Application of polygenic scores to a deeply phenotyped sample enriched for substance use disorders reveals extensive pleiotropy with psychiatric and somatic traits.

Co-occurring psychiatric, medical, and substance use disorders (SUDs) are common, but the complex pathways leading to such comorbidities are poorly understood. A greater understanding of genetic influences on this phenomenon could inform precision medicine efforts. We used the Yale-Penn dataset, a cross-sectional sample enriched for individuals with SUDs, to examine pleiotropic effects of genetic liability for psychiatric and somatic traits. Participants completed an in-depth interview that provides information on demographics, environment, medical illnesses, and psychiatric and SUDs. Polygenic scores (PGS) for psychiatric disorders and somatic traits were calculated in European-ancestry (EUR; n = 5691) participants and, when discovery datasets were available, for African-ancestry (AFR; n = 4918) participants. Phenome-wide association studies (PheWAS) were then conducted. In AFR participants, the only PGS with significant associations was bipolar disorder (BD), all of which were with substance use phenotypes. In EUR participants, PGS for major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), schizophrenia (SCZ), body mass index (BMI), coronary artery disease (CAD), and type 2 diabetes (T2D) all showed significant associations, the majority of which were with phenotypes in the substance use categories. For instance, PGSMDD was associated with over 200 phenotypes, 15 of which were depression-related (e.g., depression criterion count), 55 of which were other psychiatric phenotypes, and 126 of which were substance use phenotypes; and PGSBMI was associated with 138 phenotypes, 105 of which were substance related. Genetic liability for psychiatric and somatic traits is associated with numerous phenotypes across multiple categories, indicative of the broad genetic liability of these traits.

Identifying neurofunctional domains across substance use disorders.

Background: Substance use disorders (SUDs) are heterogeneous across multiple functional domains. Various frameworks posit that domains (e.g., executive function) contribute to the persistence of SUDs; however, the domains identified in different studies vary.Objectives: We used factor analysis to identify the underlying latent domains present in a large sample (N = 5,244, 55.8% male) with a variety of SUDs to yield findings more generalizable than studies with a narrower focus.Method: Participants (1,384 controls and 3,860 participants with one or more SUDs including alcohol, cocaine, cannabis, and/or opioid use disorders) completed the Semi-Structured Assessment for Drug Dependence and Alcoholism, the NEO Personality Inventory, and the Wisconsin Card Sorting Test. Exploratory factor analysis (EFA) and fit indices (root mean-squared error of approximation (RMSEA), Comparative Fit Index (CFI), and Tucker-Lewis Index (TLI)) were used to examine different latent variable models. A multiple indicators, multiple causes (MIMIC) approach-tested associations of the latent variables with sociodemographics, substance use, and a history of abuse/neglect.Results: A six-factor model (predominant alcohol, predominant cocaine, predominant opioid, externalizing, personality, and executive function) provided the best fit [RMSEA = 0.063 (90% CI 0.060, 0.066), CFI = 0.98, TLI = 0.96]. All factors were moderately correlated (coefficient = 0.25-0.55, p < .05) with the exception of executive function. MIMIC analysis revealed different patterns of associations (all p < .0001) with sociodemographics, substance use, and a history of abuse/neglect among the factors.Conclusions: The domains identified, particularly executive function, were parallel to those observed previously. These factors underscore the heterogeneous nature of SUDs and may be useful in developing more targeted clinical interventions.

Gene × environment effects and mediation involving adverse childhood events, mood and anxiety disorders, and substance dependence.

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. 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). Using latent variables for each phenotype, we modelled direct and indirect associations of ACEs with substance dependence, mediated by mood/anxiety disorders (the forward or 'self-medication' model) and of ACEs with mood/anxiety disorders, mediated by substance dependence (the reverse or 'substance-induced' model). In a subsample, we tested polygenic scores for the substance dependence and mood/anxiety disorder factors as moderators in the mediation models. Although there were significant indirect paths in both directions, mediation by mood/anxiety disorders (the forward model) was greater than that by substance dependence (the reverse model). Greater genetic risk for substance use disorders was associated with a weaker direct association between ACEs and substance dependence in both ancestry groups (reflecting gene × environment interactions) and a weaker indirect association in European-ancestry individuals (reflecting moderated mediation). We found greater evidence that substance dependence reflects self-medication of mood/anxiety disorders than that mood/anxiety disorders are substance induced. Among individuals at higher genetic risk for substance dependence, ACEs were less associated with that outcome. Following exposure to ACEs, multiple pathways appear to underlie the associations between mood/anxiety disorders and substance dependence. Specification of these pathways could inform individually targeted prevention and treatment approaches.

