American Journal of Psychiatry

A body of research suggests that autistic individuals are less likely to drink alcohol than neurotypicals. However, emerging studies support a link between autism and alcohol use. This complex relationship is also reflected in studies that have examined the genetic overlap between the two traits. However, it is unclear whether there is a direct causal relationship between them. To explore this, we applied a combination of polygenic score and Mendelian randomisation analyses using publicly available genome-wide summary statistics and phenotypic measures of autism and alcohol consumption from UK Biobank. LD score regression analyses did not provide evidence of a genetic correlation between genetic liability for autism and drinks consumed per week (rg=-0.08; CI95%=-0.19, 0.03). Further, findings from polygenic score analyses did not support an association between genetic liability for autism and overall monthly alcohol intake. Univariable Mendelian randomisation analyses showed little evidence for a total effect of autism, attention deficit hyperactivity disorder (ADHD) or depression on overall monthly alcohol consumption. Multivariable Mendelian randomisation analyses also showed little evidence of a direct effect of autism on drinks per week when controlling for ADHD and depression. It is plausible that genetic liability for autism does not directly increase the amount of alcohol consumed but instead operates via commonly co-occurring difficulties in the autistic community. However, our findings may be due to methodological shortcomings, including weak instruments biasing effects towards to the null. Consequently, results should be interpreted with caution and further research conducted to address these issues.

Background: As cannabis policy changes in the United States, secondhand cannabis smoke (SCS) is increasingly common, including within families. However, prevalence of exposure and clinical correlates over time in adolescents are not fully understood. Objectives: (1) To estimate the prevalence of SCS and personal cannabis use in US-based teens exposed to SCS, and (2) examine the cognitive trajectories of adolescents exposed to SCS compared to non-exposed peers. Methods: Data from the Adolescent Brain Cognitive Development (ABCD) Study was used. Participants (n=11,316 of full cohort with follow-up data; n=776 with self-reported family SCS exposure) attended yearly visits from ages 11-17, completing substance use interviews, toxicological testing, and the NIH Toolbox Cognitive battery. Youth with SCS but no personal cannabis use (n=419; 47% female) were matched on prenatal substance exposure, family substance use history, and sociodemographics to non-SCS exposed and non-cannabis-using youth with a 1:2 ratio (Controls n=838). Linear mixed-effects models assessed cognitive performance by SCS*age interactions, accounting for random effects of subject and family. Covariates included sex and alcohol, nicotine, and other substance use. Secondary models analyzed performance by cumulative waves of reported SCS exposure interacting with age. Results: Of the full cohort, 6.9% (n=776) reported exposure to SCS. Of these individuals, 46% endorsed lifetime personal cannabis use by age 17, relative to 20% of non-SCS exposed youth (OR=3.83[95%CI:3.29,4.44]). Within matched participants, SCS*age demonstrated a significant interaction on attention and inhibitory control ({beta}=-0.32, p=.028), with SCS demonstrating reduced improvement over time. More waves of exposure were also associated with worse performance over time ({beta}=-0.39, p=.057). Discussion: Almost half of those who had been exposed to SCS endorsed personal cannabis use. Cognitive findings were domain specific, similar to findings in secondhand tobacco: SCS exposed youth showed restricted improvement in attention and inhibitory control by age 17. Public health and policymakers should make efforts to curb youth SCS exposure, given the potential for risk which has not been fully explored to date.

