American Journal of Psychiatry

ObjectiveSubstance use disorders (SUD) are associated with risk of psychiatric comorbidities, with inconsistent sex differences, across studies. The objective of this study is to determine odds of psychiatric comorbidities based on sex, in persons with either primary opioid, alcohol or cannabis dependence diagnoses, from a national clinical data set. MethodsThis is a cross-sectional study of data from state-funded and state-run mental health programs in 2022 (Mental Health Client-Level Data, from the US Substance Abuse and Mental Health Administration). Data was obtained from adults (age [≥]18) with a primary diagnosis of either opioid (n=28,808), alcohol (n=23,281) or cannabis dependence (n=5,961). Individuals with each SUD were examined for psychiatric comorbidity outcomes, based on secondary diagnoses of either anxiety, bipolar, depression, schizophrenia or other psychotic disorders (SPD), or trauma and stressor-related disorders, versus no comorbidity. Data were analyzed with multinomial logistic regressions, examining sex, race, ethnicity and age as predictors. ResultsMales with primary diagnoses of either opioid, alcohol or cannabis dependence had lower adjusted odds of anxiety, bipolar, depression and trauma/stressor disorders, compared to females. However, males with opioid or cannabis dependence had higher adjusted odds of SPD, compared to females. Adjusted analyses also detected associations of race and ethnicity with specific comorbidities. ConclusionsIn this recent national clinical data set, there are specific sex-based differences in specific psychiatric comorbidities for each of these three SUDs. Future studies should examine biopsychosocial mechanisms that underlie these differences, with the goal of improving personalized care.

ObjectivePrediction of disease risk is a key component of precision medicine. Common, complex traits such as psychiatric disorders have a complex polygenic architecture making the identification of a single risk predictor difficult. Polygenic risk scores (PRS) denoting the sum of an individuals genetic liability for a disorder are a promising biomarker for psychiatric disorders, but require evaluation in a clinical setting. MethodsWe develop PRS for six psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, cross disorder, attention-deficit/hyperactivity disorder, anorexia nervosa) and 17 non-psychiatric traits in over 10,000 individuals from the Penn Medicine Biobank with accompanying electronic health records. We perform phenome-wide association analyses to test their association across disease categories. ResultsFour of the six psychiatric PRS were associated with their primary phenotypes (odds ratios between 1.2-1.6). Individuals in the highest quintile of risk had between 1.4-2.9 times higher odds of the disorder than the remaining 80% of individuals. Cross-trait associations were identified both within the psychiatric domain and across trait domains. PRS for coronary artery disease and years of education were significantly associated with psychiatric disorders, largely driven by an association with tobacco use disorder. ConclusionsWe demonstrate that the genetic architecture of common psychiatric disorders identified in a clinical setting confirms that which has been derived from large consortia. Even though the risk associated is low in this context, these results suggest that as identification of genetic markers proceeds, PRS is a promising approach for prediction of psychiatric disorders and associated conditions in clinical registries.

Genome-wide, polygenic risk scores (PRS) have emerged as a useful way to characterize genetic liability using genotypic data. There is growing evidence that PRS may prove useful to identify those at increased risk for developing certain diseases. The current utility of PRS in relation to alcohol use disorders (AUD) remains an open question. Using data from both a population-based sample [the FinnTwin12 (FT12) study] and a high risk sample [the Collaborative Study on the Genetics of Alcoholism (COGA)], we examined the association between PRSs derived from genome-wide association studies (GWASs) of 1) alcohol dependence/alcohol problems, 2) alcohol consumption, and 3) risky behaviors with AUD and other substance use disorder (SUD) symptoms. Individuals in the top 20%, 10%, and 5% of PRSs had increasingly greater odds of having an AUD compared to the lower end of the continuum in both COGA (80th % OR = 1.95; 90th % OR = 2.03; 95th % OR = 2.13) and FT12 (80th % OR = 1.77; 90th % OR = 2.27; 95th % OR = 2.39). Those in the top 5% reported greater levels of licit (alcohol and nicotine) and illicit (cannabis) SUD symptoms. PRSs can predict elevated risk for SUD in independent samples. However, clinical utility of these scores in their current form is modest. As these scores become more predictive of SUD, they may become useful to practitioners. Improvement in predictive ability will likely be dependent on increasing the size of well-phenotyped discovery samples.

