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Objective To define the relationship between the duration of smoking cessation and post-operative complications for patients with lung cancer undergoing surgical treatment. Background Smoking increases the risk of post-operative morbidity and mortality in patients with lung cancer undergoing surgical treatment. While smoking cessation before surgery can mitigate these risks, the ideal duration of pre-operative smoking cessation remains unclear. Methods Using a uniquely compiled Veterans Health Administration (VHA) dataset, we performed a retrospective cohort study of patients with clinical stage I non-small cell lung cancer (NSCLC) undergoing surgical treatment between 2006 and 2016. We characterized the relationship between duration of pre-operative smoking cessation and risk of post-operative complications or mortality within 30-days using multivariable restricted cubic spline functions. Results The study included a total of 9509 patients, of whom 6168 (64.9%) were smoking at the time of lung cancer diagnosis. Among them, only 662 (10.7%) patients stopped smoking prior to surgery. Longer duration between smoking cessation and surgery was associated with lower odds of major complication or mortality (aOR for every additional week, 0.919; 95% CI, 0.850-0.993; P = 0.03). Compared to non-smokers, patients who quit at least 3 weeks before surgery had similar odds of major complication (aOR, 1.005; 95% CI, 0.702-1.437; P = 0.98) whereas those who quit within 3 weeks of surgery had significantly higher odds of major complication (aOR, 1.698; 95% CI, 1.203-2.396; P = 0.003). Conclusion Smoking cessation at least 3 weeks prior to the surgical treatment of lung cancer is associated with reduced morbidity and mortality. Providers should aggressively encourage smoking cessation in the pre-operative period, since it can disproportionately impact outcomes in early-stage lung cancer.

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

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 (R2 range: 9.2–16%; minimum p = 7.6 × 10-8). The GWAS top hit variant rs56113850 significantly predicted nicotine metabolism biomarkers (R2 range: 14–17%; minimum p = 4.4 × 10-8). 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.

Tobacco cessation treatment for cancer patients is essential to providing comprehensive oncologic care. We have implemented a point of care tobacco treatment care model enabled by electronic health record (EHR) modifications in a comprehensive cancer center. Data are needed on the sustainability of both reach of treatment and effectiveness over time, including the COVID-19 pandemic.Using EHR data from the pre-implementation (P: 5 months) and post-implementation periods (6 month-blocks, T1-T5 for a total of 30 months), we compared two primary outcomes: 1) reach of treatment among those smoking and 2) effectiveness assessed by smoking cessation among those smoking in the subsequent 6 month period. We analyzed the data using generalized estimation equation regression models.With the point of care tobacco treatment care model, reach of treatment increased from pre to post T5 (3.2 % vs. 48.4 %, RR 15.50, 95 % CI 10.56-22.74, p0.0001). Reach of treatment in all post periods (T1-T5 including the COVID-19 pandemic time) remained significantly higher than the pre period. Effectiveness, defined by smoking cessation among those smoking, increased from pre to post T2 before the pandemic (12.4 % vs. 21.4 %, RR 1.57, 95 % CI 1.31-1.87, p0.0001). However, effectiveness, while higher in later post periods (T3, T4), was no longer significantly increased compared with the pre period.A point of care EHR-enabled tobacco treatment care model demonstrates sustained reach up to 30 months following implementation, even during the COVID-19 pandemic and changes in healthcare prioritization. Effectiveness was sustained for 12 months, but did not sustain through the subsequent 12 months.

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 (BN) and problem alcohol use (genetic correlation [rg], 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 BN 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 (MDD). Total sample sizes per phenotype ranged from ~2,400 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 (rg=0.18; false discovery rate q=0.0006), cannabis initiation and AN (rg=0.23; qwith binge-eating (rg=0.27; q=0.0016). Conversely, significant negative genetic correlations were observed between three non-diagnostic smoking phenotypes (smoking initiation, current smoking, and cigarettes per day) and AN without binge-eating (rgs=-0.19 to −0.23; qs

Because tobacco use remains one of the leading causes of disease, disability, and mortality, tobacco treatment programs should be integrated into medical systems such as hospitals and outpatient centers. Medical providers have a unique, high-impact opportunity to initiate smoking cessation treatment with patients. However, there are several barriers that may hinder the development and implementation of these programs. The purpose of this review was to address such barriers by illustrating several examples of successful tobacco treatment programs in US health-care systems that were contributed by the authors. This includes describing treatment models, billing procedures, and implementation considerations. Using an illustrative review of vignettes from existing programs, various models are outlined, emphasizing commonalities and unique features, strengths and limitations, resources necessary, and other relevant considerations. In addition, clinical research and dissemination trials from each program are described to provide evidence of feasibility and efficacy from these programs. This overview of example treatment models designed for hospitals and outpatient centers provides guidelines for any emerging tobacco cessation services within these contexts. For existing treatment programs, this review provides additional insight and ideas about improving these programs within their respective medical systems.

Introduction The purpose of this study is to examine the predictive utility of polygenic risk scores (PRSs) for smoking behaviors. Aims and Methods Using summary statistics from the 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 = 8638), and the Collaborative Genetic Study of Nicotine Dependence (COGEND) (N = 1935). 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 PRS z-score for ever smoking predicted smoking (odds ratio [OR]: 1.37; 95% confidence interval [CI]: 1.31, 1.43); the PRS z-score for age of smoking initiation was associated with age of smoking initiation (OR: 0.87; 95% CI: 0.82, 0.92); the PRS z-score for cigarettes per day was associated with heavier smoking (OR: 1.17; 95% CI: 1.11, 1.25); and the PRS z-score for smoking cessation predicted successful cessation (OR: 1.24; 95% CI: 1.17, 1.32). In the African ancestry cohort, the PRSs did not predict smoking behaviors. Conclusions 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 PRSs in a single model increases the ability to predict smoking behaviors compared with 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 PRSs, the predictive ability will continue to improve.

The implementation of pharmacogenomic (PGx) testing in psychiatry remains modest, in part due to divergent perceptions of the quality and completeness of the evidence base and diverse perspectives on the clinical utility of PGx testing among psychiatrists and other healthcare providers. Recognizing the current lack of consensus within the field, the International Society of Psychiatric Genetics assembled a group of experts to conduct a narrative synthesis of the PGx literature, prescribing guidelines, and product labels related to psychotropic medications as well as the key considerations and limitations related to the use of PGx testing in psychiatry. The group concluded that to inform medication selection and dosing of several commonly-used antidepressant and antipsychotic medications, current published evidence, prescribing guidelines, and product labels support the use of PGx testing for 2 cytochrome P450 genes (CYP2D6, CYP2C19). In addition, the evidence supports testing for human leukocyte antigen genes when using the mood stabilizers carbamazepine (HLA-A and HLA-B), oxcarbazepine (HLA-B), and phenytoin (CYP2C9, HLA-B). For valproate, screening for variants in certain genes (POLG, OTC, CSP1) is recommended when a mitochondrial disorder or a urea cycle disorder is suspected. Although barriers to implementing PGx testing remain to be fully resolved, the current trajectory of discovery and innovation in the field suggests these barriers will be overcome and testing will become an important tool in psychiatry.