Adolescent substance use is a major public health concern due to increased risk of future physical and mental health conditions. Fronto-striatal functioning - particularly regarding inhibition and reward processing - may increase vulnerability to high-risk substance use. However, it remains unclear if these neurobiological differences precede substance use or are consequences of it. The ongoing Adolescent Brain Cognitive Development (ABCD) Study follows over 10000 youth, offering an unprecedented opportunity to longitudinally examine substance use patterns throughout development. We utilized family-clustered time-varying Cox proportional hazard models to prospectively examine main and interaction effects of right Inferior Frontal Gyrus (IFG) inhibitory control and bilateral nucleus accumbens (NAc) reward response, alongside early life adversity and peer substance use as predictors of alcohol and cannabis onset in the ABCD Study. We identified a significant crossover interaction such that left NAc activity had a slight positive association with first full alcoholic drink in the context of higher right IFG activity but a negative association in the context of lower right IFG activity. However, peer alcohol and cannabis use emerged as the strongest predictors of outcomes. Alcohol onset was also more common in females, and early life adversity was associated only with cannabis onset. Findings indicate that interactions between inhibition- and reward-related brain regions may impact risk for early substance use onset, but these effects may be modest relative to socioenvironmental factors. Additionally, divergent alcohol and cannabis findings suggest that risk profiles are substance specific. Peer-focused strategies should be considered in preventive efforts.

Objective: This study examined the prevalence of psychotic experiences (PE) and how early onset and persistence of PE contribute to risk and severity of mental disorders in adolescents at familial high-risk of schizophrenia (FHR-SZ) or bipolar disorder (FHR-BP) and adolescents from a population-based control group (PBC). Methods: This is the second follow-up of a nationwide cohort study including 522 children at FHR-SZ (N=202), FHR-BP (N=120), and PBC (N=200). Participants were assessed at ages 7, 11, and 15 using a semi-structured interview to evaluate PE and mental disorders. Results: At age 15, adolescents at FHR-SZ reported more PE than PBC over the past six months (current) and the past four years, while adolescents at FHR-BP only reported more current PE. PE reported at two or three timepoints (persistent PE) predicted any Axis I disorder in mid-adolescence, corresponding to three- (OR 2.9, 95% CI [1.5-5.7]) and 21-fold (OR 21.4, 95% CI [2.8-162.3]) increased risks, respectively. Persistent PE also predicted multimorbidity, with three- (OR 2.8, 95% CI [1.0-7.6]) and four-fold (OR 4.1, 95% CI [1.2-14.1]) increased risks, respectively. This was after adjustment for sex, early mental disorders, and familial risk. Conclusions: This study demonstrates a strong link between persistent PE and mid-adolescence mental disorders. Our findings emphasize PE as important risk markers for mental disorders during mid-adolescence and highlight the importance of monitoring children with PE before age 7 who develop persistent symptoms.

Background: Substance use is common among individuals with depression. Transcranial magnetic stimulation (TMS) is an effective treatment for depression, but current clinical guidelines have discouraged TMS treatment for individuals with depression and co-occurring substance use given concerns for limited efficacy. However, limited data exists on whether substance use affects response to TMS. Methods: Using electronic health record data from patients who received a standard course of TMS for major depressive disorder at an academic medical center, we investigated associations between substance use frequency and response to TMS, defined as change in Patient Health Questionnaire-9 (PHQ-9) scores. Substance use frequency was extracted for alcohol, cannabis, nicotine, stimulants, benzodiazepines, opioids, inhalants, psychedelics, and other drugs. We performed ANCOVA and multiple regression analyses to predict change in PHQ-9 score based on substance use frequency, controlling for pre-TMS PHQ-9 score, age, sex, and number of TMS sessions received. Results: We extracted data from 219 TMS courses. Alcohol was the substance used most commonly (34.2%), followed by prescription benzodiazepines (28.3%), and prescription stimulants (21.0%). Across all substance categories, substance use was not associated with change in PHQ-9 score (all p > 0.05, Cohens d=0.00 to 0.30). In multiple regression models to compare individual levels of substance use frequency (e.g., daily use vs. no use), level of substance use was not associated with change in PHQ-9 score (all p > 0.05). The range of plausible effects of substance use frequency on PHQ-9 change was generally below the minimal clinically important difference for PHQ-9, suggesting substance use was unlikely to have a meaningful clinical effect on antidepressant response to TMS. Conclusions: Low to moderate substance use does not have a clinically significant effect on antidepressant response to TMS. Low-level substance use should not exclude individuals with depression from receiving TMS.