BackgroundRisk for Tourettes and related tic disorders (CTD) derives from a combination of genetic and environmental factors. While multiple studies have demonstrated the importance of direct additive genetic variation for CTD, little is known about the role of cross-generational transmission of genetic risks, such as maternal effects. Here, we partition sources of variation on CTD risk into direct additive genetic effect and maternal effects. MethodsThe study population consists of 2,522,677 individuals from the Swedish Medical Birth Register, born in Sweden between January 1, 1982, to December 31, 1990, and followed for a diagnosis of CTD through December 31, 2013. ResultsWe identified 6,227 (0.25%) individuals in the birth cohort diagnosed with CTD. Using generalized linear mixed models, we estimated 4.7% (95% CrI, 4.4%-4.8%) genetic maternal effects, 0.5% (95% CrI, 0.2%-7%) environmental maternal effects, and 61% (95% CrI, 59%-63%) direct additive genetic effects. Around 1% of genetic maternal effects were due to maternal effects from the individual with comorbid obsessive-compulsive disorder. ConclusionsOur results demonstrate genetic maternal effects contributing to the risk of CTD in offspring and also highlight new sources of overlapping risk between CTD and obsessive-compulsive disorder.

Ongoing efforts to identify genes involved in substance use disorders (SUDs) often focus on individual disorders despite high rates of co-occurrence with each other and other externalizing traits. Here, we investigate whether incorporating data on other externalizing traits can boost power to detect without sacrificing specificity of SUD genetic signal. We used multivariate genomic analyses and downstream biological annotation and genetic association analyses to explore this question. We found that joint analysis of SUDs and other externalizing traits resulted in increased insights into the neurobiology of broad and substance-specific SUD risk. We found no evidence of loss of specificity for SUD genetic signal but note improvements in our ability to characterize the neurobiology of broad and substance-specific SUD genetic effects. Our findings suggest that genetic risk for SUDs operates largely via pathways shared with other behaviors characterized by behavioral disinhibition, with additional substance-specific risk, and that modeling this shared disposition improves gene discovery.

ObjectivesObsessive-compulsive disorder (OCD) is a common psychiatric disorder, with two-thirds of affected individuals reporting severe impairment. Despite its substantial burden and moderate heritability, its etiology remains poorly understood, and treatments are often suboptimal. While recent genome-wide association studies (GWAS) have identified some risk loci, yet OCD remains in the linear phase of sample collection to variant association, with many more OCD-associated variants left to discover. This study aimed to develop and validate an electronic health record (EHR)-based algorithm to identify OCD cases and facilitate large-scale genetic studies. MethodsWe leveraged EHR-linked biobank data from two large hospital systems, namely Vanderbilt University Medical Center (VUMC) and Mass General Brigham (MGB), to develop a high-throughput phenotyping algorithm integrating diagnostic codes, medication records, and natural language processing (NLP) of clinical notes. Algorithm performance was evaluated through expert chart review, and genetic validation was performed using OCD polygenic risk scores (PRS). ResultsExpert chart reviews found that our algorithm combining both ICD codes and NLP achieved higher positive predictive values (PPV) for OCD cases (0.84 at VUMC and 0.91 at MGB) compared to using either ICD codes or NLP alone, albeit with a lower case yield. Furthermore, at both sites, algorithm-determined cases exhibited significantly elevated PRS derived from the latest OCD GWAS, providing genetic validation of our phenotyping approach. ConclusionOur study demonstrates a scalable and cost-efficient approach for EHR-based ascertainment of OCD cases, facilitating large-scale genetic studies and advancing understanding of the disorders complex etiology.

