Smoking Cessation Clinical Trial Research Articles

The Nicotine Metabolite Ratio is Associated With Early Smoking Abstinence Even After Controlling for Factors That Influence the Nicotine Metabolite Ratio

The decrease in smoking rates in North America has plateaued, underscoring the need for new approaches to treat nicotine dependence. Inter-individual differences in smoking behavior result, in part, from variation in the rate of CYP2A6-mediated nicotine metabolism. A phenotypic measure of CYP2A6 activity is the nicotine metabolite ratio (NMR), the ratio of 3'hydroxycotinine/cotinine. The NMR is associated with smoking cessation. However, the NMR is also associated with genetic (eg, CYP2A6 genotype) and other (eg, sex and ethnicity) factors. Here we aimed to determine if previously identified non-CYP2A6 sources of variation in the NMR mitigated the association between the NMR and short-term abstinence. The NMR was determined from blood samples collected at intake from daily smokers aged 18-65. Biochemically-verified point prevalence abstinence (exhaled carbon monoxide level ≤ 8 ppm) was measured at 1 week following the target quit date in participants from a smoking cessation clinical trial (NCT01314001). Analyses were restricted to N = 462 blacks and N = 693 whites in the intent-to-treat sample. Lower NMR (<0.31) was associated with a higher likelihood of 1-week abstinence (OR = 1.43; 95% CI = 1.12, 1.84). NMR was associated with abstinence even after controlling for treatment arm (nicotine patch or varenicline) and factors previously associated with NMR variation including sex, ethnicity, estrogen-containing hormonal therapy, body mass index, alcohol, and cigarette consumption. NMR was associated with 1-week smoking abstinence; NMR may be a useful addition to medication screening approaches evaluating treatments for nicotine dependence.

The impact of non-concordant self-report of substance use in clinical trials research

BackgroundStudies comparing self-report substance use data to biochemical verification generally demonstrate high rates of concordance. We argue that these rates are due to the relatively high true negative rate in the general population, and high degree of honestly in treatment seeking individuals. We hypothesized that high risk individuals not seeking treatment would demonstrate low concordance and a high false negative rate of self-reported substance use. MethodsA sample of 500 individuals from a smoking cessation clinical trial was assessed over 1year. Assessments included semi-structured interviews, questionnaires (e.g. Addiction Severity Index, etc.), and urine drug screen assays (UDS). Generalized estimating equations (GEEs) were used to predict false negative reports for various substances across the study and determine the influence of substance use on the primary study outcome of smoking cessation. ResultsParticipants demonstrated high false negative rates in reporting substances use, and the false negative rates increased as the study progressed. Established predictors of false negatives generalized to the current sample. High concordance and low false negative rates were found in self-report of nicotine use. A small but significant relationship was found in for effect of biochemically verified substance use on smoking cessation. ConclusionsBiochemical verification of substance use is needed in high risk populations involved in studies not directly related to the treatment of substance use, especially in populations with high threat of stigmatization. Testing should continue through the time period of the study for maximal identification of substance use.

Interaction of depressive symptoms and smoking abstinence on delay discounting rates.

Delay discounting and depressive symptomatology have strong connections with smoking. However, few studies have examined interactions across delay discounting, depressive symptoms, and smoking status. The primary goal of this secondary analysis was to assess the interrelations across these 3 variables among treatment-seeking smokers. Delay discounting and depressive symptoms were assessed in 95 smokers enrolled in a clinical trial for smoking cessation at intake and 6-month follow-up. Participants with and without depressive symptoms did not differ in their discounting rates neither at intake nor at 6-month follow-up. However, delay discounting was significantly lower among abstainers at 6-month follow-up, and changes in discounting associated with smoking status were more pronounced among participants with depressive symptoms. These results clarify the relationship between delay discounting and depressive symptoms among current and former smokers and suggest that the association between smoking abstinence and lower delay discounting is significantly greater among individuals with depressive symptoms versus those who do not have depressive symptomatology.