Background Alcohol-associated injury is a leading cause of emergency department (ED) utilization in the United States and a clinically important driver of preventable morbidity across the adult lifespan. Prior surveillance research has characterized how the rate and severity of alcohol-associated injury vary by patient age, but whether the seasonal timing of injury risk is equally predictable across age groups (a question directly relevant to the timing of clinical screening intensification and public health intervention) has not been formally tested. Methods We conducted a retrospective surveillance analysis of 45,876 alcohol-associated ED visits among adults aged 18 years and older, identified from the National Electronic Injury Surveillance System (NEISS), 2019-2025 (weighted national estimate: 2,092,319 visits), using the structured Alcohol_Involved indicator introduced into NEISS case abstraction in 2019. Patients were stratified by sex and five age groups (18-24, 25-34, 35-49, 50-64, and [&ge;]65 years). Single-harmonic cosinor (Poisson) regression was used to estimate the seasonal peak day of injury risk (acrophase) for each stratum. To assess reliability, we performed leave-one-year-out jackknife resampling (seven iterations per group), case-resampling bootstrap confidence intervals (1,000 iterations), and likelihood-ratio tests of seasonal-phase interactions. Results Peak injury timing differed significantly across age groups (X^2 [8] = 2356.2, p < .0001). Adults aged 25-64 years showed a highly reproducible early-to-mid-July peak, with jackknife estimates shifting [&le;]14 days when any single study year was excluded. Adults aged [&ge;]65 years showed significant seasonal variation annually (all p < .0001, amplitude comparable to younger groups) but a pooled peak estimate that shifted by up to 100 days across jackknife iterations. Sex-stratified analyses revealed that this instability was driven entirely by females aged [&ge;]65 years (jackknife range: 332 days, peak consistently in late October through early January) rather than males aged [&ge;]65 (jackknife range: 31 days, peak consistently in early August). Hospital admission rates increased monotonically with age from 9.0% (18-24 years) to 31.8% ([&ge;]65 years). Conclusions Alcohol-associated injury follows a reproducible, calendar-stable summer seasonal pattern in adults aged 25-64 years. Among adults [&ge;]65 years, the previously reported temporal instability is concentrated in the female subgroup, whose seasonal injury risk does not converge on a fixed calendar window. These findings suggest that fixed-calendar prevention and screening strategies are well suited to working-age adults and older men, but older women may require a year-round, individually tailored approach. Keywords: Alcohol-related injury; Emergency department; Seasonality; Age factors; Sex differences; Injury surveillance; Cosinor analysis; Older adults

Recent large-scale studies have enabled new knowledge about genetic underpinnings of morphological and electrophysiological alterations of the retina. Variation in retinal traits, often of neurodevelopmental origin, have been linked to major psychiatric disorders (MPDs). Here, we investigate the genetic overlap between MPDs and key retinal traits to identify underlying molecular mechanisms. We obtained genome-wide associations studies data for bipolar disorder (BD), major depression (MD), schizophrenia (SCZ), and the retinal traits retinal nerve fibre layer thickness (RNFL), ganglion cell inner plexiform layer thickness (GCIPL), and vertical cup-disc ratio (VCDR). We estimated the number of trait-influencing variants shared between traits with MiXeR and identified shared genetic loci with condFDR. Subsequently, we examined the biological pathways of the genes mapped to shared loci. This revealed that GCIPL shared the most genetic variants with MPDs (~60%), followed by RNFL (~40%), and VCDR (~20%). The genetic variants shared between retinal traits and MPDs showed disorder-specific patterns with more pronounced overlaps of SCZ and BD with RNFL, and MD negatively correlated with GCIPL. Gene-pathway analysis highlighted the importance of GABAergic neurotransmission and a two-stage neurodevelopmental process in SCZ, whereas the role of mitochondria and a weaker developmental component were observed in BD. The results also implicated synaptic functioning and gene-expression processes in MD. Furthermore, polygenic analysis suggested that the genetic architecture of retinal traits can distinguish between MPDs. Our findings indicate shared genetic underpinnings between retinal traits and SCZ, BD, and MD, implicating altered neurodevelopment and neurotransmission underlying the retinal link to major psychiatric disorders.