ObjectivesProblematic sexual behavior (PSB) is defined by recurrent sexual behaviors that are difficult to control, causing social and functional impairments. PSB can co-occur with reward deficiency syndrome (RDS), but this relationship remains unclear. RDS has been associated with the 10/10 genotype of 3 variable number tandem repeat (VNTR) in the dopamine transporter gene SLC6A3, which is implicated in the reward pathway. This study investigates the genetic relationship between PSB and RDS, testing their association with SLC6A3 3 VNTR genotype. MethodsPSB patients from addiction treatment facilities (n=454), and comparison participants (with non-clinical PSB=82; without PSB, n=888) were recruited. PSB was measured by the Sexual Addiction Screening Test-Revised (SAST-R) Core and RDS was measured using a composite variable from a custom test battery. DNA was collected from saliva and buccal swabs. Genotyping was performed using polymerase chain reaction (PCR), and regression analyses were conducted to investigate the association of SLC6A3 3 VNTR genotype with PSB and RDS. ResultsThe 10/10 genotype of SLC6A3 3 VNTR was associated with RDS in a combined analysis of all groups, and with non-clinical PSB in the comparison participants. In patients, rare SLC6A3 3 VNTR genotypes (3-, 6-, 8-, 11-repeat alleles) were associated with PSB. No genotype showed relationships to RDS in only PSB patients. ConclusionsThe association between the 10/10 genotype and RDS in the whole sample is consistent with previous findings associating this genotype with vulnerability to addictions. Consistent with our earlier report of PSB being related to RDS, this genotype was also associated with PSB in participants with PSB of a lesser severity. This is the first report of an association between the 10/10 genotype and PSB. In those with PSB of a greater severity, the power of the analysis was less, with a suggestive signal for an association between rare genotypes of the dopamine transporter and PSB.

Background and AimsCertain symptoms of problematic alcohol use (PAU) show associations with comorbid internalizing and externalizing disorders even after controlling for their common PAU factor. Outsized associations between PAU indicators and comorbid psychopathology may reflect distinct etiologic pathways or measurement characteristics that, if unaccounted for, could bias comorbidity estimates with PAU. Although these issues could represent a source of bias in estimates of genetic correlation with PAU, no studies have yet extended this work using genomic data. DesignGenomic structural equation modeling and the Qtrait function identified PAU indicators that showed appreciable residual genetic correlations with eleven comorbid psychiatric disorders and whose associations did not operate strictly through the latent PAU factor. SettingGenome-wide association studies (GWAS) were conducted in a variety of international locations. ParticipantsGWAS used in this study were conducted on 86,979 to 425,166 individuals of European ancestry. MeasurementsThe primary measurements were GWAS summary statistics for various forms of internalizing, externalizing, and neurodevelopmental psychiatric disorders and nine indicators from the Alcohol Use Disorder Identification Test. FindingsPAU indicators assessing alcohol-related consequences (i.e., Injuries, Failed expectations, Guilt/Remorse) each showed appreciable and positive residual genetic associations with multiple comorbid psychiatric conditions spanning various disorder spectra. Alcohol Quantity, 6+ Frequency, Blackouts, and Others concerned did not show direct genetic relationships with comorbid disorders. ConclusionsAlcohol-related consequences share unique genetic underpinnings with multiple psychiatric conditions apart from what is shared with their latent problematic alcohol use factor. Thus, alcohol-related consequences may unduly reflect dysfunction from comorbid psychiatric conditions or related third variables.

Alcohol use disorder (AUD) commonly co-occurs with posttraumatic stress disorder (PTSD), and comorbid PTSD and AUD is associated with poorer outcomes including worse treatment outcomes and significant physical health consequences. Both PTSD and AUD are polygenic in nature and genetically overlap. Previous work showed negative or non-significant associations between PTSD and alcohol consumption but positive genetic associations between PTSD and AUD. This work highlights the need for more nuanced examination of the similarities and distinctions in the associations between PTSD and alcohol consumption versus AUD. We leveraged the latest large-scale GWAS data to perform a multivariate GWAS of alcohol consumption (ALCC), problematic alcohol use (ALCP), and PTSD using GenomicSEM. Partial genetic correlations revealed that ALCP and PTSD were associated with each other (rG=0.39, p = 4.38x10-60) and with other psychiatric problems, medical conditions, and pain, while ALCC was generally only weakly correlated with PTSD (rG=-0.08, p = 2.03x10-4) and uncorrelated with most traits after accounting for its genetic overlap with ALCP and PTSD. We examined associations between GenomicSEM-derived polygenic scores (PGS) and their corresponding phenotypes in participants from the Collaborative Study on the Genetics of Alcoholism (COGA). PGS for ALCC were unrelated to PTSD diagnosis and PGS for PTSD were unrelated to drinks in a typical week. PTSD is more strongly related to alcohol problems, and much of the overlap in PTSD and consumption is accounted for by its overlap with alcohol problems. These results help demonstrate the complex partial overlaps of PTSD, AUD, and alcohol consumption.

