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This study explores familial psychiatric risk factors that are closely linked to suicide risk among patients with opioid use disorder (OUD) as measured by the Family History Assessment Module (FHAM). Data was derived from adults diagnosed with OUD (N = 389). To analyze the covariance between the 11 items of the FHAM, principal component analysis was applied to infer principal components (PC) scores. Log-binominal regression was conducted to quantify the associations between PC scores and mental health symptoms (e.g., lifetime suicidal attempt, P30D suicidal ideation, depression, and anxiety). Analyses revealed that the first 3 three PCs could account for 56% of the total variance of the FHAM items within the data. Family history of substance misuse (PC1) was positively associated with lifetime suicide attempts and severe anxiety. Family history of serious mental illness (PC2) and of suicidal behavior (PC3) were not significantly associated with any outcomes. Our findings suggest current suicide risk is associated with an array of familial psychiatric issues among people with OUD. However, family history of suicide attempts and death by suicide has less bearing on current suicide risk in OUD patients whereas family history of substance use confers significant risk. Findings underscore suicide-related preventive interventions as necessary components of treatment plans among people with OUD, who commonly report family histories of substance misuse.

Background: Attention-deficit/hyperactivity disorder (ADHD) and posttraumatic stress disorder (PTSD) are associated, but it is unclear if this is a causal relationship or confounding. We used genetic analyses and sibling comparisons to clarify the direction of this relationship. Methods: Linkage disequilibrium score regression and 2-sample Mendelian randomization were used to test for genetic correlation (r g) and bidirectional causal effects using European ancestry genome-wide association studies of ADHD (20,183 cases and 35,191 controls) and 6 PTSD definitions (up to 320,369 individuals). Several additional variables were included in the analysis to verify the independence of the ADHD-PTSD relationship. In a population-based sibling comparison (N = 2,082,118 individuals), Cox regression models were fitted to account for time at risk, a range of sociodemographic factors, and unmeasured familial confounders (via sibling comparisons). Results: ADHD and PTSD had consistent r g (r g range, 0.43–0.52; p < .001). ADHD genetic liability was causally linked with increased risk for PTSD (β = 0.367; 95% CI, 0.186–0.552; p = 7.68 × 10 −5). This result was not affected by heterogeneity, horizontal pleiotropy (Mendelian randomization Egger intercept = 4.34 × 10 −4, p = .961), or other phenotypes and was consistent across PTSD datasets. However, we found no consistent associations between PTSD genetic liability and ADHD risk. Individuals diagnosed with ADHD were at a higher risk for developing PTSD than their undiagnosed sibling (hazard ratio = 2.37; 95% CI, 1.98–3.53). Conclusions: Our findings add novel evidence supporting the need for early and effective treatment of ADHD, as patients with this diagnosis are at significantly higher risk to develop PTSD later in life.

As Alzheimer's disease (AD) biomarkers rapidly develop, tools are needed that accurately and effectively communicate risk of AD dementia.We analyzed longitudinal data from10,000 cognitively unimpaired older adults. Five-year risk of AD dementia was modeled using survival analysis.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).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.

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 (Memorygroup) were compared with a matchedControlgroup who did not have memory problems. The Random Forests model identified specific features from each domain that contributed to the classification of Memory vs. Control group (AUC=88.29%). Specifically, individuals from the Memory group manifested a predominant pattern of hyperconnectivity across the default mode network regions except some connections involving 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 5 years, such as health problems, past negative experiences, withdrawal symptoms, and the largest number of drinks in a day during the past 12 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 collected ∼18 years ago, together with personality, life experiences, polygenic risk, and alcohol consumption and related consequences, to predict alcohol-related memory problems that arise in later life.

