JAMA Psychiatry

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

BackgroundCOVID-19 has been shown to increase the risk of adverse mental health consequences. A recent electronic health record (EHR)-based observational study showed an almost two-fold increased risk of new-onset mental illness in the first 90 days following a diagnosis of acute COVID-19. MethodsWe used the National COVID Cohort Collaborative, a harmonized EHR repository with 2,965,506 COVID-19 positive patients, and compared cohorts of COVID-19 patients with comparable controls. Patients were propensity score-matched to control for confounding factors. We estimated the hazard ratio (COVID-19:control) for new-onset of mental illness for the first year following diagnosis. We additionally estimated the change in risk for new-onset mental illness between the periods of 21-120 and 121-365 days following infection. FindingsWe find a significant increase in incidence of new-onset mental disorders in the period of 21-120 days following COVID-19 (3.8%, 3.6-4.0) compared to patients with respiratory tract infections (3%, 2.8-3.2). We further show that the risk for new-onset mental illness decreases over the first year following COVID-19 diagnosis compared to other respiratory tract infections and demonstrate a reduced (non-significant) hazard ratio over the period of 121-365 days following diagnosis. Similar findings are seen for new-onset anxiety disorders but not for mood disorders. InterpretationPatients who have recovered from COVID-19 are at an increased risk for developing new-onset mental illness, especially anxiety disorders. This risk is most prominent in the first 120 days following infection. FundingNational Center for Advancing Translational Sciences (NCATS).

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

The Lundbeck Foundation Integrative Psychiatric Research (iPSYCH) consortium has almost doubled its Danish population-based Case-Cohort sample (iPSYCH2012). The newly updated cohort, named iPSYCH2015, expands the study base with 56,233 samples, to a combined total of 141,265 samples. The cohort is nested within the Danish population born between 1981 and 2008 and is a Case-Cohort design including 50,615 population controls. We added more cases to the existing phenotypes identified with, schizophrenia (Nnew=4,131/Ntotal=8,113), autism (Nnew=8,056 / Ntotal=24,975), attention-deficit/hyperactivity disorder (Nnew=10,026/Ntotal=29,668) and affective disorder (Nnew=13,999/Ntotal=40,482) of which a subset has bipolar affective disorder (N-new=1,656/Ntotal=3,819). We also added two additional focus phenotypes, schizophrenia spectrum disorder (N=16,008) and post-partum disorder (N=3,421). In total, the expanded iPSYCH2015 sample consists of 93,608 unique individuals in the case groups and 50,615 population controls. For the sample expansion, DNA was extracted and amplified from dried blood spots samples stored within the Danish Neonatal Screening Biobank and genotyped using the Illumina Global Screening Array. The Biobank sample retrieval rate was 95%, and the genotyping success rate was 92% (97% of retrieved). We expanded the follow-up period by three years, including data such as longitudinal information on health, prescribed medicine, social and socioeconomic information.

IntroductionMedical cannabis use has expanded rapidly, yet long-term real-world safety data remain limited. We evaluated adverse-event (AE) frequency, severity, and predictors in a US telehealth registry of medical cannabis patients over one year. MethodsWe analyzed 14,313 adults who completed intake between June-August 2024. Patients reported any of 30 prespecified adverse events (AEs) and rated each on a 0-10 impact scale. Weekly exposure was estimated as (days/week) x (serving size) and categorized into quintiles. We computed AE rates per 100 patients with binomial 95% confidence intervals (CI) and tested linear trends. Univariate logistic regressions assessed 20 candidate predictors within chronic-pain and anxiety subgroups. We then applied LASSO to select multivariate predictors, combining these with age, sex, race/ethnicity, smoking, and unhealthy-weeks in final logistic models. Marginal predicted-probability curves were generated across exposure, stratified by subgroup, sex, race, and age. ResultsOverall, 2.6% of patients reported [&ge;]1 AE. The most common symptoms were increased appetite (23.8%), fatigue (20.3%), and anxiety (19.9%) with mean impact <4/10. In adjusted models, having been to the doctor because of their condition remained the sole AE predictor for patients with anxiety (OR 4.03, 95% CI: 2.44-6.87); age was a significant predictor for patients with chronic pain (OR 0.981, 95% CI: 0.97-0.99). Marginal curves remained flat ([~]2-3% AE probability) across weekly cannabis exposure. Ad hoc analysis of non-missing-at-random data suggests possible AE rates are in line with current literature. DiscussionIn this large cohort, AEs were infrequent and mild, and weekly cannabis frequency did not independently increase odds. Healthcare engagement likely reflects underlying health complexity driving AE reporting. These findings support the safety of medical cannabis.