Combining Transdiagnostic and Disorder-Level GWAS Enhances Precision of Psychiatric Genetic Risk Profiles in a Multi-Ancestry Sample.

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

Candidate Genes from an FDA-Approved Algorithm Fail to Predict Opioid Use Disorder Risk in Over 450,000 Veterans.

Importance: Recently, the Food and Drug Administration gave pre-marketing approval to algorithm based on its purported ability to identify genetic risk for opioid use disorder. However, the clinical utility of the candidate genes comprising the algorithm has not been independently demonstrated. Objective: To assess the utility of 15 variants in candidate genes from an algorithm intended to predict opioid use disorder risk. Design: This case-control study examined the association of 15 candidate genetic variants with risk of opioid use disorder using available electronic health record data from December 20, 1992 to September 30, 2022. Setting: Electronic health record data, including pharmacy records, from Million Veteran Program participants across the United States. Participants: Participants were opioid-exposed individuals enrolled in the Million Veteran Program (n = 452,664). Opioid use disorder cases were identified using International Classification of Disease diagnostic codes, and controls were individuals with no opioid use disorder diagnosis. Exposures: Number of risk alleles present across 15 candidate genetic variants. Main Outcome and Measures: Predictive performance of 15 genetic variants for opioid use disorder risk assessed via logistic regression and machine learning models. Results: Opioid exposed individuals (n=33,669 cases) were on average 61.15 (SD = 13.37) years old, 90.46% male, and had varied genetic similarity to global reference panels. Collectively, the 15 candidate genetic variants accounted for 0.4% of variation in opioid use disorder risk. The accuracy of the ensemble machine learning model using the 15 genes as predictors was 52.8% (95% CI = 52.1 - 53.6%) in an independent testing sample. Conclusions and Relevance: Candidate genes that comprise the approved algorithm do not meet reasonable standards of efficacy in predicting opioid use disorder risk. Given the algorithm's limited predictive accuracy, its use in clinical care would lead to high rates of false positive and negative findings. More clinically useful models are needed to identify individuals at risk of developing opioid use disorder.

A multi-ancestry genetic study of pain intensity in 598,339 veterans.

Chronic pain is a common problem, with more than one-fifth of adult Americans reporting pain daily or on most days. It adversely affects the quality of life and imposes substantial personal and economic costs. Efforts to treat chronic pain using opioids had a central role in precipitating the opioid crisis. Despite an estimated heritability of 25-50%, the genetic architecture of chronic pain is not well-characterized, in part because studies have largely been limited to samples of European ancestry. To help address this knowledge gap, we conducted a cross-ancestry meta-analysis of pain intensity in 598,339 participants in the Million Veteran Program, which identified 126 independent genetic loci, 69 of which are new. Pain intensity was genetically correlated with other pain phenotypes, level of substance use and substance use disorders, other psychiatric traits, education level and cognitive traits. Integration of the genome-wide association studies findings with functional genomics data shows enrichment for putatively causal genes (n = 142) and proteins (n = 14) expressed in brain tissues, specifically in GABAergic neurons. Drug repurposing analysis identified anticonvulsants, ß-blockers and calcium-channel blockers, among other drug groups, as having potential analgesic effects. Our results provide insights into key molecular contributors to the experience of pain and highlight attractive drug targets.

Application of polygenic scores to a deeply phenotyped sample enriched for substance use disorders reveals extensive pleiotropy with psychiatric and medical traits.

Co-occurring psychiatric, medical, and substance use disorders (SUDs) are common, but the complex pathways leading to such comorbidities are poorly understood. A greater understanding of genetic influences on this phenomenon could inform precision medicine efforts. We used the Yale-Penn dataset, a cross-sectional sample enriched for individuals with SUDs, to examine pleiotropic effects of genetic liability for psychiatric and medical traits. Participants completed an in-depth interview that provides information on demographics, environment, medical illnesses, and psychiatric and SUDs. Polygenic scores (PGS) for psychiatric disorders and medical traits were calculated in European-ancestry (EUR; n=5,691) participants and, when discovery datasets were available, for African-ancestry (AFR; n=4,918) participants. Phenome-wide association studies (PheWAS) were then conducted. In AFR participants, the only PGS with significant associations was bipolar disorder (BD), all of which were with substance use phenotypes. In EUR participants, PGS for major depressive disorder (MDD), generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), schizophrenia (SCZ), body mass index (BMI), coronary artery disease (CAD), and type 2 diabetes (T2D) all showed significant associations, the majority of which were with phenotypes in the substance use categories. For instance, PGS MDD was associated with over 200 phenotypes, 15 of which were depression-related (e.g., depression criterion count), 55 of which were other psychiatric phenotypes, and 126 of which were substance use phenotypes; and PGS BMI was associated with 138 phenotypes, 105 of which were substance related. Genetic liability for psychiatric and medical traits is associated with numerous phenotypes across multiple categories, indicative of the broad genetic liability of these traits.

