Publications by authors named "Sarah M Hartz"

47 Publications

An examination between treatment type and treatment retention in persons with opioid and co-occurring alcohol use disorders.

Drug Alcohol Depend 2021 09 25;226:108886. Epub 2021 Jun 25.

Departments of Family and Community Medicine and Health and Outcomes Research, St. Louis University, 1008 South Spring, SLUCare Academic Pavilion, 3rd Floor, St. Louis, MO, 63110, United States.

Background And Aims: Persons with opioid use disorder (OUD) and co-occurring alcohol use disorder (AUD) are understudied. We identified whether co-occurring AUD was associated with OUD treatment type, compared associations between treatment type and six-month treatment retention and determined whether co-occurring AUD moderated these relationships.

Methods: We used an observational cohort study design to analyze insurance claims data from 2011 to 2016 from persons aged 12-64 with an opioid abuse or opioid dependence diagnosis and OUD treatment claim. Our unit of analysis was the treatment episode; we used logistic regression for analyses.

Results: Of 211,047 treatment episodes analyzed, 14 % had co-occurring alcohol abuse or dependence diagnoses. Among persons with opioid dependence, persons with co-occurring alcohol dependence were 25 % less likely to receive medication treatment relative to those without AUD. Further, alcohol dependence was associated with decreased likelihood of treatment with buprenorphine (AOR 0.47, 95 % CI 0.44-0.49) or methadone (AOR 0.31, 95 % CI 0.28-0.35) and increased likelihood of treatment with extended-release (AOR 1.36, 95 % CI 1.21-1.54) or oral (AOR 1.73, 95 % CI 1.57-1.90) naltrexone relative to psychosocial treatment. Buprenorphine and methadone were associated with highest retention prevalence regardless of OUD or AUD severity. Co-occurring alcohol abuse or dependence did not meaningfully change retention prevalence associated with buprenorphine or methadone. Co-occurring AUD was not associated with improved retention among persons receiving either formulation of naltrexone.

Conclusions: Buprenorphine and methadone are associated with relatively high likelihood of treatment retention among persons opioid and alcohol dependence, but are disproportionately under-prescribed.
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http://dx.doi.org/10.1016/j.drugalcdep.2021.108886DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8370094PMC
September 2021

A cascade of care for alcohol use disorder: Using 2015-2019 National Survey on Drug Use and Health data to identify gaps in past 12-month care.

Alcohol Clin Exp Res 2021 06 16;45(6):1276-1286. Epub 2021 May 16.

Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.

Background: Although effective treatments exist, alcohol use disorder (AUD) is undertreated. We used a cascade of care framework to understand gaps in care for persons with AUD.

Methods: Using 2015-2019 National Survey on Drug Use and Health data, we evaluated the following steps in the cascade of care: (1) adult prevalence of AUD; (2) proportion of adults with AUD who utilized health care in the past 12 months; (3) proportion with AUD screened about their alcohol use; (4) proportion with AUD who received a brief intervention about their alcohol misuse; (5) proportion with AUD who received information about treatment for alcohol misuse; and (6) proportion with AUD who received treatment. Analyses were stratified by AUD severity.

Results: Of the 214,505 persons included in the sample, the weighted prevalence of AUD was 7.8% (95% CI 7.6-8.0%). Cascades of care showed the majority of individuals with AUD utilized health care in the past 12 months [81.4% (95% CI 80.7-82.1%)] and were screened about alcohol use [69.9% (95% CI 68.9-70.8%)]. However, only a minority of individuals received subsequent steps of care, including 11.6% (95% CI 11.0-12.2%) who reported receiving a brief intervention, 5.1% (95% CI 4.6-5.6%) who were referred to treatment, and 5.8% (95% CI 5.4-6.3%) who received treatment. Similar patterns were observed when cascades of care were stratified by AUD severity.

Conclusions: Persons with AUD commonly utilize health care and are often screened about alcohol use, but few receive treatment. Healthcare settings-particularly primary care settings-represent a prime opportunity to implement AUD treatment to improve outcomes in this high-risk population.
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http://dx.doi.org/10.1111/acer.14609DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8254783PMC
June 2021

Studying the utility of using genetics to predict smoking-related outcomes in a population-based study and a selected cohort.

Nicotine Tob Res 2021 May 15. Epub 2021 May 15.

Department of Psychiatry, Washington University School of Medicine, St. Louis, MO.

Objective: The purpose of this study is to examine the predictive utility of polygenic risk scores (PRSs) for smoking behaviors.

Methods: Using summary statistics from the GWAS and Sequencing Consortium of Alcohol and Nicotine use consortium, we generated PRSs of ever smoking, age of smoking initiation, cigarettes smoked per day, and smoking cessation for participants in the population-based Atherosclerosis Risk in Communities (ARIC) study (N=8,638), and the Collaborative Genetic Study of Nicotine Dependence (COGEND) (N=1,935). The outcomes were ever smoking, age of smoking initiation, heaviness of smoking, and smoking cessation.

Results: In the European ancestry cohorts, each PRS was significantly associated with the corresponding smoking behavior outcome. In the ARIC cohort, the z-score ever smoking PRS predicted ever smoking (odds ratio [OR]: 1.37; 95% confidence interval [CI]: 1.31, 1.43); the z-score age of smoking initiation PRS was associated with earlier age of smoking initiation (OR:0.87: 95% CI: 0.82, 0.92); the z-score cigarettes per day PRS was associated with heavier smoking (OR:1.17: 95% CI: 1.11, 1.25); and the z-score smoking cessation PRS predicted with successful cessation (OR: 1.24: 95% CI: 1.17, 1.32). In the African ancestry cohort, the PRSs did not predict smoking behaviors.

Conclusion: Smoking-related PRSs were associated with smoking-related behaviors in European ancestry populations. This improvement in prediction is greatest in the lowest and highest genetic risk categories. The lack of prediction in African ancestry populations highlights the urgent need to increase diversity in research so that scientific advances can be applied to populations other than those of European ancestry.

