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. Author manuscript; available in PMC: 2023 Jun 1.
Published in final edited form as: Exp Clin Psychopharmacol. 2021 Apr 15;30(3):359–364. doi: 10.1037/pha0000461

Correlates of the Nicotine Metabolite Ratio in Alaska Native People who Smoke Cigarettes

Erin A Vogel 1, Neal L Benowitz 2, Jordan Skan 3, Matthew Schnellbaecher 3, Judith J Prochaska 1
PMCID: PMC8517031  NIHMSID: NIHMS1731673  PMID: 33856821

Abstract

Research on nicotine metabolism has primarily focused on white adults. This study examined associations between nicotine metabolism, tobacco use, and demographic characteristics among Alaska Native adults who smoke cigarettes. Participants (N=244) were Alaska Native adults who smoked and who provided a plasma sample at baseline (70.1%) or follow-up (29.9%) of a randomized controlled trial of a cardiovascular risk behavior intervention. At baseline, participants self-reported age, sex, Alaska Native heritage, cigarettes per day, time to first cigarette upon wakening, menthol use, perceived difficulty staying quit, tobacco withdrawal symptoms, and past-month tobacco product use, binge drinking, and cannabis use. At 3, 6, 12, and 18-month follow-ups, participants self-reported seven-day point prevalence abstinence from smoking. Height and weight were measured to calculate body mass index (BMI). Participants’ nicotine metabolite ratio (NMR), calculated as the ratio of plasma cotinine and trans-3’ hydroxycotinine, was log-transformed. The sample (52.0% male, age M=47.0 years [SD=13.8], 60.3% of Inupiaq heritage) averaged 12.5 cigarettes per day (SD=10.5); 64.0% smoked within 30 minutes of wakening. NMR was not significantly associated with age, sex, Alaska Native heritage, BMI, cigarettes per day, time to first cigarette upon wakening, menthol use, perceived difficulty staying quit, past-month dual tobacco product use, withdrawal symptoms, past-month binge drinking, past-month cannabis use, or abstinence from smoking (all p-values >0.050). Characteristics that relate to NMR in Alaska Native adults may differ from those typically identified among white adults. Specifically, results may suggest that Alaska Native adults with slower nicotine metabolism do not titrate their nicotine intake when smoking.

Keywords: nicotine metabolite ratio, Alaska Native, tobacco, smoking, nicotine metabolism

Introduction

The rate of nicotine metabolism varies across individuals (Benowitz, Jacob, Jones, & Rosenberg, 1982). Genetic variations affect the activity of liver enzymes CYP2A6 and CYP2B6 that metabolize nicotine into cotinine and trans-3’ hydroxycotinine (3HC). The ratio of 3HC to cotinine, called the nicotine metabolite ratio (NMR), is a biomarker of the metabolic clearance of nicotine by CYP2A6 (Dempsey et al., 2004). NMR captures both genetic and environmental influences on nicotine metabolism (Ray, Tyndale, & Lerman, 2009). Compared to slower metabolizers, individuals with higher NMR metabolize nicotine quickly and tend to smoke more cigarettes per day (West, Hajek, & McRobbie, 2011), have higher levels of inflammation and exposure to toxicants (Carroll et al., 2020), have a poorer response to nicotine patch (Lerman et al., 2015; Lerman et al., 2006; Schnoll et al., 2009) and placebo NRT or placebo bupropion (Ho et al., 2009; Patterson et al., 2008), and experience greater cravings and withdrawal symptoms (Kubota et al., 2006; Lerman et al., 2006; Liakoni et al., 2019). Faster metabolizers respond better to varenicline than to nicotine patch, while slow metabolizers do not experience this benefit and may have more side effects from varenicline (Lerman et al., 2015). Therefore, understanding variation in NMR has important smoking cessation treatment implications (Allenby, Boylan, Lerman, & Falcone, 2016).