Adverse Childhood Events, Mood and Anxiety Disorders, and Substance Dependence: Gene X Environment Effects and Moderated Mediation.

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.

Medications for treating alcohol use disorder: A narrative review.

Chronic heavy alcohol use impacts all major neurotransmitter systems and is associated with multiple medical, psychiatric, and social problems. Available evidence-based medications to treat alcohol use disorder (AUD) are underutilized in clinical practice. These medications promote abstinence or reduce alcohol consumption, though there are questions regarding their optimal dosage, length of treatment, and utility in combination with one another. Pharmacogenetic approaches, which use a patient's genetic make-up to inform medication selection, have garnered great interest but have yet to yield results robust enough to incorporate them in routine clinical care. This narrative review summarizes the evidence both for medications approved by the Food and Drug Administration (disulfiram, oral naltrexone, acamprosate, and extended-release naltrexone) and those commonly used off-label (e.g., gabapentin, baclofen, and topiramate) for AUD treatment. We discuss these drugs' mechanisms of action, clinical use, pharmacogenetic findings, and treatment recommendations. We conclude that the most consistent evidence supporting the pharmacotherapy of AUD is for the opioid antagonists, naltrexone and nalmefene (which is not approved in the United States), and topiramate. These medications demonstrate consistent small or moderate effects in reducing the frequency of drinking and/or heavy drinking. Lastly, we make suggestions for research needed to refine and expand the current literature on effective pharmacotherapy for AUD.

Racial and Ethnic Bias in the Diagnosis of Alcohol Use Disorder in Veterans.

OBJECTIVE: Studies show that racially and ethnically minoritized veterans have a higher prevalence of alcohol use disorder (AUD) than White veterans. The investigators examined whether the relationship between self-reported race and ethnicity and AUD diagnosis remains after adjusting for alcohol consumption, and if so, whether it varies by self-reported alcohol consumption. METHODS: The sample included 700,012 Black, White, and Hispanic veterans enrolled in the Million Veteran Program. Alcohol consumption was defined as an individual's maximum score on the consumption subscale of the Alcohol Use Disorders Identification Test (AUDIT-C), a screen for unhealthy alcohol use. A diagnosis of AUD, the primary outcome, was defined by the presence of relevant ICD-9 or ICD-10 codes in electronic health records. Logistic regression with interactions was used to assess the association between race and ethnicity and AUD as a function of maximum AUDIT-C score. RESULTS: Black and Hispanic veterans were more likely than White veterans to have an AUD diagnosis despite similar levels of alcohol consumption. The difference was greatest between Black and White men; at all but the lowest and highest levels of alcohol consumption, Black men had 23%-109% greater odds of an AUD diagnosis. The findings were unchanged after adjustment for alcohol consumption, alcohol-related disorders, and other potential confounders. CONCLUSIONS: The large discrepancy in the prevalence of AUD across groups despite a similar distribution of alcohol consumption levels suggests that there is racial and ethnic bias, with Black and Hispanic veterans more likely than White veterans to receive an AUD diagnosis. Efforts are needed to reduce bias in the diagnostic process to address racialized differences in AUD diagnosis.

Does polygenic risk for substance-related traits predict ages of onset and progression of symptoms?

