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ImportanceCurrent Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) diagnoses of substance use disorders rely on criterion count-based approaches, disregarding severity grading indexed by individual criteria.ObjectiveTo examine correlates of alcohol use disorder (AUD) across count-based severity groups (ie, mild, moderate, mild-to-moderate, severe), identify specific diagnostic criteria indicative of greater severity, and evaluate whether specific criteria within mild-to-moderate AUD differentiate across relevant correlates and manifest in greater hazards of severe AUD development.Design, setting, and participantsThis cohort study involved 2 cohorts from the family-based Collaborative Study on the Genetics of Alcoholism (COGA) with 7 sites across the United States: cross-sectional (assessed 1991-2005) and longitudinal (assessed 2004-2019). Statistical analyses were conducted from December 2022 to June 2023.Main outcomes and measuresSociodemographic, alcohol-related, psychiatric comorbidity, brain electroencephalography (EEG), and AUD polygenic score measures as correlates of DSM-5 AUD levels (ie, mild, moderate, severe) and criterion severity-defined mild-to-moderate AUD diagnostic groups (ie, low-risk vs high-risk mild-to-moderate).ResultsA total of 13 110 individuals from the cross-sectional COGA cohort (mean [SD] age, 37.8 [14.2] years) and 2818 individuals from the longitudinal COGA cohort (mean baseline [SD] age, 16.1 [3.2] years) were included. Associations with alcohol-related, psychiatric, EEG, and AUD polygenic score measures reinforced the role of increasing criterion counts as indexing severity. Yet within mild-to-moderate AUD (2-5 criteria), the presence of specific high-risk criteria (eg, withdrawal) identified a group reporting heavier drinking and greater psychiatric comorbidity even after accounting for criterion count differences. In longitudinal analyses, prior mild-to-moderate AUD characterized by endorsement of at least 1 high-risk criterion was associated with more accelerated progression to severe AUD (adjusted hazard ratio [aHR], 11.62; 95% CI, 7.54-17.92) compared with prior mild-to-moderate AUD without endorsement of high-risk criteria (aHR, 5.64; 95% CI, 3.28-9.70), independent of criterion count.Conclusions and relevanceIn this cohort study of a combined 15 928 individuals, findings suggested that simple count-based AUD diagnostic approaches to estimating severe AUD vulnerability, which ignore heterogeneity among criteria, may be improved by emphasizing specific high-risk criteria. Such emphasis may allow better focus on individuals at the greatest risk and improve understanding of the development of AUD.

Memory problems are common among older adults with a history of alcohol use disorder (AUD). Employing a machine learning framework, the current study investigates the use of multi-domain features to classify individuals with and without alcohol-induced memory problems. A group of 94 individuals (ages 50-81 years) with alcohol-induced memory problems (the memory group) were compared with a matched control group who did not have memory problems. The random forests model identified specific features from each domain that contributed to the classification of the memory group vs. the control group (AUC = 88.29%). Specifically, individuals from the memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except for some connections involving the anterior cingulate cortex, which were predominantly hypoconnected. Other significant contributing features were: (i) polygenic risk scores for AUD, (ii) alcohol consumption and related health consequences during the past five years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past twelve months, and (iii) elevated neuroticism and increased harm avoidance, and fewer positive "uplift" life events. At the neural systems level, hyperconnectivity across the default mode network regions, including the connections across the hippocampal hub regions, in individuals with memory problems may indicate dysregulation in neural information processing. Overall, the study outlines the importance of utilizing multidomain features, consisting of resting-state brain connectivity data collected ~18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict the alcohol-related memory problems that arise in later life.

