American Journal of Psychiatry

Alcohol use disorder (AUD) remains a major public health problem, with few effective medications and suboptimal adherence. L-type calcium channel blockers (LTCCBs) have genetic and preclinical support as potential treatments for AUD. We evaluated whether brain penetrant (BP)-LTCCBs are associated with reduced alcohol consumption by conducting two preregistered (https://osf.io/huawv) observational cohort studies using electronic health records (EHRs) from the US Department of Veterans Affairs (VA) and Kaiser Permanente Northern California (KPNC). New users of BP-LTCCBs (nifedipine or felodipine) were compared with new users of a non-BP-LTCCB (amlodipine) and with unexposed patients sampled from the same clinics, following a 180-day washout and requiring at least 60 days supply. Propensity score matching was conducted separately for BP-LTCCB versus unexposed, non-BP-LTCCB versus unexposed, and BP-versus non-BP-LTCCB. The primary outcome was change in drinks per week from the most recent pre-index screen to end of follow-up, estimated using difference-in-differences (DiD) models. Prespecified subgroup analyses were conducted by AUD diagnosis, baseline drinking level, and sex. Across both health systems, BP-LTCCB initiation was not associated with greater reductions in drinks per week than either comparator, with broadly consistent findings across all subgroups. In two large, preregistered EHR-based cohorts with rigorous confounding control, BP-LTCCBs were not associated with reduced drinking relative to comparators. Despite compelling genetic and preclinical evidence, these results do not support repurposing BP-LTCCBs for AUD, highlighting the need to prioritize alternative pharmacologic targets, potentially within etiologically informed subgroups.

IntroductionMaternal mental health conditions, comprising maternal suicide and drug overdose, are currently the leading cause of maternal mortality in the United States. However, the relationship between suicidality and drug use behavior in the perinatal period is not well understood. We examined the association between suicidality and drug use behavior among perinatal individuals. Given the racial disparities in both drug use and suicide rates in the U.S., we also examined any differences in suicidality and drug use behavior by race. MethodsParticipants were recruited from a High-Risk Obstetric & Gynecological Clinic in the Midwestern U.S that specializes in providing obstetric care to perinatal individuals who have histories or current use of opioids and other illicit drugs. Participants (N = 66) were a sub-sample of a larger cohort enrolled in an mHealth intervention to support recovery from opioid and stimulant use disorders. We performed chi-square tests and t-tests to examine any significant associations between lifetime suicidality and drug use behavior during the perinatal period. ResultsThe final analytic sample included participants who had responded to the suicidality survey questions (n=43). Nearly 40% (n=16) of our sample endorsed a lifetime history of suicidal thoughts and behaviors (SITB). Of those, 87% (n=15) reported a previous suicide attempt. SITB was significantly associated with cravings for opioids during the perinatal period (p = .01) as well as comorbidities with perinatal anxiety symptoms? ( p < .05), depression symptoms? (p < .05), and bipolar disorder (p < .05). A higher proportion of recent cannabis use was found among mothers with SITB, compared to those without SITB (p=0.04). Mothers with SITB also had a strong positive correlation between preconception and postnatal nicotine use compared to mothers without SITB (p < .01). Finally, while white mothers endorsed more lifetime overdoses (p= 0.01), Black mothers endorsed higher cravings for opioids during pregnancy (p = 0.03). ConclusionsA history of SITB is a distinct risk factor for both illicit and recreational drug use behavior in the perinatal period, and frequently co-occurs with other perinatal mental health conditions. Further research is needed to better understand the directionality of this relationship and the complex interplay between high risk drug use behavior and suicidality.

Tobacco use disorder (TUD) remains a leading cause of preventable mortality, and existing pharmacotherapies yield 12-month abstinence rates below 30%. As cannabis legalization expands, approximately 18-22% of people who use tobacco report concurrent cannabis use, yet the impact of co-use on cessation outcomes and the therapeutic potential of endocannabinoid system (ECS) modulation remain unclear. We conducted a translational systematic review and meta-analysis following PRISMA 2020 guidelines, searching Ovid MEDLINE, Embase, APA PsycInfo, and Web of Science through January 2026 (PROSPERO: CRD420250652724). Three study categories were eligible: observational studies of cannabis co-use and cessation outcomes; preclinical studies of cannabinoid modulators on nicotine-related behaviors; and human experimental studies of ECS-targeted interventions. Of 4,869 records screened, 52 studies met inclusion criteria. Meta-analysis of 18 observational studies (N=229,630) revealed that cannabis use was associated with 35% lower odds of achieving tobacco smoking cessation (OR=0.65; 95% CI: 0.55-0.78; p<0.0001; I{superscript 2}=88.1%). Preclinical evidence (15 studies) demonstrated that CB1 receptor antagonists robustly reduced nicotine self-administration and reinstatement, while cannabidiol (CBD) attenuated both nicotine intake and withdrawal without affecting food reinforcement. Clinical translation of CB1 receptor inverse agonists failed due to psychiatric adverse effects, but CBD showed promise by reducing cigarette consumption by 40%, reversing attentional bias to smoking cues, and alleviating withdrawal severity. These findings distinguish naturalistic cannabis exposure from potentially beneficial targeted ECS modulation, and support CBD as a promising candidate for adequately powered tobacco cessation trials.