Implications: This study shows that including both genetic ancestry and polygenic risk scores in a single model increases the ability to predict smoking behaviors compared to the model including only demographic characteristics. This finding is observed for every smoking-related outcome. Even though adding genetics is more predictive, the demographics alone confer substantial and meaningful predictive power. However, with increasing work in polygenic risk scores, the predictive ability will continue to improve.
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http://dx.doi.org/10.1093/ntr/ntab100DOI Listing
May 2021

Association Between Benzodiazepine or Z-Drug Prescriptions and Drug-Related Poisonings Among Patients Receiving Buprenorphine Maintenance: A Case-Crossover Analysis.

Am J Psychiatry 2021 07 3;178(7):651-659. Epub 2021 Mar 3.

Department of Psychiatry, Health and Behavior Research Center, Washington University School of Medicine, St. Louis (Xu, Presnall, Mintz, Hartz, Bierut, Grucza); Department of Biomedical Data Science, Center for Technology and Behavioral Health, Geisel School of Medicine, Dartmouth, Hanover, N.H. (Borodovsky); Alvin J. Siteman Cancer Center, Barnes Jewish Hospital and Washington University School of Medicine, St. Louis (Bierut); and Departments of Family and Community Medicine and Health and Outcomes Research, St. Louis University, St. Louis (Grucza).

Objective: Persons with opioid use disorder who take benzodiazepines are at high risk for overdose. The objective of this study was to evaluate the association of benzodiazepine and Z-drug use with drug-related poisonings among patients receiving buprenorphine maintenance treatment.

Methods: A case-crossover study design was used to analyze prescription claims among persons ages 12-64 with opioid use disorder who had buprenorphine prescriptions and had claims data in the IBM MarketScan databases (2006-2016), encompassing 14,213,075 person-days of observation time for 23,036 individuals who experienced drug-related poisoning. The exposures were buprenorphine prescriptions and benzodiazepine or Z-drug prescriptions, standardized as daily diazepam-equivalent milligram doses and separated by pharmacologic properties (short-acting or long-acting benzodiazepines, Z-drugs). The outcome of interest was nonfatal drug-related poisoning. Conditional logistic regression was used to evaluate variation in benzodiazepine or Z-drug and buprenorphine use between poisoning and nonpoisoning days.

Results: Buprenorphine treatment days were associated with a nearly 40% reduction in the risk of poisoning events (odds ratio=0.63, 95% CI=0.60, 0.66) compared with nontreatment days, whereas benzodiazepine or Z-drug treatment days were associated with an 88% increase in the risk of such events (95% CI=1.78, 1.98). In stratified analyses by dose, we observed a 78% (95% CI=1.67, 1.88) and 122% (95% CI=2.03, 2.43) increase in poisonings associated with low-dose and high-dose benzodiazepine or Z-drug treatment days, respectively. High-dose, but not low-dose, benzodiazepine or Z-drug treatment was associated with increased poisonings in combination with buprenorphine cotreatment (odds ratio=1.64, 95% CI=1.39, 1.93), but this was lower than the odds risk associated with benzodiazepine or Z-drug treatment in the absence of buprenorphine (low-dose: odds ratio=1.69, 95% CI=1.60, 1.79; high-dose: odds ratio=2.23, 95% CI=2.04, 2.45).

Conclusions: Increased risk of nonfatal drug-related poisoning is associated with benzodiazepine or Z-drug treatment in patients with opioid use disorder, but this risk is partially mitigated by buprenorphine treatment. Dose reduction of benzodiazepines or Z-drugs while maintaining buprenorphine treatment may provide the advantage of lowering drug-related poisoning risk.
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http://dx.doi.org/10.1176/appi.ajp.2020.20081174DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8286284PMC
July 2021

Prolactin and Estrogen Levels in Postmenopausal Women Receiving Aripiprazole Augmentation Treatment for Depression.

J Clin Psychopharmacol 2021 Jan/Feb 01;41(1):31-35

From the Department of Psychiatry, Washington University in St. Louis, School of Medicine, St Louis, MO.

Background: Antipsychotic drugs are well established to alter serum prolactin levels, often resulting in adverse effects including amenorrhea, galactorrhea, osteoporosis, and loss of libido. There is growing preclinical evidence that prolactin-elevating drugs can instigate the progression of precancerous lesions to breast cancer and that genes activated by prolactin are associated with the development and proliferation of breast cancer. Current guides advise a cautious approach (weighing risks and benefits) to the administration of prolactin-elevating antipsychotic drugs in women with a previously detected breast cancer. Aripiprazole is known to be a prolactin-sparing antipsychotic; however, data regarding its effects on prolactin and estrogens in postmenopausal women are lacking.

Methods: We examined serum hormone levels in n = 66 women who participated in a randomized, double-blind, placebo-controlled, multicenter trial of aripiprazole (high and low doses) added to an antidepressant in adults older than 60 years. Aripiprazole or placebo tablets were administered for 12 weeks as an augmentation strategy in venlafaxine-treated women. The primary outcomes were the difference in prolactin and estrogen levels.

Results: There was no significant effect of aripiprazole treatment on prolactin or estrogen levels, including in models that divided groups into low and high doses: prolactin (P = 0.075), estrone (P = 0.67), and estradiol (P = 0.96).

Conclusions: Aripiprazole addition to an antidepressant did not alter serum estrogens or prolactin. These findings may be relevant in the treatment of some postmenopausal women with depression.
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http://dx.doi.org/10.1097/JCP.0000000000001335DOI Listing
September 2021

Association Between Benzodiazepine Use With or Without Opioid Use and All-Cause Mortality in the United States, 1999-2015.

JAMA Netw Open 2020 12 1;3(12):e2028557. Epub 2020 Dec 1.