Correspondence between self-reported and biochemical measures of smoking in opioid-dependent pregnant women

Aims: Smoking exacerbates adverse outcomes among opioiddependent pregnant women (e.g., a more severe neonatal abstinence syndrome in exposed neonates). Vermont birth certificate data for opioid-dependent pregnant women indicates a significant decrease in their self-reported smoking rate over the course of pregnancy, from a mean of 17.9 cigarettes per day (CPD) prior to pregnancy to 13.8, 10.9, and 9.7 in the first, second and third trimesters, respectively. This study examined self-reported smoking rate andbiochemicalmeasures of smoking to testwhether self-reported decreases in smokingwere paralleled by decreases in biochemical measures. Methods: Participants were 18 opioid-dependent pregnant women enrolled in clinical trials for smoking cessation. All women continued to smoke throughout their pregnancies. Self-reported CPD prior to pregnancy were collected at the Intake Assessment. CPD, breathCO, andurine cotininewere collected at intake, at a second assessment 1 month later (Early Pregnancy Assessment), and againat theendofpregnancy (≥28weeksgestation; LatePregnancy Assessment). Results: Like birth certificate data, self-reported smoking rates decreased from amean of 22.6 prior to pregnancy to 15.5 at intake. During pregnancy, self-reported CPD decreased significantly from 15.5, 7.5, and 9.0 at Intake and Early and Late Pregnancy Assessments, respectively (p< .001). However, parallel changes were not evident in biochemical measures of smoking. Mean CO was 13.3, 10.0, and 12.3ppm (p= .11) and mean urine cotinine was 1422.8, 1387.8, and 1294.1ng/ml (p= .71) at the three assessments. Conclusions: Discrepancies between self-report and biochemical measures may be explained by misrepresentation of self-reported smoking or reductions in CPD offset by changes in smoking topography (i.e., compensatory smoking). Further research is needed to understand changes in smoking among opioid-dependent pregnant women. Financial support:NICHDR01HD075669, NIDAR01DA014028 and R01 DA031928, and FDA/NIDA P50 DA036114.

Attitudes and interest in technology-based treatment and the remote monitoring of smoking among adolescents and emerging adults.

Despite the public health relevance of smoking in adolescents and emerging adults, this group remains understudied and underserved. High technology utilization among this group may be harnessed as a tool for better understanding of smoking, yet little is known regarding the acceptability of mobile health (mHealth) integration. Participants (ages 14-21) enrolled in a smoking cessation clinical trial provided feedback on their technology utilization, perceptions, and attitudes; and interest in remote monitoring for smoking. Characteristics that predicted greater technology acceptability for smoking treatment were also explored. Participants (N=87) averaged 19 years old and were mostly male (67%). Technology utilization was high for smart phone ownership (93%), Internet use (98%), and social media use (94%). Despite this, only one-third of participants had ever searched the Internet for cessation tips or counseling (33%). Participants showed interest in mHealth-enabled treatment (48%) and felt that it could be somewhat helpful (83%). Heavier smokers had more favorable attitudes toward technology-based treatment, as did those with smartphones and unlimited data. Our results demonstrate high technology utilization, favorable attitudes towards technology, and minimal concerns. Technology integration among this population should be pursued, though in a tailored fashion, to accomplish the goal of providing maximally effective, just-in-time interventions.

Integrating smoking cessation and alcohol use treatment in homeless populations: study protocol for a randomized controlled trial.

BackgroundDespite progress in reducing cigarette smoking in the general U.S. population, smoking rates, cancer morbidity and related heart disease remain strikingly high among the poor and underserved. Homeless individuals’ cigarette smoking rate remains an alarming 70 % or greater, and this population is generally untreated with smoking cessation interventions. Furthermore, the majority of homeless smokers also abuse alcohol and other drugs, which makes quitting more difficult and magnifies the health consequences of tobacco use.Methods/DesignParticipants will be randomized to one of three groups, including (1) an integrated intensive smoking plus alcohol intervention using cognitive behavioral therapy (CBT), (2) intensive smoking intervention using CBT or (3) usual care (i.e., brief smoking cessation and brief alcohol counseling). All participants will receive 12-week treatment with a nicotine patch plus nicotine gum or lozenge. Counseling will include weekly individual sessions for 3 months, followed by monthly booster group sessions for 3 months. The primary smoking outcome is cotinine-verified 7-day smoking abstinence at follow-up week 52, and the primary alcohol outcome will be breathalyzer-verified 90-day alcohol abstinence at week 52.DiscussionThis study protocol describes the design of the first community-based controlled trial (n = 645) designed to examine the efficacy of integrating alcohol abuse treatment with smoking cessation among homeless smokers. To further address the gap in effectiveness of evidence-based smoking cessation interventions in the homeless population, we are conducting a renewed smoking cessation clinical trial called Power to Quit among smokers experiencing homelessness.Trial registrationClinicalTrials.gov Identifier: NCT01932996. Date of registration: 20 November 2014.