BackgroundPrevious studies have shown that brain volume is negatively associated with cigarette smoking, but there is an ongoing debate whether smoking causes lowered brain volume or a lower brain volume is a risk factor for smoking. We address this debate through multiple methods that evaluate causality: Bradford Hill’s Criteria to understand a causal relationship in epidemiological studies, mediation analysis, and Mendelian Randomization.MethodsIn 28,404 participants of European descent from the UK Biobank dataset, we examined relationships between a history of daily smoking and brain imaging phenotypes as well as associations of genetic predisposition to smoking initiation with brain volume.ResultsA history of daily smoking is strongly associated with decreased brain volume, and a history of heavier smoking is associated with a greater decrease in brain volume. The strongest association was between total grey matter volume and a history of daily smoking (p-value = 8.28 × 10−33), and there was a dose response relationship with more pack years smoked associated with a greater decrease in brain volume. A polygenic risk score (PRS) for smoking initiation was strongly associated with a history of daily smoking (p-value = 4.09 ×10−72), yet only modestly associated with total grey matter volume (p-value = 0.02). Mediation analysis indicated that a history of daily smoking is a mediator between smoking initiation PRS and total grey matter volume. Mendelian Randomization showed a causal effect of daily smoking on total grey matter volume (p-value = 0.022).ConclusionsThese converging findings strongly support the hypothesis that smoking causes decreased brain volume.

Completer analyses and Expectation-Maximization algorithm analyses

Change in smoking by RiskProfile status, smoking heaviness, and level of genetic risk

PDF file - 67K, Imputed SNP associations from the case-control analysis of lung cancer (by histology) in African-Americans (P

PDF file - 561K, Association of SNPs on chromosome 15q25.1 with lung cancer, with and without adjustment for missense variant rs16969968. Black circles depict association of SNPs with lung cancer, adjusted for sex, age, study site, % African ancestry, % European ancestry, and number of pack-years smoked. Open squares depict association of SNPs with lung cancer, adjusted for sex, age, study site, and % African ancestry, % European ancestry, number of pack-years smoked, AND conditioned on rs16969968 (marked with an X).

Background: Genome-wide association studies of European and East Asian populations have identified lung cancer susceptibility loci on chromosomes 5p15.33, 6p22.1-p21.31, and 15q25.1. We investigated whether these regions contain lung cancer susceptibly loci in African-Americans and refined previous association signals by using the reduced linkage disequilibrium observed in African-Americans.Methods: 1,308 African-American cases and 1,241 African-American controls from 3 centers were genotyped for 760 single-nucleotide polymorphisms (SNP) spanning 3 regions, and additional SNP imputation was carried out. Associations between polymorphisms and lung cancer risk were estimated using logistic regression, stratified by tumor histology where appropriate.Results: The strongest associations were observed on 15q25.1 in/near CHRNA5, including a missense substitution [rs16969968: OR, 1.57; 95% confidence interval (CI), 1.25–1.97; P, 1.1 × 10−4) and variants in the 5′-UTR. Associations on 6p22.1-p21.31 were histology specific and included a missense variant in BAT2 associated with squamous cell carcinoma (rs2736158: OR, 0.64; 95% CI, 0.48–0.85; P, 1.82 × 10−3). Associations on 5p15.33 were detected near TERT, the strongest of which was rs2735940 (OR, 0.82; 95% CI, 0.73–0.93; P, 1.1 × 10−3). This association was stronger among cases with adenocarcinoma (OR, 0.75; 95% CI, 0.65–0.86; P, 8.1 × 10−5).Conclusions: Polymorphisms in 5p15.33, 6p22.1-p21.31, and 15q25.1 are associated with lung cancer in African-Americans. Variants on 5p15.33 are stronger risk factors for adenocarcinoma and variants on 6p21.33 associated only with squamous cell carcinoma.Impact: Results implicate the BAT2, TERT, and CHRNA5 genes in the pathogenesis of specific lung cancer histologies. Cancer Epidemiol Biomarkers Prev; 22(2); 251–60. ©2012 AACR.

PDF file - 559K, Association of SNPs on chromosome 15q25.1 with lung cancer, with and without adjustment for pack-years smoked. Black circles depict association of SNPs with lung cancer, adjusted for sex, age, study site, % African ancestry, % European ancestry, and number of pack-years smoked. Open squares depict association of SNPs with lung cancer, adjusted for sex, age, study site, and % African ancestry, % European ancestry, but NOT number of pack-years smoked.

PDF file - 926K, Triangle plot showing estimated admixture in the African-American case-control population. Estimates were performed using 102 AIMs and data from the International HapMap Project on 167 Yoruban African (Green dots), 165 European (Red dots), 84 Chinese (Yellow dots) and 86 Japanese (Dark Blue dots) founders. The figure depicts ancestry in 1308 African-American lung cancer cases (Pink dots) and 1241 controls (Light Blue dots).