NMR varies by personal characteristics, with evidence of racial differences. Overall, white individuals typically have faster nicotine metabolism than Black individuals (Chenoweth et al., 2014; Kandel, Hu, Schaffran, Udry, & Benowitz, 2007; Patterson et al., 2008; Pérez-Stable, Herrera, Jacob, & Benowitz, 1998; Schnoll et al., 2009). Among both white and Black individuals, NMR is higher among females, older individuals, and those with lower body mass index (BMI) (Benowitz, Lessov-Schlaggar, Swan, & Jacob, 2006; Chenoweth et al., 2014; Ho et al., 2009; Kandel et al., 2007; Liakoni et al., 2019; Mooney et al., 2008; Mwenifumbo, Sellers, & Tyndale, 2007; Patterson et al., 2008; Schnoll et al., 2009; St. Helen et al., 2019; Swan et al., 2009). Additionally, use of estrogenic medication appears to increase NMR in both white and Black women (Benowitz et al., 2006; Chenoweth et al., 2014). One study found a positive association between NMR and alcohol use (Chenoweth et al., 2014); however, others have not (Liakoni et al., 2019; Mwenifumbo et al., 2007; Swan et al., 2009). One study examined the association between NMR and cannabis use, finding no significant relationship (Mwenifumbo et al., 2007).

Notably, a systematic review found weaker than expected relationships between NMR and cigarettes per day or commonly used self-report dependence measures (e.g., Fagerström Test of Nicotine Dependence, Hooked on Nicotine Checklist, Wisconsin Index of Smoking Motives) (West et al., 2011). The relationship between NMR and nicotine dependence may be less apparent in groups that tend to smoke fewer cigarettes per day, potentially due to differences in smoking motives. For example, NMR has a weaker association with cigarettes per day among Black than white people who smoke (Ross et al., 2016), and it has been suggested that positive reinforcement (i.e., “peak-seeking”; Russell & Feyerabend, 1978) may drive smoking among Black individuals, while relief from withdrawal symptoms and stabilization of mood (i.e., “trough-maintaining”; Russell & Feyerabend, 1978) may drive smoking in white individuals (Ross et al., 2016). Indeed, white individuals with higher NMR typically titrate their cigarettes per day to match their rate of nicotine metabolism and maintain a constant nicotine level; however, Black individuals, for whom cigarettes per day tend to be lower (Centers for Disease Control and Prevention [CDC], 2019a), typically do not titrate their cigarettes per day (Ross et al., 2016; St. Helen et al., 2019). Furthermore, menthol use can decrease nicotine metabolism (Benowitz, Herrera, & Jacob, 2004), and disproportionately high menthol use among Black individuals who smoke may partially—but not entirely—explain the association between NMR and race (Ho et al., 2009; Ross et al., 2016).

Less is known about nicotine metabolism among Alaska Native people. Despite smoking fewer cigarettes per day on average than White people who smoke (CDC, 2019b), Alaska Native people have high smoking prevalence and high tobacco-related disease burden (CDC, 2019b). Alaska Native people frequently experience discrimination, lack of access to health insurance, wealth inequality, and other structural inequities that increase their risk for smoking and cardiovascular disease (Breathett et al., 2020). Additionally, previous research has identified differences in variant allele frequencies in Alaska Native Yup’ik people that influence nicotine metabolism (Binnington et al., 2012; Zhu, Renner, Hatsukami, Benowitz, & Tyndale, 2013). Similar to white people who smoke cigarettes, Yup’ik individuals with slower nicotine metabolism had lower daily nicotine intake, as assessed by urine total nicotine equivalents (Zhu, Binnington, et al., 2013). Nonetheless, similar to Black people who smoke, Yup’ik individuals did not reduce their cigarettes per day to compensate for slower nicotine metabolism (Zhu, Binnington, et al., 2013). It remains unclear whether previously identified correlates of NMR in white and Black individuals apply to Alaska Native people who smoke cigarettes. Moreover, previous research on nicotine metabolism in Alaska Native people has primarily recruited participants of Yup’ik descent.

This study examined correlates of NMR in a diverse sample of Alaska Native people living in the Norton Sound Region of Alaska. Approximately 82% of Norton Sound residents are Alaska Native, primarily Inupiaq and Yup’ik (Boedeker & Foster, 2011). More than half (52.3%) of Alaska Native adults in Norton Sound report current cigarette smoking (Dilley, Peterson, Hiratsuka, & Rohde, 2013).