Background: Cognitive impairment is the leading cause of disability in schizophrenia with limited treatments. A major barrier to treatment development is the absence of reproducible, mechanistically grounded neural targets. Cross-sectional studies have identified dorsomedial prefrontal cortex (DMPFC)-somatomotor connectivity as a neural marker of cognitive performance on the Auditory Continuous performance task (ACPT), a measure of attention. To test the stability of this marker, we tested the relationship between DMPFC-somatomotor connectivity and ACPT performance in a longitudinal psychosis sample. Methods: Individuals with early psychosis (n=251) and matched controls (n=90) were enrolled and underwent resting-state neuroimaging and neurocognitive assessment. A subset completed longitudinal assessments over 2-4 years. We calculated DMPFC-somatomotor resting-state functional connectivity using a previously identified DMPFC region and a seed in the somatomotor cortex. We performed linear mixed effects models to predict ACPT performance based on connectivity, time, psychosis type, and their interaction. Results: In the psychosis sample, time (p=.0037) and affective psychosis diagnosis (p<.0001) predicted better ACPT performance. In a model predicting ACPT performance, we observed a significant interaction effect of DMPFC-somatomotor connectivity*psychosis subtype (p=.0079) such that DMPFC-somatomotor connectivity predicted ACPT performance only in individuals with non-affective psychosis (p=.0051). We then tested the specificity of this connectivity-cognitive performance relationship. In a model predicting DMPFC-somatomotor connectivity, only ACPT performance (p=.017), but not fluid cognition, was a significant predictor. Conclusions: DMPFC-somatomotor connectivity is longitudinally associated with cognitive performance in early psychosis. This relationship is strongest in nonaffective psychosis, suggesting a novel, reliable target for intervention for cognitive deficits in early psychosis.

Psychosis has long been conceptualized as a disorder of disrupted hierarchical integration across distributed brain systems, yet it remains unclear whether alterations in macroscale cortical hierarchy are already present before illness onset and are associated with subsequent transition to psychosis. Using connectome gradient mapping, we characterized baseline cortical hierarchical architecture along the unimodal-to-transmodal axis in 580 participants from the NAPLS-3 cohort, including converters (CHR-C, n = 56), non-converters (CHR-NC, n = 434), and healthy controls (HC, n = 90). Group differences were assessed at regional, network, and global levels. Group comparisons revealed that CHR-C individuals, relative to the other two groups, exhibited bidirectional alterations selectively along the sensorimotor-to-association gradient, with reduced values in the visual network alongside elevated values in the default mode network, indicating greater separation between sensory and transmodal systems along the gradient. At the global level, CHR-C showed increased explained variance, range, and variation of this gradient, collectively indicating hierarchical expansion. Notably, greater explained variance of this gradient was associated with a shorter time to conversion to psychosis, while increased gradient range and variation were associated with higher positive symptom severity across CHR individuals. These findings indicate that expansion of the sensorimotor-to-association connectome hierarchy is already present before psychosis onset in individuals who subsequently convert to psychosis. This altered hierarchical organization may reflect greater decoupling between sensory and transmodal systems and may characterize neurobiological changes associated with progression from a clinical high-risk state to psychotic illness.

Suicidal behavior results from complex behavioral and biological changes. Previous cross-sectional studies indicate that proinflammatory immunobiological factors are often increased in close temporal proximity to a suicide attempt. Suicidal individuals may also exhibit a biological trait vulnerability to stress and inflammation, due to persistent epigenetic modifications. We enrolled 130 individuals with major depressive disorder (MDD), 83 with suicidal behavior at intake, and followed them for 12 months with up to eight clinical assessments. Quantification of plasma inflammatory markers and metabolites was performed by high-sensitivity electrochemiluminescence and Ultra High-Performance-Liquid-Mass Spectrometry (UPLC-MS), respectively. Epigenetic changes were identified using Illumina EPIC arrays. We identified 15 genes with altered DNA-methylation associated with suicidal behavior and attempts at baseline. Childhood trauma predicted lifetime suicide attempts and was associated with altered methylation of seven genes. Increased neutrophils and lower plasma serotonin at baseline predicted future suicide attempts over the following year (neutrophil estimate = 0.42, P = 0.016; serotonin OR = 0.58, 95% CI: 0.39-1.13). Utilizing biomarkers from baseline and epigenetic data from the genes with highest predictive values (STBD1 ,PRDM8, and TRIM15), we achieved an area under the curve (AUC) of 0.84 for suicide attempts over the year. Suicidal behavior in MDD was associated with specific epigenetic signatures. Several of the identified genes, such as MAD1L1, have been implicated in psychiatric disease, suicidal behavior and the immune response. These findings support the usefulness of epigenetic and immunometabolic blood markers for identifying suicidal individuals in clinical settings, potentially enhancing preventative efforts.