PDF file - 57K

Supplementary Figure 1 from Identification of a Novel Tumor Suppressor Gene p34 on Human Chromosome 6q25.1

Supplementary Table 1 from The CHRNA5-CHRNA3-CHRNB4 Nicotinic Receptor Subunit Gene Cluster Affects Risk for Nicotine Dependence in African-Americans and in European-Americans

Supplementary Table 2 from The CHRNA5-CHRNA3-CHRNB4 Nicotinic Receptor Subunit Gene Cluster Affects Risk for Nicotine Dependence in African-Americans and in European-Americans

In this study, we observed loss of heterozygosity (LOH) in human chromosomal fragment 6q25.1 in sporadic lung cancer patients. LOH was observed in 65% of the 26 lung tumors examined and was narrowed down to a 2.2-Mb region. Single-nucleotide polymorphism (SNP) analysis of genes located within this region identified a candidate gene, termed p34. This gene, also designated as ZC3H12D, C6orf95, FLJ46041, or dJ281H8.1, carries an A/G nonsynonymous SNP at codon 106, which alters the amino acid from lysine to arginine. Nearly 73% of heterozygous lung cancer tissues with LOH and the A/G SNP also exhibited loss of the A allele. In vitro clonogenic and in vivo nude mouse studies showed that overexpression of the A allele exerts tumor suppressor function compared with the G allele. p34 is located within a recently mapped human lung cancer susceptibility locus, and association of the p34 A/G SNP was tested among these families. No significant association between the less frequent G allele and lung cancer susceptibility was found. Our results suggest that p34 may be a novel tumor suppressor gene involved in sporadic lung cancer but it seems not to be the candidate familial lung cancer susceptibility gene linked to chromosomal region 6q23-25. [Cancer Res 2007;67(1):93–9]

Study Objectives In adult populations, women are more likely than men to be prescribed benzodiazepines. However, such disparities have not been investigated in people with opioid use disorder (OUD) and insomnia receiving buprenorphine, a population with particularly high sedative/hypnotic receipt. This retrospective cohort study used administrative claims data from Merative MarketScan Commercial and MultiState Medicaid Databases (2006–2016) to investigate sex differences in the receipt of insomnia medication prescriptions among patients in OUD treatment with buprenorphine. Methods We included people aged 12–64 years with diagnoses of insomnia and OUD-initiating buprenorphine during the study timeframe. The predictor variable was sex (female versus male). The primary outcome was receipt of insomnia medication prescription within 60 days of buprenorphine start, encompassing benzodiazepines, Z-drugs, or non-sedative/hypnotic insomnia medications (e.g. hydroxyzine, trazodone, and mirtazapine). Associations between sex and benzodiazepine, Z-drug, and other insomnia medication prescription receipt were estimated using Poisson regression models. Results Our sample included 9510 individuals (female n = 4637; male n = 4873) initiating buprenorphine for OUD who also had insomnia, of whom 6569 (69.1%) received benzodiazepines, 3891 (40.9%) Z-drugs, and 8441 (88.8%) non-sedative/hypnotic medications. Poisson regression models, adjusting for sex differences in psychiatric comorbidities, found female sex to be associated with a slightly increased likelihood of prescription receipt: benzodiazepines (risk ratio [RR], RR = 1.17 [1.11–1.23]), Z-drugs (RR = 1.26 [1.18–1.34]), and non-sedative/hypnotic insomnia medication (RR = 1.07, [1.02–1.12]). Conclusions Sleep medications are commonly being prescribed to individuals with insomnia in OUD treatment with buprenorphine, with sex-based disparities indicating a higher prescribing impact among female than male OUD treatment patients.

While smoking is a well-established risk factor for surgical complications, it is unclear how frequently guideline-concordant tobacco treatments are prescribed to surgical patients. In this cross-sectional study including 164673 unique patients evaluated in outpatient surgery clinics at a single institution in 2020, despite a relatively high smoking prevalence (14.7%), guideline-concordant treatment rates were very low, with only 12.7% of patients receiving pharmacotherapy and 31.7% receiving any treatment. Addressing disparities in smoking cessation treatments are critical given the disproportionate impact of smoking on surgical outcomes.