Background and AimsAlcohol use disorder (AUD) remains a major public health concern, with persistent disparities in access to evidence-based treatment. This study aimed to examine associations between perceived discrimination in healthcare settings (PDHS), patient-clinician communication (PCC), and receipt of treatment for AUD, and compared these with sociodemographic and insurance-related factors. DesignCross-sectional analysis using structural equation modeling (SEM), logistic and multinomial logistic regression, and machine learning approaches including SHapley Additive exPlanations (SHAP). SettingUnited States, using data from the National Institutes of Health All of Us Research Program. ParticipantsA total of 5,287 adults with AUD (mean age 61 years; 57% men), including 71.6% non-Hispanic White, 12.2% Black, and 8.6% Hispanic participants. Insurance coverage included 52% government (Medicaid/Medicare), 37% private, and 21% military with 19% reporting more than one type. MeasurementsPrimary outcomes were receipt of Food and Drug Administration-approved pharmacotherapy and/or psychotherapy for AUD, examined as binary and multinomial outcomes. The primary exposure was PDHS, measured using a 7-item scale (range 7-35), with higher scores indicating more frequent discrimination. PCC, assessed using a 2-item scale (range 2-8) with higher scores indicating poorer communication, was examined as a potential mediator. Models were adjusted for age group, sex at birth, race/ethnicity, insurance type (government, private, military), household income, and Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) scores (range 0-12). FindingsPDHS was associated with poorer PCC ({beta} = 0.209, p < 0.001), although communication was not independently associated with treatment receipt. The indirect pathway from discrimination to treatment via communication was not supported. Military insurance was the strongest predictor of treatment receipt, with 6-7 times higher odds compared with other insurance types. Higher AUDIT-C scores and greater PDHS were also associated with increased likelihood of treatment. In analyses restricted to civilian participants, PDHS showed a stronger association with treatment receipt, while PCC demonstrated more modest effects. Machine learning models identified PDHS, AUDIT-C, and PCC as strong contributors, with the impact of poor communication most pronounced among individuals with lower income. ConclusionsAccess to treatment for alcohol use disorder is most strongly associated with insurance coverage, particularly military insurance. PDHS and PCC also contribute to treatment engagement, with differential effects across socioeconomic groups. These findings highlight the importance of addressing structural and interpersonal barriers to improve equitable access to evidence-based AUD treatment.

Background At present, there are no approved pharmacological treatments for people at clinical high risk for psychosis (CHR-P). We sought to assess the acceptability of cannabidiol (CBD): a promising candidate treatment for this population. Methods CHR-P individuals completed a survey which assessed their views on the acceptability of CBD, its expected effectiveness and side effects, and on formulation preferences. Results The sample comprised 55 CHR-P individuals (24.3 years and 69% female). Most (91%) were familiar with CBD, and had previously used cannabis (64%), and around half (42%) had tried over-the-counter CBD. 75% were willing to take CBD as an intervention for mental health problems. Most participants anticipated fewer side effects with CBD than with existing medications, and preferred tablet or capsule formulations over liquids. Discussion CBD is perceived as a highly acceptable treatment among CHR-P individuals.

In 2024, approximately 30% of U.S. adolescents reported having consumed alcohol at least once in their lifetime, with about 25% of these individuals engaging in binge drinking. Adolescent alcohol use is associated with neurodevelopmental impairments, elevated risk of later alcohol use, and mental health disorders. These findings underscore the importance of identifying the variables driving adolescent alcohol use and leveraging them for early identification and targeted intervention. Previous studies have typically developed machine-learning classification models that use neuroimaging data in combination with limited clinical measurements. Neuroimaging data are expensive and difficult to obtain at scale, whereas clinical measures are more practical for large-scale screening due to their low cost and widespread accessibility. However, clinical-only approaches for alcohol drinking classification remain largely underexplored. Furthermore, prior studies have often focused on adults, limiting generalizability to the broader adolescent population. Additionally, confounding factors such as age and substance use, which are strongly correlated with alcohol consumption, have often been inadequately addressed, potentially inflating classification performance. Finally, class imbalance remains a persistent challenge, with prior attempts yielding only limited improvements. To address these limitations, we propose FocalTab, a framework that integrates TabPFN with focal loss for robust generalization and effective mitigation of class imbalance. The approach also incorporates an initial preprocessing step to remove confounding factors to account for age and substance-use. We compare FocalTab against state-of-the-art methods across different variable selections and dataset settings. FocalTab achieves the highest accuracy (84.3%) and specificity (80.0%) in the most stringent setting, in which both age and substance use variables were excluded, whereas competing models drop to near-chance specificity (12-24%). We further applied SHapley Additive exPlanations (SHAP) analysis to identify key clinical predictors of drinker classification, supporting enhanced screening and early intervention.