BACKGROUND AND AIMS: Genetic risk can influence disease progression. We measured the impact of genetic risk for substance use disorders (SUDs) on substance use onset and progression of symptoms. DESIGN, SETTING, PARTICIPANTS: Using findings from genome-wide association studies (GWASs) of alcohol use disorder (AUD), opioid use disorder (OUD) and smoking trajectory (SMK) as discovery samples, we calculated polygenic risk scores (PRSs) in a deeply phenotyped independent target sample. Participants in the target sample were recruited from 2000 to 2020 from US inpatient or outpatient settings or through advertisements and comprised 5692 European-ancestry individuals (EUR) (56.2% male) and 4918 African-ancestry individuals (AFR) (54.9% male). MEASUREMENTS: This study measured age of first substance use, regular use, reported problems and dependence diagnosis and progression from regular use to onset of problems and dependence for alcohol, opioids and smoking. We examined the contribution of PRS to each milestone and progression measure. FINDINGS: EUR and males reported an earlier onset and shorter progression times than AFR and females, respectively. Among EUR, higher AUD PRS predicted earlier onset and more rapid progression to alcohol-related milestones (P < 0.001). Although the AUD PRS was a stronger moderator of problem onset among females (P = 0.017), it was more predictive of the progression to problems among males (P = 0.005). OUD and SMK PRS in EUR also predicted earlier onset of the respective milestones (P < 0.001). Among AFR, where power is lower due to the smaller discovery sample, AUD PRS predicted age of regular alcohol use (P = 0.039) and dependence (P = 0.001) and progression from regular use to diagnosis (P = 0.045), while SMK PRS predicted earlier age of initiation (P = 0.036). CONCLUSIONS: Genetic risk for SUDs appears to predict substance use milestones and symptom progression among European-ancestry individuals and, to a lesser extent, African-ancestry individuals.

Effects of topiramate therapy on serum bicarbonate concentration in a sample of 10,279 veterans.

BACKGROUND: Topiramate, which is increasingly being used to treat alcohol use disorder (AUD), is commonly associated with reduced serum bicarbonate concentrations. However, estimates of the prevalence and magnitude of this effect are from small samples and do not address whether topiramate's effects on acid-base balance differ in the presence of an AUD or by topiramate dosage. METHODS: Veterans Health Administration electronic health record (EHR) data were used to identify patients with a minimum of 180 days of topiramate prescription for any indication and a propensity score-matched control group. We differentiated patients into two subgroups based on the presence of a diagnosis of AUD in the EHR. Baseline alcohol consumption was determined using Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) scores in the EHR. Analysis also included a three-level measure representing mean daily dosage. The topiramate-associated changes in serum bicarbonate concentration were estimated in difference-in-differences linear regression models. A serum bicarbonate concentration <17 mEq/L was considered to represent possible clinically significant metabolic acidosis. RESULTS: The cohort comprised 4287 topiramate-treated patients and 5992 propensity score-matched controls with a mean follow-up period of 417 days. The mean topiramate-associated reductions in serum bicarbonate concentration were <2 mEq/L in the low (≤88.75), medium (>88.75 and ≤141.70), and high (>141.70) mg/day dosage tertiles, irrespective of AUD history. Concentrations <17 mEq/L occurred in 1.1% of topiramate-treated patients and 0.3% of controls and were not associated with alcohol consumption or an AUD diagnosis. CONCLUSIONS: The excess prevalence of metabolic acidosis associated with topiramate treatment does not differ with dosage, alcohol consumption, or the presence of an AUD. Baseline and periodic serum bicarbonate concentration measurements are recommended during topiramate therapy. Patients prescribed topiramate should be educated about the symptoms of metabolic acidosis and urged to report their occurrence promptly to a healthcare provider.

Phenome-wide Association Analysis of Substance Use Disorders in a Deeply Phenotyped Sample.

BACKGROUND: Substance use disorders (SUDs) are associated with a variety of co-occurring psychiatric disorders and other SUDs, which partly reflects genetic pleiotropy. Polygenic risk scores (PRSs) and phenome-wide association studies are useful in evaluating pleiotropic effects. However, the comparatively low prevalence of SUDs in population samples and the lack of detailed information available in electronic health records limit these data sets' informativeness for such analyses. METHODS: We used the deeply phenotyped Yale-Penn sample (n = 10,610 with genetic data; 46.3% African ancestry, 53.7% European ancestry) to examine pleiotropy for 4 major substance-related traits: alcohol use disorder, opioid use disorder, smoking initiation, and lifetime cannabis use. The sample includes both affected and control subjects interviewed using the Semi-Structured Assessment for Drug Dependence and Alcoholism, a comprehensive psychiatric interview. RESULTS: In African ancestry individuals, PRS for alcohol use disorder, and in European individuals, PRS for alcohol use disorder, opioid use disorder, and smoking initiation were associated with their respective primary DSM diagnoses. These PRSs were also associated with additional phenotypes involving the same substance. Phenome-wide association study analyses of PRS in European individuals identified associations across multiple phenotypic domains, including phenotypes not commonly assessed in phenome-wide association study analyses, such as family environment and early childhood experiences. CONCLUSIONS: Smaller, deeply phenotyped samples can complement large biobank genetic studies with limited phenotyping by providing greater phenotypic granularity. These efforts allow associations to be identified between specific features of disorders and genetic liability for SUDs, which help to inform our understanding of the pleiotropic pathways underlying them.