BackgroundPolysubstance use (PSU), defined as the use of multiple psychoactive substances, often begins during adolescence. Since PSU is associated with heightened risk of subsequent health issues, including substance use disorders, understanding its antecedents could ultimately have significant public health impacts. We investigated how genetic susceptibility, and environmental exposures together influence the initiation of PSU in adolescents. MethodsWe analyzed data from 11,868 adolescents (aged 11-15 years) in the Adolescent Brain and Cognitive Development study. PSU was assessed through interviews and toxicology. We examined the associations of polygenic scores (PGSs) for addiction, derived from GenomicSEM analysis, environmental factors, and their interactions, with the initiation of PSU, while controlling for multiple covariates. OutcomesOur sample included 7,898 adolescents (mean age 12.9 [0.6] years; 4,150 [53%] male). Overall, 541 (6.8%) had initiated single substance use (SSU), and 162 (2.1%) reported PSU. PGSs for general addiction risk were significantly associated with PSU (Odds Ratios [OR]=1.62, 95% CI=1.30-2.01) but not with SSU. Key environmental risk factors for PSU included prenatal substance use and peer victimization, while planned pregnancy and positive family dynamics acted as protective factors. Notably, gene-environment interaction analyses showed that peer victimization (OR=2.4, 95% CI=1.4-4.2), prenatal substance use (OR=2.1, 95% CI=1.2-3.6), and substance availability (OR=2.3, 95% CI=1.3-3.9) increased PSU risk among adolescents with high genetic susceptibility but had negligible effects at low genetic risk levels (all p < 0.05 after multiple testing correction). InterpretationThis study provides the first evidence linking polygenic risk to PSU in early adolescence and demonstrates that PSU represents more severe manifestation of substance use liability, driven by heightened genetic vulnerability and adverse environmental exposures. This underscores the importance of studying PSU explicitly, identifying high-risk individuals early, and implementing tailored interventions to mitigate the risk of escalating substance use behaviors. FundingNational Institutes of Health. Research in ContextO_ST_ABSEvidence before this studyC_ST_ABSWe conducted a literature search on PubMed for research articles published in English from inception to August 30, 2024, using the search terms: ("polygenic risk" OR "polygenic score" OR "genetic risk") AND ("adolescents" OR "adolescence") AND ("polysubstance use" OR "multiple substance use" OR "number of substances"). This search yielded no studies examining the relationship between polygenic risk of substance use and polysubstance use (PSU) in adolescence, revealing a critical gap in the field. To investigate environmental factors, we searched PubMed using the terms: (("environmental risk" OR "environmental factor" OR "social determinants") AND ("adolescents" OR "adolescence") AND ("polysubstance use" OR "multiple substance use" OR "number of substances")). This search highlighted consistent associations between adolescent PSU and adverse social determinants, such as socioeconomic disadvantage, prenatal exposure, parental monitoring, peer influence, and trauma experience. However, evidence for interactions between genetic and environmental factors was limited to a small number of candidate gene studies, with largely inconclusive findings. Added value of this studyThis study offers several important contributions to the field. First, to our knowledge, it is the first population-based study in the U.S. to examine PSU in early adolescence within a large, diverse cohort, leveraging data from the Adolescent Brain Cognitive Development (ABCD) Study. The ABCD studys comprehensive and inclusive sampling enables analyses across socioeconomic and racial/ethnic backgrounds, strengthening the generalizability of findings. Second, our results reveal a previously unreported association between polygenic risk and PSU in adolescents. Specifically, we show that genetic risk shared across five adult substance use disorders (SUDs) including alcohol, nicotine, cannabis, cocaine, and opioid use disorders, significantly relates to PSU outcomes among adolescents aged 11-15 years. Third, the ABCD studys in-depth characterization of participants allows us to examine a wide array of contributory individual and socio-environmental factors. Finally, this study highlights specific environmental contexts--such as prenatal substance use, family dynamics, and peer relationships--that interact with genetic predisposition to shape PSU behaviors in adolescence. This integrative approach provides insights into gene-environment (GxE) interactions that contribute to PSU, offering a more nuanced understanding of the mechanisms underlying health disparities in adolescent substance use outcomes. Implications of All Available EvidenceOur study shows that integrating polygenic risk with assessments of environmental factors in adolescent population improves understanding of PSU risk. Adolescents exposed to adverse social determinants are particularly vulnerable to PSU, especially when they have a higher genetic predisposition to SUDs. These insights support more explicit study of PSU and targeted early prevention strategies addressing both genetic vulnerabilities and environmental stressors to reduce PSU risk in preadolescence.