BackgroundSerious mental illnesses, including schizophrenia, bipolar disorder and depression are heritable, highly multifactorial disorders and major causes of disability worldwide. Polygenic risk scores (PRS) aggregate variants identified from genome-wide association studies (GWAS) into individual-level estimates of liability, and are a promising tool for clinical risk stratification. MethodsBy leveraging the VAs extensive electronic health record (EHR) and a cohort of 9,378 individuals with confirmed diagnoses of schizophrenia or bipolar I disorder, we validated automated case-control assignments based on ICD-9/10 codes, and benchmarked the performance of current PRS for schizophrenia, bipolar disorder, and major depression in 400,000 Million Veteran Program (MVP) participants. We explored broader relationships between PRS and 1,650 disease categories via phenome-wide association studies (PheWAS). Finally, we applied genomic structural equation modeling (gSEM) to derive novel PRS indexing common and disorder-specific latent genetic factors. FindingsAmong 3,953 and 5,425 individuals with diagnoses of schizophrenia or bipolar disorder type I that were confirmed by structured clinical interviews, 95% were correctly identified using ICD-9/10 codes (2 or more). Current PRS were robustly associated with case status in European (p<10-254) and African (p<10-5) participants and were higher among more frequently hospitalized patients (p<10-4). PheWAS confirmed previous associations among higher neuropsychiatric PRS and elevated risk for psychiatric and physical health problems and extended these findings to African Americans. InterpretationUsing diagnoses confirmed by in-person structured clinical interviews and current neuropsychiatric PRS, we demonstrated the validity of an EHR-based phenotyping approach in US veterans, highlighting the potential of PRS for disentangling biological and mediated pleiotropy. FundingDepartment of Veterans Affairs Cooperative Studies Program (CSP) #572; Million Veteran Program (MVP-000, MVP-006); Office of Research and Development, Department of Veterans Affairs.

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

Harmonized phenotyping and diverse population-specific studies are crucial for advancing gene discovery in psychiatric genetics. We conducted a genome-wide association (GWAS) mega-analysis of DSM-defined lifetime major depressive disorder (MDD) in 64 941 participants (25.7% cases) from the Dutch BIObanks Netherlands Internet Collaboration (BIONIC) consortium. SNP-based heritability was estimated at 13.4%, exceeding recent global meta-analyses, with a high genetic correlation (rG = 0.89) to the latest major depression GWAS by the Psychiatric Genetics Consortium (PGC-MD). We identified a novel genome-wide significant locus in PALMD (p = 3.26 x 10-), that was confirmed by GWAS-by-subtraction. Polygenic scores (PGSs) based on BIONIC predicted MDD in UK Biobank, and PGSs from PGC-MD predicted into BIONIC, with within-family analyses indicating minimal confounding. Genetic causal inference revealed associations with over 30 phenotypes. Twin concordance for MDD increased with polygenic burden, reinforcing its genetic architecture. This study emphasizes the power of harmonized phenotyping and regional biobanks in uncovering the genetic architecture of MDD, highlighting the value of population-specific studies for improving risk prediction and advancing psychiatric genetics.

Tobacco use is the top preventable cause of early mortality in schizophrenia, but the underlying pathophysiology remains unknown. In schizophrenia, small studies have linked default mode network (DMN) organization to tobacco use and showed that nicotine normalizes DMN disorganization. We sought to 1) validate the relationship between DMN organization and tobacco use using a large psychosis-spectrum sample (Bipolar-Schizophrenia Network on Intermediate Phenotypes 2, B-SNIP2); and 2) test if targeting this network with single and multiple sessions of repetitive transcranial magnetic stimulation (rTMS) affects craving. In B-SNIP2, we tested associations between DMN connectivity and tobacco use. In the Single Session DMN-targeted rTMS study, individuals received single rTMS sessions (intermittent theta burst stimulation, iTBS; continuous theta burst stimulation, cTBS; sham) with pre-/post-neuroimaging and craving assessment. In the Accelerated, Multi-Session DMN-targeted cTBS study, individuals received 5 sessions of cTBS with pre-/post-neuroimaging and craving assessment. In B-SNIP2 (n=596), current smokers had lower DMN connectivity than former (p=.017) and never smokers (p=.021). These differences were also observed in the psychosis group (current vs. former p=.044; current vs. never p=.011). In the Single Session DMN-targeted rTMS study (n=10), there was a nonsignificant treatment*time interaction (p=.059) where iTBS increased craving (padj=.015) compared to cTBS and sham. In the Accelerated, Multi-Session DMN-targeted cTBS study (n=12), DMN-targeted cTBS reduced craving after each session (p<.001) and reduced DMN connectivity (p=.052). We identified a mechanism of nicotine use in psychosis and demonstrated that engaging this target reduces craving, suggesting a novel target for nicotine interventions in psychosis.

