reported, differed by source: gender (n=528, male: 78.6% PD vs. 50.1% CP, p<.001), race/ethnicity (n=468, Black: 15.8% PD vs. 7.8% CP; Hispanic: 6.7% PD vs. 13.2% CP, p<.05), and associated overdose (n=242, fatal: 62.9% vs. 10.9%, p<.001). Emergent substances were detected a median of 249 days sooner in CP than co-located PD samples, though drugs exhibiting concerning patterns (e.g., unexpected fentanyl in stimulants) had similar, swift detection times. ConclusionDrug samples differ based on PD vs. CP source in significant ways that may introduce bias when drawing conclusions about drug supply trends but also offer unique insights for public health and responses to emerging drugs. Modern drug monitoring should include a broad range of sources to best prepare for changes the illicit supply may bring to overdose prevention, public safety, and health systems.

0.Clozapine is the gold-standard for treatment-resistant schizophrenia despite its severe metabolic complications, including metabolic syndrome (MetS) and type 2 diabetes (T2D) risk. A better understanding of the genetic factors influencing clozapine pharmacokinetics and their relationship to metabolic risk could help inform precision medicine approaches to clozapine prescribing. Using a series of genetic-epidemiological approaches, we aimed to identify candidate biomarkers associated with clozapine-induced metabolic dysfunction. We first used two-sample Mendelian randomization (MR) leveraging genome-wide association summary data to investigate evidence of causal relationships between clozapine metabolism and cardiometabolic traits. These analyses indicated that higher plasma clozapine levels and a higher clozapine-norclozapine ratio were both associated with a higher risk of T2D and higher blood pressure. We then applied a Phenome-scan-colocalization-MR pipeline to identify traits influenced by clozapine-metabolism loci that might serve as biomarkers of cardiometabolic risk. This pipeline identified 28 colocalizing candidate biomarkers associated with clozapine metabolising genetic loci. Subsequent MR highlighted associations for 16 of these 28 biomarker candidates with cardiometabolic outcomes, which included haematological markers and excretory traits (e.g. gamma-glutamyl transferase, red cell distribution width, and urea). These findings may inform the development of biomarker-guided monitoring approaches for risk stratification and early intervention, enabling a shift from reactive monitoring to predictive approaches in managing clozapine-induced metabolic dysfunction with appropriate clinical validation. These findings may also help to mitigate the risk of metabolic dysfunction associated with other antipsychotic medications.

BackgroundKetamine is a rapid-acting antidepressant that produces acute dissociative symptoms. In routine care, the respective contributions of therapeutic expectations and dissociative symptoms to antidepressant response, and the directionality of their associations with depressive symptom change, remain poorly characterized. MethodsWe conducted a retrospective longitudinal observational cohort study of 100 adults with major depressive disorder or bipolar depression receiving six open-label intravenous racemic ketamine infusions over 3 weeks. Therapeutic expectations were rated at baseline and before each infusion. Post-infusion, dissociative symptoms (CADSS) were assessed first, followed by depressive symptom severity (MADRS) within the same session. Linear mixed-effects, mediation, and random intercept cross-lagged panel models (RI-CLPM) were used to distinguish within-person from between-person effects. ResultsDepressive symptoms improved across the induction course, with 45% of participants meeting response criteria. At each session, therapeutic expectations consistently predicted post-infusion improvement in depressive symptoms at the within-person level, independently of dissociative symptoms. Moreover, expectations became stronger across sessions in treatment responders. Dissociative symptoms were associated with improvement when examined alone, but were not observed after adjustment for expectations, and were not linked to improvement in depression at the within-person level from session to session. They were associated with greater overall antidepressant benefit at the between-person level. A notable indirect pathway was identified at the between-person level, where expectations determined changes in depression through initial dissociative symptoms and early depressive symptom reductions. This pathway explained 3.2% of the total effect of expectations on improvement in depression by the end of treatment. ConclusionsTherapeutic expectations and dissociative symptoms contributed to antidepressant response through distinct pathways: expectations functioned at the individual level as a dynamic within-person driver, whereas dissociative propensity served on the group level as a stable between-person marker of outcome, highlighting complementary clinical targets to optimize treatment response in routine care.

BackgroundCognitive-behavioral therapy (CBT) is a widely used treatment for mental disorders, yet the biological mechanisms underlying its effects, and the factors contributing to response, remain poorly understood. DNA methylation, an epigenetic mechanism shaped by both genetic and environmental factors, may offer insights into individual differences in psychotherapy outcomes. MethodsSaliva samples were collected before treatment, after treatment, and three months post-treatment from individuals with OCD undergoing the Bergen 4-Day Treatment (n = 889). DNA methylation was measured using the Illumina EPIC v02 array, followed by epigenome-wide DNA methylation analyses of CBT response. ResultsWe identified ten differentially methylated regions (DMRs) associated with treatment response at baseline, 23 DMRs showing consistent associations with response across multiple time points, and three DMRs displaying longitudinal methylation changes associated with response. These loci were annotated to genes with roles in neuroplasticity, stress response, immune function, mitochondrial processes, and gene regulation. Baseline and stable methylation signals were largely influenced by genetic variation, whereas all longitudinal associations appeared to be confounded by psychoactive medication use and psychiatric comorbidities. In addition, changes in monocyte and CD4+T cell proportions were associated with treatment response. ConclusionsWe identified DNA methylation markers associated with CBT response in OCD at baseline. Stable methylation patterns associated with treatment response are likely driven by genetic factors. Longitudinal methylation analyses should be interpreted cautiously, as medication and comorbidities can exert substantial effects - even when they remain unchanged over time. Baseline methylation profiles may ultimately help predict treatment outcomes, thereby advancing precision psychiatry.

Recent evidence suggests that psychosis involves glutamatergic dysfunction and altered activity/connectivity within corticolimbic circuitry. While altered relationships between corticolimbic glutamatergic metabolite levels and resting-state functional connectivity (FC) have been described in schizophrenia and first-episode psychosis (FEP), whether these disruptions are also present prior to psychosis onset remains unclear. We measured Glx (glutamate + glutamine) levels in the anterior cingulate cortex (ACC) and hippocampus with magnetic resonance spectroscopy (MRS), and resting-state FC between corticolimbic regions of interest (ACC, hippocampus, amygdala and nucleus accumbens (NAc)) in antipsychotic-naive participants at clinical high-risk for psychosis (CHR-P, n=22), compared to healthy controls (HC, n=23) and FEP participants (n=10). Primary analyses compared corticolimbic Glx-FC interactions between CHR-P and HC groups. FEP individuals were included in secondary Glx comparisons but were excluded from FC analyses due to insufficient sample size after quality control. There was a significant interaction between group and ACC Glx for FC between the NAc and the bilateral amygdala and hippocampus (p-FDR=0.021), which was driven by a significant negative association in the CHR-P group (p-FDR=0.005). Complementary seed-to-whole-brain analyses revealed additional negative associations between ACC Glx and FC with the left middle temporal gyrus, and between hippocampal Glx and FC with the parahippocampal and temporal fusiform cortices in CHR-P individuals, which were absent in HC. FEP showed higher Glx than HC across both regions (p=0.015), but there were no significant Glx differences between CHR-P and HC. These data suggest that increased risk for psychosis is associated with altered relationships between corticolimbic connectivity and glutamatergic function.