PURPOSE To determine if physicians' self-reported knowledge, attitudes, and practices regarding genetic counseling and testing (GCT) vary by patients' race. METHODS We conducted a nationwide 49-item survey among breast oncology physicians in the United States. We queried respondents about their own demographics, clinical characteristics, knowledge, attitudes, practices, and perceived barriers in providing GCT to patients with breast cancer. RESULTS Our survey included responses from 277 physicians (females, 58.8%; medical oncologists, 75.1%; academic physicians, 61.7%; and Whites, 67.1%). Only 1.8% indicated that they were more likely to refer a White patient than refer an African American patient for GCT, and 66.9% believed that African American women with breast cancer have lower rates of GCT than White women. Regarding perceived barriers to GCT, 63.4% of respondents indicated that African American women face more barriers than White women do and 21% felt that African American women require more information and guidance during the GCT decision-making process than White women. Although 32% of respondents indicated that lack of trust was a barrier to GCT in all patients, 58.1% felt that this was a greater barrier for African American women ( P < .0001). Only 13.9% believed that noncompliance with GCT is a barrier for all patients, whereas 30.6% believed that African American women are more likely than White women to be noncompliant ( P < .0001). CONCLUSION We demonstrated that racial differences exist in oncology physicians' perceived barriers to GCT for patients with breast cancer. This nationwide survey will serve as a basis for understanding physicians' determinants of GCT for African American women and highlights the necessity of education and interventions to address bias among physicians. Awareness of such physician biases can enable further work to address inequities, ultimately leading to improved GCT equity for African American women with breast cancer.

Cannabis is widely used worldwide, yet its links to health outcomes are not fully understood. DNA methylation can serve as a mediator to link environmental exposures to health outcomes. We conducted an epigenome-wide association study (EWAS) of peripheral blood-based DNA methylation and lifetime cannabis use (ever vs. never) in a meta-analysis including 9,436 participants (7,795 European and 1,641 African ancestry) from seven cohorts. Accounting for effects of cigarette smoking, our trans-ancestry EWAS meta-analysis revealed four CpG sites significantly associated with lifetime cannabis use at a false discovery rate of 0.05 (p< 5.85 × 10−7): cg22572071 near geneADGRF1, cg15280358 inADAM12, cg00813162 inACTN1, and cg01101459 nearLINC01132. Additionally, our EWAS analysis in participants who never smoked cigarettes identified another epigenome-wide significant CpG site, cg14237301 annotated toAPOBR. We used a leave-one-out approach to evaluate methylation scores constructed as a weighted sum of the significant CpGs. The best model can explain 3.79% of the variance in lifetime cannabis use. These findings unravel the DNA methylation changes associated with lifetime cannabis use that are independent of cigarette smoking and may serve as a starting point for further research on the mechanisms through which cannabis exposure impacts health outcomes.

BACKGROUND: Stimulant use has substantially increased among people with opioid use disorder (OUD) and is associated with worse treatment outcomes. This study’s objective was to compare risk of stimulant-related emergency department (ED) and hospital admissions associated with exposure to bupropion, OUD medication (buprenorphine, naltrexone, and methadone), and SSRIs (active comparator) relative to days without active prescriptions for medication. METHODS: This recurrent-event, case-crossover study used insurance claims from 51,084 individuals with OUD enrolled in the IBM(®) MarketScan(®) (2006–2016) databases who had at least one stimulant-related ED or hospital admission. Conditional logistic regression models estimated the risk of admissions between days without active prescriptions and days with prescriptions for bupropion, OUD medication, and SSRIs. Secondary analyses were conducted by stimulant subtype (cocaine; amphetamine) and event subtype (psychotic events; falls, injuries, or poisonings). RESULTS: Compared to days without active prescriptions, days with bupropion treatment were associated with decreased odds of stimulant-related ED or hospital admissions (odds ratio [OR]=0.77, 95% Confidence Interval [CI]:0.72–0.82) Among OUD medications, we observed strong protective associations with decreased admissions for buprenorphine (OR=0.67, 95% CI:0.64–0.71), naltrexone (OR=0.65, 95% CI:0.60–0.70]), and methadone (OR=0.59, 95% CI:0.51–0.67). The SSRI active comparator group was associated with a small protective association with decreased admissions (OR=0.90, 95% CI:0.86–0.93). These effects were sustained in secondary analyses stratifying by stimulant and event subtype. CONCLUSIONS: Bupropion and OUD medication, including both naltrexone and opioid agonists, are associated with fewer stimulant-related ED or hospital admissions in patients with OUD. Bupropion may show promise as adjunctive therapy targeting stimulant-specific poisoning risk.