BackgroundCannabis use is highly prevalent among people who use unregulated drugs. While daily cannabis use has been hypothesized to provide protective effects through substitution or tolerance mechanisms, the relationship between cannabis use frequency and overdose risk remains poorly understood, particularly for infrequent users. MethodsWe conducted a secondary analysis of cross-sectional interview data from people who use unregulated drugs in Vancouver, British Columbia, collected during the fentanyl crisis (November 2019-July 2021; n=657). Binary logistic regression examined associations between self-reported cannabis use frequency (five categories: less than monthly, 1-3 times per month, weekly, more than weekly and daily) and non-fatal overdose in the preceding six months. Daily use served as the reference category. Models adjusted for age, gender, ethnicity, homelessness, mental health, HIV status, incarceration and daily use of alcohol, opioids, fentanyl, cocaine and stimulants. ResultsAmong 657 participants, 95 (14.5%) reported non-fatal overdose in the past six months. In adjusted models with daily cannabis use as the reference, infrequent cannabis use was associated with significantly increased odds of overdose: use 1-3 times per month (aOR=3.17, 95% CI: 1.50-6.69, p=.002) and more than weekly use (aOR=3.13, 95% CI: 1.70-5.76, p<.001) showed approximately three-fold increased odds compared to daily use. Less frequent use showed non-significant trends in the same direction (less than monthly: aOR=1.73, 95% CI: 0.89-3.37, p=.109; weekly: aOR=1.44, 95% CI: 0.59-3.51, p=.421). Sensitivity analysis restricted to participants with daily stimulant or fentanyl use (n=148) revealed even stronger associations. ConclusionsInfrequent cannabis use was associated with substantially increased overdose risk compared to daily use. This frequency-dependent relationship, with infrequent users at highest risk, likely reflects tolerance differences: infrequent users lack tolerance to synergistic cannabis-opioid effects. These findings were completely obscured in preliminary analyses that dichotomized cannabis use as daily versus less-than-daily, demonstrating how analytical choices can mask critical public health insights. Current harm reduction approaches, including cannabis distribution programs, should incorporate frequency-dependent risk communication and develop strategies to protect infrequent users who may be at heightened overdose risk.

Despite decades of clinical implementation of medications for opioid use disorder (OUD), overdose mortality rates remain high, underscoring a critical gap in treatments that target brain mechanisms driving addiction. Mindfulness-Oriented Recovery Enhancement (MORE) has demonstrated efficacy in reducing opioid use and craving, hypothetically by restructuring the salience of drug and natural rewards. Yet, to date, MOREs neurobiological mechanisms remain unclear. In this first functional magnetic resonance imaging (fMRI) randomized controlled trial (RCT) of MORE for OUD (NCT04112186), we tested whether compared with an active psychoeducational supportive therapy (PST) control group, MORE rebalanced neural responses to drug and natural reward cues in inpatients with OUD receiving standard of care including medications. Compared with PST, eight weeks of MORE significantly reduced drug-biased activity in the dorsolateral prefrontal cortex (dlPFC) and posterior regions of the default mode network including the precuneus during downregulation of responses to drug cues relative to upregulation of responses to natural reward cues (even when controlling for passive cue viewing). The shift from drug to natural reward responses in the lateral and ventromedial PFC was associated with lower cue-induced craving exclusively in the MORE group. MORE also reduced medial PFC synchronization to naturalistic drug-related movie scenes and significantly extended abstinence duration at follow-up ([~]4 months post-treatment) relative to PST. Together, this neuroimaging RCT demonstrates that MORE normalizes function in PFC nodes of the reward, salience, and control systems, positioning MORE as a biologically-grounded adjunct to pharmacotherapy for OUD.