AimsWe performed a latent class analysis (LCA) in a sample ascertained for addiction phenotypes to investigate cocaine use disorder (CoUD) subgroups related to polysubstance addiction (PSA) patterns and characterized their differences with respect to psychiatric and somatic comorbidities. DesignCross-sectional study SettingUnited States ParticipantsAdult participants aged 18-76, 39% female, 47% African American, 36% European American with a lifetime DSM-5 diagnosis of CoUD (N=7,989) enrolled in the Yale-Penn cohort. The control group included 2,952 Yale-Penn participants who did not meet for alcohol, cannabis, cocaine, opioid, or tobacco use disorders. MeasurementsPsychiatric disorders and related traits were assessed via the Semi-structured Assessment for Drug Dependence and Alcoholism. These features included substance use disorders (SUD), family history of substance use, sociodemographic information, traumatic events, suicidal behaviors, psychopathology, and medical history. LCA was conducted using diagnoses and diagnostic criteria of alcohol, cannabis, opioid, and tobacco use disorders. FindingsOur LCA identified three subgroups of PSA (i.e., low, 17%; intermediate, 38%; high, 45%) among 7,989 CoUD participants. While these subgroups varied by age, sex, and racial-ethnic distribution (p<0.001), there was no difference on education or income (p>0.05). After accounting for sex, age, and race-ethnicity, the CoUD subgroup with high PSA had higher odds of antisocial personality disorder (OR=21.96 vs. 6.39, difference-p=8.08x10-6), agoraphobia (OR=4.58 vs. 2.05, difference-p=7.04x10-4), mixed bipolar episode (OR=10.36 vs. 2.61, difference-p=7.04x10-4), posttraumatic stress disorder (OR=11.54 vs. 5.86, difference-p=2.67x10-4), antidepressant medication use (OR=13.49 vs. 8.02, difference-p=1.42x10-4), and sexually transmitted diseases (OR=5.92 vs. 3.38, difference-p=1.81x10-5) than the low-PSA CoUD subgroup. ConclusionsWe found different patterns of PSA in association with psychiatric and somatic comorbidities among CoUD cases within the Yale-Penn cohort. These findings underscore the importance of modeling PSA severity and comorbidities when examining the clinical, molecular, and neuroimaging correlates of CoUD.

ObjectivePolygenic scores (PGS) are widely used in psychiatric genetic associations studies due to their impressive power to predict focal outcomes. However, they lack in discriminatory power, in part due to the high degree of genetic overlap between psychiatric disorders. The lack of prediction specificity limits the clinical utility of psychiatric PGS, particularly for diagnostic applications. The goal of the study was to enhance the discriminatory power of psychiatric PGS for two highly comorbid and genetically correlated neurodevelopmental disorders in ADHD and autism spectrum disorder (ASD). MethodsGenomic structural equation modeling (GenomicSEM) was used to generate novel PGS for ADHD and ASD by accounting for the genetic overlap between these disorders (and eight others) to achieve greater discriminatory power in non-focal outcome predictions. PGS associations were tested in two large independent samples - the Philadelphia Neurodevelopmental Cohort (N=4,789) and the Simons Foundation Powering Autism Research for Knowledge (SPARK) ASD and sibling controls (N=5,045) cohort. ResultsPGS from GenomicSEM achieved superior discriminatory power in terms of showing significantly attenuated associations with non-focal outcomes relative to traditionally computed PGS for these disorders. Additionally, genetic correlations between GenomicSEM PGS for ASD and ADHD were significantly attenuated in cross-trait associations with other psychiatric disorders and outcomes. ConclusionsPsychiatric PGS associations are likely inflated by the high degree of genetic overlap between the psychiatric disorders. Methods such as GenomicSEM can be used to refine PGS signals to be more disorder-specific, thereby enhancing their discriminatory power for future diagnostic applications.