Background: Variation 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. Methods: To 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. Findings: We 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 (r g 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. Interpretation: These 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. Funding: National 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.

Stimulant medication use is common among individuals receiving buprenorphine for opioid use disorder (OUD). Associations between prescription stimulant use and treatment outcomes in this population have been understudied.To investigate whether use of prescription stimulants was associated with (1) drug-related poisoning and (2) buprenorphine treatment retention.This retrospective, recurrent-event cohort study with a case-crossover design used a secondary analysis of administrative claims data from IBM MarketScan Commercial and Multi-State Medicaid databases from January 1, 2006, to December 31, 2016. Primary analyses were conducted from March 1 through August 31, 2021. Individuals aged 12 to 64 years with an OUD diagnosis and prescribed buprenorphine who experienced at least 1 drug-related poisoning were included in the analysis. Unit of observation was the person-day.Days of active stimulant prescriptions.Primary outcomes were drug-related poisoning and buprenorphine treatment retention. Drug-related poisonings were defined using International Classification of Diseases, Ninth Revision, and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, codes; treatment retention was defined by continuous treatment claims until a 45-day gap was observed.There were 13 778 567 person-days of observation time among 22 946 individuals (mean [SD] age, 32.8 [11.8] years; 50.3% men) who experienced a drug-related poisoning. Stimulant treatment days were associated with 19% increased odds of drug-related poisoning (odds ratio [OR], 1.19 [95% CI, 1.06-1.34]) compared with nontreatment days; buprenorphine treatment days were associated with 38% decreased odds of poisoning (OR, 0.62 [95% CI, 0.59-0.65]). There were no significant interaction effects between use of stimulants and buprenorphine. Stimulant treatment days were associated with decreased odds of attrition from buprenorphine treatment (OR, 0.64 [95% CI, 0.59-0.70]), indicating that stimulants were associated with 36% longer mean exposure to buprenorphine and its concomitant protection.Among persons with OUD, use of prescription stimulants was associated with a modest increase in per-day risk of drug-related poisoning, but this risk was offset by the association between stimulant use and improved retention to buprenorphine treatment, which is associated with protection against overdose.

Despite prevalent polysubstance use, treatment patterns and outcomes for individuals with opioid use disorder (OUD) and cooccurring substance use disorders (SUD) are understudied.To evaluate the distribution of buprenorphine and naltrexone initiation among individuals with OUD with vs without cooccurring SUD and to assess the comparative effectiveness associated with buprenorphine and naltrexone against drug-related poisonings.This observational comparative effectiveness study used insurance claims from 2011 to 2016 from the US IBM MarketScan databases to study initiation of medications for OUD (MOUD) among treatment-seeking individuals aged 12 to 64 years with a primary diagnosis of OUD. Cooccurring SUD was defined as SUD diagnosed concurrent with or in the 6 months prior to OUD treatment initiation. Treatment was codified as psychosocial treatment without MOUD or initiation or buprenorphine or naltrexone (including extended-release or oral). Methadone recipients were excluded from analysis. Data were analyzed from February 3, 2021, through February 26, 2022.MOUD.Associations between cooccurring SUD diagnoses with treatment type were assessed with multivariable regression. The association of drug-related poisoning admissions with days covered with buprenorphine or naltrexone prescriptions vs days without prescriptions was assessed among MOUD initiators. Odds ratios from within-person fixed effects models were estimated as a function of MOUD and stratified by cooccurring SUDs.Among 179 280 individuals with OUD (mean [SD] age, 33.2 [11.0] years; 90 196 [50.5%] men), 102 930 (57.4%) received psychosocial treatment without MOUD. Across 47 488 individuals with cooccurring SUDs, 33 449 (70.4%) did not receive MOUD, whereas across 131 792 individuals without cooccurring SUDs, 69 481 (52.7%) did not receive MOUD. Cooccurring SUD was associated with decreased odds of initiating buprenorphine (risk ratio [RR], 0.55 [95% CI, 0.54-0.56]) but increased odds of initiating naltrexone (extended release: RR, 1.12 [95% CI, 1.05-1.20]; oral: RR, 1.95 [95% CI, 1.86-2.03]). Among 12 485 individuals initiating MOUD who experienced at least 1 drug-related poisoning during insurance enrollment, buprenorphine treatment days were associated with decreased poisonings compared with days without MOUD for individuals with cooccurring SUD (odds ratio [OR], 0.56 [95% CI, 0.48-0.65]) and individuals without cooccurring SUD (OR, 0.57 [95% CI, 0.53-0.63]), with comparable associations observed for extended-release naltrexone. No protective association was observed for oral naltrexone.These findings suggest that individuals with OUD and polysubstance use were less likely to initiate buprenorphine and naltrexone than individuals without polysubstance use. Among individuals initiating MOUD, polysubstance use was associated with decreased buprenorphine and increased naltrexone initiation, despite buprenorphine's protective associations against drug-related poisoning.