BackgroundClinical biobanks linking electronic health records (EHRs) with genotype data are expanding, enabling investigation of genomic risk factors for psychiatric disorders. However, few recall-by-genotype (RbG) studies have been published--particularly for psychiatric risk variants in diverse healthcare systems--indicating a need for further research to inform implementation. Some rare copy number variants (CNVs) confer substantially increased risk for neurodevelopmental disorders (NDDs) and cognitive impairment. We recalled NDD CNV carriers from BioMe, a multi-ancestry biobank within the Mount Sinai Health System, for in-depth phenotyping and empirical insights into the implementation of RbG in psychiatry. MethodsFrom BioMe, 892 adults were recontacted: 335 NDD CNV carriers, 217 with schizophrenia, and 340 neurotypical controls. Of these, 18% responded to recontact, 12% were screened for participation, and 10% began the study. Participants completed structured clinical and cognitive assessments. ResultsSeventy-three participants (8% of those recontacted) completed the study: 30 NDD CNV carriers, 20 schizophrenia cases, and 23 controls. The mean age was 48.8 years, 66% were female, and ancestry was 37% African, 34% Hispanic, and 26% European. Seventy percent of NDD CNV carriers had at least one neuropsychiatric or developmental condition, including 40% with mood or anxiety disorders. Among 22 NDD CNV carriers at loci previously examined for cognitive effects, performance was impaired on digit span backward ({beta}= -1.76, FDR = 0.04) and sequencing ({beta}= -2.01, FDR = 0.04) compared with controls but outperformed schizophrenia cases on verbal learning ({beta}= 4.5, FDR = 0.05). ConclusionsThis proof-of-concept RbG study of rare psychiatric risk variants from a multi-ancestry biobank demonstrates both opportunities and challenges for recontact within healthcare systems. Despite modest enrollment, recalling individuals--including those affected by psychiatric illness and cognitive impairment--yielded a genotypically defined cohort and phenotypes not captured in EHRs, underscoring the potential of RbG to advance precision psychiatry.

BackgroundIndividuals with mental and behavioural disorders face increased risk of age-related diseases and premature mortality. Accelerated biological ageing may contribute to this disparity. We investigated differences in metabolomic ageing between individuals with and without mental disorders. MethodsThe UK Biobank is a community-based health study of middle-aged and older adults. Mental disorders were identified from hospital inpatient, primary care, death registry and self-reported physician diagnosis data. Plasma metabolites were profiled using the Nightingale Health platform. We examined differences in MileAge delta, the difference between metabolite-predicted and chronological age, across broad ICD-10 diagnostic groups and for 45 individual diagnoses. We further investigated sex-specific associations and tested whether polygenic scores for mental disorders were associated with MileAge delta. ResultsAmongst 225,212 participants (54% female; mean age = 56.97 years), 38,524 had at least one mental disorder diagnosis preceding baseline. Substance use, psychotic, affective and neurotic disorders were associated with a metabolite-predicted age exceeding chronological age. In contrast, obsessive-compulsive and eating disorders were associated with a younger MileAge, particularly in females. Associations were generally stronger in males, with several diagnoses showing sex-specific patterns. Higher genetic liability to major depression, autism and ADHD was associated with a MileAge exceeding chronological age, whereas psychosis, tobacco use disorder, obsessive-compulsive disorder and anorexia nervosa polygenic scores were associated with a younger MileAge. ConclusionsMetabolomic ageing varies across mental disorders, with direction and strength of association differing by diagnosis and sex. These findings highlight the heterogeneity of biological ageing across mental disorders and contribute to our understanding of the biological processes linking mental disorders to excess morbidity and premature mortality.