New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes

Dynamic treatment regimes (DTRs) are sequential decision rules for individual patients that can adapt over time to an evolving illness. The goal is to accommodate heterogeneity among patients and find the DTR which will produce the best long-term outcome if implemented. We introduce two new statistical learning methods for estimating the optimal DTR, termed backward outcome weighted learning (BOWL), and simultaneous outcome weighted learning (SOWL). These approaches convert individualized treatment selection into an either sequential or simultaneous classification problem, and can thus be applied by modifying existing machine learning techniques. The proposed methods are based on directly maximizing over all DTRs a nonparametric estimator of the expected long-term outcome; this is fundamentally different than regression-based methods, for example, Q-learning, which indirectly attempt such maximization and rely heavily on the correctness of postulated regression models. We prove that the resulting rules are consistent, and provide finite sample bounds for the errors using the estimated rules. Simulation results suggest the proposed methods produce superior DTRs compared with Q-learning especially in small samples. We illustrate the methods using data from a clinical trial for smoking cessation. Supplementary materials for this article are available online.

Multivariate longitudinal data analysis with mixed effects hidden Markov models.

Multiple longitudinal responses are often collected as a means to capture relevant features of the true outcome of interest, which is often hidden and not directly measurable. We outline an approach which models these multivariate longitudinal responses as generated from a hidden disease process. We propose a class of models which uses a hidden Markov model with separate but correlated random effects between multiple longitudinal responses. This approach was motivated by a smoking cessation clinical trial, where a bivariate longitudinal response involving both a continuous and a binomial response was collected for each participant to monitor smoking behavior. A Bayesian method using Markov chain Monte Carlo is used. Comparison of separate univariate response models to the bivariate response models was undertaken. Our methods are demonstrated on the smoking cessation clinical trial dataset, and properties of our approach are examined through extensive simulation studies.

Misreporting in a randomized clinical trial for smoking cessation in adolescents

IntroductionMisreporting smoking behavior is common among younger smokers participating in clinical trials for smoking cessation. This study focused on the prevalence of and factors associated with adolescent misreporting of smoking behaviors within the context of a randomized clinical trial for smoking cessation. MethodsAdolescent smokers (N=129) participated in a randomized clinical trial that compared two brief interventions for smoking cessation. Following the final (6-month) follow-up, a confidential, self-administered exit questionnaire examined the extent to which participants admitted to having misreported smoking quantity, frequency and/or consequences during the study. Factors associated with under- and over-reporting were compared to accurate-reporting. ResultsOne in 4 adolescent smokers (25.6%) admitted to under-reporting during the study and 14.7% admitted to over-reporting; 10.9% of the adolescents admitted to both under- and over-reporting. Rates of admitted misreporting did not differ between treatment conditions or recruitment site. Compared to accurate-reporting, under- and over-reporting were significantly associated with home smoking environment and the belief among adolescents that the baseline interviewer wanted them to report smoking more or less than they actually smoked. Compared to accurate reporters, over-reporters were more likely to be non-White and to report being concerned with the confidentiality of their responses. ConclusionsA post-study confidential debriefing questionnaire can be a useful tool for estimating rates of misreporting and examining whether potential differences in misreporting might bias the interpretation of treatment effects. Future studies are needed to thoroughly examine potentially addressable reasons that adolescents misreport their smoking behavior and to develop methods for reducing misreporting.

A naturalistic study of the association between antidepressant treatment and outcome of smoking cessation treatment.