Anxiety and depression commonly occur together resulting in worse health outcomes than when they occur in isolation. We aimed to determine whether the genetic liability for combined anxiety and depression was greater than when anxiety or depression occurred alone. Data from 12,558 genotyped twins (ages 38-85) were analysed, including 1,986 complete monozygotic and 1,809 complete dizygotic pairs. Outcomes were prescription of antidepressant and anxiolytic drugs, as demined by the World Health Organization Anatomical Therapeutic Chemical Classimication System (ATC) convention, for combined anxiety and depression (n = 1054), anxiety only (n = 744), and depression only (n = 511). Heritability of each outcome was estimated using twin modelling, and the inmluence of common genetic variation was assessed from polygenic scores (PGS) for depressive symptoms, anxiety, and 40 other traits. Heritability of combined anxiety and depression was 79% compared with 41% for anxiety and 50% for depression alone. The PGS for depressive symptoms likewise predicted more variation in combined anxiety and depression (adjusted odds ratio per SD PGS = 1.53, 95% CI = 1.43-1.63; {Delta}R2 = .031, {Delta}AUC = .044) than the other outcomes, with nearly identical results when combined anxiety and depression was demined by International Classimication of Diseases (ICD) diagnoses (adjusted odds ratio per SD PGS = 1.70, 95% CI = 1.53-1.90; {Delta}R2 = .036, {Delta}AUC = .051). Individuals in the highest decile of PGS for depressive symptoms had over 5 times higher odds of being prescribed medication for combined anxiety and depression compared to those in the lowest decile. We conclude that genetic factors explain substantially more variation in combined anxiety and depression than anxiety or depression alone.

AimsEstimate trends in levels of cannabis use among adults with and without serious psychological distress (SPD) in the United States from 2009-2019, and to ascertain whether cannabis use among individuals with SPD was associated with inpatient psychiatric hospitalization and outpatient mental health care. DesignUsing multivariable logistic regression models and predictive margin methods, we estimated linear time trends in levels of cannabis use by year and SPD status and rates of psychiatric hospitalization and outpatient service use. SettingThe United States: National Survey on Drug Use and Health (NSDUH), an annual cross-sectional survey, 2009-19 public use files. Participants447,228 adults aged [&ge;] 18 years. MeasurementsIn the past year, self-report of any and greater-than-weekly cannabis use, any inpatient psychiatric hospitalization, and any outpatient mental health care. FindingsRates of any and weekly-plus cannabis use increased similarly among individuals with SPD compared to those without from 2009-2014 but more rapidly in SPD from 2015-2019 (p<0.001). Among individuals with SPD, probability of psychiatric hospitalization was greater among individuals with less than weekly (5.2%, 95% CI 4.4-5.9%, p=0.011), and weekly-plus cannabis use (5.4%, 95% CI 4.6-6.1, p=0.002) compared to no use (4.1%, 95% CI 3.8-4.4%). For outpatient mental health care, no use was associated with a 27.4% probability (95% CI 26.7-8.1%) of any outpatient care, significantly less than less than weekly use (32.7% probability, 95% CI 31.3-34.1% p<0.001) and weekly-plus use (29.9% probability, 95% CI 28.3-31.5% p=0.006). ConclusionsCannabis use is increasing more rapidly among individuals with SPD than the general population, and is associated with increased rates of psychiatric hospitalization as well as increased outpatient service use. These findings can inform policy makers looking to better tailor regulations on advertising for medical and adult use cannabis and develop public health messaging on the use of cannabis in people with mental illness.

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.

Large-scale mega-analyses of worldwide combined Magnetic Resonance Imaging (MRI) studies have demonstrated brain differences between individuals with mental disorders and controls. However, the potential of large-scale observational studies using population-based clinical MRI data remains unexplored. We analyzed clinical MRI data from 23,545 patients in the Eastern half of Denmark (Capital Region of Denmark and Region Zealand). 2,774 patients with mental disorders and 2,062 non-psychiatric controls fulfilled our inclusion and exclusion criteria. Patients with mental disorders exhibited smaller thalamic (d=-0.298) and amygdala volumes (d=-0.250), with larger ventricles (d=0.272), and thinner insula (d=-0.177), all p<0.0001. Analysis across all ROIs revealed a widespread pattern of thinner cortex (d=-0.180), especially in the temporal pole (d=-0.234) and superior frontal (d=-0.212) regions, and increased extracerebral cerebrospinal fluid (d=0.264). For volumetric measurements, findings were consistent across different inclusion and exclusion criteria but varied for cortical thickness measurements. Utilizing this currently largest population-based MRI cohort for mental disorders, we demonstrate that clinical MRI scans can detect brain structural differences among patients with mental disorders in real-world clinical settings, aiding in the stratification of patients without mental disorders. Cross-disorder analyses reveal shared neuroanatomical changes, including globally smaller brain volumes and thinner cortex. Integrating large-scale clinical MRI data with electronic health records holds promise for improved patient stratification and tracking of disease progression for future longitudinal cross-disorder studies, bridging real-world MRI data with clinical trajectories for further biological subgrouping.