BackgroundParental genetics matters for childrens behavioural difficulties, but the extent to which this is due to direct genetic transmission versus environmentally mediated indirect genetic effects remains unclear. MethodsWe studied eight European birth cohorts with over 33,000 family-based trio samples. We analysed polygenic scores (PGSs) for 13 mental health and neurodevelopmental conditions and their composite indices (PC1 and mean) representing general neuropsychiatric liabilities, as well as educational attainment (EA) and alcohol and cigarette use, from children (PGSc), mothers (PGSm), and fathers. Child internalising, externalising, and total difficulties reported by mothers and/or fathers were examined at preschool and school ages. We then conducted multivariate meta-analyses to combine cohort-level results. FindingsWe observed several direct genetic effects on externalising difficulties, while indirect genetic influences were mainly identified for internalising difficulties. Specifically, child PGSs for attention-deficit/hyperactivity disorder (ADHD) and EA predicted higher and lower levels, respectively, of child externalising and total difficulties (all pFDR<0{middle dot}001; for school-aged externalising difficulties, PGSc-ADHD: {beta}=0{middle dot}121 [95% CI 0{middle dot}091 to 0{middle dot}151], pFDR<0{middle dot}0001; PGSc-EA: {beta}=-0{middle dot}095 [95% CI -0{middle dot}127 to -0{middle dot}063], pFDR<0{middle dot}0001), whereas maternal PGSs for major depressive disorder (MDD) and general neuropsychiatric liabilities were associated with internalising and total difficulties across parental raters and child ages (all pFDR<0{middle dot}05; for school-aged internalising difficulties, PGSm-MDD: {beta}=0{middle dot}049 [95% CI 0{middle dot}017 to 0{middle dot}081], pFDR=0{middle dot}016; PGSm-PC1: {beta}=0{middle dot}056 [95% CI 0{middle dot}022 to 0{middle dot}091], pFDR=0{middle dot}011). No statistically significant effects from paternal PGSs were identified. InterpretationIn this multi-cohort study, findings across multiple traits, raters, and ages supported several direct genetic effects of ADHD and EA on child externalising difficulties and indirect genetic effects on internalising difficulties, especially maternal depression and general neuropsychiatric liabilities. These suggest that child internalising difficulties are not solely driven by direct genetic transmission. More comprehensive research is needed to better understand the mechanisms involved, and ultimately how to ameliorate child behavioural difficulties. FundingEU, ERC, RCN, RCF, UKRI, SERI, DFG Research in contextO_ST_ABSEvidence before this studyC_ST_ABSIndirect genetic effects (IGEs) refer to the influence of parental genotypes on offspring outcomes beyond direct genetic effects (DGEs), for example via environmental pathways. While IGEs on offspring cognitive traits are well-established for educational attainment, evidence for IGEs of parental liabilities to mental health and neurodevelopmental conditions remains limited. To assess the current state of evidence, we conducted a systematic search of published studies applying trio-based polygenic score (PGS) designs to child and adolescent mental health outcomes. We identified 141 primary studies in MEDLINE, Embase, PsycInfo, and Web of Science, by 6 March 2025, after removing duplicates; following screening, 12 studies met inclusion criteria (see supplement for a full description including results). Ten out of the 12 studies focused on externalising outcomes, with little or inconsistent support for IGEs. When observed, IGEs were mainly driven by maternal liabilities to autism, educational attainment, and cognitive performance on child outcomes. The current evidence was too limited and heterogeneous to synthesize findings quantitatively, therefore a qualitative synthesis was conducted. Many studies were statistically underpowered, and the observed IGEs were in all cases sample-specific. There were no published multi-cohort studies. Added value of this studyWe integrated information across over 33,000 mother-father-child trios from eight European cohorts, investigating 18 PGSs from parents and children, using maternal and paternal ratings of offsprings internalising, externalising, and total difficulties as outcomes at both preschool and school age. We mainly observed DGEs on externalising difficulties, consistent with previous studies. Some evidence of IGEs was found for internalising and total difficulties. IGEs were often found to be maternally driven, with the most robust evidence across ages and raters emerging for maternal depression and general neuropsychiatric liabilities. Implications of all the available evidenceThe current evidence suggests that childrens behavioural difficulties, especially internalising difficulties, may be partly driven by the environment shaped by maternal neuropsychiatric liabilities. Ours and previous findings highlight a pressing need for more comprehensive studies across different cohorts, raters, outcomes, and time points to understand the true extent of IGEs in the intergenerational transmission of mental health.