BackgroundVariation in liability to cannabis use disorder has a strong genetic component (estimated twin and family heritability about 50-70%) and is associated with negative outcomes, including increased risk of psychopathology. The aim of the study was to conduct a large genome-wide association study (GWAS) to identify novel genetic variants associated with cannabis use disorder.MethodsTo conduct this GWAS meta-analysis of cannabis use disorder and identify associations with genetic loci, we used samples from the Psychiatric Genomics Consortium Substance Use Disorders working group, iPSYCH, and deCODE (20 916 case samples, 363 116 control samples in total), contrasting cannabis use disorder cases with controls. To examine the genetic overlap between cannabis use disorder and 22 traits of interest (chosen because of previously published phenotypic correlations [eg, psychiatric disorders] or hypothesised associations [eg, chronotype] with cannabis use disorder), we used linkage disequilibrium score regression to calculate genetic correlations.FindingsWe identified two genome-wide significant loci: a novel chromosome 7 locus (FOXP2, lead single-nucleotide polymorphism [SNP] rs7783012; odds ratio [OR] 1·11, 95% CI 1·07-1·15, p=1·84 × 10-9) and the previously identified chromosome 8 locus (near CHRNA2 and EPHX2, lead SNP rs4732724; OR 0·89, 95% CI 0·86-0·93, p=6·46 × 10-9). Cannabis use disorder and cannabis use were genetically correlated (rg 0·50, p=1·50 × 10-21), but they showed significantly different genetic correlations with 12 of the 22 traits we tested, suggesting at least partially different genetic underpinnings of cannabis use and cannabis use disorder. Cannabis use disorder was positively genetically correlated with other psychopathology, including ADHD, major depression, and schizophrenia.InterpretationThese findings support the theory that cannabis use disorder has shared genetic liability with other psychopathology, and there is a distinction between genetic liability to cannabis use and cannabis use disorder.FundingNational Institute of Mental Health; National Institute on Alcohol Abuse and Alcoholism; National Institute on Drug Abuse; Center for Genomics and Personalized Medicine and the Centre for Integrative Sequencing; The European Commission, Horizon 2020; National Institute of Child Health and Human Development; Health Research Council of New Zealand; National Institute on Aging; Wellcome Trust Case Control Consortium; UK Research and Innovation Medical Research Council (UKRI MRC); The Brain & Behavior Research Foundation; National Institute on Deafness and Other Communication Disorders; Substance Abuse and Mental Health Services Administration (SAMHSA); National Institute of Biomedical Imaging and Bioengineering; National Health and Medical Research Council (NHMRC) Australia; Tobacco-Related Disease Research Program of the University of California; Families for Borderline Personality Disorder Research (Beth and Rob Elliott) 2018 NARSAD Young Investigator Grant; The National Child Health Research Foundation (Cure Kids); The Canterbury Medical Research Foundation; The New Zealand Lottery Grants Board; The University of Otago; The Carney Centre for Pharmacogenomics; The James Hume Bequest Fund; National Institutes of Health: Genes, Environment and Health Initiative; National Institutes of Health; National Cancer Institute; The William T Grant Foundation; Australian Research Council; The Virginia Tobacco Settlement Foundation; The VISN 1 and VISN 4 Mental Illness Research, Education, and Clinical Centers of the US Department of Veterans Affairs; The 5th Framework Programme (FP-5) GenomEUtwin Project; The Lundbeck Foundation; NIH-funded Shared Instrumentation Grant S10RR025141; Clinical Translational Sciences Award grants; National Institute of Neurological Disorders and Stroke; National Heart, Lung, and Blood Institute; National Institute of General Medical Sciences.

To provide insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing 4503 OD cases, 4173 opioid-exposed controls, and 32,500 opioid-unexposed controls, including participants of European and African descent (EUR and AFR, respectively). Among the variants identified, rs9291211 was associated with OE (exposed vs. unexposed controls; EUR z = -5.39, p = 7.2 × 10-8). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N > 360,000) found association of this variant with propensity to use dietary supplements (p = 1.68 × 10-8). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (EUR + AFR z = 4.69, p = 10-6), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (AFR z = 5.55, p = 2.9 × 10-8) and a significant association with musculoskeletal disorders in the UK Biobank (p = 4.88 × 10-7). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (n = 466,571) was positively associated with OD (OD vs. unexposed controls, p = 8.1 × 10-5; OD cases vs. exposed controls, p = 0.054) and OE (exposed vs. unexposed controls, p = 3.6 × 10-5). A PRS based on a GWAS of neuroticism (n = 390,278) was positively associated with OD (OD vs. unexposed controls, p = 3.2 × 10-5; OD vs. exposed controls, p = 0.002) but not with OE (p = 0.67). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls in studies of addiction.