Background Compared to white smokers, Black smokers are at disproportionately higher risk for smoking-related disease, despite consuming fewer cigarettes per day (CPD). To examine racial disparities in biobehavioral influences on smoking and disease risk, we analyzed the relationship between self-reported tobacco dependence and intensity of tobacco smoke exposure per cigarette, on the one hand, and intensity of nicotine intake per cigarette, on the other. Methods In 270 Black and 516 white smokers, smoke exposure was measured by expired carbon monoxide (CO), and nicotine intake was measured by plasma cotinine (COT) and cotinine+3′-hydroxycotinine ([COT + 3HC]). Using linear regression analyses, we analyzed how the Fagerstrom Test for Cigarette Dependence (FTCD) predicted intensity of smoke exposure per cigarette (CO/CPD) and intensity of nicotine intake per cigarette (COT/CPD; [COT + 3HC]/CPD), and how race moderated these relations. Results Overall, Black smokers consumed fewer CPD than white smokers and had higher levels of CO/CPD, COT/CPD, and [COT + 3HC]/CPD. These elevations were most pronounced at lower levels of dependence: amongst Black smokers, FTCD negatively predicted intensity of smoke exposure as measured by CO/CPD (B = −0.12, 95% CI = −0.18, −0.05, p = 0.0003) and intensity of nicotine intake as measured by [COT + 3HC]/CPD (B = −1.31, 95% CI = −2.15, −0.46, p = 0.002). Conclusions Low-dependence Black smokers had higher intensities of both smoke exposure and nicotine intake per cigarette compared to similarly dependent white smokers, suggesting that measures of dependence, exposure, and intake underestimate incremental risk of each cigarette to Black smokers.

Relatively little is known about the possible effects of personalized genetic risk information on smoking, the leading preventable cause of morbidity and mortality. We examined the acceptability and potential behavior change associated with a personalized genetically informed risk tool (RiskProfile) among current smokers. Current smokers (n = 108) were enrolled in a pre-post study with three visits. At visit 1, participants completed a baseline assessment and genetic testing via 23andMe. Participants’ raw genetic data (CHRNA5 variants) and smoking heaviness were used to create a tailored RiskProfile tool that communicated personalized risks of smoking-related diseases and evidence-based recommendations to promote cessation. Participants received their personalized RiskProfile intervention at visit 2, approximately 6 weeks later. Visit 3 involved a telephone-based follow-up assessment 30 days after intervention. Of enrolled participants, 83% were retained across the three visits. Immediately following intervention, acceptability of RiskProfile was high (M = 4.4; SD = 0.6 on scale of 1 to 5); at 30-day follow-up, 89% of participants demonstrated accurate recall of key intervention messages. In the full analysis set of this single-arm trial, cigarettes smoked per day decreased from intervention to 30-day follow-up [11.3 vs. 9.8; difference = 1.5; 95% confidence interval (0.6–2.4); P = 0.001]. A personalized genetically informed risk tool was found to be highly acceptable and associated with a reduction in smoking, although the absence of a control group must be addressed in future research. This study demonstrates proof of concept for translating key basic science findings into a genetically informed risk tool that was used to promote progress toward smoking cessation. Prevention Relevance: This study demonstrates that personal genetic information can be incorporated into a risk feedback tool that was highly acceptable to current smokers and associated with reductions in smoking. These findings may pave the way for effectiveness and implementation research on genetically-informed behavior change interventions to enhance cancer prevention efforts.

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 × 10-81 ), lung cancer (z-score = 8.91; p = 5.02 × 10-19 ), and COPD (z-score = 4.04; p = 5.40 × 10-5 ). Surprisingly, this allele-C (rs56113850) was associated with increased smoking cessation (z-score = -8.17; p = 2.52 × 10-26 ). 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.

The clinical translation of genetic research on nicotine dependence and treatment response requires acceptance of genetic testing by smokers. This study determines (1) which current smokers are receptive to genetic susceptibility testing for nicotine dependence and (2) to what potential extent smokers motivated to quit desire to take smoking cessation medication when hypothetical genetic results predict their pharmacogenetic medication response. Current smokers from a genetic nicotine dependence study (n = 1306) and an ongoing smoking cessation trial (n = 209) were surveyed on their hypothetical interest in seeing genetic testing results related to risk of nicotine dependence. Most current smokers (84.8%) reported high interest in receiving genetic testing results. Factors associated with high interest included age ≥ 40 years, having a college degree, and a positive medical history (≥1 medical condition). In the ongoing smoking cessation trial, current smokers motivated to quit (n = 474) were surveyed on their desire to take smoking cessation medication given hypothetical below or above average pharmacogenetic responses to the medication. When the hypothetical medication response changed from below to above average, significantly more smokers reported a desire to take medication (from 61.0% to 97.5%, p .0001). These preliminary findings suggest that genetic testing for personalized smoking cessation treatment is well-received by smokers and that a positive hypothetical pharmacogenetic response increases desire to take smoking cessation medication among current smokers motivated to quit. Graphical abstract ᅟ.

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(2)(g)) of each of the smoking phenotypes was computed using LD score regression (ever/never smoker h(2)(g) =0.08, age of onset of smoking h(2)(g)=0.06, CPD h(2)(g)=0.06, nicotine dependence h(2)(g)=0.15, current/former smoker h(2)(g)=0.07, p

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

Introduction: Human genetic research has succeeded in definitively identifying multiple genetic variants associated with risk for nicotine dependence and heavy smoking. To build on these advances, and to aid in reducing the prevalence of smoking and its consequent health harms, the next frontier is to identify genetic predictors of successful smoking cessation and also of the efficacy of smoking cessation treatments ("pharmacogenomics"). More broadly, additional biomarkers that can be quantified from biosamples also promise to aid "Precision Medicine" and the personalization of treatment, both pharmacological and behavioral. Aims and Methods: To motivate ongoing and future efforts, here we review several compelling genetic and biomarker findings related to smoking cessation and treatment. Results: These Key results involve genetic variants in the nicotinic receptor subunit gene CHRNA5, variants in the nicotine metabolism gene CYP2A6, and the nicotine metabolite ratio. We also summarize reports of epigenetic changes related to smoking behavior. Conclusions: The results to date demonstrate the value and utility of data generated from biosamples in clinical treatment trial settings. This article cross-references a companion paper in this issue that provides practical guidance on how to incorporate biosample collection into a planned clinical trial and discusses avenues for harmonizing data and fostering consortium-based, collaborative research on the pharmacogenomics of smoking cessation. Implications: Evidence is emerging that certain genotypes and biomarkers are associated with smoking cessation success and efficacy of smoking cessation treatments. We review key findings that open potential avenues for personalizing smoking cessation treatment according to an individual's genetic or metabolic profile. These results provide important incentive for smoking cessation researchers to collect biosamples and perform genotyping in research studies and clinical trials.