Psychiatric, medical, and substance use comorbidities are highly prevalent among smokers, and many of these comorbidities have been found to be associated with reduced rate of success in clinical trials for smoking cessation. While much has been established about the best available treatments from these clinical trials, little is known about the effect of concomitant psychiatric medications on quit rates in smoking cessation programs. On the basis of results in populations with tobacco dependence and other substance use disorders, we hypothesized that smokers taking antidepressants would have a lower rate of quitting in an outpatient smoking cessation program. We performed a naturalistic chart review of veterans (N = 144) enrolled in the Veterans Affairs Greater Los Angeles Mental Health Clinic Smoking Cessation Program from March 2011 through July 2013, who met DSM-IV-TR criteria for nicotine dependence. The primary outcome was smoking cessation with treatment, as evidenced by a patient report of at least 1 week of abstinence and an exhaled carbon monoxide level of ≤ 6 ppm (if available) at the end of acute treatment, with comparators including concomitant psychotropic medication treatment, psychiatric and medical comorbidities, and the presence of a substance use disorder history. We utilized stepwise binary logistic regression as the main statistical technique. We found that current antidepressant treatment (P = .003) and history of substance use disorder (P = .01) (particularly cocaine [P = .02]) were associated with a lower rate of quitting smoking. Furthermore, the association between antidepressant treatment and reduced rate of smoking cessation was primarily seen in patients with a history of substance use disorder (P = .003). While preliminary, these results suggest an important clinical interaction meriting future study. If these findings are confirmed, clinicians may want to consider the risk of reduced ability to quit smoking in patients with a history of substance use disorder who are taking antidepressants.

Genetic Influences on Smoking and Clinical Disease. Understanding Behavioral and Biological Pathways with Mediation Analysis

Genetic Influences on Smoking and Clinical Disease. Understanding Behavioral and Biological Pathways with Mediation Analysis

Culturally-Tailored Smoking Cessation for Adult American Indian Smokers: A Clinical Trial.

This collaborative, community-engaged project developed and tested a Culturally-Tailored Treatment (CTT) for American Indian/Alaska Native (AI/AN) smokers in the Menominee tribal community. One hundred three adult AI/AN smokers were randomized to receive either Standard Treatment (n= 53) or CTT (n = 50) for smoking cessation. Both treatment conditions included 12 weeks of varenicline and four individual counseling sessions but differed in terms of cultural tailoring of the counseling. The primary outcome was 7-day biochemically-confirmed point-prevalence abstinence (PPA) at the 6-month end-of-study visit. Both intention-to-treat (ITT) and responder-only analyses were conducted. There were no statistically significant group differences in 7-day PPA. The overall ITT abstinence rate at 6 months was 20%; the responder-only rate was 42%. The current study represents the first randomized smoking cessation clinical trial testing a culturally-tailored smoking cessation intervention designed for a specific AI/AN tribal community that combined FDA-approved cessation medication (varenicline) and innovative cultural intervention components.

Examining the timing of changes in cigarette smoking upon learning of pregnancy

Objective and methodTimeline Follow-back interviews were conducted with 107 pregnant women enrolling in smoking cessation and relapse prevention clinical trials in the Burlington, VT area between 2006 and 2009 to examine the time course of changes in smoking between learning of pregnancy and the first prenatal care visit. We know of no systematic studies of this topic. ResultsWomen reported learning of pregnancy at 5.1±2.2weeks gestation and attending a first prenatal care visit at 10.1±3.6weeks gestation. In the intervening five weeks, 22% of women became abstainers, 62% reduced their smoking, and 16% maintained or increased their smoking. Women who made changes typically reported doing so within the first 2days after learning of pregnancy, with few changes occurring beyond the first week after learning of pregnancy. ConclusionIn this first effort to systematically characterize the time course of changes in smoking upon learning of pregnancy, the majority of pregnant smokers who quit or made reductions reported doing so soon after receiving the news. Further research is needed to assess the reliability of these results and to examine whether devising strategies to provide early interventions for women who continue smoking after learning of pregnancy is warranted.

Varenicline for smoking cessation in bipolar disorder: a randomized, double-blind, placebo-controlled study.