BackgroundAlcoholism is a multifactorial disorder influenced by multiple gene loci, each with small effect. Studies suggest shared genetic influences across DSM-IV alcohol dependence symptoms, but shared effects across DSM-5 alcohol use disorder remains unknown. We aimed to test the assumption of genetic homogeneity across the 11 criteria of DSM-5 alcohol use disorder (AUD).MethodsData from 2596 alcohol using individuals of European ancestry from the Study of Addiction: Genetics and Environment were used to examine the genomewide SNP-heritability (h2SNP) and SNP-covariance (rGSNP) between 11 DSM-5 AUD symptoms. Phenotypic relationships between symptoms were examined to confirm an underlying liability of AUD and the SNP-heritability of the observed latent trait and the co-heritabilityamong AUD symptoms was assessed using Genomic-Relatedness-Matrix-Restricted-Maximum-Likelihood. Genetic covariance among symptoms was examined using factor analysis.ResultsPhenotypic relationships confirmed a unidimensional underlying liability to AUD. Factor and parallel analyses of the observed genetic variance/covariance provided evidence of genetic homogeneity. Additive genetic effects on DSM-5 AUD symptoms varied from 0.10 to 0.37 and largely overlapped (rG-SNP across symptoms ranged from 0.49 - 0.92). The additive genetic effect on the DSM-5 AUD factor was 0.36, 0.14 for DSM-5 AUD diagnosis, and was 0.22 for DSM-5 AUD severity.ConclusionsCommon genetic variants influence DSM-5 AUD symptoms. Despite evidence for a common AUD factor, the evidence of only partially overlapping genetic effects across AUD symptoms further substantiates the need to simultaneously model common and symptom-specific genetic effects in molecular genetic studies in order to best characterize the genetic liability.

Liability to alcohol dependence (AD) is heritable, but little is known about its complex polygenic architecture or its genetic relationship with other disorders. To discover loci associated with AD and characterize the relationship between AD and other psychiatric and behavioral outcomes, we carried out the largest genome-wide association study to date of DSM-IV-diagnosed AD. Genome-wide data on 14,904 individuals with AD and 37,944 controls from 28 case-control and family-based studies were meta-analyzed, stratified by genetic ancestry (European, n = 46,568; African, n = 6,280). Independent, genome-wide significant effects of different ADH1B variants were identified in European (rs1229984; P = 9.8 × 10-13) and African ancestries (rs2066702; P = 2.2 × 10-9). Significant genetic correlations were observed with 17 phenotypes, including schizophrenia, attention deficit-hyperactivity disorder, depression, and use of cigarettes and cannabis. The genetic underpinnings of AD only partially overlap with those for alcohol consumption, underscoring the genetic distinction between pathological and nonpathological drinking behaviors.

Cytochrome P450 2A6 (CYP2A6) encodes the enzyme responsible for the majority of nicotine metabolism. Previous studies support that slow metabolizers smoke fewer cigarettes once nicotine dependent but provide conflicting results on the role of CYP2A6 in the development of dependence. By focusing on the critical period of young adulthood, this study examines the relationship of CYP2A6 variation and smoking milestones. A total of 1209 European American young adults enrolled in the Collaborative Study on the Genetics of Alcoholism were genotyped for CYP2A6 variants to calculate a previously well-validated metric that estimates nicotine metabolism. This metric was not associated with the transition from never smoking to smoking initiation nor with the transition from initiation to daily smoking (P > 0.4). But among young adults who had become daily smokers (n = 506), decreased metabolism was associated with increased risk of nicotine dependence (P = 0.03) (defined as Fagerström Test for Nicotine Dependence score ≥4). This finding was replicated in the Collaborative Genetic Study of Nicotine Dependence with 335 young adult daily smokers (P = 0.02). Secondary meta-analysis indicated that slow metabolizers had a 53 percent increased odds (OR = 1.53, 95 percent CI 1.11-2.11, P = 0.009) of developing nicotine dependence compared with normal metabolizers. Furthermore, secondary analyses examining four-level response of time to first cigarette after waking (>60, 31-60, 6-30, ≤5 minutes) demonstrated a robust effect of the metabolism metric in Collaborative Study on the Genetics of Alcoholism (P = 0.03) and Collaborative Genetic Study of Nicotine Dependence (P = 0.004), illustrating the important role of this measure of dependence. These findings highlight the complex role of CYP2A6 variation across different developmental stages of smoking behaviors.