Funding Information: This work was supported by the National Institute of Mental Health / U.S. Army Medical Research and Development Command (Grant No. R01MH106595 [to CMN, IL, MBS, KJRe, and KCK], National Institutes of Health (Grant No. 5U01MH109539 to the Psychiatric Genomics Consortium ), and Brain & Behavior Research Foundation (Young Investigator Grant [to KWC]). Genotyping of samples was provided in part through the Stanley Center for Psychiatric Genetics at the Broad Institute supported by Cohen Veterans Bioscience . Statistical analyses were carried out on the LISA/Genetic Cluster Computer ( https://userinfo.surfsara.nl/systems/lisa ) hosted by SURFsara. This research has been conducted using the UK Biobank resource (Application No. 41209). This work would have not been possible without the financial support provided by Cohen Veterans Bioscience, the Stanley Center for Psychiatric Genetics at the Broad Institute, and One Mind. Funding Information: MBS has in the past 3 years received consulting income from Actelion, Acadia Pharmaceuticals, Aptinyx, Bionomics, BioXcel Therapeutics, Clexio, EmpowerPharm, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals, and Roche/Genentech and has stock options in Oxeia Biopharmaceuticals and Epivario. In the past 3 years, NPD has held a part-time paid position at Cohen Veterans Bioscience, has been a consultant for Sunovion Pharmaceuticals, and is on the scientific advisory board for Sentio Solutions for unrelated work. In the past 3 years, KJRe has been a consultant for Datastat, Inc., RallyPoint Networks, Inc., Sage Pharmaceuticals, and Takeda. JLM-K has received funding and a speaking fee from COMPASS Pathways. MU has been a consultant for System Analytic. HRK is a member of the Dicerna scientific advisory board and a member of the American Society of Clinical Psychopharmacology Alcohol Clinical Trials Initiative, which during the past 3 years was supported by Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Dicerna, Ethypharm, Indivior, Lundbeck, Mitsubishi, and Otsuka. HRK and JG are named as inventors on Patent Cooperative Treaty patent application number 15/878,640, entitled “Genotype-guided dosing of opioid agonists,” filed January 24, 2018. RP and JG are paid for their editorial work on the journal Complex Psychiatry. OAA is a consultant to HealthLytix. All other authors report no biomedical financial interests or potential conflicts of interest. Funding Information: This work was supported by the National Institute of Mental Health/ U.S. Army Medical Research and Development Command (Grant No. R01MH106595 [to CMN, IL, MBS, KJRe, and KCK], National Institutes of Health (Grant No. 5U01MH109539 to the Psychiatric Genomics Consortium), and Brain & Behavior Research Foundation (Young Investigator Grant [to KWC]). Genotyping of samples was provided in part through the Stanley Center for Psychiatric Genetics at the Broad Institute supported by Cohen Veterans Bioscience. Statistical analyses were carried out on the LISA/Genetic Cluster Computer (https://userinfo.surfsara.nl/systems/lisa) hosted by SURFsara. This research has been conducted using the UK Biobank resource (Application No. 41209). This work would have not been possible without the financial support provided by Cohen Veterans Bioscience, the Stanley Center for Psychiatric Genetics at the Broad Institute, and One Mind. This material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting true views of the U.S. Department of the Army or the Department of Defense. We thank the investigators who comprise the PGC-PTSD working group and especially the more than 206,000 research participants worldwide who shared their life experiences and biological samples with PGC-PTSD investigators. We thank Mark Zervas for his critical input. Full acknowledgments are in Supplement 1. MBS has in the past 3 years received consulting income from Actelion, Acadia Pharmaceuticals, Aptinyx, Bionomics, BioXcel Therapeutics, Clexio, EmpowerPharm, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals, and Roche/Genentech and has stock options in Oxeia Biopharmaceuticals and Epivario. In the past 3 years, NPD has held a part-time paid position at Cohen Veterans Bioscience, has been a consultant for Sunovion Pharmaceuticals, and is on the scientific advisory board for Sentio Solutions for unrelated work. In the past 3 years, KJRe has been a consultant for Datastat, Inc. RallyPoint Networks, Inc. Sage Pharmaceuticals, and Takeda. JLM-K has received funding and a speaking fee from COMPASS Pathways. MU has been a consultant for System Analytic. HRK is a member of the Dicerna scientific advisory board and a member of the American Society of Clinical Psychopharmacology Alcohol Clinical Trials Initiative, which during the past 3 years was supported by Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Dicerna, Ethypharm, Indivior, Lundbeck, Mitsubishi, and Otsuka. HRK and JG are named as inventors on Patent Cooperative Treaty patent application number 15/878,640, entitled ?Genotype-guided dosing of opioid agonists,? filed January 24, 2018. RP and JG are paid for their editorial work on the journal Complex Psychiatry. OAA is a consultant to HealthLytix. All other authors report no biomedical financial interests or potential conflicts of interest. Publisher Copyright: © 2021 Society of Biological Psychiatry Background: Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs). Methods: A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms. Results: GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program. Conclusions: Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods. publishersversion published