Virtually no clinical trials for smoking cessation have been undertaken in bipolar disorder. Varenicline has shown efficacy for smoking cessation, but warnings about neuropsychiatric adverse events have been issued. We assessed the efficacy and safety of varenicline in euthymic bipolar subjects motivated to quit smoking. Clinically stable adult patients with DSM-IV bipolar disorder (n = 60) who smoked ≥ 10 cigarettes per day were randomized to a 3-month, double-blind, placebo-controlled varenicline trial and a 3-month follow-up. Study enrollment was completed from February 2010 through March 2013. Varenicline was dosed using standard titration, and smoking cessation counseling was provided to all patients. The primary outcome was defined as a 7-day point prevalence of self-reported no smoking verified by expired carbon monoxide level < 10 ppm at 12 weeks. Psychopathology and side-effects were assessed at each visit. At 3 months (end of treatment), significantly more subjects quit smoking with varenicline (n/n = 15/31, 48.4%) than with placebo (n/n = 3/29, 10.3%) (OR = 8.1; 95% CI, 2.03-32.5; P < .002). At 6 months, 6 of 31 varenicline-treated subjects (19.4%) remained abstinent compared to 2 of 29 (6.90%) assigned to placebo (OR = 3.2; 95% CI, 0.60-17.6; P = .17). Psychopathology scores remained stable. Ten serious adverse events occurred (n = 6, varenicline; n = 4, placebo). Abnormal dreams occurred significantly more often in varenicline-treated subjects (n/n = 18/31, 61.3%) than in those receiving placebo (n/n = 9/29, 31%; Fisher exact test, P = .04). Eight varenicline-treated and 5 placebo-assigned subjects expressed fleeting suicidal ideation, a nonsignificant difference. Varenicline shows efficacy for initiating smoking cessation in bipolar patients, but medication trials of longer duration are warranted for maintaining abstinence. Vigilance for neuropsychiatric adverse events is prudent when initiating varenicline for smoking cessation in this patient population. ClinicalTrials.gov identifier: NCT01010204.

How low should you go? Determining the optimal cutoff for exhaled carbon monoxide to confirm smoking abstinence when using cotinine as reference.

Confirming abstinence during smoking cessation clinical trials is critical for determining treatment effectiveness. Several biological methods exist for verifying abstinence (e.g., exhaled carbon monoxide [CO], cotinine), and while cotinine provides a longer window of detection, it is not easily used in trials involving nicotine replacement therapy. The Society for Research on Nicotine and Tobacco's Subcommittee on Biochemical Verification cite 8-10 parts per million (ppm) for CO as a viable cutoff to determine abstinence; however, recent literature suggests this cutoff is likely too high and may overestimate the efficacy of treatment. This study examined the relationship between CO and cotinine in a sample of 662 individuals participating in a smoking cessation clinical trial. A receiver operating characteristics curve was calculated to determine the percentage of false positives and false negatives at given CO levels when using cotinine as confirmation of abstinence. Differences were also examined across race and gender. A CO cutoff of 3 ppm (97.1% correct classification) most accurately distinguished smokers from nonsmokers. This same cutoff was accurate for both racial and gender groups. The standard cutoffs of 8 ppm (14.0% misclassification of smokers as abstainers) and 10 ppm (20.6% misclassification of smokers as abstainers) produced very high false-negative rates and inaccurately identified a large part of the sample as being abstinent when their cotinine test identified them as still smoking. It is recommended that researchers and clinicians adopt a more stringent CO cutoff in the range of 3-4 ppm when complete abstinence from smoking is the goal.

Functional data analysis for dynamical system identification of behavioral processes.

Efficient new technology has made it straightforward for behavioral scientists to collect anywhere from several dozen to several thousand dense, repeated measurements on one or more time-varying variables. These intensive longitudinal data (ILD) are ideal for examining complex change over time but present new challenges that illustrate the need for more advanced analytic methods. For example, in ILD the temporal spacing of observations may be irregular, and individuals may be sampled at different times. Also, it is important to assess both how the outcome changes over time and the variation between participants' time-varying processes to make inferences about a particular intervention's effectiveness within the population of interest. The methods presented in this article integrate 2 innovative ILD analytic techniques: functional data analysis and dynamical systems modeling. An empirical application is presented using data from a smoking cessation clinical trial. Study participants provided 42 daily assessments of pre-quit and post-quit withdrawal symptoms. Regression splines were used to approximate smooth functions of craving and negative affect and to estimate the variables' derivatives for each participant. We then modeled the dynamics of nicotine craving using standard input-output dynamical systems models. These models provide a more detailed characterization of the post-quit craving process than do traditional longitudinal models, including information regarding the type, magnitude, and speed of the response to an input. The results, in conjunction with standard engineering control theory techniques, could potentially be used by tobacco researchers to develop a more effective smoking intervention.