Health and Behavior Research Center, Department of Psychiatry, Washington University School of Medicine in St Louis, St Louis, Missouri.

Importance: Although overall rates of opioid use have been plateauing, coprescriptions of benzodiazepines and opioids have increased greatly in recent years. It is unknown whether this combination is an independent risk factor for all-cause mortality as opposed to being more frequently used by persons with a baseline elevated risk of death.

Objective: To evaluate whether benzodiazepine use, with or without opioid use, is associated with increased all-cause mortality relative to the use of low-risk antidepressants.

Design, Setting, And Participants: This retrospective cohort study used a large, nationally representative US data set (the National Health and Nutrition Examination Surveys [NHANES]) from 1999 to 2015. Eight cycles of NHANES data were used, spanning 37 610 person-years of follow-up time among 5212 individuals. Statistical analysis was performed from August 24, 2019, through May 23, 2020.

Exposures: The primary exposure variable was benzodiazepine and opioid coprescriptions. Individuals taking selective serotonin reuptake inhibitors (SSRIs) served as an active comparator reference group.

Main Outcomes And Measures: All-cause mortality was obtained via linkage of NHANES to the National Death Index. Propensity scores were calculated from covariates associated with sociodemographic factors, comorbidities, and medication use for more than 1000 prescription types. Propensity score-weighted mortality hazards were calculated from Cox proportional hazards regression models.

Results: Of 5212 participants aged 20 years or older (1993 men [38.2%]; mean [SD] age, 54.8 [16.9] years) followed up for a median of 6.7 years (range, 0.2-16.8 years), 101 deaths (33.0 per 1000 person-years) occurred among those receiving cotreatment, 236 deaths (26.5 per 1000 person-years) occurred among those receiving only benzodiazepines, and 227 deaths (20.2 per 1000 person-years) occurred among SSRI recipients taking neither opioids nor benzodiazepines. After propensity score weighting, a significant increase in all-cause mortality was associated with benzodiazepine and opioid cotreatment (hazard ratio, 2.04 [95% CI, 1.65-2.52]) and benzodiazepines without opioids (hazard ratio, 1.60 [95% CI, 1.33-1.92]). Subgroup analyses revealed an increased risk of mortality for individuals receiving cotreatment who were 65 years or younger but not for those older than 65 years; similar findings were observed for those receiving benzodiazepines without opioids.

Conclusions And Relevance: This study found a significant increase in all-cause mortality associated with benzodiazepine use with or without opioid use in comparison with SSRI use. Benzodiazepine and opioid cotreatment, in particular, was associated with a 2-fold increase in all-cause mortality even after taking into account medical comorbidities and polypharmacy burden.
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http://dx.doi.org/10.1001/jamanetworkopen.2020.28557DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7726637PMC
December 2020

A Cascade of Care for Alcohol Use Disorder: Using 2015-2018 National Survey on Drug Use and Health Data to Identify Gaps in Care.

medRxiv 2020 Nov 4. Epub 2020 Nov 4.

Background: Although effective treatments exist, alcohol use disorder (AUD) is undertreated. We used a cascade of care framework to understand gaps in care between diagnosis and treatment for persons with AUD.

Methods: Using 2015-2018 National Survey on Drug Use and Health data, we evaluated the following steps in the cascade of care: 1) prevalence of adults with AUD; 2) proportion of adults who utilized health care in the past 12 months; 3) were screened about alcohol use; 4) received a brief intervention about alcohol misuse; 5) received information about treatment for alcohol misuse; and 6) proportion of persons with AUD who received treatment. Analyses were stratified by AUD severity.

Results: Of the 171,766 persons included in the sample, weighted prevalence of AUD was 7.9% (95% CI 7.7-8.0%). Persons with AUD utilized health care settings at similar rates as those without AUD. Cascades of care showed the majority of individuals with AUD utilized health care and were screened about alcohol use, but the percent who received the subsequent steps of care decreased substantially. For those with severe AUD, 83.5% (CI: 78.3%-88.7%) utilized health care in the past 12 months, 73.5% (CI: 68.1%-78.9%) were screened for alcohol use, 22.7% (CI: 19.4%-26.0%) received a brief intervention, 12.4% (CI: 10%-14.7%) received information about treatment, and 20.5% (CI: 18%-23.1%) were treated for AUD. The greatest decrease in the care continuum occurred from screening to brief intervention and referral to treatment. More persons with severe AUD received treatment than were referred, indicating other pathways to treatment outside of the healthcare system.

Conclusions: Persons with AUD utilize health care at high rates and are frequently screened about alcohol use, but few receive treatment. Health care settings-particularly primary care settings-represent a prime opportunity to implement pharmacologic treatment for AUD to improve outcomes in this high-risk population.
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http://dx.doi.org/10.1101/2020.10.30.20222695DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654865PMC
November 2020

A large-scale genome-wide association study meta-analysis of cannabis use disorder.

Lancet Psychiatry 2020 12 20;7(12):1032-1045. Epub 2020 Oct 20.

Stanford University Graduate School of Education, Stanford University, Stanford, CA, USA.

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) 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). Cannabis use disorder and cannabis use were genetically correlated (r 0·50, p=1·50 × 10), 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.
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http://dx.doi.org/10.1016/S2215-0366(20)30339-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7674631PMC
December 2020

Dissecting the genetic overlap of smoking behaviors, lung cancer, and chronic obstructive pulmonary disease: A focus on nicotinic receptors and nicotine metabolizing enzyme.

Genet Epidemiol 2020 10 16;44(7):748-758. Epub 2020 Aug 16.

Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri.