Method

Participants, Design, and Procedure

Participants were enrolled in the Healing and Empowering Alaskan Lives Toward Healthy-Hearts (HEALTHH) Project, a randomized clinical trial of two culturally-tailored interventions for tobacco use and other cardiovascular disease risk behaviors among Alaska Native people (J. J. Prochaska et al., 2018). Eligible participants were self-identified Alaska Native adults, age 19+ years, who spoke English, resided in the Norton Sound region, smoked 5+ cigarettes per day at study entry, and had high blood pressure or high cholesterol. Participants were recruited in 2015–2019 through community outreach including radio announcements, flyers, and communications from local health providers (J. J. Prochaska et al., 2018). Participants were randomized to receive a culturally and individually tailored intervention for tobacco cessation or blood pressure/cholesterol management based on the transtheoretical model (J. O. Prochaska & DiClemente, 1983). Assessments were conducted at baseline and 3, 6, 12, and 18 months at the local community clinics (J. J. Prochaska et al., 2018). The present study analyzed data from the baseline assessment, which consisted of a self-report survey and body measures, and self-reported cigarette smoking status at follow-up assessments. Participants were compensated $30 for completing the baseline assessment, $40 for completing assessments at 3, 6, and 12 months, and $50 for completing the 18-month assessment (J. J. Prochaska et al., 2018). Participants were asked to provide a blood sample at baseline to test for NMR. If missed, the blood sample was requested at a follow-up assessment, if still smoking cigarettes. For the current analyses, all self-report measures (except abstinence from smoking) and 70.1% of plasma samples were obtained at baseline. Ethical approval was granted by the Stanford University School of Medicine Institutional Review Board.

Measures

NMR.

NMR was calculated using the ratio of plasma 3HC to cotinine. Plasma samples were analyzed using liquid chromatography with tandem mass spectrometry (Jacob et al., 2011).

Demographic Characteristics.

Participants reported their age, sex (male/female), Alaska Native heritage (Inupiaq, Yup’ik, other, or multiple heritages) and identification with their heritage (1=not at all, 4=very much), Norton Sound community of residence (coded as Nome vs. other), education (elementary, some high school, high school graduate/GED, some college, college graduate, graduate degree), subjective social status (a validated measure of socioeconomic status; 1=lowest standing in one’s community, 10=highest standing; Adler, Epel, Castellazzo, & Ickovics, 2000), employment (employed; unemployed; retired, on disability, or other), and general health (1=poor, 5 =excellent). BMI was calculated from height and weight measured at the time of plasma sample collection or the closest available assessment point.

Tobacco and Other Substance Use.

Smoking characteristics measured were cigarettes per day, time to first cigarette (within/not within 30 min. of waking), menthol cigarette use (non-menthol only, menthol only, both menthol and non-menthol), perceived difficulty with sustaining a quit attempt (1=lowest difficulty, 10=greatest difficulty; Hall, Havassy, & Wasserman, 1990), and nicotine withdrawal symptoms (8-item Minnesota Tobacco Withdrawal Scale, rated 0=none to 4=severe; Hughes & Hatsukami, 1986). Participants reported the date of last use of: snuff or chewing tobacco, Iqmik or Blackbull, cigar or pipe tobacco, and e-cigarettes. Past-month use of any non-cigarette tobacco product was categorized as positive vs. none. Past-month binge drinking (yes/no) was determined by self-reported number of days having 4+ drinks (for women) or 5+ drinks (for men) in a row within a couple of hours. Participants also reported past-month cannabis use (yes/no). Seven-day point prevalence abstinence from smoking was indicated by self-reported zero past-week cigarettes at any follow-up assessment (yes/no).

Analyses

NMR and cigarettes per day were log-transformed for analyses (log-transformed NMR = Ln[cotinine/3HC+1]). Associations between participant characteristics and log-transformed NMR were tested using Pearson’s correlations, independent samples t-tests, and one-way ANOVAs. Because the association between sex and NMR is presumably driven by estrogen and may weaken among post-menopausal females (Benowitz et al., 2006; Chenoweth et al., 2014), linear regression tested the relationship between NMR and sex, adjusting for age. As a sensitivity analysis, participant characteristics were tested in association with NMR dichotomized by quartile into slow versus normal/fast metabolism (i.e., lowest quartile of NMR versus three higher quartiles) using independent-samples t-tests, chi-square tests, and logistic regression. As a proxy for daily nicotine dose (Ross et al., 2016), the molar sum of 3HC and cotinine was tested in association with participant characteristics using Pearson’s correlations, independent samples t-tests, and one-way ANOVAs. Prior research found that the molar sum of 3HC and cotinine was strongly correlated with daily intake of nicotine (r=.92) (Benowitz, Dains, Dempsey, Yu, & Jacob III, 2010).