Objective To measure the accuracy of clinical coding, clinician review, and a locally deployed large language model (LLM) in identifying alcohol, drug, and self-harm involvement in emergency department (ED) attendances, and quantify prevalence. Design Two-phase diagnostic accuracy study. In a validation week, the identification strategies were assessed against a conflict-adjudicated reference standard (n=2,256); the LLM was then applied to n=105,096 annual attendances at the same site. Setting UK Type 1 Emergency Department treating patients [&ge;]16yrs. Main outcome measures Prevalence quantification compared with the reference standard; sensitivity, specificity, and balanced accuracy of each strategy; monthly identification rates and adjusted annual prevalence. Results The reference standard identified 12.1% of attendances as involving alcohol, drugs, or self-harm (coding 6.0%; clinician 10.0%, LLM 15.6%). LLM balanced accuracy matched or outperformed clinician review in all three domains (alcohol 0.942 v 0.930, p=0.635; drug 0.959 v 0.791, p<0.001; self-harm 0.982 v 0.908, p=0.004). Coding recorded 1.07 domains per identified patient against 1.32 in the reference standard. Adjusted annual prevalence corresponded to 12,890 domain involvements per year not identifiable in coded data. Subdomain classification found at least 81.6% of self-harm attendances required medical assessment for injury or overdose before psychiatric review. Conclusions Clinical coding identified fewer than half of presentations involving alcohol, drugs, and self-harm and rarely captured co-occurring domains; under-recording was present across a full year. A locally deployed LLM generated more complete structured data from existing clinical text within NHS infrastructure, at a scale which is not feasible for manual review.

Major depressive disorder (MDD) is strongly associated with dysregulation of the hypothalamic-pituitary-adrenal (HPA) axis, systemic inflammation, and gut microbiota dysbiosis. Although selective serotonin reuptake inhibitors such as escitalopram are standard treatments, their efficacy is often constrained by partial response and gastrointestinal adverse effects. In this 12-week, randomized, double-blind, placebo-controlled trial, we evaluated the clinical efficacy and microecological mechanisms of adjunctive Lactiplantibacillus plantarum PS128 (PS128; 6*1010CFU/day) in MDD patients on stable escitalopram therapy. Adjunctive PS128 significantly enhanced clinical response compared to placebo, yielding substantial reductions in HAMD-17 and MADRS, alongside a higher remission rate. 16S rRNA sequencing and PICRUSt2 profiling revealed that PS128 enriched key short-chain fatty acid producers (Faecalibacterium, Coprococcus), counteracting the Klebsiella expansion seen in placebo. Functionally, PS128 up-regulated neuroprotective cofactor, B vitamins, biosynthesis and down-regulated the neurotoxic kynurenine pathway. Network analysis demonstrated that PS128 maintained a resilient, integrated microbial co-occurrence topology, whereas the placebo network showed structural segregation. This stabilized ecosystem attenuated peripheral inflammatory signaling and normalized salivary cortisol levels. Overall, adjunctive PS128 augments escitalopram efficacy by enhancing gut network stability, supporting cellular energetics, and modulating neuroendocrine activity, offering a promising multimodal strategy for MDD.