BackgroundTheta-band oscillation is integral to fronto-parietal connectivity in the executive control network and its top-down regulation on subcortical areas. External frontoparietal synchronization using theta-frequency transcranial alternating current (tACS) is a technology to potentially engage this network. In this pre-registered, triple-blind, sham-controlled trial (NCT03907644), we tested this intervention targeting the right frontoparietal network in people with opioid use disorder (OUD) to measure network engagement and behavioral outcomes. MethodSixty male participants with OUD were randomized to receive 20 minutes of active or sham 6 Hz tACS (HD electrodes over F4 and P4). Structural, resting-state, task-based fMRI drug cue reactivity, and repeated cue-induced craving assessments were collected immediately before and after stimulation. Pre-registered outcome measures were analyzed using timexgroup interaction models to examine (1) modulation of drug cue-related brain activity, (2) changes in craving, (3) alterations in functional connectivity, and (4) relationship between electric field, neural responses, and craving behavior. Results(1) A significant Time x Group interaction revealed decreased post-stimulation opioid cue-related activity in the active group relative to sham, involving key nodes in reward processing (ventral striatum, amygdala and ventral tegmental area) (FWE corrected =0.05) (2) subjective craving did not differ significantly between groups (3) Group by time generalized psychophysiological interaction analyses showed increased right frontoparietal network engagement ({beta}=2.63, p=0.0308) following stimulation, and increased top-down inhibitory regulation of frontoparietal network on right ventral striatum ({beta}=1.99, p=0.037) and left medial amygdala ({beta}=1.97, p=0.039) (4) Electric field strength in the right frontal/parietal node predicted frontoparietal network engagement in the active group (r=0.43, p=0.02). ConclusionTogether, these findings demonstrate that theta-band frontoparietal tACS can modulate activity and task-dependent coupling within cortical-subcortical circuits in OUD, supporting network-targeted neuromodulation as a potential intervention for addiction. Significance StatementAddiction is linked to imbalances in cortico-subcortical brain circuits that control reward processing and craving. This study tested whether a non-invasive brain stimulation method-- theta-band transcranial alternating current stimulation (tACS)--can rebalance these circuits in people with opioid use disorder. Using advanced brain imaging, we found that tACS strengthened communication within frontoparietal brain regions involved in self-control while reducing their connections with reward and emotion centers. These brain changes were linked to reduced craving responses to drug cues. Our results demonstrate that dual-site, network-targeted tACS modulates neural activity and task-dependent engagement of brain circuits during drug cue reactivity in addiction, supporting its potential as a novel therapeutic approach.

Perinatal depression (PD) is common and disabling, yet its longitudinal comorbidity patterns and predictability remain poorly understood. This study leveraged 8,804 women with delivery records in the All of Us cohort, including 438 with clinically diagnosed postpartum depression (PPD), to characterize multimorbidity trajectories and develop integrated prediction models. Comorbidities were grouped into 38 conditions across psychiatric, autoimmune, metabolic, neurological/pain, and reproductive/gynecological categories and examined both cross-sectionally and in monthly time bins from 250 months before to 500 months after delivery. Latent class analysis identified three pre- and post-delivery multimorbidity profiles and transitions between classes, while polygenic risk scores for depression and obstetric, clinical and socioeconomic variables were combined in machine learning models to predict PPD, post-delivery class membership, and symptom worsening among initially low-burden women. PPD cases showed higher odds of several psychiatric, autoimmune, and metabolic conditions and a tendency toward greater post-delivery comorbidity accumulation, particularly among women who were healthy pre-pregnancy. Multimorbidity profiles based on latent classes captured clinically meaningful risk gradients, and transition analyses revealed that incident PPD in previously healthy women marked a shift toward more symptomatic post-delivery profiles. Machine learning models achieved moderate discrimination for PPD and comorbidity outcomes and highlighted the importance of genetic liability, obstetric complications, and socioeconomic disadvantage, but low positive predictive values limit clinical implementation. These findings position PPD as a critical event in womens psychiatric, cardiometabolic, and pain-related health trajectories and support life-course, multimorbidity-informed screening and prevention strategies that extend beyond the traditional postpartum period.

BackgroundSubstance use disorders (SUD) are associated with a high global burden of disease, with 5.4% of all disability-adjusted life years lost due to alcohol and illicit drugs. Highly prevalent multimorbidity includes polysubstance use, mental health conditions, and other non-communicable and infectious diseases. Where traditional treatments are insufficient alone, music therapy (MT) is highly engaging and improves motivation and reduces craving; however, its long-term effects are unknown. The present study aims to examine long-term effects of active music groups (AMG) and music listening groups (MLG) versus treatment as usual (TAU) on addiction severity, recovery, and other outcomes in people with SUD Immediate and short-term effects, as well as mechanisms of these interventions, will also be examined. MethodsIn individuals with SUD across a wide range of age, gender, socioeconomic, and cultural backgrounds, a parallel 3-arm assessor-blinded pragmatic multinational randomised controlled trial (RCT) with embedded exploratory trials and mechanistic studies will determine long-term effects of AMG and MLG versus TAU on addiction severity (primary endpoint: 1 year), recovery, and other outcomes. Embedded trials will examine immediate effects of AMG or MLG combined with individual components of TAU combined to determine the best combinations of interventions. Experimental studies will examine mechanisms using cognitive testing and brain imaging. With 600 participants in 7 countries randomised, the trial will have 80% power on the primary outcome. Patient representatives, health technology assessment (HTA) bodies, and interventionists have been involved from conception and will ensure feasibility and applicability of the intervention across Europe. DiscussionThis document describes the FALCO RCT, the main part of the FALCO project, which aims to reduce disease burden through innovative, effective, and affordable treatment, and will strengthen research and innovation expertise. Recommendations from FALCO will inform intervention delivery across Europe and beyond, leading to increased safety, effectiveness, and cost-effectiveness, and improved quality of life for individuals with SUD. Stakeholders will be involved in communicating findings across all European countries and regions and ensuring that findings are effectively implemented. Trial registrationClinicalTrials.gov, NCT07028983, registered 11th of June 2025. https://clinicaltrials.gov/study/NCT07028983