Patients with Bipolar disorder smoke more than the general population. Smoking negatively impacts mortality and clinical course in Bipolar disorder patients. Prior studies have shown contradictory results regarding the impact of psychosis on smoking behavior in Bipolar disorder. We analyzed a large sample of Bipolar disorder and Schizoaffective disorder, Bipolar Type patients and predicted those with a history of psychosis would be more likely to be nicotine dependent. Data from subjects and controls were collected from the Genomic Psychiatry Cohort (GPC). Subjects were diagnosed with Bipolar disorder without psychosis (N = 610), Bipolar disorder with psychosis (N = 1544). Participants were classified with or without nicotine dependence. Diagnostic groups were compared to controls (N = 10065) using logistic regression. Among smokers (N = 6157), those with Bipolar disorder had an increased risk of nicotine dependence (OR = 2.5; P

The mu1 opioid receptor gene, OPRM1, has long been a high-priority candidate for human genetic studies of addiction. Because of its potential functional significance, the non-synonymous variant rs1799971 (A118G, Asn40Asp) in OPRM1 has been extensively studied, yet its role in addiction has remained unclear, with conflicting association findings. To resolve the question of what effect, if any, rs1799971 has on substance dependence risk, we conducted collaborative meta-analyses of 25 datasets with over 28,000 European-ancestry subjects. We investigated non-specific risk for "general" substance dependence, comparing cases dependent on any substance to controls who were non-dependent on all assessed substances. We also examined five specific substance dependence diagnoses: DSM-IV alcohol, opioid, cannabis, and cocaine dependence, and nicotine dependence defined by the proxy of heavy/light smoking (cigarettes-per-day >20 vs. ≤ 10). The G allele showed a modest protective effect on general substance dependence (OR = 0.90, 95% C.I. [0.83-0.97], p value = 0.0095, N = 16,908). We observed similar effects for each individual substance, although these were not statistically significant, likely because of reduced sample sizes. We conclude that rs1799971 contributes to mechanisms of addiction liability that are shared across different addictive substances. This project highlights the benefits of examining addictive behaviors collectively and the power of collaborative data sharing and meta-analyses.

This paper presents the development of the fusion model skills diagnosis system (fusion model system), which can help integrate standardized testing into the learning process with both skills-level examinee parameters for modeling examinee skill mastery and skills-level item parameters, giving information about the diagnostic power of the test. The development of the fusion model system involves advancements in modeling, parameter estimation, model-fitting methods, and model-fit evaluation procedures, which are described in detail in the paper. To document the accuracy of the estimation procedure and the effectiveness of the model-fitting and model-fit evaluation procedures, this paper also presents a series of simulation studies. Special attention is given to evaluating the robustness of the fusion model system to violations of various modeling assumptions. The results demonstrate that the fusion model system is a promising tool for skills diagnosis that merits further research and development.

ImportanceAlthough early mortality in severe psychiatric illness is linked to smoking and alcohol, to our knowledge, no studies have comprehensively characterized substance use behavior in severe psychotic illness. In particular, recent assessments of substance use in individuals with mental illness are based on population surveys that do not include individuals with severe psychotic illness.ObjectiveTo compare substance use in individuals with severe psychotic illness with substance use in the general population.Design, setting, and participantsWe assessed comorbidity between substance use and severe psychotic disorders in the Genomic Psychiatry Cohort. The Genomic Psychiatry Cohort is a clinically assessed, multiethnic sample consisting of 9142 individuals with the diagnosis of schizophrenia, bipolar disorder with psychotic features, or schizoaffective disorder, and 10,195 population control individuals.Main outcomes and measuresSmoking (smoked >100 cigarettes in a lifetime), heavy alcohol use (>4 drinks/day), heavy marijuana use (>21 times of marijuana use/year), and recreational drug use.ResultsRelative to the general population, individuals with severe psychotic disorders have increased risks for smoking (odds ratio, 4.6; 95% CI, 4.3-4.9), heavy alcohol use (odds ratio, 4.0; 95% CI, 3.6-4.4), heavy marijuana use (odds ratio, 3.5; 95% CI, 3.2-3.7), and recreational drug use (odds ratio, 4.6; 95% CI, 4.3-5.0). All races/ethnicities (African American, Asian, European American, and Hispanic) and both sexes have greatly elevated risks for smoking and alcohol, marijuana, and drug use. Of specific concern, recent public health efforts that have successfully decreased smoking among individuals younger than age 30 years appear to have been ineffective among individuals with severe psychotic illness (interaction effect between age and severe mental illness on smoking initiation, P = 4.5 × 105).Conclusions and relevanceIn the largest assessment of substance use among individuals with severe psychotic illness to date, we found the odds of smoking and alcohol and other substance use to be dramatically higher than recent estimates of substance use in mild mental illness.