Background: Major depressive disorder (MDD) is a severe mental illness associated with severe clinical consequences and substantial societal burden. It's characterized by immune-inflammatory dysregulation and immune sensitization. Objective: To determine whether in vitro ketamine attenuates phytohemagglutinin (PHA)/lipopolysaccharide (LPS)-induced immune sensitization in patients with MDD and healthy controls (HCs). Methods: Whole blood from 18 patients with MDD and 18 HCs was stimulated with PHA/LPS and exposed to ketamine (0.3 M, 0.6 M, and 6 M) for 72 hours. Cytokines, chemokines, growth factors, and composite immune profiles, including M1/M2 macrophages, T helper (Th)1/2/17, the immune-inflammatory response system (IRS), and compensatory immunoregulatory system (CIRS), were synthesized and determined. Results: Under PHA and LPS stimulation in vitro, the MDD group exhibited markedly elevated immune profiles, including M1, M2, Th1, Th2, Th17, IRS, CIRS, chemokines, and growth factors, consistent with immune sensitization. Significant group-by-treatment interactions were observed for Th1-Th2, M2, growth factors, IL-12(p70), M1, and chemokines. Ketamine produced minimal changes in HCs but broader suppression in MDD, particularly at the highest concentration, without normalizing the sensitized immune phenotype. Among the immune markers with no notable group-by-treatment interactions, ketamine exerted diagnosis-independent effects, decreasing MIP-1{beta}, IL-1&{beta}, Th1, TNF-{beta} IRS, IFN-{gamma}, and IL-2 compared to the control condition. Conclusions: Ketamine exhibited two distinct immunoregulatory patterns: selective, disease-dependent attenuation of sensitized immune pathways and broader, diagnosis-independent suppression of the stimulated immune response, predominantly at higher concentrations. However, these effects were insufficient to normalize the immune-sensitized phenotype of MDD.

Introduction: Risk of recurrent opioid use during buprenorphine-naloxone (bup-nx) treatment is dynamic and remains elevated after initiation, with vulnerability shaped in part by treatment intensity and gaps between visits, yet routine outpatient care relies on episodic encounters and retrospective data. This mismatch can delay recognition of emerging instability and limit timely treatment adjustments. This paper reports the development and specification of an intervention strategy to address this mismatch. Methods: We used a structured, multi-phase design process to specify and configure a measurement-based care (MBC) strategy for bup-nx treatment (Bup-MBC) in outpatient addiction clinics through three phases: (1) a systematic review of patient-reported outcome measures (PROMs) for substance use treatment; (2) a qualitative needs assessment using the Theoretical Domains Framework and COM-B (Capability, Opportunity, Motivation-Behavior) model to identify gaps in risk monitoring, agency, and trust; and (3) iterative co-design with multidisciplinary clinicians to refine workflow fit and trust-preserving use of data. Patients informed item and feedback content during the needs assessment but did not participate in the co-design cycles. Results: Bup-MBC integrates (1) brief between-visit PROMs (e.g., withdrawal, craving, adherence); (2) immediate non-punitive patient feedback; (3) clinician-facing summaries and non-directive prompts in the electronic health record (EHR); and (4) an opt-in between-visit outreach pathway with predefined safety triggers, all configured within existing EHR and patient portal infrastructure. It targets patient and clinician capability to recognize changes in risk, opportunity for action through structured monitoring and visit preparation, and trust and agency through non-punitive communication, without adding substantial burden. The full measure set, severity bands, and question-to-action map are provided as supplementary material. Key trade-offs included prioritizing single-item measures for feasibility, balancing opt-in outreach with safety overrides, and assuming routine clinician use of summaries. Conclusion: This development study specifies an EHR-integrated MBC strategy for outpatient bup-nx treatment. As single-center design work with co-design limited to clinicians and delivery contingent on portal or text-message access, its outputs are hypotheses about mechanism and fit rather than demonstrated effects. Feasibility studies are needed to evaluate uptake, acceptability, workflow fit, and effects on treatment.