Cross-ancestry meta-analysis of opioid use disorder uncovers novel loci with predominant effects in brain regions associated with addiction.

Despite an estimated heritability of ~50%, genome-wide association studies of opioid use disorder (OUD) have revealed few genome-wide significant loci. We conducted a cross-ancestry meta-analysis of OUD in the Million Veteran Program (N = 425,944). In addition to known exonic variants in OPRM1 and FURIN, we identified intronic variants in RABEPK, FBXW4, NCAM1 and KCNN1. A meta-analysis including other datasets identified a locus in TSNARE1. In total, we identified 14 loci for OUD, 12 of which are novel. Significant genetic correlations were identified for 127 traits, including psychiatric disorders and other substance use-related traits. The only significantly enriched cell-type group was CNS, with gene expression enrichment in brain regions previously associated with substance use disorders. These findings increase our understanding of the biological basis of OUD and provide further evidence that it is a brain disease, which may help to reduce stigma and inform efforts to address the opioid epidemic.

Treating Alcohol Use Disorder With Hallucinogens-Renewed Interest After a 50-Year Hiatus.

This Viewpoint discusses the use of hallucinogens to treat alcohol use disorder.

Association of topiramate prescribed for any indication with reduced alcohol consumption in electronic health record data.

BACKGROUND AND AIMS: Topiramate is a medication that is widely prescribed to treat a variety of conditions, including alcohol use disorder (AUD). We used electronic health record (EHR) data to measure topiramate's effects on drinking in individuals differentiated by a history of AUD. DESIGN: Parallel-groups comparison of patients prescribed topiramate and a propensity score-matched comparison group. SETTING: A large US integrated health-care system. PARTICIPANTS: Patients with Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) scores prior to and after a minimum of 180 days of topiramate prescription for any indication and a propensity score-matched group. The sample included 5918 patients with an electronic health record diagnosis of alcohol use disorder at any time (AUD-hx-pos) (1738 topiramate-exposed and 4180 controls) and 23 614 patients with no EHR diagnosis of AUD (AUD-hx-neg) (6324 topiramate-exposed and 17 290 controls). MEASUREMENTS: Regression analyses compared difference-in-difference (DiD) estimates, separately by AUD history. DiD estimates represent exposure-group (i.e. topiramate versus control) differences on the pre-post difference in AUDIT-C score. Effects of baseline AUDIT-C score and daily topiramate dosage were also tested. FINDINGS: AUD-hx-neg patients who received topiramate had a greater reduction in AUDIT-C score (-0.11) than matched controls (-0.04). This yielded a DiD score of -0.07 [95% confidence interval (CI) = -0.11,-0.03; P = 0.002], with the greatest effect among AUD-hx-neg patients with a baseline AUDIT-C score of 4+ (DiD = -0.35, 95% CI = -0.49, -0.21; P < 0.0001) and those prescribed > 150 mg/day of the medication (DiD = -0.15, 95%CI = -0.23, -0.07; P < 0.001). DISCUSSION: Among individuals with no history of alcohol use disorder, topiramate appears to be associated with reduced drinking. This small effect is most evident among patients with higher baseline drinking levels and at a higher average daily topiramate dosage.

Alcohol withdrawal in past-year drinkers with unhealthy alcohol use: Prevalence, characteristics, and correlates in a national epidemiologic survey.