Smoking is a major contributor to lung cancer and chronic obstructive pulmonary disease (COPD). Two of the strongest genetic associations of smoking-related phenotypes are the chromosomal regions 15q25.1, encompassing the nicotinic acetylcholine receptor subunit genes CHRNA5-CHRNA3-CHRNB4, and 19q13.2, encompassing the nicotine metabolizing gene CYP2A6. In this study, we examined genetic relations between cigarettes smoked per day, smoking cessation, lung cancer, and COPD. Data consisted of genome-wide association study summary results. Genetic correlations were estimated using linkage disequilibrium score regression software. For each pair of outcomes, z-score-z-score (ZZ) plots were generated. Overall, heavier smoking and decreased smoking cessation showed positive genetic associations with increased lung cancer and COPD risk. The chromosomal region 19q13.2, however, showed a different correlational pattern. For example, the effect allele-C of the sentinel SNP (rs56113850) within CYP2A6 was associated with an increased risk of heavier smoking (z-score = 19.2; p = 1.10 × 10 ), lung cancer (z-score = 8.91; p = 5.02 × 10 ), and COPD (z-score = 4.04; p = 5.40 × 10 ). Surprisingly, this allele-C (rs56113850) was associated with increased smoking cessation (z-score = -8.17; p = 2.52 × 10 ). This inverse relationship highlights the need for additional investigation to determine how CYP2A6 variation could increase smoking cessation while also increasing the risk of lung cancer and COPD likely through increased cigarettes smoked per day.
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http://dx.doi.org/10.1002/gepi.22331DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7793026PMC
October 2020

Extreme Overvalued Beliefs.

J Am Acad Psychiatry Law 2020 Sep 14;48(3):319-326. Epub 2020 May 14.

Dr. Rahman and Dr. Hartz are Associate Professors, Washington University in St. Louis, Missouri. Dr. Xiong is Assistant Professor, University of Maryland, College Park, Maryland. Dr. Meloy is Clinical Professor, University of California, San Diego. Dr. Janofsky is Associate Professor, Johns Hopkins Hospital, Baltimore, Maryland. Dr. Harry is Associate Professor, University of Missouri-Columbia. Dr. Resnick is Professor, Case Western Reserve University, Cleveland, Ohio.

An extreme overvalued belief is shared by others in a person's cultural, religious, or subcultural group. The belief is often relished, amplified, and defended by the possessor of the belief and should be differentiated from a delusion or obsession. Over time, the belief grows more dominant, more refined, and more resistant to challenge. The individual has an intense emotional commitment to the belief and may carry out violent behavior in its service. Study participants ( = 109 forensic psychiatrists) were asked to select among three definitions (i.e., obsession, delusion, and extreme overvalued belief) as the motive for the criminal behavior seen in 12 randomized fictional vignettes. Strong interrater agreement (kappa = 0.91 [95% CI 0.83-0.98]) was seen for vignettes representing extreme overvalued belief. Vignettes representing delusion and obsession also had strong reliability (kappa = 0.99 for delusion and 0.98 for obsession). This preliminary report suggests that forensic psychiatrists, given proper definitions, possess a substantial ability to identify delusion, obsession, and extreme overvalued belief. The rich historical foundation of extreme overvalued belief and this small survey study highlight the benefit of inclusion of "extreme overvalued belief" in future glossaries of the Diagnostic and Statistical Manual.
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http://dx.doi.org/10.29158/JAAPL.200001-20DOI Listing
September 2020

Identifying blood pressure loci whose effects are modulated by multiple lifestyle exposures.

Genet Epidemiol 2020 09 29;44(6):629-641. Epub 2020 Mar 29.

Division of Biostatistics, Washington University School of Medicine, St. Louis, Missouri.

Although multiple lifestyle exposures simultaneously impact blood pressure (BP) and cardiovascular health, most analysis so far has considered each single lifestyle exposure (e.g., smoking) at a time. Here, we exploit gene-multiple lifestyle exposure interactions to find novel BP loci. For each of 6,254 Framingham Heart Study participants, we computed lifestyle risk score (LRS) value by aggregating the risk of four lifestyle exposures (smoking, alcohol, education, and physical activity) on BP. Using the LRS, we performed genome-wide gene-environment interaction analysis in systolic and diastolic BP using the joint 2 degree of freedom (DF) and 1 DF interaction tests. We identified one genome-wide significant (p < 5 × 10 ) and 11 suggestive (p < 1 × 10 ) loci. Gene-environment analysis using single lifestyle exposures identified only one of the 12 loci. Nine of the 12 BP loci detected were novel. Loci detected by the LRS were located within or nearby genes with biologically plausible roles in the pathophysiology of hypertension, including KALRN, VIPR2, SNX1, and DAPK2. Our results suggest that simultaneous consideration of multiple lifestyle exposures in gene-environment interaction analysis can identify additional loci missed by single lifestyle approaches.
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http://dx.doi.org/10.1002/gepi.22292DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7717887PMC
September 2020

Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium.

Mol Psychiatry 2020 08 26;25(8):1673-1687. Epub 2020 Feb 26.

Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, USA.

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). 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). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (EUR + AFR z = 4.69, p = 10), 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) and a significant association with musculoskeletal disorders in the UK Biobank (p = 4.88 × 10). 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; OD cases vs. exposed controls, p = 0.054) and OE (exposed vs. unexposed controls, p = 3.6 × 10). A PRS based on a GWAS of neuroticism (n = 390,278) was positively associated with OD (OD vs. unexposed controls, p = 3.2 × 10; 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.
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http://dx.doi.org/10.1038/s41380-020-0677-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7392789PMC
August 2020

Shared genetic risk between eating disorder- and substance-use-related phenotypes: Evidence from genome-wide association studies.

Addict Biol 2021 01 16;26(1):e12880. Epub 2020 Feb 16.

Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, RWTH Aachen University, Aachen, Germany.