Obesity is a significant public health concern. Early-onset obesity in the context of rare disease can reflect genetically-mediated pathology or elevated susceptibility through indirect mechanisms. Mapping the diverse characteristics and needs of young people with obesity in the rare disease population is a first step toward mechanistic and translational research. We carried out a retrospective comparative analysis of demographic, genotypic, phenotypic and health service utilisation data for young people with obesity (cases: n=500) and without obesity (controls: n=11,444) from the UK 100,000 Genomes Project rare disease cohort. Cases and controls were recruited prior to genomic diagnosis, across clinical disorder categories. We observed significant association between socioeconomic deprivation and obesity risk. Young people with obesity had significantly higher utilisations of acute care and mental health services, indicating an overall higher health burden. A curated panel of 519 candidate obesity-associated genes demonstrated aggregate association with obesity, although no single gene reached significance. Phenotypic comparison between cases and controls highlighted increased multi-organ and neurological system involvement, highlighting the overlap between neurodevelopmental and obesity risks. Within the case group, we conducted cluster analysis to identify early-onset obesity groups with different phenotypic profiles, potentially arising from different causal pathways - this identified six obesity subgroups of interest, with differing involvement of neurodevelopmental and other systems. Our study confirms that obesity co-occurs with a wide range of factors within the rare disease population, and is associated with significant physical and mental health needs, requiring holistic lifelong care.

Background: Metabolomic studies of depression have yielded heterogeneous findings, potentially because metabolic correlates differ across symptoms and metabolic states. We examined symptom-specific metabolomic associations and whether body mass index (BMI) modifies these relationships. Methods: We analyzed 83,717 Estonian Biobank participants (70.6% female) with 249 Nightingale metabolite measures and 14 lifetime depressive symptoms. Logistic regression models progressively adjusted for sociodemographic, lifestyle, medication, and BMI factors. BMI-related attenuation and metabolite x BMI interactions were evaluated, followed by self-organizing map analyses of broader metabolic context. Results: Before BMI adjustment, 660 metabolite-symptom associations were Bonferroni-significant; 136 were significant after BMI adjustment, including 105 retained associations. Weight-related associations showed the strongest BMI dependence: none of 199 weight-gain associations and 2 of 115 weight-loss associations were retained. Among 691 preselected metabolite-symptom pairs, 211 (30.5%) showed significant metabolite x BMI interactions after false discovery rate correction. Six systemic metabolic profiles were identified, but only 3 of 211 BMI-sensitive pairs showed additional profile-dependent heterogeneity. Conclusions: Circulating metabolic correlates of depressive symptoms are heterogeneous and strongly dependent on symptom phenotype and BMI-related metabolic context. These findings suggest that metabolic biomarkers in depression should be interpreted in relation to both symptom presentation and metabolic state rather than as uniform correlates of the disorder.

Background: Bipolar disorder is associated with persistent social, cognitive, and functional impairment during euthymia, yet the neural mechanisms underlying these deficits remain unclear. Alterations to self-referential processing are a candidate mechanism, but existing electrophysiological studies rely on emotionally valenced paradigms that potentially confound self-processing with emotional biases. Methods: We analysed electroencephalography from 28 patients with bipolar disorder (type I or II) and 28 age- and sex-matched healthy controls during an emotionally neutral colour judgment task with self-related (preference) and non-self-related (similarity) conditions. Late positive potentials, temporal generalisation decoding, and frequency band decoding (theta, alpha, beta) were used to characterise the temporal dynamics and oscillatory correlates of self versus non-self processing. Results: Controls showed higher overall event-related potential amplitudes and greater self versus non-self differentiation than patients (condition by group interaction, 337 to 946 ms). Broadband temporal generalisation decoding revealed extensive cross-temporal generalisation of the self versus non-self representation in controls, spanning most of the trial, but no significant generalisation in patients. Frequency analyses showed that alpha and beta carried self versus non-self information in both groups, with broader extent in controls, and that anterior theta carried this information in patients but not controls. Exploratory correlations linked decoding measures to rumination and anxiety but not to manic symptoms. Conclusions: The neural representation distinguishing self-referential from externally guided processing was both smaller in amplitude and less temporally sustained in bipolar disorder. Reduced persistence is not detectable by conventional amplitude analyses, and may bear on the self-related and social cognitive difficulties reported in this population.