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

Patients with serious mental illness have high smoking prevalence and early mortality. Inadequate implementation of evidence-based smoking cessation treatment in community mental health centers (CMHCs) contributes to this disparity. This study evaluates the effects of quality improvement strategies on treatment and cessation outcomes. Low-burden strategies of decision support and academic detailing with data-driven feedback were implemented in 4 clinics from 2014 to 2016. Pre- and post-implementation data from pharmacy and medical records were analyzed. With these quality improvement strategies, we found an increase in patient receipt of cessation medication from 4.6% to 18.0% (p

Tobacco cessation after cancer diagnosis leads to better patient outcomes. However, tobacco treatment services are frequently unavailable in cancer care settings, and multilevel implementation challenges can impede uptake of new programs. The National Cancer Institute (NCI) dedicated Cancer Moonshot funding through the Cancer Center Cessation Initiative (C3I) for NCI-Designated Cancer Centers to implement or enhance the implementation of tobacco treatment services. We examined a pragmatic application of the RE-AIM framework (reach, effectiveness, adoption, implementation, and maintenance) to evaluate tobacco treatment programs implemented within Cancer Centers funded through C3I. Using three C3I-funded Centers as examples, we describe how each RE-AIM construct was operationalized to evaluate the implementation of a wide range of cessation services (e.g., tobacco use screening, counseling, Quitline referral, pharmacotherapy) in this heterogeneous group of cancer care settings. We discuss the practical challenges encountered in assessing RE-AIM constructs in real world situations, including using the electronic health record (EHR) to aid in assessment. Reach and effectiveness evaluation required that Centers define the setting(s) where cessation services were implemented (to determine the "denominator"), enumerate the patient population, report current patient tobacco use, patient engagement in tobacco treatment, and 6-month cessation outcomes. To reduce site heterogeneity, increase data accuracy, and reduce burden, reach was frequently captured via standardized EHR enhancements that improved the identification of current smokers and tobacco treatment referrals. Effectiveness was determined by cessation outcomes (30-day point prevalence abstinence at 6-months post-engagement) assessed through a variety of data collection approaches. Adoption was measured by the characteristics and proportion of targeted cancer care settings and clinicians engaged in cessation service delivery. Implementation was assessed by examining the delivery of tobacco screening assessments and intervention components across sites, and provider-level implementation consistency. Maintenance assessments identified whether tobacco treatment services continued in the setting after implementation and documented the sustainability plan and organizational commitment to continued delivery. In sum, this paper demonstrates a pragmatic approach to using RE-AIM as an evaluation framework that yields relevant outcomes on common implementation metrics across widely differing tobacco treatment approaches and settings.

Tobacco smoking is an important risk factor for cancer incidence, an effect modifier for cancer treatment, and a negative prognostic factor for disease outcomes. Inadequate implementation of evidence-based smoking cessation treatment in cancer centers, a consequence of numerous patient-, provider-, and system-level barriers, contributes to tobacco-related morbidity and mortality. This study provides data for a paradigm shift from a frequently used specialist referral model to a point-of-care treatment model for tobacco use assessment and cessation treatment for outpatients at a large cancer center. The point-of-care model is enabled by a low-burden strategy, the Electronic Health Record-Enabled Evidence-Based Smoking Cessation Treatment program, which was implemented in the cancer center clinics on June 2, 2018. Five-month pre- and post-implementation data from the electronic health record (EHR) were analyzed. The percentage of cancer patients assessed for tobacco use significantly increased from 48% to 90% (z = 126.57, p < .001), the percentage of smokers referred for cessation counseling increased from 0.72% to 1.91% (z = 3.81, p < .001), and the percentage of smokers with cessation medication significantly increased from 3% to 17% (z = 17.20, p < .001). EHR functionalities may significantly address barriers to point-of-care treatment delivery, improving its consistent implementation and thereby increasing access to and quality of smoking cessation care for cancer center patients.

Background Whether medical or recreational cannabis legalization impacts alcohol or cigarette consumption is a key question as cannabis policy evolves, given the adverse health effects of these substances. Relatively little research has examined this question. The objective of this study was to examine whether medical or recreational cannabis legalization was associated with any change in state-level per capita alcohol or cigarette consumption. Methods Dependent variables included per capita consumption of alcohol and cigarettes from all 50 U.S. states, estimated from state tax receipts and maintained by the Centers for Disease Control and National Institute for Alcohol Abuse and Alcoholism, respectively. Independent variables included indicators for medical and recreational legalization policies. Three different types of indicators were separately used to model medical cannabis policies. Indicators for the primary model were based on the presence of active medical cannabis dispensaries. Secondary models used indicators based on either the presence of a more liberal medical cannabis policy (“non-medicalized”) or the presence of any medical cannabis policy. Difference-in-difference regression models were applied to estimate associations for each type of policy. Results Primary models found no statistically significant associations between medical or recreational cannabis legalization policies and either alcohol or cigarette sales per capita. In a secondary model, both medical and recreational policies were associated with significantly decreased per capita cigarette sales compared to states with no medical cannabis policy. However, post hoc analyses demonstrated that these reductions were apparent at least two years prior to policy adoption, indicating that they likely result from other time-varying characteristics of legalization states, rather than cannabis policy. Conclusion We found no evidence of a causal association between medical or recreational cannabis legalization and changes in either alcohol or cigarette sales per capita.

Little is known about patients' electronic cigarette use, interest in and use of smoking cessation treatments, and providers' attitude towards such treatment. We assessed patients (N = 231) and providers (45 psychiatrists, 97 case workers) in four Community Mental Health Centers. Interestingly, 50% of smokers reported interest in using electronic cigarettes to quit smoking, and 22% reported current use. While 82% of smokers reported wanting to quit or reduce smoking, 91% of psychiatrists and 84% of case workers reported that patients were not interested in quitting as the lead barrier, limiting the provision of cessation interventions. Providers' assumption of low patient interest in treatment may account for the low rate of smoking cessation treatment. In contrast, patients report interest and active use of electronic cigarettes to quit smoking. This study highlights the need for interventions targeting different phases of smoking cessation in these patients suffering disproportionately from tobacco dependence.