Eating disorders and substance use disorders frequently co-occur. Twin studies reveal shared genetic variance between liabilities to eating disorders and substance use, with the strongest associations between symptoms of bulimia nervosa and problem alcohol use (genetic correlation [r ], twin-based = 0.23-0.53). We estimated the genetic correlation between eating disorder and substance use and disorder phenotypes using data from genome-wide association studies (GWAS). Four eating disorder phenotypes (anorexia nervosa [AN], AN with binge eating, AN without binge eating, and a bulimia nervosa factor score), and eight substance-use-related phenotypes (drinks per week, alcohol use disorder [AUD], smoking initiation, current smoking, cigarettes per day, nicotine dependence, cannabis initiation, and cannabis use disorder) from eight studies were included. Significant genetic correlations were adjusted for variants associated with major depressive disorder and schizophrenia. Total study sample sizes per phenotype ranged from ~2400 to ~537 000 individuals. We used linkage disequilibrium score regression to calculate single nucleotide polymorphism-based genetic correlations between eating disorder- and substance-use-related phenotypes. Significant positive genetic associations emerged between AUD and AN (r = 0.18; false discovery rate q = 0.0006), cannabis initiation and AN (r = 0.23; q < 0.0001), and cannabis initiation and AN with binge eating (r = 0.27; q = 0.0016). Conversely, significant negative genetic correlations were observed between three nondiagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge eating (r = -0.19 to -0.23; qs < 0.04). The genetic correlation between AUD and AN was no longer significant after co-varying for major depressive disorder loci. The patterns of association between eating disorder- and substance-use-related phenotypes highlights the potentially complex and substance-specific relationships among these behaviors.
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http://dx.doi.org/10.1111/adb.12880DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7429266PMC
January 2021

The etiology of DSM-5 alcohol use disorder: Evidence of shared and non-shared additive genetic effects.

Drug Alcohol Depend 2019 08 14;201:147-154. Epub 2019 Jun 14.

Department of Human Development and Family Studies, Purdue University, USA.

Background: Alcoholism 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).

Methods: Data 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.

Results: Phenotypic 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.

Conclusions: Common 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.
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http://dx.doi.org/10.1016/j.drugalcdep.2018.12.034DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6929687PMC
August 2019

Changes in associations of prescription opioid use disorder and illegal behaviors among adults in the United States from 2002 to 20.

Addiction 2019 12 12;114(12):2150-2159. Epub 2019 Jun 12.

Department of Psychiatry, Washington University in St Louis School of Medicine, St Louis, MO, USA.

Background And Aims: In the United States, the availability of prescription opioids has decreased in recent years. Whether there have been corresponding changes in the likelihood of people with prescription opioid use disorder (POUD) to engage in illegal behaviors related to drug use remains unknown. We examined changes in prevalence of illegal behaviors between people with and without POUD over time, and how transactions for obtaining opioids have changed among people with POUD over time.

Design: Temporal trend analysis of repeated cross-sectional data.

Setting: United States household dwelling population from all 50 states and District of Columbia.

Participants: Adult subsamples from the 2002-14 National Survey of Drug Use and Health (n = 5393 people with POUD; n = 486 768 people without POUD).

Measurements: Outcome variables were selected illegal behaviors and sources of opioids used non-medically. POUD was defined using the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, criteria. Time was treated as a continuous variable. The variable of interest for each illegal behavior analysis was the interaction between POUD diagnosis and time. Covariates included age, sex and race/ethnicity.

Findings: During the 13-year period examined, the adjusted interaction odds ratio (AIOR) describing the change in association between POUD and selling illicit drugs increased by a factor of 2.41 [95% confidence interval (CI) = 1.56-3.71, P < 0.001]. Similar trends were noted for stealing (AIOR = 2.12, 95% CI = 1.31-3.44, P = 0.002) and for life-time history of arrest (AIOR = 1.53, 95% CI = 1.06-2.19, P = 0.021). People with POUD became less likely to receive opioids for free from friends and family [adjusted odds ratio (AOR) = 0.42, 95% CI = 0.25-0.71, P = 0.001] and more likely to buy them from friends and family (AOR = 3.29, 95% CI = 1.76-6.13, P < 0.001) from 2005 to 2014.

Conclusions: In the United States, against a backdrop of a decreasing prescription opioid supply, rates of some crimes potentially related to drug use increased among people with prescription opioid use disorder compared with those without prescription opioid use disorder from 2002 to 2014.
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http://dx.doi.org/10.1111/add.14638DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6819203PMC
December 2019

Transancestral GWAS of alcohol dependence reveals common genetic underpinnings with psychiatric disorders.

Nat Neurosci 2018 12 26;21(12):1656-1669. Epub 2018 Nov 26.

NIH/NIAAA, Laboratory of Neurogenetics, Bethesda, MD, USA.

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) and African ancestries (rs2066702; P = 2.2 × 10). 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.
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http://dx.doi.org/10.1038/s41593-018-0275-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6430207PMC
December 2018

Use of polygenic risk scores of nicotine metabolism in predicting smoking behaviors.

Pharmacogenomics 2018 12 16;19(18):1383-1394. Epub 2018 Nov 16.

Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, USA.

Aim: This study tests whether polygenic risk scores (PRSs) for nicotine metabolism predict smoking behaviors in independent data.

Materials & Methods: Linear regression, logistic regression and survival analyses were used to analyze nicotine metabolism PRSs and nicotine metabolism, smoking quantity and smoking cessation.

Results: Nicotine metabolism PRSs based on two genome wide association studies (GWAS) meta-analyses significantly predicted nicotine metabolism biomarkers (R range: 9.2-16%; minimum p = 7.6 × 10). The GWAS top hit variant rs56113850 significantly predicted nicotine metabolism biomarkers (R range: 14-17%; minimum p = 4.4 × 10). There was insufficient evidence for these PRSs predicting smoking quantity and smoking cessation.

Conclusion: Results suggest that nicotine metabolism PRSs based on GWAS meta-analyses predict an individual's nicotine metabolism, so does use of the top hit variant. We anticipate that PRSs will enter clinical medicine, but additional research is needed to develop a more comprehensive genetic score to predict smoking behaviors.
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http://dx.doi.org/10.2217/pgs-2018-0081DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6562697PMC
December 2018

E-cigarette Usage Is Associated With Increased Past-12-Month Quit Attempts and Successful Smoking Cessation in Two US Population-Based Surveys.