Results

Participant characteristics are presented in Table 1. The sample (N=244) was 52.0% male, with a mean age of 47 years (SD=13.8), and mean BMI of 28.3 (SD=7.2), which is in the overweight range. Most participants were of Inupiaq (60.3%) or Yup’ik (27.3%) heritage. Most (85.2%) identified with their heritage “somewhat” to “very much.” Mean NMR was 0.58 (SD=0.35) with a range of 0.02 to 2.65 and a median of 0.51 (IQR: 0.35 – 0.71). On average, participants smoked 12.5 cigarettes per day (SD=10.5), with a range of 1 to 70 and a median of 10. Smoking within 30 minutes of wakening, an indicator of dependence, was reported by 64% of participants. On a scale of 1 (least) to 10 (most), participants rated the difficulty of staying quit at M=6.1 (SD=3.0). Most (65.8%) reported smoking non-menthol cigarettes only; 10.2% used other tobacco products in the past month; 31.1% reported past-month binge drinking; 54.2% past-month cannabis use, and 11.1% reported abstinence from smoking cigarettes at one or more follow-up assessments.

Table 1.

Sample Descriptive Characteristics

M (SD) or N (%)
Demographic characteristics
Age 47.0 (13.8)
Male sex 127 (52.0%)
Body mass index (BMI) 28.2 (7.2)
AN heritage
 Inupiaq 146 (60.3%)
 Yup’ik 66 (27.3%)
 Other (Aleut, Athabascan, Eskimo, Tlingit) 11 (4.5%)
 Multiple 19 (7.9%)
Identification with AN heritage
 Not at all 2 (0.8%)
 Very little 34 (14.0%)
 Somewhat 64 (26.3%)
 Very much 143 (58.8%)
Residence in Nome 61 (25.0%)
Education
 Less than high school 50 (20.6%)
 High school graduate / GED 142 (58.4%)
 Some college or higher 51 (21.0%)
Subjective standing in communitya 5.3 (2.1)
Employment
 Employed 107 (44.0%)
 Unemployed 91 (37.4%)
 Retired, on disability, or other 45 (18.5%)
General health
 Excellent or very good 35 (14.7%)
 Good 94 (39.5%)
 Fair or poor 109 (45.8%)
Tobacco and other substance use
Cigarettes per day 12.5 (10.5)
Smokes within 30 min of waking 153 (64.0%)
Non-menthol use only 160 (65.8%)
Perceived difficulty staying quitb 6.1 (3.0)
Past month other tobacco product use 25 (10.2%)
Tobacco withdrawal symptomsc 7.9 (6.4)
Past-month binge drinking 76 (31.1%)
Past-month cannabis use 130 (54.2%)
Reported abstinence (% yes)d 27 (11.1%)
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a

Range: 1= lowest to 10 = highest perceived standing in community

b

Range: 1 = lowest to 10 = highest perceived difficulty

c

Range: 0 = no symptoms to 32 = eight severe symptoms

d

Self-reported abstinence from cigarette use in the past 7 days at 1+ follow-up assessments

NMR was not associated with age (r=.09, p=.182), sex (t[242]=−.22, p=.824), BMI (r=−.12, p=.059), Alaska Native heritage (F[3, 238]=1.44, p=.233), cigarettes per day (r=−.03, p=.617), time to first cigarette upon wakening (t[237]=.84, p=.404), menthol use (t[241]=.95, p=.342), perceived difficulty staying quit (r=−.03, p=.614), past-month dual tobacco product use (t[242]=.65, p=.519), tobacco withdrawal symptoms (r=−.02, p=.793), past-month binge drinking (t[242]=.87, p=.386), past-month cannabis use (t[238]=1.87, p=.063), or abstinence from smoking at a follow-up (t[242]=.74, p=.462). When adjusting for age, sex was still not significantly associated with NMR (B<.001, p=.987). Comparing slow metabolizers to normal/fast metabolizers produced the same pattern of results (p-values >.05), except that slow metabolizers were less likely to have used cannabis in the past month than normal/fast metabolizers (40.0% versus 59.1%; χ²[1]=7.27, p=.007). Defining slow metabolism as NMR < .31 (Lerman et al., 2015) produced the same pattern of results (n=46 with slow metabolism). To determine whether the null association between CPD and NMR was specific to light smokers, we examined the correlation between CPD and NMR within the highest and lowest tertiles of CPD, and found no association (p-values > .05).