Current phenotype classifiers for large biobanks with coupled electronic health records EHR and multi-omic data rely on ICD-10 codes for definition. However, ICD-10 codes are primarily designed for billing purposes, and may be insufficient for research. Nuanced phenotypes composed of a patients experience in the EHR will allow us to create precision psychiatry to predict disease risk, severity, and trajectories in EHR and clinical populations. Here, we create a phenotype risk score (PheRS) for major depressive disorder (MDD) using 2,086 cases and 31,000 individuals from Mount Sinais biobank BioMe . Rather than classifying individuals as cases and controls, PheRS provide a whole-phenome estimate of each individuals likelihood of having a given complex trait. These quantitative scores substantially increase power in EHR analyses and may identify individuals with likely missing diagnoses (for example, those with large numbers of comorbid diagnoses and risk factors, but who lack explicit MDD diagnoses). Our approach applied ten-fold cross validation and elastic net regression to select comorbid ICD-10 codes for inclusion in our PheRS. We identified 158 ICD-10 codes significantly associated with Moderate MDD (F33.1). Phenotype Risk Score were significantly higher among individuals with ICD-10 MDD diagnoses compared to the rest of the population (Kolgorov-Smirnov p<2.2e-16), and were significantly correlated with MDD polygenic risk scores (R2>0.182). Accurate classifiers are imperative for identification of genetic associations with psychiatric disease; therefore, moving forward research should focus on algorithms that can better encompass a patients phenome.

BackgroundLong COVID, also known as post-acute sequelae of COVID-19 (PASC), is a poorly understood condition with symptoms across a range of biological domains that often have debilitating consequences. Some have recently suggested that lingering SARS-CoV-2 virus in the gut may impede serotonin production and that low serotonin may drive many Long COVID symptoms across a range of biological systems. Therefore, selective serotonin reuptake inhibitors (SSRIs), which increase synaptic serotonin availability, may prevent or treat Long COVID. SSRIs are commonly prescribed for depression, therefore restricting a study sample to only include patients with depression can reduce the concern of confounding by indication. MethodsIn an observational sample of electronic health records from patients in the National COVID Cohort Collaborative (N3C) with a COVID-19 diagnosis between September 1, 2021, and December 1, 2022, and pre-existing major depressive disorder, the leading indication for SSRI use, we evaluated the relationship between SSRI use at the time of COVID-19 infection and subsequent 12-month risk of Long COVID (defined by ICD-10 code U09.9). We defined SSRI use as a prescription for SSRI medication beginning at least 30 days before COVID-19 infection and not ending before COVID-19 infection. To minimize bias, we estimated the causal associations of interest using a nonparametric approach, targeted maximum likelihood estimation, to aggressively adjust for high-dimensional covariates. ResultsWe analyzed a sample (n = 506,903) of patients with a diagnosis of major depressive disorder before COVID-19 diagnosis, where 124,928 (25%) were using an SSRI. We found that SSRI users had a significantly lower risk of Long COVID compared to nonusers (adjusted causal relative risk 0.90, 95% CI (0.86, 0.94)). ConclusionThese findings suggest that SSRI use during COVID-19 infection may be protective against Long COVID, supporting the hypothesis that serotonin may be a key mechanistic biomarker of Long COVID.

BackgroundWhether associations between psychiatric disorders and cardiovascular diseases (CVDs) can be modified by disease susceptibility and the temporal pattern of these associated CVDs remain unknown. MethodsWe conducted a matched cohort study of UK Biobank including 35,227 patients with common psychiatry disorders (anxiety, depression, and stress-related disorders) between 1997 and 2019, together with 176,135 sex- and birth year-individually matched unexposed individuals. ResultsThe mean age at the index date was 51.76 years, and 66.0% of participants were females. During a mean follow-up of 11.94 years, we observed an elevated risk of CVD among patients with studied psychiatry disorders, compared with matched unexposed individuals (hazard ratios [HRs]=1.16, 95% confidence interval [CI]: 1.14-1.19), especially during the first six months of follow-up (HR=1.59 [1.42-1.79]). To assess the modification role of disease susceptibility, we stratified analyses by family history of CVD and by CVD PRS, which obtained similar estimates between subgroups with different susceptibilities to CVD. We conducted trajectory analysis to visualize the temporal pattern of CVDs after common psychiatry disorders, identifying primary hypertension, acute myocardial infarction, and stroke as three main intermediate steps leading to further increased risk of other CVDs. ConclusionsThe association between common psychiatry disorders and subsequent CVD is not modified by predisposition to CVD. Hypertension, acute myocardial infarction, and stroke are three initial CVDs linking psychiatric disorders to other CVD squeals, highlighting a need of timely intervention on these targets to prevent further CVD squeals among all individuals with common psychiatric disorders.