Nicotine Tob Res 2019 09;21(10):1331-1338

Department of Psychiatry, Washington University School of Medicine, St. Louis, MO.

Introduction: We examined past-12-month quit attempts and smoking cessation from 2006 to 2016 while accounting for demographic shifts in the US population. In addition, we sought to understand whether the current use of electronic cigarettes was associated with a change in past-12-month quit attempts and successful smoking cessation at the population level.

Methods: We analyzed data from 25- to 44-year-olds from the National Health Interview Survey (NHIS) from 2006 to 2016 (N = 26,354) and the Tobacco Use Supplement to the Current Population Survey (TUS-CPS) in 2006-2007, 2010-2011, and 2014-2015 (N = 33,627). Data on e-cigarette use were available in the 2014-2016 NHIS and 2014-2015 TUS-CPS surveys.

Results: Past-12-month quit attempts and smoking cessation increased in recent years compared with 2006. Current e-cigarette use was associated with higher quit attempts (adjusted odds ratio [aOR] = 2.29, 95% confidence interval [CI] = 1.87 to 2.81, p < .001) and greater smoking cessation (aOR = 1.64, 95% CI = 1.21 to 2.21, p = .001) in the NHIS. Multivariable logistic regression of the TUS-CPS data showed that current e-cigarette use was similarly significantly associated with increased past-12-month quit attempts and smoking cessation. Significant interactions were found for smoking frequency (everyday and some-day smoking) and current e-cigarette use for both outcomes (p < .0001) with the strongest positive effects seen in everyday smokers.

Conclusions: Compared with 2006, past-12-month quit attempts and smoking cessation increased among adults aged 25-44 in recent years. Current e-cigarette use was associated with increased past-12-month quit attempts and successful smoking cessation among established smokers. These findings are relevant to future tobacco policy decisions.

Implications: E-cigarettes were introduced into the US market over the past decade. During this period, past-12-month quit attempts and smoking cessation have increased among US adults aged 25-44. These trends are inconsistent with the hypothesis that e-cigarette use is delaying quit attempts and leading to decreased smoking cessation. In contrast, current e-cigarette use was associated with significantly higher past-12-month quit attempts and past-12-month cessation. These findings suggest that e-cigarette use contributes to a reduction in combustible cigarette use among established smokers.
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http://dx.doi.org/10.1093/ntr/nty211DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6751520PMC
September 2019

Daily Drinking Is Associated with Increased Mortality.

Alcohol Clin Exp Res 2018 11 3;42(11):2246-2255. Epub 2018 Oct 3.

Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri.

Background: There is evidence that low-level alcohol use, drinking 1 to 2 drinks on occasion, is protective for cardiovascular disease, but increases the risk of cancer. Synthesizing the overall impact of low-level alcohol use on health is therefore complex. The objective of this paper was to examine the association between frequency of low-level drinking and mortality.

Methods: Two data sets with self-reported alcohol use and mortality follow-up were analyzed: 340,668 individuals from the National Health Interview Survey (NHIS) and 93,653 individuals from the Veterans Health Administration (VA) outpatient medical records. Survival analyses were conducted to evaluate the association between low-level drinking frequency and mortality.

Results: The minimum risk drinking frequency among those who drink 1 to 2 drinks per occasion was found to be 3.2 times weekly in the NHIS data, based on a continuous measure of drinking frequency, and 2 to 3 times weekly in the VA data. Relative to these individuals with minimum risk, individuals who drink 7 times weekly had an adjusted hazard ratio (HR) of all-cause mortality of 1.23 (p < 0.0001) in the NHIS data, and individuals who drink 4 to 7 times weekly in the VA data also had an adjusted HR of 1.23 (p = 0.01). Secondary analyses in the NHIS data showed that the minimum risk was drinking 4 times weekly for cardiovascular mortality, and drinking monthly or less for cancer mortality. The associations were consistent in stratified analyses of men, women, and never smokers.

Conclusions: The minimum risk of low-level drinking frequency for all-cause mortality appears to be approximately 3 occasions weekly. The robustness of this finding is highlighted in 2 distinctly different data sets: a large epidemiological data set and a data set of veterans sampled from an outpatient clinic. Daily drinking, even at low levels, is detrimental to one's health.
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http://dx.doi.org/10.1111/acer.13886DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214719PMC
November 2018

From genes to treatments: a systematic review of the pharmacogenetics in smoking cessation.

Pharmacogenomics 2018 07 19;19(10):861-871. Epub 2018 Jun 19.

Department of Psychiatry, Washington University School of Medicine, St Louis, MO 63110, USA.

Smoking cessation treatment outcomes may be heavily influenced by genetic variations among smokers. Therefore, identifying specific variants that affect response to different pharmacotherapies is of major interest to the field. In the current study, we systematically review all studies published in or after the year 1990 which examined one or more gene-drug interactions for smoking cessation treatment. Out of 644 citations, 46 articles met the inclusion criteria for the systematic review. We summarize evidence on several genetic polymorphisms (CHRNA5-A3-B4, CYP2A6, DBH, CHRNA4, COMT, DRD2, DRD4 and CYP2B6) and their potential moderating pharamacotherarpy effects on patient cessation efficacy rates. These findings are promising and call for further research to demonstrate the effectiveness of genetic testing in personalizing treatment decision-making and improving outcome.
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http://dx.doi.org/10.2217/pgs-2018-0023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6219447PMC
July 2018

Toward the implementation of genomic applications for smoking cessation and smoking-related diseases.

Transl Behav Med 2018 01;8(1):7-17

Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA.