Alcohol use disorder (AUD) is common and associated with increased morbidity. The degree to which AUD currently factors into workplace absenteeism needs further characterization in the US.To examine the association between AUD and workplace absenteeism in a nationally representative sample.This cross-sectional study used data from a nationally representative sample of noninstitutionalized US residents from the 2015-2019 National Survey on Drug Use and Health to examine the association of AUD with workplace absenteeism. Eligible respondents were aged 18 years and older who reported full-time employment. Data were analyzed from March to September 2021.Primary outcomes were markers of workplace absenteeism as defined by the number of days missed from work because of illness or injury and days skipped from work in the last 30 days. Descriptive statistics, prevalence ratios, and logistic regression analyses were performed to assess the association between AUD and absenteeism.A total of 110 701 adults aged 18 years and older reported current full-time employment (58 948 [53.2%] men, 51 753 [46.8%] women; 12 776 [11.5%] Black, 18 096 [16.3%] Hispanic, and 69 506 [62.8%] White respondents). Weighted prevalence of AUD in this sample of working adults was 9.3% (95% CI, 9.0%-9.5%); 6.2% (95% CI, 6.0%-6.4%) of respondents met criteria for mild AUD, 1.9% (95% CI, 1.7%-2.0%) for moderate AUD, and 1.2% (95% CI, 1.1%-1.3%) for severe AUD. Mean days missed from work annually increased in a stepwise fashion with increasing AUD severity (no AUD, 13.0 days; 95% CI, 12.7-13.2 days; mild AUD, 17.7 days; 95% CI, 16.4-19.1 days; moderate AUD, 23.6 days; 95% CI, 21.5-25.7 days; severe AUD, 32.3 days; 95% CI, 27.5-37.0 days). People with AUD represented 9.3% of the full-time workforce and contributed to 14.1% of total reported workplace absences.In this cross-sectional study, AUD was disproportionately associated with an increased prevalence of workplace absenteeism, with individuals with AUD contributing over 232 million missed workdays annually. These results provide economic incentive for increased investment in AUD prevention and treatment, both for employers and policy makers.

Despite major advancements in genomic medicine, research to optimize the design and communication of genetically informed interventions in behavioral health has lagged. The goal of this study was to engage potential end users in participatory codesign of a personalized genetically informed risk tool to intervene on high-risk health behaviors. We used structured interviews to examine end-user attitudes and interest in personalized genetics, qualitative interviews to guide iterative design of a genetically informed tool, and questionnaires to assess acceptability and potential utility of the tool. Participants expressed strong demand for using personal genetics to inform smoking and alcohol-related disease risk and guide treatment (78%–95% agreed). Via iterative design feedback, we cocreated a genetically informed risk profile featuring (i) explanation of genetic and phenotypic markers used to construct a risk algorithm, (ii) personalized risks and benefits of healthy behavior change, and (iii) recommended actions with referral to freely available resources. Participants demonstrated sufficient understanding and cited motivating behavior change as the most useful purpose of the tool. In three phases, we confirmed strong desire for personalized genetics on high-risk health behaviors; codesigned a genetically informed profile with potential end users; and found high acceptability, comprehensibility, and perceived usefulness of the profile. As scientific discovery of genomic medicine advances in behavioral health, we must develop the tools to communicate these discoveries to consumers who stand to benefit. The potential of genomic medicine to engage populations and personalize behavioral health treatment depends, in part, on preparatory studies to design for the future implementation of genetically informed interventions.