Background Recent meta-analyses show that individuals with high risk variants in CHRNA5 on chromosome 15q25 are likely to develop lung cancer earlier than those with low-risk genotypes. The same high-risk genetic variants also predict nicotine dependence and delayed smoking cessation. It is unclear whether smoking cessation confers the same benefits in terms of lung cancer risk reduction for those who possess CHRNA5 risk variants versus those who do not. Methods Meta-analyses examined the association between smoking cessation and lung cancer risk in 15 studies of individuals with European ancestry who possessed varying rs16969968 genotypes (N = 12,690 ever smokers, including 6988 cases of lung cancer and 5702 controls) in the International Lung Cancer Consortium. Results Smoking cessation (former vs. current smokers) was associated with a lower likelihood of lung cancer (OR = 0.48, 95%CI = 0.30–0.75, p = 0.0015). Among lung cancer patients, smoking cessation was associated with a 7-year delay in median age of lung cancer diagnosis (HR = 0.68, 95%CI = 0.61–0.77, p = 4.9 ∗ 10–10). The CHRNA5 rs16969968 risk genotype (AA) was associated with increased risk and earlier diagnosis for lung cancer, but the beneficial effects of smoking cessation were very similar in those with and without the risk genotype. Conclusion We demonstrate that quitting smoking is highly beneficial in reducing lung cancer risks for smokers regardless of their CHRNA5 rs16969968 genetic risk status. Smokers with high-risk CHRNA5 genotypes, on average, can largely eliminate their elevated genetic risk for lung cancer by quitting smoking- cutting their risk of lung cancer in half and delaying its onset by 7 years for those who develop it. These results: 1) underscore the potential value of smoking cessation for all smokers, 2) suggest that CHRNA5 rs16969968 genotype affects lung cancer diagnosis through its effects on smoking, and 3) have potential value for framing preventive interventions for those who smoke., Highlights • CHRNA5 rs16969968 confers risk for earlier lung cancer diagnosis, but quitting produces benefit regardless of genotype. • Smokers can cut their risk of lung cancer in half and delay its onset by 7 years among those diagnosed. • Precision prevention allows clinicians to provide personalized health benefits of smoking cessation. This is a report on whether smoking cessation confers the same benefits in terms of lung cancer risk reduction for those who possess CHRNA5 risk variants versus those who do not. We determined that quitting smoking is highly beneficial in reducing lung cancer risk levels for smokers regardless of their CHRNA5 rs16969968 genetic risk status. Although CHRNA5 rs16969968 increases risk for earlier lung cancer by 4 years, quitting produces essentially the same benefit for smokers with either high or low genetic risks. Smokers can cut their risk of lung cancer in half and delay its onset by 7 years among those diagnosed. These results are important for smokers to prevent cancer. On average, smokers at all genetic risk levels can largely eliminate their elevated risk for lung cancer by quitting smoking.

Several studies have reported that cigarette smoking is inversely associated with the risk of melanoma. This study further tested whether incorporating genetic factors will provide another level of evaluation of mechanisms underlying the association between smoking and risk of melanoma. We investigated the association between SNPs selected from genome-wide association studies (GWAS) on smoking behaviors and risk of melanoma using 2,298 melanoma cases and 6,654 controls. Among 16 SNPs, three (rs16969968 [A], rs1051730 [A] and rs2036534 [C] in the 15q25.1 region) reached significance for association with melanoma risk in men (0.01 < = P values < = 0.02; 0.85 < = Odds Ratios (ORs)

Rural populations face significant smoking-related health disparities, such as a higher prevalence of lung cancer and cancer mortality, higher prevalence of smoking, and lower likelihood of receiving cessation treatment than urban counterparts. A significant proportion of health disparities in rural populations could be eliminated with low-barrier, easy-access treatment delivery methods for smoking cessation. In this study, we assessed treatment engagement among patients in rural and urban settings. Then, we examined the effect of an electronic health record-based smoking cessation module on patient receipt of evidence-based cessation care. As part of a quality improvement project, we retrospectively observed 479,798 unique patients accounting for 1,426,089 outpatient clinical encounters from June 2018&ndash, March 2019 across 766 clinics in the greater St. Louis, southern Illinois, and mid-Missouri regions. Smoking prevalence was higher in rural versus urban clinics (20.7% vs. 13.9%, 6.7% [6.3, 7.1], odds ratio = 1.6 [1.6, 1.6], p &lt, 0.0001), and yet rural smokers were nearly three times less likely than their urban counterparts to receive any smoking cessation treatment after adjusting for patients clustering within clinics (9.6% vs. 25.8%, &minus, 16.2% [&minus, 16.9, &minus, 15.5], odds ratio = 0.304 [0.28, 0.33], p &lt, 0.0001). Although not yet scaled up in the rural setting, we examined the effects of a low-burden, point-of-care smoking module currently implemented in cancer clinics. After adjusting for patient clustering within clinics, patients were more likely to receive smoking treatment in clinics that implemented the module versus clinics that did not implement the module (31.2% vs. 17.5%, 13.7% [10.8, 16.6], odds ratio = 2.1 [1.8, 2.6], p &lt, 0.0001). The point-of-care treatment approach offers a promising solution for rural settings, both in and outside the context of cancer care.

BACKGROUND: Incorporating tobacco treatment within lung cancer screening programs has the potential to influence cessation in high-risk smokers. We aimed to better understand the characteristics of smokers within a screening cohort, correlate those variables with downstream outcomes, and identify predictors of continued smoking. METHODS: This study is a secondary analysis of the National Lung Screening Trial randomized clinical study. Tobacco dependence was evaluated by using the Fagerstrӧm Test for Nicotine Dependence, the Heaviness of Smoking Index, and time to first cigarette (TTFC); descriptive statistics were performed. Clinical outcomes (smoking cessation, lung cancer, and mortality) were assessed with descriptive statistics and χ(2) tests stratified according to nicotine dependence. Logistic and Cox regression models were used to study the influence of dependence on smoking cessation and mortality, respectively. RESULTS: Patients with high dependence scores were less likely to quit smoking compared with low dependence smokers (TTFC OR, 0.50 [95% CI, 0.42-0.60]). Indicators of high dependence, as measured according to all three metrics, were associated with worsening clinical outcomes. TTFC showed that patients who smoked within 5 min of waking (indicating higher dependence) had higher rates of lung cancer (2.07% for > 60 min after waking vs 5.92% ≤ 5 min after waking; hazard ratio [HR], 2.56 [95% CI, 1.49-4.41]), all-cause mortality (5.38% for > 60 min vs 11.21% ≤ 5 min; HR, 2.19 [95% CI, 1.55-3.09]), and lung cancer-specific mortality (0.55% for > 60 min vs 2.92% for ≤ 5 min; HR, 4.46 [95% CI, 1.63-12.21]). CONCLUSIONS: Using TTFC, a one-question assessment of tobacco dependence, at the time of lung cancer screening has implications for personalizing tobacco treatment and improving risk assessment.