The incorporation of genomic information into routine care settings is a burgeoning area for investigation in behavioral medicine. The past decade has witnessed rapid advancements in knowledge of genetic biomarkers associated with smoking behaviors and tobacco-related morbidity and mortality, providing the basis for promising genomic applications in clinical and community settings. We assessed the current state of readiness for implementing genomic applications involving variation in the α5 nicotinic cholinergic receptor subunit gene CHRNA5 and smoking outcomes (behaviors and related diseases) using a process that could be translatable to a wide range of genomic applications in behavioral medicine. We reviewed the scientific literature involving CHRNA5 genetic variation and smoking cessation, and then summarized and synthesized a chain of evidence according to analytic validity, clinical validity, clinical utility, and ethical, legal, and social implications (ACCE), a well-established set of criteria used to evaluate genomic applications. Our review identified at least three specific genomic applications for which implementation may be considered, including the use of CHRNA5 genetic test results for informing disease risk, optimizing smoking cessation treatment, and motivating smoking behavior change. For these genomic applications, we rated analytic validity as convincing, clinical validity as adequate, and clinical utility and ethical, legal, and social implications as inadequate. For clinical genomic applications involving CHRNA5 variation and smoking outcomes, research efforts now need to focus on establishing clinical utility. This approach is compatible with pre-implementation research, which is also needed to accelerate translation, improve innovation design, and understand and refine system processes involved in implementation. This study informs the readiness to incorporate smoking-related genomic applications in real-world settings and facilitates cross-disciplinary collaboration to accelerate the integration of evidence-based genomics in behavioral medicine.
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http://dx.doi.org/10.1093/tbm/ibx060DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6065540PMC
January 2018

Association Between Substance Use Disorder and Polygenic Liability to Schizophrenia.

Biol Psychiatry 2017 Nov 6;82(10):709-715. Epub 2017 Jun 6.

Washington University in St. Louis, St. Louis, Missouri.

Background: There are high levels of comorbidity between schizophrenia and substance use disorder, but little is known about the genetic etiology of this comorbidity.

Methods: We tested the hypothesis that shared genetic liability contributes to the high rates of comorbidity between schizophrenia and substance use disorder. To do this, polygenic risk scores for schizophrenia derived from a large meta-analysis by the Psychiatric Genomics Consortium were computed in three substance use disorder datasets: the Collaborative Genetic Study of Nicotine Dependence (ascertained for tobacco use disorder; n = 918 cases; 988 control subjects), the Collaborative Study on the Genetics of Alcoholism (ascertained for alcohol use disorder; n = 643 cases; 384 control subjects), and the Family Study of Cocaine Dependence (ascertained for cocaine use disorder; n = 210 cases; 317 control subjects). Phenotypes were harmonized across the three datasets and standardized analyses were performed. Genome-wide genotypes were imputed to the 1000 Genomes reference panel.

Results: In each individual dataset and in the mega-analysis, strong associations were observed between any substance use disorder diagnosis and the polygenic risk score for schizophrenia (mega-analysis pseudo-R range 0.8-3.7%; minimum p = 4 × 10).

Conclusions: These results suggest that comorbidity between schizophrenia and substance use disorder is partially attributable to shared polygenic liability. This shared liability is most consistent with a general risk for substance use disorder rather than specific risks for individual substance use disorders and adds to increasing evidence of a blurred boundary between schizophrenia and substance use disorder.
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http://dx.doi.org/10.1016/j.biopsych.2017.04.020DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5643224PMC
November 2017

Capsule Commentary on Gryczynski et al., Validation of the TAPS-1: A Four-Item Screening Tool to Identify Unhealthy Substance Use in Primary Care.

Authors:
Sarah M Hartz

J Gen Intern Med 2017 09;32(9):1026

Department of Psychiatry, Washington University School of Medicine in St. Louis, St. Louis, MO, USA.

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http://dx.doi.org/10.1007/s11606-017-4119-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5570757PMC
September 2017

Genetic correlation between smoking behaviors and schizophrenia.

Schizophr Res 2018 04 8;194:86-90. Epub 2017 Mar 8.

Washington University School of Medicine in St. Louis, United States.

Nicotine dependence is highly comorbid with schizophrenia, and the etiology of the comorbidity is unknown. To determine whether there is a genetic correlation of smoking behavior with schizophrenia, genome-wide association study (GWAS) meta-analysis results from five smoking phenotypes (ever/never smoker (N=74,035), age of onset of smoking (N=28,647), cigarettes smoked per day (CPD, N=38,860), nicotine dependence (N=10,666), and current/former smoker (N=40,562)) were compared to GWAS meta-analysis results from schizophrenia (N=79,845) using linkage disequilibrium (LD) score regression. First, the SNP heritability (h) of each of the smoking phenotypes was computed using LD score regression (ever/never smoker h=0.08, age of onset of smoking h=0.06, CPD h=0.06, nicotine dependence h=0.15, current/former smoker h=0.07, p<0.001 for all phenotypes). The SNP heritability for nicotine dependence was statistically higher than the SNP heritability for the other smoking phenotypes (p<0.0005 for all two-way comparisons). Next, a statistically significant (p<0.05) genetic correlation was observed between schizophrenia and three of the five smoking phenotypes (nicotine dependence r=0.14, CPD r=0.12, and ever/never smoking r=0.10). These results suggest that there is a component of common genetic variation that is shared between smoking behaviors and schizophrenia.
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http://dx.doi.org/10.1016/j.schres.2017.02.022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5811408PMC
April 2018

Associations between Polygenic Risk for Psychiatric Disorders and Substance Involvement.

Front Genet 2016 15;7:149. Epub 2016 Aug 15.

Department of Psychological and Brain Sciences, Washington University in St. Louis St. Louis, MO, USA.