Neighbourhood deprivation is one of the few potential policy-modifiable risk factors for psychiatric and neurological disorders, but the neurobiological pathways underlying these associations remain unclear. We investigated these relationships across three cohorts spanning the life span: the Healthy Brain and Child Development (HBCD) Study (n = 84, aged 0 to 4 weeks postnatal), the Adolescent Brain Cognitive Development (ABCD) Study (n = 4,792, aged 9 to 10 years), and the UK Biobank (UKB; approximately 500,000 adults, aged 44 to 87 years). Neighbourhood deprivation was associated with elevated disease risk, and individual lifestyle factors accounted for only a small fraction of this burden, indicating that the much larger residual effect reflects broader contextual characteristics of deprived environments rather than individual behaviours alone. Across all cohorts, greater deprivation consistently predicted lower cortical and subcortical brain volume, with effects detectable in early development and substantially stronger in adulthood. Across disorders, regional brain volume emerged as a consistent neuroanatomical mediator linking neighbourhood deprivation to neuropsychiatric disease. We further showed that deprivation preferentially affects brain regions intrinsically vulnerable to neuropsychiatric disorders. Spatial decoding analyses implicated dopaminergic and serotonergic neurotransmitter systems together with specific excitatory and inhibitory neuronal classes. Importantly, both the deprivation effects and their neuroanatomical mediation patterns were replicated across independent populations. Our study delivers a translational framework linking neighbourhood deprivation to brain health, which could inform public health policies and preventive interventions.

The Autism Spectrum Quotient (AQ-50) is widely used to assess autistic traits, yet its Hungarian version has not been psychometrically evaluated. We assessed the reliability, factor structure, temporal stability, convergent validity, and clinical utility of the Hungarian AQ-50 and a revised translation (AQ-50-HU-R) in two samples (N1 = 1967; N2 = 423), including autistic and non-autistic participants. The AQ-50-HU-R showed high internal consistency and test-retest reliability. A bifactor model provided the best fit ({chi}2[1125] = 1650.433, p < 0.001; CFI = 0.991; TLI = 0.990; RMSEA = 0.033 [90% CI = 0.030-0.037]; SRMR = 0.083), with 71% of common variance attributable to a general autistic traits factor. The total score distinguished clinically verified autistic participants from participants reporting no ASD diagnosis (AUC = 0.906), with a cutoff of 25. Associations with ADOS scores were weak or nonsignificant. The AQ-50-HU-R is best interpreted as a reliable total-score screening measure, supporting referral for comprehensive autism assessment.

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition defined by differences in social communication and restricted, repetitive behaviours. As diagnostic criteria have broadened, ASD is now recognised across a wider range of individuals, raising key questions about its structure: does ASD have discrete sub-types, or is it better conceptualised as a continuous, possibly multidimensional, condition? We aim to explore whether a multidimensional continuum model more accurately captures the variability within ASD. We analysed a large SPARK phenotypic dataset of medical history and diagnostic surveys (background history, SCQ, RBS-R; n=36,710 individuals). We apply and compare two traditional statistical approaches, Factor Analysis and Gaussian Mixture Models, with a modern machine learning technique, the Variational Autoencoder (VAE). VAEs reconstructed unseen test data with ~4-fold better accuracy than Factor Analysis, and ~8-fold better accuracy than Gaussian Mixture Models. We identified four stable latent factors across 100 independently trained VAEs. These four dimensions provide an individual behavioural profile that can be visualized using radar-plots, offering a compact way to compare profiles at the person level. Through further analysis, we found evidence for 3 overlapping clusters or subtypes of ASD identified within the 4D latent space. This work aims to inform new ways of modelling ASD using a VAE that will be able to discern between a continuum or a clustered output and that go beyond binary diagnosis, instead reflecting the complex range of trait profiles, with implications for personalised diagnosis and intervention.