BackgroundCannabinoid hyperemesis syndrome (CHS) is characterized by episodes of severe nausea, vomiting, and abdominal pain among those with heavy cannabis use. We estimated differences between those reporting CHS symptoms and other daily and less frequent cannabis users on drug use, psychiatric problems, other health problems, antisocial behavior, and personality. MethodsThe National Firearms, Alcohol, Cannabis, and Suicide survey was administered to 7034 US adults in 2025. Survey items assessed substance use, common psychiatric symptoms, personality traits, and symptoms of CHS. ResultsThose with CHS symptoms reported the highest rates and greatest variety of drug use compared to others who used cannabis. Those with CHS symptoms reported higher rates of other drug use than those who used cannabis daily without CHS symptoms across a variety of drug classes, including opioids, hallucinogens, and sedatives, higher rates of drug overdoses, and greater use of all drug classes than those with less-than-daily cannabis use. Those with CHS symptoms also reported more depression, anxiety, sleep problems, chronic pain, antisocial behavior, intimate partner violence, and disinhibited personality traits than those who used daily (mean d = 0.58) and less frequently (mean d = 0.69) and those with no cannabis use in the past 12 months (mean d = 0.99). ConclusionsThose with CHS symptoms exhibit a variety of psychological and behavioral problems including higher rates of other drug use, psychiatric symptoms, antisocial behavior, and dysfunctional personality traits. Results highlight the importance of understanding and addressing the broader psychosocial challenges faced by people experiencing CHS symptoms. Highlights O_LICHS symptoms are linked to greater polysubstance use and overdose risk C_LIO_LICHS symptoms are associated with depression, anxiety, sleep, and pain problems C_LIO_LICHS tied to antisocial behavior and intimate partner violence C_LIO_LICHS shows disinhibited personality traits and low well-being C_LIO_LINational survey identifies high-risk psychosocial CHS profile C_LI

Background and HypothesisAbnormal default mode network (DMN) connectivity was observed in both tobacco use and psychotic spectrum disorders, but it remains unknown how psychosis impacts the relationship between connectivity and tobacco use. Interventions targeting the left lateral parietal DMN node (LLPDMN) have modulated DMN connectivity and nicotine craving in psychosis. We aimed to investigate relationships between DMN connectivity, psychotic illness, and tobacco use. Study Design336 participants (psychosis: n=161, control: n=175) reported their tobacco use history and underwent resting-state functional magnetic resonance imaging. We calculated connectivity within DMN and salience network (SN), between DMN-SN, and from LLPDMN to other DMN and SN nodes. Logistic and LASSO regression with bootstrapping were performed to investigate diagnosis-by-connectivity interactions on lifetime tobacco use. Exploratory brainwide analysis was conducted by regressing brainwide connectivity to LLPDMN against daily cigarette use. Study ResultsWe observed a significant diagnosis-by-DMN connectivity interaction for lifetime tobacco use (p=0.0281, coefficient=0.457, OR=1.579, 95% CI=[1.063, 2.411]); in the psychosis group, higher DMN connectivity was associated with higher odds of lifetime tobacco use. LASSO regression yielded four predictors of lifetime tobacco use: age, diagnosis, LLPDMN connectivity to a prefrontal SN node, and the interaction between diagnosis and LLPDMN connectivity to a right parietal DMN node. Brainwide analysis identified bilateral somatomotor clusters where higher connectivity to LLPDMN correlated with higher daily cigarette use (voxel-wise p<0.001, cluster p<0.05). ConclusionsPsychosis diagnosis modified relationship between DMN connectivity and tobacco use. Modulating DMN connectivity may provide a psychosis-specific treatment target for tobacco dependence.