In an era of Precision Medicine, it is vital to collect biological data within clinical trials and to integrate their analysis within the outcomes of the trial. The identification of genomic biomarkers that affect treatment response to smoking cessation treatment, both pharmacological and behavioral, or susceptibility to medication-related adverse reactions, holds real promise to improve treatment efficacy and to tailor the treatment approach to the individual. However, a clear challenge in identifying reliable biomarkers is in obtaining adequate sample sizes. Consortium-based approaches will likely be necessary to yield real successes. Thus, meta-analyses of data from individual smoking cessation trials will become crucial and will be facilitated by standardized trial designs, assessments, and outcomes and harmonizable measures. To foster increased collection of high-quality genetics data in clinical trials, we discuss (1) genetically informed trial design, (2) biological samples (collection requirements, storage, and analysis with a focus on genomic data) and genetics consortia, (3) participant consent and data sharing requirements for Institutional Review Board (IRB) approvals, and (4) information on phenotype characterization and meta-analysis. This work aligns with the objectives of the Precision Medicine Initiative and offers guidance for integrating treatment research and genetics/genomics within the nicotine and tobacco research community. It is intended to promote the collection and genotyping of biosamples in existing subject samples as well as the integration of genetic research elements into future study designs. This article crossreferences a companion paper in this issue that reviews current evidence on genetic and epigenetic markers in cessation trials. Implications: This article outlines a framework for the consistent integration of biological data/ samples into smoking cessation pharmacotherapy trials, aligned with the objectives of the recently unveiled Precision Medicine Initiative. Our goal is to encourage and provide support for treatment researchers to consider biosample collection and genotyping their existing samples as well as integrating genetic analyses into their study design in order to realize precision medicine in treatment of nicotine dependence.

Overall Abstract Introduction Recent genetic studies have identified variants in specific gene regions showing not only evidence of association but also biological mechanisms underlying the association. The goal of this symposium is to demonstrate how human genetic association findings can be translated into improved understanding of biological mechanism, disease prognosis, and treatment response using examples across different psychiatric disorders such as mood disorders, schizophrenia, eating disorder, and addiction. Methods A variety of methods are used including randomized treatment trials, large genome wide association studies or meta-analyses, bioinformatics, and epigenetics. Results The first talk by Dr. Schulze is on genotype-phenotype relationships in bipolar and related traits. He will demonstrate how applying the friendly data sharing approach spanning five continents has helped pave the way for a better understanding of the genetic basis of response to lithium treatment, serving as an example for successful precision medicine research. The 2nd talk by Dr. Muller is on current evidence, evidence adjudication, implementation challenges of pharmacogenetics testing for antidepressants and antipsychotics use. He will present a review of the evidence that was considered in formulating that guidance, along with a summary of the main recommendations and associated ethical issues. He will also discuss the kinds of evidence still needed to incorporate genetic testing in clinical decision-making. The 3rd talk by Dr. Breen is on translation of genetic findings from large GWAS studies in Psychiatric Genomics Consortium (PGC) into druggable genomes. He will discuss how discovery of the biological pathways can not only reveal etiological pathways but also lead to drug discovery. The 4th talk by Dr. Chen demonstrates the significant genetic variants that predict nicotine dependence, smoking cessation, and response to cessation pharmacotherapy. These data suggest that genetic risks may predict smoking cessation outcomes and moderate the effect of pharmacological treatments. Some pharmacogenetic findings have been replicated in meta-analyses of multiple smoking cessation trials. The variation in efficacy between smokers with different genetic markers suggests that personalized smoking cessation pharmacotherapy based upon genotype could maximize the efficiency of such treatment while minimizing side effects. As a result, these genetic markers are useful to predict clinical predictor such as the number needed to treat (NNT) and the number needed to harm. Conclusion Different psychiatric disorders serve as successful examples in how genetic findings in human association studies inform biological experiments to uncover the underlying mechanisms and treatments tailored to individual genetic background. Each presentation represents unique and complementary research paradigms in understanding how genomics inform personalized medicine by linking the biology and treatment in different psychiatric disorders. Learning Objectives 1) human genetic studies inform the biological mechanisms of psychiatric disorders, 2) pharmacogenetic studies inform personalized treatments with maximized efficacy and minimized risk of side effects.

The goal of this study was to examine participant responses to disclosure of genetic results in a minority population at high risk for depression and anxiety. Eighty-two subjects in a genetic study of nicotine dependence were offered personalized genetic results. All were nicotine-dependent and 64% self-identified as African American. Pathway Genomics was used to evaluate genetic risks for five complex diseases. Participants returned 4–8 weeks after enrollment for in-person genetic counseling interviews and evaluation of baseline measures. A telephone follow-up was performed 4–8 weeks later to assess responses to results. Fifty of the 82 subjects (61%) were interested in receiving genetic results. These participants had multiple risk factors, including high baseline measures of depression (66%) and anxiety (32%), as well as low rates of employment (46%), adequate health literacy (46%), and health insurance (45%). Pathway Genomics reported “increased risk” for at least one disease in 77% of subjects. Ninety-five percent of participants reported that they appreciated the genetic results, and receiving these results was not associated with changes in symptoms of depression or anxiety. Furthermore, after return of genetic results, smoking cessation attempts increased (P = 0.003). Even in an underserved population at high risk for adverse psychological reactions, subjects responded positively to personalized genetic results. Genet Med 17 5, 374–379.

Cigarette smoking leads to large healthcare and morbidity costs, and mortality losses, and smoking cessation plays a key role in reducing health risk and economic costs. While medical evidence suggests that some smokers are more likely to respond to medication treatment than others depending on genetic markers, it remains unexplored whether pharmacogenetic testing is cost-effective in treating potential quitters of smoking. We address this knowledge gap by developing a lifecycle model in which individuals make smoking, health investment and consumption-savings decisions. Depending on an individual's genotype, smoking may bring enjoyment but deteriorates one's health, and the dynamic evolution of health capital determines life expectancy. In addition to heterogeneous genotypes, individuals also differ in demographics. We calibrate this model to fit key economic and medical observations in the U.S. We then propose three smoking cessation policies, two with standard treatments and one personalized depending on genetic markers, all under the same program costs. We construct two unified measures of effectiveness and subsequently compute the cost-effectiveness ratio. We find that personalized treatment is the most cost-effective: for each dollar of program cost, it generates $8.94 value in effectiveness, which is 22-45% higher than those under standard treatments.

Introduction Effective smoking cessation medications are readily available but may be underutilized in hospital settings. In our large, tertiary care hospital, we aimed to (1) characterize patient tobacco use prevalence across medical specialties, (2) determine smoking cessation pharmacotherapy prescription variation across specialties, and (3) identify opportunities for improvement in practice. Methods Using electronic health records at Barnes Jewish Hospital, we gathered demographic data, admitting service, admission route, length of stay, self-reported tobacco use, and smoking cessation prescriptions over a 6-year period, from 2010 to 2016. We then compared tobacco use prevalence and smoking cessation prescriptions across medical specialties using a cross-sectional, retrospective design. Results Past 12-month tobacco use was reported by patients in 27.9% of inpatient admissions; prescriptions for smoking cessation pharmacotherapy were provided during 21.5% of these hospitalizations. The proportion of patients reporting tobacco use was highest in psychiatry (55.3%) and lowest in orthopedic surgery (17.1%). Psychiatric patients who reported tobacco use were most likely to receive pharmacotherapy (71.8% of admissions), and plastic surgery patients were least likely (4.7% of admissions). Compared with Caucasian tobacco users, African American patients who used tobacco products were less likely to receive smoking cessation medications (adjusted odds ratio [aOR] = 0.65; 95% confidence interval [CI] = 0.62 to 0.68). Conclusions Among hospitalized tobacco users, safe and cost-effective pharmacotherapies are under-prescribed. We identified substantial variation in prescribing practices across different medical specialties and demographic groups, suggesting the need for an electronic medical record protocol that facilitates consistent tobacco use cessation pharmacotherapy treatment. Implications Tobacco use cessation pharmacotherapy is underutilized during hospitalization, and prescription rates vary greatly across medical specialties and patient characteristics. Hospitals may benefit from implementing policies and practices that standardize and automate the offer of smoking pharmacotherapy for all hospitalized patients who use tobacco.