Despite evidence of substantial comorbidity between psychiatric disorders and substance involvement, the extent to which common genetic factors contribute to their co-occurrence remains understudied. In the current study, we tested for associations between polygenic risk for psychiatric disorders and substance involvement (i.e., ranging from ever-use to severe dependence) among 2573 non-Hispanic European-American participants from the Study of Addiction: Genetics and Environment. Polygenic risk scores (PRS) for cross-disorder psychopathology (CROSS) were generated based on the Psychiatric Genomics Consortium's Cross-Disorder meta-analysis and then tested for associations with a factor representing general liability to alcohol, cannabis, cocaine, nicotine, and opioid involvement (GENSUB). Follow-up analyses evaluated specific associations between each of the five psychiatric disorders which comprised CROSS-attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (AUT), bipolar disorder (BIP), major depressive disorder (MDD), and schizophrenia (SCZ)-and involvement with each component substance included in GENSUB. CROSS PRS explained 1.10% of variance in GENSUB in our sample (p < 0.001). After correction for multiple testing in our follow-up analyses of polygenic risk for each individual disorder predicting involvement with each component substance, associations remained between: (A) MDD PRS and non-problem cannabis use, (B) MDD PRS and severe cocaine dependence, (C) SCZ PRS and non-problem cannabis use and severe cannabis dependence, and (D) SCZ PRS and severe cocaine dependence. These results suggest that shared covariance from common genetic variation contributes to psychiatric and substance involvement comorbidity.
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http://dx.doi.org/10.3389/fgene.2016.00149DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4983546PMC
August 2016

The significant impact of education, poverty, and race on Internet-based research participant engagement.

Genet Med 2017 02 28;19(2):240-243. Epub 2016 Jul 28.

Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA.

Purpose: Internet-based technologies are increasingly being used for research studies. However, it is not known whether Internet-based approaches will effectively engage participants from diverse racial and socioeconomic backgrounds.

Methods: A total of 967 participants were recruited and offered genetic ancestry results. We evaluated viewing Internet-based genetic ancestry results among participants who expressed high interest in obtaining the results.

Results: Of the participants, 64% stated that they were very or extremely interested in their genetic ancestry results. Among interested participants, individuals with a high school diploma (n = 473) viewed their results 19% of the time relative to 4% of the 145 participants without a diploma (P < 0.0001). Similarly, 22% of participants with household income above the federal poverty level (n = 286) viewed their results relative to 10% of the 314 participants living below the federal poverty level (P < 0.0001). Among interested participants both with a high school degree and living above the poverty level, self-identified Caucasians were more likely to view results than self-identified African Americans (P < 0.0001), and females were more likely to view results than males (P = 0.0007).

Conclusion: In an underserved population, engagement in Internet-based research was low despite high reported interest. This suggests that explicit strategies should be developed to increase diversity in Internet-based research.Genet Med 19 2, 240-243.
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http://dx.doi.org/10.1038/gim.2016.91DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5274598PMC
February 2017

Nicotine dependence and psychosis in Bipolar disorder and Schizoaffective disorder, Bipolar type.

Am J Med Genet B Neuropsychiatr Genet 2016 06 15;171(4):521-4. Epub 2015 Oct 15.

Department of Psychiatry and the Behavioral Sciences, Keck School of Medicine of the University of Southern California, Los Angeles, California.

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 < 0.0001). Patients with Bipolar disorder with psychosis were more likely to be dependent than Bipolar disorder patients without psychosis (OR = 1.3; P = 0.03). Schizoaffective disorder, Bipolar Type patients had more risk of nicotine dependence when compared to Bipolar disorder patients with or without psychosis (OR = 1.2; P = 0.02). Bipolar disorder patients experiencing more severity of psychosis have more risk of nicotine dependence. © 2015 Wiley Periodicals, Inc.
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http://dx.doi.org/10.1002/ajmg.b.32385DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4950843PMC
June 2016

When Does Choice of Accuracy Measure Alter Imputation Accuracy Assessments?

PLoS One 2015 12;10(10):e0137601. Epub 2015 Oct 12.

Department of Genetics, Washington University, St. Louis, Missouri, United States of America.

Imputation, the process of inferring genotypes for untyped variants, is used to identify and refine genetic association findings. Inaccuracies in imputed data can distort the observed association between variants and a disease. Many statistics are used to assess accuracy; some compare imputed to genotyped data and others are calculated without reference to true genotypes. Prior work has shown that the Imputation Quality Score (IQS), which is based on Cohen's kappa statistic and compares imputed genotype probabilities to true genotypes, appropriately adjusts for chance agreement; however, it is not commonly used. To identify differences in accuracy assessment, we compared IQS with concordance rate, squared correlation, and accuracy measures built into imputation programs. Genotypes from the 1000 Genomes reference populations (AFR N = 246 and EUR N = 379) were masked to match the typed single nucleotide polymorphism (SNP) coverage of several SNP arrays and were imputed with BEAGLE 3.3.2 and IMPUTE2 in regions associated with smoking behaviors. Additional masking and imputation was conducted for sequenced subjects from the Collaborative Genetic Study of Nicotine Dependence and the Genetic Study of Nicotine Dependence in African Americans (N = 1,481 African Americans and N = 1,480 European Americans). Our results offer further evidence that concordance rate inflates accuracy estimates, particularly for rare and low frequency variants. For common variants, squared correlation, BEAGLE R2, IMPUTE2 INFO, and IQS produce similar assessments of imputation accuracy. However, for rare and low frequency variants, compared to IQS, the other statistics tend to be more liberal in their assessment of accuracy. IQS is important to consider when evaluating imputation accuracy, particularly for rare and low frequency variants.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0137601PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4601794PMC
June 2016

Association of the OPRM1 Variant rs1799971 (A118G) with Non-Specific Liability to Substance Dependence in a Collaborative de novo Meta-Analysis of European-Ancestry Cohorts.

Behav Genet 2016 Mar 21;46(2):151-69. Epub 2015 Sep 21.

Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.

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.
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http://dx.doi.org/10.1007/s10519-015-9737-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4752855PMC
March 2016
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