Objective: Posttraumatic stress disorder (PTSD) after a traumatic birth is a serious but overlooked maternal morbidity, affecting ~20% of women following medically complicated deliveries. PTSD can undermine maternal caregiving. Rapid screening tools suited to busy obstetric settings are lacking. We developed and evaluated a brief screener, derived from the 20-item PTSD Checklist for DSM-5 (PCL-5), to identify PTSD related to childbirth. Study Design: We enrolled 107 women with traumatic childbirth. Participants completed the PCL-5 and the gold-standard clinician diagnostic interview for PTSD (CAPS-5); depression was measured with the Edinburgh Postnatal Depression Scale (EPDS). Bootstrap resampling with LASSO regression identified PCL-5 items most associated with PTSD. Firth logistic regression models estimated diagnostic accuracy. Sensitivity, specificity, area under the ROC curve (AUC), and Youden's J statistic determined performance and optimal cut-off. Results: A six-item version of the PCL-5 (PCL-5 R6), statistically derived from the full scale, showed excellent discrimination for PTSD compared with clinician evaluation (AUC = 0.95; 95% CI, 0.89-1.00). A cut-off score of 7 yielded high sensitivity (0.96) and good specificity (0.83), with an overall diagnostic efficiency of 0.86, detecting most PTSD cases while minimizing false positives. The PCL-5 R6 correlated moderately with the EPDS (rho = 0.53), showing that a depression screen alone cannot reliably detect PTSD. Conclusions: A short, 6-item PCL-5 provides a valid, efficient tool for detecting childbirth PTSD. Its brevity and accuracy make it practical for integration into routine postpartum care, enabling timely mental health screening.

IntroductionRecreational and medical cannabis use (CU) information is often available within the electronic health record (EHR) in a format that is impractical for health care provider use. Transformation of free-text EHR documentation in notes to discrete elements is possible using natural language processing (NLP) and has the potential to characterize CU efficiently. The objective of this study was to develop an NLP algorithm to identify documentation of CU within EHR unstructured clinical notes. MethodsWe identified EHR notes with cannabis-related terminologies through a keyword search among all Geisinger patients with at least one encounter between 1/1/2013 and 6/30/2022. We trained four NLP models to classify notes into six categories based on time, context, and reliability of CU documentation identified through manual annotation. We compared the demographic characteristics of patients with positive classification for CU using the best-performing model to those of the overall population. ResultsOf the over 1.7 million eligible patients, 150,726 (8.6%) were flagged as cannabis users. The Bio-ClinicalBERT, a transformer-based NLP model, achieved close to human performance in classifying CU (weighted Precision=91.4, Recall=93.3, F-score=92.4). Cannabis users had higher BMI and were at least nine-fold more likely to use tobacco, alcohol, and illicit substances. ConclusionOur study evaluated the prevalence of CU documentation across the entire corpus of EHR notes data without population segmentation. The NLP methodologies used achieved performance close to that of human annotation and laid the foundation for identifying and classifying CU within unstructured data sources, with future applications in research and patient care. Plain Language SummaryMarijuana, also known as cannabis, may impact the health of patients, yet it is not routinely captured in medical records, and when documented, it is often found in unstructured formats (e.g., progress notes) rather than in discrete fields. Incomplete and unstructured capture limits many functional capabilities within the EHR that enhance patient care (e.g., drug interactions, notifications) and limit researchers from identifying patients routinely exposed to marijuana use. The transformation of free-text documentation of cannabis use (CU) into discrete elements can be performed using natural language processing (NLP). The objective of this study was to develop an NLP model to identify CU in unstructured clinical notes in the EHR. We examined the EHRs of Geisinger patients in Pennsylvania over a 10-year period. Among 1.7 million patients, 9% were identified as CU. One of the NLP models tested, Bio-ClinicalBERT, achieved the highest performance. Cannabis users had a higher BMI and were ten-fold more likely to be tobacco users, ten-fold more likely to use alcohol, and nine-fold more likely to use illicit substances. NLP can be used to better understand the risks and benefits of CU at a population level and may improve patient identification to assist clinical decision-making. Future CU epidemiological research should continue to explore other avenues to automate and improve CU documentation by leveraging rapidly evolving technologies, such as artificial intelligence-driven tools.

ObjectiveAs cannabis use has increased in the United States, so has cannabinoid hyperemesis syndrome (CHS), a disorder characterized by severe nausea, vomiting, and abdominal pain among heavy cannabis users. We previously showed that CHS symptoms are associated with several behavioral and psychological characteristics linked to psychosocial impairment. We examined links between CHS symptoms and suicidal thoughts, behaviors, and proximal suicide risk factors. MethodsWe used data from the National Firearms, Alcohol, Cannabis, and Suicide survey, a nationally representative survey of 7,034 US adults. Items assessed symptoms of CHS and suicidal thoughts and behaviors. Comparisons focused on: those with daily cannabis use and CHS symptoms (n = 191), those with daily cannabis use without CHS symptoms (n = 882), those with past year cannabis use but not daily use (n = 1288), and those without past year cannabis use (n = 4673). ResultsThose with CHS symptoms reported the highest prevalence of suicidal thoughts and behaviors with most lifetime rates being significantly higher than those with daily cannabis use without CHS symptoms. Those with CHS symptoms also reported higher mean-levels of thoughts and feelings associated with suicide (i.e., perceived burdensomeness, thwarted belongingness, defeat, entrapment) than all the other groups. ConclusionsThose with CHS symptoms reported especially high rates of suicidal thoughts, behaviors, and attempts even when compared to others with daily cannabis use. People with CHS symptoms appear to be at high risk of suicide, possibly related to distress from their gastrointestinal symptoms and psychiatric, substance use, and medical comorbidities.