The etiology of substance use disorders (SUDs) and psychiatric disorders reflects a combination of both transdiagnostic (i.e., common) and disorder-level (i.e., independent) genetic risk factors. We applied genomic structural equation modeling to examine these genetic factors across SUDs, psychotic, mood, and anxiety disorders using genome-wide association studies (GWAS) of European-(EUR) and African-ancestry (AFR) individuals. In EUR individuals, transdiagnostic genetic factors represented SUDs (143 lead single nucleotide polymorphisms [SNPs]), psychotic (162 lead SNPs), and mood/anxiety disorders (112 lead SNPs). We identified two novel SNPs for mood/anxiety disorders that have probable regulatory roles on FOXP1, NECTIN3, and BTLA genes. In AFR individuals, genetic factors represented SUDs (1 lead SNP) and psychiatric disorders (no significant SNPs). The SUD factor lead SNP, although previously significant in EUR- and cross-ancestry GWAS, is a novel finding in AFR individuals. Shared genetic variance accounted for overlap between SUDs and their psychiatric comorbidities, with second-order GWAS identifying up to 12 SNPs not significantly associated with either first-order factor in EUR individuals. Finally, common and independent genetic effects showed different associations with psychiatric, sociodemographic, and medical phenotypes. For example, the independent components of schizophrenia and bipolar disorder had distinct associations with affective and risk-taking behaviors, and phenome-wide association studies identified medical conditions associated with tobacco use disorder independent of the broader SUDs factor. Thus, combining transdiagnostic and disorder-level genetic approaches can improve our understanding of co-occurring conditions and increase the specificity of genetic discovery, which is critical for psychiatric disorders that demonstrate considerable symptom and etiological overlap.

Whereas attention-deficit/hyperactive disorder (ADHD) is correlated with later risk of depression, anxiety, and substance misuse, the relationship with other health endpoints is uncertain. In a full-nation birth cohort study, we used a phenotype-wide approach to explore the influence of an ADHD diagnosis in childhood/adolescence with later disease and injury. Comprising 53147 (25731 female) children born in a single year, the 1987 Finnish Birth Cohort was generated from linkage of routinely collected data. Using international classification disease codes, ADHD diagnosis was captured from in- and out-patient hospital records up to age 18 years and study members continued to be surveilled for other diagnoses until 2020 (aged 33 years). In logistic regression analyses, effect estimates were adjusted for education achievement, family socioeconomic status, and multiple comparisons. Pre-adulthood, 0.43% (N=228) of study members were diagnosed with ADHD. In people with ADHD relative to population controls, there was a heightened risk of developing all 17 specific health endpoints examined. Of these, only 5 reached statistical significance after correction for socioeconomic status, education, and multiple comparison (odds ratio; 99.7%): substance abuse disorders (2.27; 1.28, 3.81), mood disorders (2.46; 1.50, 3.90), neurotic disorders (2.12; 1.25, 3.43), epilepsy (4.65; 1.86, 9.75), and poisoning (2.30; 1.02, 4.51). In the present study, children and adolescents with ADHD had an increased future burden of psychological and neurological conditions but not somatic disorders.

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

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

BackgroundSuicide is a major public health challenge, and a suicide attempt is an indicator of future mortality. This study provides a comprehensive analysis of initial suicide attempts. MethodsUsing Swedish national registers, we conducted a population-based cohort study of 3.7 million individuals followed from age 10 to a maximum age of 57. Suicide attempts were identified in hospital and death registers using ICD self-harm codes (intentional, with lethal methods, or leading to hospitalization or death). We investigated incidence, risk factors, outcomes, and familial aggregation, heritability, genetic correlations with psychiatric disorders, and healthcare visits in the month before and after initial suicide attempt. FindingsThe lifetime risk of suicide attempt in the study population was 4.6%, with greater risk in females and highest risk among ages 18-24. Overdose/poisoning were the most common methods. Prior history of psychiatric disorders, general medical diseases, and adverse life events were associated with increased risk of initial suicide attempt, while higher socioeconomic status was protective. Individuals with an initial suicide attempt were at substantially elevated risks of subsequent attempts (hazard ratio, HR, 23.4), suicide mortality (HR 16.4), and all-cause mortality (HR 7.3). One in ten families in Sweden had at least one individual who attempted suicide, and it tended to aggregate within families. The estimate of heritability was 42%, and genetic correlations of suicide attempts with psychiatric disorders ranged 0.48-0.85. At least 60% of those who made an initial suicide attempt had a healthcare contact in the month preceding the attempt. InterpretationThe study provides comprehensive insights into suicidal behavior. Suicide attempts are major markers of poor mental health and risk for subsequent morbidity and mortality; indeed, they may carry the greatest mortal risk seen in clinical psychiatry. Our results underscore the need for systematic prevention efforts for individuals who have recently attempted suicide.

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.