BACKGROUND: We explore the factor structure of DSM-5 cannabis use disorders, examine its prevalence across European- and African-American respondents as well as its genetic underpinnings, utilizing data from a genome-wide study of single nucleotide polymorphisms (SNPs). We also estimate the heritability of DSM-5 cannabis use disorders explained by these common SNPs. METHODS: Data on 3053 subjects reporting a lifetime history of cannabis use were utilized. Exploratory and confirmatory factor analyses were conducted to create a factor score, which was used in a genome-wide association analysis. p-values from the single SNP analysis were examined for evidence of gene-based association. The aggregate effect of all SNPs was also estimated using Genome-Wide Complex Traits Analysis. RESULTS: The unidimensionality of DSM-5 cannabis use disorder criteria was demonstrated. Comparing DSM-IV to DSM-5, a decrease in prevalence of cannabis use disorders was only noted in European-American respondents and was exceedingly modest. For the DSM-5 cannabis use disorders factor score, no SNP surpassed the genome-wide significance testing threshold. However, in the European-American subsample, gene-based association testing resulted in significant associations in 3 genes (C17orf58, BPTF and PPM1D) on chromosome 17q24. In aggregate, 21% of the variance in DSM-5 cannabis use disorders was explained by the genome-wide SNPs; however, this estimate was not statistically significant. CONCLUSIONS: DSM-5 cannabis use disorder represents a unidimensional construct, the prevalence of which is only modestly elevated above the DSM-IV version. Considerably larger sample sizes will be required to identify individual SNPs associated with cannabis use disorders and unequivocally establish its polygenic underpinnings.

Introduction: As Alzheimer's disease (AD) biomarkers rapidly develop, tools are needed that accurately and effectively communicate risk of AD dementia. Methods: We analyzed longitudinal data from >10,000 cognitively unimpaired older adults. Five‐year risk of AD dementia was modeled using survival analysis. Results: A demographic model was developed and validated on independent data with area under the receiver operating characteristic curve (AUC) for 5‐year prediction of AD dementia of 0.79. Clinical and cognitive variables (AUC = 0.79), and apolipoprotein E genotype (AUC = 0.76) were added to the demographic model. We then incorporated the risk computed from the demographic model with hazard ratios computed from independent data for amyloid positron emission tomography status and magnetic resonance imaging hippocampal volume (AUC = 0.84), and for plasma amyloid beta (Aβ)42/Aβ40 (AUC = 0.82). Discussion: An adaptive tool was developed and validated to compute absolute risks of AD dementia. This approach allows for improved accuracy and communication of AD risk among cognitively unimpaired older adults. [ABSTRACT FROM AUTHOR]

Background: Alcohol use disorder (AUD) and schizophrenia (SCZ) frequently co-occur, and large-scale genome-wide association studies (GWAS) have identified significant genetic correlations between these disorders. Methods: We used the largest published GWAS for AUD (total cases = 77 822) and SCZ (total cases = 46 827) to identify genetic variants that influence both disorders (with either the same or opposite direction of effect) and those that are disorder specific. Results: We identified 55 independent genome-wide significant single nucleotide polymorphisms with the same direction of effect on AUD and SCZ, 8 with robust effects in opposite directions, and 98 with disorder-specific effects. We also found evidence for 12 genes whose pleiotropic associations with AUD and SCZ are consistent with mediation via gene expression in the prefrontal cortex. The genetic covariance between AUD and SCZ was concentrated in genomic regions functional in brain tissues (p = 0.001). Conclusions: Our findings provide further evidence that SCZ shares meaningful genetic overlap with AUD. [ABSTRACT FROM AUTHOR]