Psychotic disorders are increasingly recognized as the extreme end of a progressive psychopathology continuum, with less advanced stages including the asymptomatic familial high-risk state (FHR), the help-seeking clinical high-risk state (CHR), and first episode psychosis (FEP). However, we lack a comprehensive study of clinical, cognitive, functional, and neuroanatomical markers across all three early stages of psychosis, limiting our understanding of how the multimodal phenotypes which define psychotic disorders emerge in the broader course of psychopathology. We leveraged a sample of 70 FEP, 40 CHR, 43 FHR, and 41 healthy participants recruited from the same clinical and sociodemographic setting - the first such dataset to be described in the literature. Several markers were elevated in CHR but did not worsen in FEP, including depression/anxiety and difficulties functioning, while FEP was uniquely defined by cognitive impairments and cortical thickness reductions characteristic of those seen in schizophrenia. Across the sample, the dominant axis of joint brain-behaviour variability captured a relationship between reduced cortical thickness and lower cognitive performance, a pattern which was equally established in both CHR and FEP. Initial longitudinal data revealed that depressive and negative symptoms best predicted lower functioning at 6-month follow-up, regardless of group status. Together, our analysis suggests that affective and functional disturbances emerge in earlier stages of psychosis, while cognitive and anatomical abnormalities characterize more advanced ones - though the overlap we observed across groups demonstrates that clinically relevant phenotypes can cut across group boundaries, requiring personalized care to manage.

BackgroundLipidomic alterations have been reported across schizophrenia (SCZ) and bipolar disorder (BD), but findings are heterogeneous and often overlap across diagnoses, limiting diagnostic specificity. Associations between lipid profiles and illness severity have also been inconsistent when assessed using single symptom scales, raising the possibility that unidimensional measures fail to capture biologically relevant variation. Whether plasma lipidomic alterations relate to multidimensional psychosis severity, and how they relate to polygenic liability, remains unclear. MethodsWe examined associations among psychiatric and cognitive polygenic risk scores (PRS), plasma lipidomics (361 species across 16 classes), and a machine-learning-derived severe psychosis probability score in a transdiagnostic cohort of individuals with SCZ or BD (PRS n=1,320; lipid subset n=428). Regression and lipid class enrichment analyses tested severity associations. Mediation and canonical correlation analyses assessed integrated genetic-lipid-severity relationships. ResultsSCZ-PRS (positive), BD-PRS (negative), and educational attainment PRS (negative) showed modest associations ({beta} = |0.02|) with severe psychosis probability. Lipid class enrichment analysis identified nine classes associated with severity, including increased sphingolipids (dSM, dCer), phosphatidylcholines (PC), triacylglycerides (TAG), and phosphatidylethanolamine plasmalogens (PE-P), alongside decreased phosphatidylcholine plasmalogens (PC-P). Most lipid class associations were robust to adjustment for diagnosis and medication. No significant mediation or shared multivariate genetic-lipid structure was observed. ConclusionsPlasma lipidomic variation tracks multidimensional psychosis severity across diagnostic boundaries. These findings suggest that lipidomic alterations may reflect transdiagnostic biological processes linked to illness burden that are not fully captured by categorical diagnoses, single symptom scales, or common-variant polygenic risk.

BackgroundPsychotherapy is proven efficacious for the treatment of alcohol use disorder (AUD). However, the patient-perceived importance of its effect is not fully appreciated in the evidence base. The smallest worthwhile difference (SWD) represents the smallest beneficial effect of an intervention that patients deem worthwhile in exchange for the harms, expenses, and inconveniences associated with the intervention, and facilitates the interpretation of patient perceived worthiness of an intervention. MethodsThe proposed study will estimate the SWD of NIAAA recommended psychotherapies for AUD treatment with English-speaking American respondents aged 18 and older. Primary participants will be recruited using the Prolific research crowdsourcing site. The SWD will be estimated using the Benefit-Harm Trade-off Method, presenting survey respondents with variable, hypothetical magnitudes of psychotherapy outcomes to find the smallest acceptable effect over a natural remission alternative. The overall average SWD, and subgroup distributions by participant AUD treatment experiences and AUD symptomology will be described. Secondary findings will estimate the smallest recommendable risk difference for AUD psychotherapy from providers and criminal justice professionals. Expected ResultsWe expect to find an estimate of the SWD for AUD psychotherapy. Further, we expect that the SWD will vary between clinical subgroups based on AUD symptomology and treatment experiences. We expect differences in SWDs between the general population and those of providers and criminal justice professionals. Findings from this project will inform the treatment decision process about psychotherapy during the clinical consultation for people with AUD.