In epidemiological studies, obsessive-compulsive disorder (OCD) is robustly associated with increased risk of cardiometabolic disorders, including cardiovascular diseases, type 2 diabetes, and obesity. However, the mechanisms behind these associations are unclear. We conducted genetic correlation analyses to explore shared genetic etiology and bi-directional summary-level Mendelian randomization (MR) to explore potential causal effects between genetic liability to OCD and 14 cardiometabolic phenotypes (e.g., coronary artery disease, blood pressure, body mass index [BMI]). If causal effects were observed, we planned to conduct multivariable-MR to explore indirect effects via health behaviors. We found no evidence for genetic correlations between OCD and any of the cardiometabolic phenotypes under study, except for a negative correlation with BMI (rG=-0.123, SE=0.029, p<0.001). Summary-level MR showed no evidence for causal effects. Therefore, multivariable-MR was not conducted. We found limited evidence for shared genetic etiology or causal effects. However, we were only powered to detect medium effects in the direction of OCD to cardiometabolic traits, leaving the possibility of smaller causal effects existing. Future studies with larger, more representative samples will help to further interpret findings.

BackgroundBipolar disorder (BIP) frequently co-occurs with heightened substance use (SU) and substance use disorders (SUDs). Although the strong co-occurrence of these heritable traits points to shared genetic susceptibility, the extent to which there are differences in how SU and SUD overlap with BIP genetic architecture remains unclear. MethodsWe quantified the polygenic overlap between BIP and SUDs (alcohol, cannabis, opioid, and tobacco), and BIP and SU traits (drinks per week, lifetime cannabis use, prescription_opioid use, and smoking initiation) using GWAS summary statistics and trivariate MiXeR. We then isolated the general and unique genetic contributions of SUD and SU using GWAS-by-subtraction via GenomicSEM. Next, we tested associations between polygenic risk scores (PRSs) derived from these latent factors and diagnostic and behavioral outcomes in the Norwegian Mother, Father and Child Cohort Study. Finally, we applied GSA-MiXeR to explore pleiotropic pathway enrichment shared between the latent factors and BIP. ResultsWe found extensive polygenic overlap between traits, with SUDs being more genetically correlated with BIP than SU traits. The unique SUD factor correlated positively with psychiatric disorders, whereas unique SU correlated negatively. PRSs for BIP, shared SUD/SU, and unique SUD were significantly associated with BIP, SUD, and comorbid SUD-BIP; PRS for unique SU was only associated with self-reported lifetime SU. GSA-MiXeR revealed richer gene-set enrichment for SUD/BIP than SU/BIP implicating dopamine signaling and interneuron function. ConclusionBy dissecting the genetic liability to SUD and SU and investigating their relationship with BIP we find a genetic link driven by substance dependence but not substance use more broadly.

Background: Adverse childhood events (ACEs) contribute to the development of mood and anxiety disorders and substance dependence. However, the extent to which these effects are direct or indirect and whether genetic risk moderates them is unclear. Methods: We examined associations among ACEs, mood/anxiety disorders, and substance dependence in 12,668 individuals (44.9% female, 42.5% African American/Black, 42.1% European American/White). We generated latent variables for each phenotype and modeled direct and indirect effects of ACEs on substance dependence, mediated by mood/anxiety disorders (forward or "self-medication" model) and of ACEs on mood/anxiety disorders, mediated by substance dependence (reverse or "substance-induced" model). In a sub-sample, we also generated polygenic scores for substance dependence and mood/anxiety disorder factors, which we tested as moderators in the mediation models. Results: Although there were significant indirect effects in both directions, mediation by mood/anxiety disorders (forward model) was greater than by substance dependence (reverse model). Greater genetic risk for substance dependence was associated with a weaker direct effect of ACEs on substance dependence in both the African- and European-ancestry groups (i.e., gene-environment interaction) and a weaker indirect effect in European-ancestry individuals (i.e., moderated mediation). Conclusion: We found greater evidence that substance dependence results from self-medication of mood/anxiety disorders than that mood/anxiety disorders are substance induced. Among individuals at higher genetic risk for substance dependence who are more likely to develop a dependence diagnosis, ACEs exert less of an effect in promoting that outcome. Following exposure to ACEs, multiple pathways lead to mood/anxiety disorders and substance dependence. Specification of these pathways could inform individually targeted prevention and treatment approaches.

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.