INTRODUCTION: Genetic variants associated with nicotine dependence have previously been identified, primarily in European-ancestry populations. No genome-wide association studies (GWAS) have been reported for smoking behaviors in Hispanics/Latinos in the United States and Latin America, who are of mixed ancestry with European, African, and American Indigenous components. METHODS: We examined genetic associations with smoking behaviors in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) (N = 12 741 with smoking data, 5119 ever-smokers), using ~2.3 million genotyped variants imputed to the 1000 Genomes Project phase 3. Mixed logistic regression models accounted for population structure, sampling, relatedness, sex, and age. RESULTS: The known region of CHRNA5, which encodes the α5 cholinergic nicotinic receptor subunit, was associated with heavy smoking at genome-wide significance (p ≤ 5 × 10(–8)) in a comparison of 1929 ever-smokers reporting cigarettes per day (CPD) > 10 versus 3156 reporting CPD ≤ 10. The functional variant rs16969968 in CHRNA5 had a p value of 2.20 × 10(–7) and odds ratio (OR) of 1.32 for the minor allele (A); its minor allele frequency was 0.22 overall and similar across Hispanic/Latino background groups (Central American = 0.17; South American = 0.19; Mexican = 0.18; Puerto Rican = 0.22; Cuban = 0.29; Dominican = 0.19). CHRNA4 on chromosome 20 attained p < 10(–4), supporting prior findings in non-Hispanics. For nondaily smoking, which is prevalent in Hispanic/Latino smokers, compared to daily smoking, loci on chromosomes 2 and 4 achieved genome-wide significance; replication attempts were limited by small Hispanic/Latino sample sizes. CONCLUSIONS: Associations of nicotinic receptor gene variants with smoking, first reported in non-Hispanic European-ancestry populations, generalized to Hispanics/Latinos despite different patterns of smoking behavior. IMPLICATIONS: We conducted the first large-scale genome-wide association study (GWAS) of smoking behavior in a US Hispanic/Latino cohort, and the first GWAS of daily/nondaily smoking in any population. Results show that the region of the nicotinic receptor subunit gene CHRNA5, which in non-Hispanic European-ancestry smokers has been associated with heavy smoking as well as cessation and treatment efficacy, is also significantly associated with heavy smoking in this Hispanic/Latino cohort. The results are an important addition to understanding the impact of genetic variants in understudied Hispanic/Latino smokers.

BackgroundThere are high levels of comorbidity between schizophrenia and substance use disorder, but little is known about the genetic etiology of this comorbidity.MethodsHere, we test 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: COGEND (ascertained for nicotine dependence n=918 cases, 988 controls), COGA (ascertained for alcohol dependence n=643 cases, 384 controls), and FSCD (ascertained for cocaine dependence n=210 cases, 317 controls). Phenotypes were harmonized across the three datasets and standardized analyses were performed. Genome-wide genotypes were imputed to 1000 Genomes reference panel.ResultsIn 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 R2 range 0.8%-3.7%, minimum p=4×10-23).ConclusionsThese 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.

ObjectivesTo generate system insights on patient and provider levers and strategies that must be activated to improve hospital-based smoking cessation treatment.DesignMixed methods study including a series of in-depth group model building sessions, which informed the design of an online survey completed by healthcare providers and a structured interview protocol administered at the bedside to patients who smoke.SettingLarge, tertiary care hospital in the Midwestern United States.ParticipantsGroup model building: 28 healthcare providers and 22 previously-hospitalised patients;Online survey: 308 healthcare providers;Bedside interviews: 205 hospitalised patients.Primary and secondary outcome measuresHypothesis-generating, participatory qualitative methods informed the examination of the following quantitative outcomes: patient interest versus provider perception of patient interest in smoking cessation and treatment; patient-reported receipt versus provider-reported offering of inpatient smoking cessation interventions; and priority ratings of importance and feasibility of strategies to improve treatment.ResultsSystem insightsincluded patients frequently leaving the floor to smoke, which created major workflow disruption.Leverage pointsincluded interventions to reduce withdrawal symptoms, andaction ideasincluded nurse-driven protocols for timely administration of nicotine replacement therapy. Quantitative data corroborated system insights; for instance, 80% of providers reported that patients frequently leave the floor to smoke, leading to safety risks, missed assessments and inefficient use of staff time. Patients reported significantly lower rates of receiving any smoking cessation interventions, compared with provider reports (mean difference=17.4%–33.7%, pConclusionsEngaging hospital providers and patients in participatory approaches to develop an implementation strategy revealed discrepant perceptions of patient interest and frequency of hospital-based treatment for smoking. These findings spurred adoption of standardised point-of-care treatment for cigarette smoking, which remains highly prevalent yet undertreated among hospitalised patients.

Introduction: Cigarette smoking is highly addictive and prevalent among individuals with mental illness. Modern genetic research has identified robust genetic influences on nicotine dependence. An important step in translating these genetic findings is to identify the genetic factors affecting smoking cessation in order to enhance current smoking cessation treatments. Methods We investigated the significant genetic variants that predict nicotine dependence, smoking cessation, and response to cessation pharmacotherapy using both large cohort studies of smokers and randomized controlled trials. Results We find that genetic risks may predict prognosis and response to pharmacological treatments. For prognosis, the nicotinic receptor genotypes predict delayed quitting and earlier lung cancer diagnoses after adjusting for smoking quantity in large meta-analyses of studies of lung cancer and nicotine dependence. For treatment response, some pharmacogenetic findings have been replicated in meta-analyses or multiple smoking cessation trials for both pharmacodynamic markers (e.g., CHRNA5) and pharmacokinetic marker (e.g., CYP2A6). The variation in efficacy between smokers with different genetic markers supports the notion that personalized smoking cessation intervention based upon genotype could maximize the efficiency of such treatment while minimizing side effects, thus influencing the number needed to treat (NNT) and the number needed to harm. Conclusion Current evidence strongly suggests that cessation failure and cessation pharmacotherapy effectiveness are modulated by biomarkers such as nicotinic receptor genotypes, nicotine metabolism ratio (NMR), or polygenic risk scores for nicotine dependence. These findings strengthen the case for further research on genetics of smoking cessation success, and the development and rigorous testing of treatments for smokers with different genetic risk profiles. Learning Objectives 1) human genetic studies can inform the biological mechanisms of nicotine dependence and health consequences such as lung cancer, 2) pharmacogenetic studies may inform personalized treatments for tobacco use disorder, possibly increasing efficacy and reducing risk of side effects.