Larval Zebrafish Model for FDA-Approved Drug Repositioning for Tobacco Dependence Treatment

Cigarette smoking remains the most preventable cause of death and excess health care costs in the United States, and is a leading cause of death among alcoholics. Long-term tobacco abstinence rates are low, and pharmacotherapeutic options are limited. Repositioning medications approved by the U.S. Food and Drug Administration (FDA) may efficiently provide clinicians with new treatment options. We developed a drug-repositioning paradigm using larval zebrafish locomotion and established predictive clinical validity using FDA-approved smoking cessation therapeutics. We evaluated 39 physician-vetted medications for nicotine-induced locomotor activation blockade. We further evaluated candidate medications for altered ethanol response, as well as in combination with varenicline for nicotine-response attenuation. Six medications specifically inhibited the nicotine response. Among this set, apomorphine and topiramate blocked both nicotine and ethanol responses. Both positively interact with varenicline in the Bliss Independence test, indicating potential synergistic interactions suggesting these are candidates for translation into Phase II clinical trials for smoking cessation.

The association between changes in alternative reinforcers and short-term smoking cessation

BackgroundWhile more than 50% of smokers make a serious quit attempt each year, less than 10% quit permanently. Evidence from studies of adolescent smoking and other substances of abuse suggest that alternative reinforcers, a construct of Behavioral Economic Theory, may contribute to the likelihood of smoking cessation in adults. This study examined the behavioral economics of smoking cessation within a smoking cessation clinical trial and evaluated how depressive symptoms and behavioral economic variables are associated with smoking cessation. MethodsA sample of 469 smokers, enrolled in an effectiveness trial that provided counseling and 8 weeks of 21mg nicotine patches, was analyzed. Alternative reinforcers (substitute and complementary reinforcers) and depressive symptoms were examined in relation to 7-day point prevalence abstinence, verified with breath carbon monoxide, 8 weeks after the quit date. ResultsControlling for covariates associated with cessation (nicotine dependence, age of smoking initiation, patch adherence), participants who were abstinent at week 8 showed significantly higher substitute reinforcers at all time-points, compared to those who were smoking (p's<.05). Participants who were abstinent at week 8 showed lower complementary reinforcers and depressive symptoms at all time-points, compared to those who were smoking, but significant differences were confined to week 8 (p's<.01). There was no significant interaction between alternative reinforcers and depressive symptoms across the 8 weeks on week 8 abstinence. ConclusionsThese results support continued examination of Behavioral Economic Theory in understanding adult smoking cessation in order to inform future treatments and guidelines.

Continuous-time system identification of a smoking cessation intervention

Cigarette smoking is a major global public health issue and the leading cause of preventable death in the United States. Toward a goal of designing better smoking cessation treatments, system identification techniques are applied to intervention data to describe smoking cessation as a process of behaviour change. System identification problems that draw from two modelling paradigms in quantitative psychology (statistical mediation and self-regulation) are considered, consisting of a series of continuous-time estimation problems. A continuous-time dynamic modelling approach is employed to describe the response of craving and smoking rates during a quit attempt, as captured in data from a smoking cessation clinical trial. The use of continuous-time models provide benefits of parsimony, ease of interpretation, and the opportunity to work with uneven or missing data.

The relationship between the nicotine metabolite ratio and three self-report measures of nicotine dependence across sex and race.

Variability in the rate of nicotine metabolism, measured by the nicotine metabolite ratio (NMR), is associated with smoking behavior. However, data linking the NMR with nicotine dependence measured by the Fagerström test for nicotine dependence (FTND) are mixed. Few past studies have examined alternative measures of nicotine dependence and how this relationship may vary by sex and race. Using data from smokers undergoing eligibility evaluation for a smoking cessation clinical trial (n = 833), this study examined variability in the relationship between NMR and nicotine dependence across sex and race and using three measures of nicotine dependence: FTND, time-to-first-cigarette (TTFC), and the heaviness of smoking index (HSI). Controlling for sex and race, nicotine metabolism was associated with nicotine dependence only when using the HSI (p < 0.05). Male normal metabolizers of nicotine were more likely to have high nicotine dependence based on the FTND and HSI (p < 0.05), but NMR was not related to measures of nicotine dependence in women. For African Americans, the NMR was associated with nicotine dependence only for the TTFC (p < 0.05), but NMR was not associated with nicotine dependence among Caucasians. Post hoc analyses indicated that the NMR was associated with cigarettes per day, overall, and among men and Caucasians (p < 0.05). While there was some variation in the relationship between nicotine metabolism and nicotine dependence across measures and sex and race, the results indicate that this relationship may be more attributable to the association between NMR and cigarettes per day.