BACKGROUND: Despite its potential to produce serious adverse outcomes, DSM-5 alcohol withdrawal syndrome (AWS) has not been widely studied in the general population. METHODS: We used cross-sectional data from 36,309 U.S. adults from the 2012-2013 National Epidemiologic Survey on Alcohol and Related Conditions-III to examine the past-year prevalence of AWS and its correlates. We focused on an important clinical population-past-year drinkers with unhealthy alcohol use-i.e., those with a positive score on the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) questionnaire. We also examined the association of AWS with sociodemographic measures, psychiatric disorders, alcohol-related measures, and healthcare utilization. RESULTS: Approximately one-third (n = 12,634) of respondents reported unhealthy alcohol use (AUDIT-C+). Of these, 14.3% met criteria for a DSM-5 AWS diagnosis. The mean (SE) number of withdrawal symptoms among individuals with AWS was 2.83 (1.88), with the most common being nausea/vomiting and insomnia (19.8% and 11.6%, respectively). Among AUDIT-C+ respondents, the odds of AWS were significantly higher among males (adjusted odds ratio [aOR] = 1.17 [95% CI, 1.02-1.33]), unmarried participants (aOR = 1.55 [95% CI, 1.25-1.92]), and those at the lowest (vs. highest) income levels (aOR = 1.62 [95% CI, 1.37-1.92]). Among AUDIT-C+ respondents, AWS was also associated with psychiatric disorders (with aORs that ranged from 2.08 [95% CI, 1.79-2.41]) for major depressive disorder to 3.14 (95% CI, 1.79-2.41) for borderline personality disorder. AUDIT-C+ respondents with AWS also had higher odds of past-year alcohol use disorder (aOR = 11.2 [95% CI, 9.66-13.07]), other alcohol-related features (e.g., binge drinking), and healthcare utilization. CONCLUSIONS: Among individuals with unhealthy alcohol use, AWS is prevalent, highly comorbid, and disabling. Given the risk of AWS among unhealthy drinkers, a comparatively large segment of the general population, clinicians should seek to identify individuals with AWS and intervene with them to prevent serious adverse outcomes.

Genetic liability for substance use associated with medical comorbidities in electronic health records of African- and European-ancestry individuals.

Polygenic risk scores (PRS) represent an individual's summed genetic risk for a trait and can serve as biomarkers for disease. Less is known about the utility of PRS as a means to quantify genetic risk for substance use disorders (SUDs) than for many other traits. Nonetheless, the growth of large, electronic health record-based biobanks makes it possible to evaluate the association of SUD PRS with other traits. We calculated PRS for smoking initiation, alcohol use disorder (AUD), and opioid use disorder (OUD) using summary statistics from the Million Veteran Program sample. We then tested the association of each PRS with its primary phenotype in the Penn Medicine BioBank (PMBB) using all available genotyped participants of African or European ancestry (AFR and EUR, respectively) (N = 18,612). Finally, we conducted phenome-wide association analyses (PheWAS) separately by ancestry and sex to test for associations across disease categories. Tobacco use disorder was the most common SUD in the PMBB, followed by AUD and OUD, consistent with the population prevalence of these disorders. All PRS were associated with their primary phenotype in both ancestry groups. PheWAS results yielded cross-trait associations across multiple domains, including psychiatric disorders and medical conditions. SUD PRS were associated with their primary phenotypes; however, they are not yet predictive enough to be useful diagnostically. The cross-trait associations of the SUD PRS are indicative of a broader genetic liability. Future work should extend findings to additional population groups and for other substances of abuse.

Analysis of genetic and clinical factors associated with buprenorphine response.

BACKGROUND: Buprenorphine, approved for treating opioid use disorder (OUD), is not equally efficacious for all patients. Candidate gene studies have shown limited success in identifying genetic moderators of buprenorphine treatment response. METHODS: We studied 1616 European-ancestry individuals enrolled in the Million Veteran Program, of whom 1609 had an ICD-9/10 code consistent with OUD, a 180-day buprenorphine treatment exposure, and genome-wide genotype data. We conducted a genome-wide association study (GWAS) of buprenorphine treatment response [defined as having no opioid-positive urine drug screens (UDS) following the first prescription]. We also examined correlates of buprenorphine treatment response in multivariable analyses. RESULTS: Although no variants reached genome-wide significance, 6 loci were nominally significant (p < 1 × 10-5), four of which were located near previously characterized genes: rs756770 (ADAMTSL2), rs11782370 (SLC25A37), rs7205113 (CRISPLD2), and rs13169373 (LINC01947). A higher maximum daily buprenorphine dosage (aOR = 0.98; 95 %CI: 0.97, 0.995), greater number of UDS (aOR = 0.97; 95 %CI: 0.96, 0.99), and history of hepatitis C (HCV) infection (aOR = 0.71; 95 %CI: 0.57, 0.88) were associated with a reduced odds of buprenorphine response. Older age (aOR: 1.01; 95 %CI: 1.000, 1.02) was associated with increased odds of buprenorphine response. CONCLUSIONS: This study had limited statistical power to detect genetic variants associated with a complex human phenotype like buprenorphine treatment response. Meta-analysis of multiple data sets is needed to ensure adequate statistical power for a GWAS of buprenorphine treatment response. The most robust phenotypic predictor of buprenorphine treatment response was intravenous drug use, a proxy for which was HCV infection.