Cigarette smoking is highly addictive, and modern genetic research has identified robust genetic influences on nicotine dependence. An important step in translating these genetic findings is to identify the genetic factors affecting smoking cessation in order to enhance current smoking cessation treatments. We review the significance of variants in the nicotinic receptor gene cluster ( CHRNA5–CHRNA3–CHRNB4 ) in the prediction of smoking quantity, smoking cessation, and response to cessation medication in multiple studies of smoking cessation. Three common haplotypes (low-risk, intermediate-risk, and high-risk) in the CHRNA5–CHRNA3–CHRNB4 region are defined by rs16969968 and rs680244. The genetic variants in the CHRNA5–CHRNA3–CHRNB4 region that predict nicotine dependence also predicted a later age of smoking cessation in a community-based sample. In a smoking cessation trial, these variants predicted abstinence at the end of treatment in individuals receiving placebo medication, but not among individuals receiving active medication. Pharmacological treatments moderate the genetic risk in affecting cessation success. These pharmacogenetic interactions have been reproduced by a recent meta-analysis of smoking cessation trials. The number needed to treat was four for smokers with the high-risk haplotype, seven for smokers with the intermediate-risk haplotype, and > 1000 for smokers with the low-risk haplotype. The wide variation in number needed to treat between smokers with different haplotypes supports the notion that personalized smoking cessation intervention based upon genotype could meaningfully increase the efficiency of such treatment. In summary, variants in the CHRNA5–CHRNA3–CHRNB4 region identify individuals at increased risk of cessation failure, and this increased risk can be ameliorated by cessation pharmacotherapy.

When smokers were separated by their nicotinic receptor gene variants, those with the low-risk genotype responded equally well to pharmacological treatments, including both nicotine replacement and bupropion, and nonpharmacological therapies. Those with the high-risk genotype, as identified by DNA sequencing, responded only to pharmacological treatments. Clinicians advising patients on smoking cessation can suspect genetic risk on the basis of early onset of heavy smoking and direct those smokers specifically to pharmacological treatments.

BACKGROUND: Several studies have found replicable associations between nicotine dependence and specific variants in the nicotinic receptor genes CHRNA5(rs16969968) and CHRNA3(rs3743078). How these newly identified genetic risks combine with known environmental risks is unknown. This study examined whether the level of parent monitoring during early adolescence modified the risk of nicotine dependence associated with these genetic variants. METHODS: In a cross-sectional case control study of US-based community sample of 2027 subjects, we use a systematic series of regression models to examine the effect of parent monitoring on risk associated with two distinct variants in the nicotinic receptor genes CHRNA5(rs16969968) and CHRNA3(rs3743078). RESULTS: Low parent monitoring as well as the previously identified genetic variants were associated with an increased risk of nicotine dependence. An interaction was found between the SNP(rs16969968) and parent monitoring (p=0.034). The risk for nicotine dependence increased significantly with the risk genotype of SNP(rs16969968) when combined with lowest quartile parent monitoring. In contrast, there was no evidence of an interaction between SNP(rs3743078) and parent monitoring (p=0.80). CONCLUSIONS: The genetic risk of nicotine dependent associated with rs16969968 was modified by level of parent monitoring, while the genetic risk associated with rs3743078 was not, suggesting that the increased risk due to some genes may be mitigated by environmental factors such as parent monitoring.

Item does not contain fulltext BACKGROUND: Recent meta-analyses show strong evidence of associations among genetic variants in CHRNA5 on chromosome 15q25, smoking quantity, and lung cancer. This meta-analysis tests whether the CHRNA5 variant rs16969968 predicts age of smoking cessation and age of lung cancer diagnosis. METHODS: Meta-analyses examined associations between rs16969968, age of quitting smoking, and age of lung cancer diagnosis in 24 studies of European ancestry (n = 29 072). In each dataset, we used Cox regression models to evaluate the association between rs16969968 and the two primary phenotypes (age of smoking cessation among ever smokers and age of lung cancer diagnosis among lung cancer case patients) and the secondary phenotype of smoking duration. Heterogeneity across studies was assessed with the Cochran Q test. All statistical tests were two-sided. RESULTS: The rs16969968 allele (A) was associated with a lower likelihood of smoking cessation (hazard ratio [HR] = 0.95, 95% confidence interval [CI] = 0.91 to 0.98, P = .0042), and the AA genotype was associated with a four-year delay in median age of quitting compared with the GG genotype. Among smokers with lung cancer diagnoses, the rs16969968 genotype (AA) was associated with a four-year earlier median age of diagnosis compared with the low-risk genotype (GG) (HR = 1.08, 95% CI = 1.04 to 1.12, P = 1.1*10(-5)). CONCLUSION: These data support the clinical significance of the CHRNA5 variant rs16969968. It predicts delayed smoking cessation and an earlier age of lung cancer diagnosis in this meta-analysis. Given the existing evidence that this CHRNA5 variant predicts favorable response to cessation pharmacotherapy, these findings underscore the potential clinical and public health importance of rs16969968 in CHRNA5 in relation to smoking cessation success and lung cancer risk.

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.

Objective Recent evidence suggests that the efficacy of smoking cessation pharmacotherapy can vary across patients based on their genotypes. This study tests whether the coding variant rs16969968 in the CHRNA5 nicotinic receptor gene predicts the effects of combination nicotine replacement therapy (cNRT) and varenicline on treatment outcomes. Method In two randomized smoking cessation trials comparing cNRT vs. placebo, and varenicline vs. placebo, we used logistic regression to model associations between CHRNA5 rs16969968 and abstinence at end of treatment. Results For abstinence at end of treatment, there was an interaction between cNRT and rs16969968 (X2 = 8.15, df = 2, omnibus-p = 0.017 for the interaction); individuals with the high-risk AA genotype were more likely to benefit from cNRT. In contrast, varenicline increased abstinence, but its effect did not vary with CHRNA5. However, the genetic effects differed between the placebo control groups across two trials (wald = 3.94, df = 1, p = 0.047), this non-replication can alter the interpretation of pharmacogenetic findings. Conclusions Results from two complementary smoking cessation trials demonstrate inconsistent genetic results in the placebo arms. This evidence highlights the need to compare the most effective pharmacotherapies with the same placebo control to establish pharmacogenetic evidence to aid decisions on medication choice for patients trying to quit smoking.

To assess if the allelic variations of rs16969968/rs1051730 in the CHRNA5-CHRNA3-CHRNB4 gene cluster are associated with smoking cessation after nicotine replacement therapy (NRT).We searched for NRT studies published from 2000-2013 that reported counts for allelic variation of rs16969968/rs1051730 and measured abstinence rates at the end of NRT treatment. We identified four studies which met the criteria, giving us a test sample of 2036 participants.There was no effect of rs16969968/rs1051730 in influencing the success rate at the end of NRT (n = 6, effect size [ES]: 0.969, 95% CI: 0.77 to 1.23, z = 0.27, p = 0.791).There is no robust evidence that allelic variations of rs16969968 or rs1051730 are associated with smoking cessation after NRT. Original submitted 26 November 2014; Revision submitted 9 March 2015.