Where sufficiently large genome-wide association study (GWAS) samples are not currently available or feasible, methods that leverage increasing knowledge of the biological function of variants may illuminate discoveries without increasing sample size. We comprehensively evaluated 18 functional weighting methods for identifying novel associations. We assessed the performance of these methods using published results from multiple GWAS waves across each of five complex traits. Although no method achieved both high sensitivity and positive predictive value (PPV) for any trait, a subset of methods utilizing pleiotropy and expression quantitative trait loci nominated variants with high PPV (>75%) for multiple traits. Application of functionally weighting methods to enhance GWAS power for locus discovery is unlikely to circumvent the need for larger sample sizes in truly underpowered GWAS, but these results suggest that applying functional weighting to GWAS can accurately nominate additional novel loci from available samples for follow-up studies.

BackgroundThe utility of electronic cigarettes (‘e-cigarettes’) as a smoking cessation adjunct remains unclear. Similarly, it is unclear if formal tobacco treatment (pharmacotherapy and/or behavioural support) augments smoking cessation in individuals who use both cigarettes and e-cigarettes.MethodsWe performed a longitudinal cohort study of adult outpatients evaluated in our tertiary care medical centre (6/2018–6/2020). E-cigarette use, smoking status and formal tobacco treatment (deterrent pharmacotherapy and/or behavioural support) were assessed in 6-month blocks (eg, cohort 1 (C1)=6/2018–12/2018, C2=1/2019–6/2019 and so on) using our electronic health record. We assessed the relationship between e-cigarette use (either with or without formal tobacco treatment) and point prevalence of smoking cessation at 6 and 12 months.Results111 823 unique patients were included in the study. The prevalence of dual use of cigarettes and e-cigarettes increased significantly over the study period (C1=0.8%; C2=1.1%; C3=1.8%; C4=2.3%; pInterpretationThese results suggest that dual users of cigarettes and e-cigarettes benefit from formal tobacco treatment. Clinicians should consider offering formal tobacco treatment to such patients, though future trials are needed.

Genome-wide association studies (GWAS) have made impactful discoveries for complex diseases, often by amassing very large sample sizes. Yet, GWAS of many diseases remain underpowered, especially for non-European ancestries. One cost-effective approach to increase sample size is to combine existing cohorts, which may have limited sample size or be case-only, with public controls, but this approach is limited by the need for a large overlap in variants across genotyping arrays and the scarcity of non-European controls. We developed and validated a protocol, Genotyping Array-WGS Merge (GAWMerge), for combining genotypes from arrays and whole-genome sequencing, ensuring complete variant overlap, and allowing for diverse samples like Trans-Omics for Precision Medicine to be used. Our protocol involves phasing, imputation, and filtering. We illustrated its ability to control technology driven artifacts and type-I error, as well as recover known disease-associated signals across technologies, independent datasets, and ancestries in smoking-related cohorts. GAWMerge enables genetic studies to leverage existing cohorts to validly increase sample size and enhance discovery for understudied traits and ancestries.

Genome-wide association studies (GWAS) have made impactful discoveries for complex diseases, often by amassing very large sample sizes. Yet, GWAS of many diseases remain underpowered, especially for non-European ancestries. One cost-effective approach to increase sample size is to combine existing case-only cohorts with public controls, but this approach is limited by the need for a large overlap in variants across genotyping arrays and the scarcity of non-European controls. We developed and validated a protocol, Genotyping Array-WGS Merge (GAWMerge), for combining genotypes from arrays and whole genome sequencing, ensuring complete variant overlap, and allowing for diverse samples like Trans-Omics for Precision Medicine to be used. Our protocol involves phasing, imputation, and filtering. We illustrated its ability to control type I error and recover known disease-associated signals across technologies, independent datasets, and ancestries in smoking-related cohorts. GAWMerge enables genetic studies to leverage existing cohorts to validly increase sample size and enhance discovery.