Background Inflammatory bowel disease (IBD) is a chronic luminal disease with genetic and environmental factors affecting phenotype. This study evaluated the relationship between CHRNA5, a nicotinic receptor subunit gene, and smoking in predicting IBD-related surgery as well as the relationship between CHRNA5 and nicotine dependence. Methods Participants completed a smoking questionnaire and were genotyped for CHRNA5 rs16969968. Demographic and clinical data were obtained from medical records. Wilcoxon, ANOVA, Chi square, and Fisher's exact tests were used for comparisons. Logistic regression was used to evaluate the effect of clinical and genetic predictors on surgery, stratified by disease subtype given paradoxical effects of smoking. Kaplan-Meier curves were used to examine the effect of smoking and genotype on time to surgery. (Significance: P AG > GG; P = 0.08]). Smoking and genotype were not independently associated with surgery in UC or CD. However, interaction between rs16969968 and smoking in predicting surgery was observed for both UC (OR = 2.72; P = 0.05) and CD (OR = 2.88; P = 0.1). CHRNA5 genotype, but not smoking, predicted time to surgery in patients with UC (P = 0.007) but not in patients with CD. The interaction between smoking and genotype was not significantly associated with time to surgery in UC or CD. Conclusions The CHRNA5 rs16969968 A variant interacts with smoking to influence IBD-related surgery. 10.1093/ibd/izx094_video1izx094.video15775248538001.

Rationale: The CHRNA5-CHRNA3-CHRNB4 locus is associated with self-reported smoking behavior and also harbors the strongest genetic associations with chronic obstructive pulmonary disease (COPD) and lung cancer. Because the associations with lung disease remain after adjustment for self-reported smoking behaviors, it has been asserted that CHRNA5-CHRNA3-CHRNB4 variants increase COPD and lung cancer susceptibility independently of their effects on smoking. Objectives: To compare the genetic associations of exhaled carbon monoxide (CO), a biomarker of current cigarette exposure, with self-reported smoking behaviors. Methods: A total of 1,521 European American and 247 African American current smokers recruited into smoking cessation studies were assessed for CO at intake before smoking cessation. DNA samples were genotyped using the Illumina Omni2.5 microarray. Genetic associations with CO and smoking behaviors (cigarettes smoked per day, Fagerstrom test for nicotine dependence) were studied. Measurements and Main Results: Variants in the CHRNA5-CHRNA3-CHRNB4 locus, including rs16969968, a nonsynonymous variant in CHRNA5, are genomewide association study–significantly associated with CO (β = 2.66; 95% confidence interval [CI], 1.74–3.58; P = 1.65 × 10−8), and this association remains strong after adjusting for smoking behavior (β = 2.18; 95% CI, 1.32–3.04; P = 7.47 × 10−7). The correlation between CO and cigarettes per day is statistically significantly lower (z = 3.43; P = 6.07 × 10−4) in African Americans (r = 0.14; 95% CI, 0.02–0.26; P = 0.003) than in European-Americans (r = 0.36; 95% CI, 0.31–0.40; P = 0.0001). Conclusions: Exhaled CO, a biomarker that is simple to measure, captures aspects of cigarette smoke exposure in current smokers beyond the number of cigarettes smoked per day. Behavioral measures of smoking are therefore insufficient indices of cigarette smoke exposure, suggesting that genetic associations with COPD or lung cancer that persist after adjusting for self-reported smoking behavior may still reflect genetic effects on smoking exposure.

INTRODUCTION While smoking is a major modifiable risk factor for secondary prevention of myocardial infarction (MI), active smoking is common among patients hospitalized with acute MI. Recent studies suggest that nicotinic receptor variants, and specifically the high-risk CHRNA5 rs16969968 A allele, are associated with cessation failure among noncardiac patients. This study investigates the association between CHRNA5 rs16969968 and smoking cessation in patients hospitalized with acute MI. METHODS Using data from the TRIUMPH study, we ascertained smoking status at the time of index hospitalization for acute MI and 1 year after hospitalization. After adjusting for age and sex, we used logistic regression to model the association between smoking cessation and CHRNA5 rs16969968. RESULTS At index admission, 752 Caucasian subjects were active smokers and 699 were former smokers. Among these ever-smokers, the A allele was associated with significantly decreased abstinence (45.0% abstinence for A allele carriers vs. 51.7% for GG homozygotes; odds ratio [OR] = 0.70, 95% confidence interval [CI] = 0.56-0.88, p = .0027). The A allele was also significantly associated with decreased abstinence at 1 year (69.1% abstinence for A allele carriers vs. 76.0% for GG homozygotes; OR = 0.70, 95% CI = 0.53-0.94, p = .0185). CONCLUSIONS Among patients who have smoked and who are hospitalized with acute MI, the high-risk CHRNA5 allele was associated with lower likelihood of quitting before hospitalization and significantly less abstinence 1 year after hospitalization with MI. The CHRNA5 rs16969968 genotype may therefore identify patients who would benefit from aggressive, personalized smoking cessation intervention.

This study tests whether the genetic predictor (CHRNA5 nicotine receptor gene variants) and an environmental risk factor (partner smoking) interact in the prediction of smoking reduction.Subjects were from a community-based, longitudinal study of women (n=1856) who smoked before pregnancy, and a randomized comparative effectiveness smoking cessation trial (n=1065). Smoking reduction was defined as the trajectory of self-reported smoking quantities over time in the observational study, and as the trajectory of alveolar CO levels in the cessation trial.In the pregnancy study, rs16969968 genotype and partner smoking status interacted such that the smoking reduction was lowest for expectant mothers with high genetic risk and partner smoking, and highest for those with high genetic risk but not partner smoking (interaction of genotype×partner smoking on smoking quantity trajectory slope β=0.071, 95%CI=0.013, 0.13, p=0.017). In the clinical trial, a similar interaction was found (interaction β=0.20, 95%CI=0.049, 0.36, p=0.010). Furthermore, these associations were moderated by pharmacotherapy such that the interactive relation of genetic and environmental factors occurred in the placebo group, but not in the active pharmacotherapy group (interaction of genotype×partner smoking×pharmacotherapy on CO trajectory slope β=-0.25, 95%CI=-0.42, -0.091, p=0.0023).The CHRNA5 genetic risk synergized the effect of partner smoking, producing an especially low likelihood of successful smoking reduction in two complementary studies. This suggests that the genetic vulnerability may be mitigated by altering environmental factors. In addition, cessation pharmacotherapy neutralizes the increase in cessation failure associated with combined genetic and environmental risks, which has possible relevance to treatment algorithms.