The molar sum of cotinine and 3HC was positively (though weakly) associated with cigarettes per day (r=.17, p=.010), reflecting greater daily nicotine intake among participants who smoked more cigarettes at baseline. The molar sum of cotinine and 3HC was not significantly associated with NMR (r=−.02, p=.785). Molar sum of cotinine and 3HC per cigarette averaged 0.19 (SD=0.22, range=0–2.30). Results suggest that smoking behavior was not driven by rate of nicotine metabolism. Additionally, participants who quit smoking during the follow-up period had lower average daily nicotine intake (t[242]=2.18, p=.031); however, daily nicotine intake was not associated with any other demographic or smoking characteristics.

Discussion

The current study examined correlates of the rate of nicotine metabolism in a sample of 244 Alaska Native people who smoke cigarettes living in the Norton Sound Region of Alaska. NMR was not significantly associated with smoking behavior or any measured sociodemographic characteristic or other substance use variable. Despite lighter smoking, Alaska Native people have disproportionately high tobacco-related disease burden (CDC, 2019b). The current study found that the number of cigarettes smoked per day was not significantly associated with NMR, suggesting that Alaska Native people who smoke cigarettes did not titrate their cigarette use to match their nicotine metabolism. The finding is similar to that found in Black individuals (Ross et al., 2016; St. Helen et al., 2019), who also experience disproportionately high prevalence of tobacco-related disease despite tending to smoke fewer cigarettes per day than white individuals (CDC, 2019a). Also similar to a study of Black people with light smoking, the current study found that sex and time to first cigarette were not associated with NMR in Alaska Native people who smoke cigarettes (St. Helen et al., 2019). Findings suggest that Alaska Native men and women in the current sample metabolized nicotine at similar rates, and individuals with faster nicotine metabolism were not necessarily more dependent on smoking. Of 13 primary tests of association that were run to examine correlates of NMR, none were significant, despite a reasonable sample size.

There are several possible reasons for the pattern of null results observed in the current study. First, similar to Black individuals who smoke on average fewer cigarettes per day than white individuals (Ross et al., 2016), Alaska Native individuals may engage in “peak-seeking” smoking, such that they smoke primarily for positive reinforcement (Russell & Feyerabend, 1978). “Peak seekers” presumably obtain their smoking reward from the peak nicotine level immediately following a cigarette. Peak nicotine level is unaffected by NMR. Cigarettes per day are known to be correlated with urine total nicotine equivalents, but if Alaska Native people are more likely to engage in “peak-seeking” smoking, total nicotine equivalents may correlate with cigarettes per day, whereas nicotine metabolism may not. In contrast, “trough maintainers” presumably obtain their smoking reward from a consistent nicotine level throughout the day, possibly related in part to desensitization of nicotinic receptors. Thus, trough maintainers would be more likely to titrate their daily intake of nicotine to their NMR (Ross et al., 2016). Future research could assess smoking motives in Alaska Native people to better understand peak seeking versus trough maintaining smoking behavior. Second, Alaska Native individuals have a faster average nicotine metabolism than white or Black individuals (Binnington et al., 2012). Studies of the relationship between NMR and cigarettes per day find that the greatest impact is seen in the slowest quartile of metabolizers, with relatively little change across higher levels of metabolism (Lerman et al., 2015; Schnoll et al., 2009). Importantly, in this study, a sensitivity analysis of NMR in quartiles still found no significant difference between slow metabolizers and normal/fast metabolizers in the association between NMR and cigarettes per day. Third, social determinants of health may be stronger predictors of cigarette smoking behavior than biological phenotypes such as NMR. Racism and other adverse experiences appear to contribute to cigarette smoking behavior among AN people (Breathett et al., 2020).

Findings in the current study showed no significant associations between NMR and participants’ demographic and smoking characteristics. Notably, other studies have found weak associations between individuals’ characteristics and their NMR. For example, in a study of Black and white people who smoke cigarettes, race, sex, estrogen use, BMI, cigarettes per day, and alcoholic beverages per week each accounted for less than 2% of total NMR variation (Chenoweth et al., 2014). In a study of Black people who engaged in light smoking, sex, cigarettes per day, and time to first cigarette were not associated with NMR (St. Helen et al., 2019). Although slow metabolizers were less likely to have used cannabis, compared to normal/fast metabolizers, no other associations were significant in sensitivity analyses. Overall, results suggest no meaningful differences in smoking or demographic characteristics between slow and normal/fast metabolizers in this study.

Moreover, over the 18-month follow-up period, current study participants’ likelihood of quitting smoking cigarettes was not associated with their NMR despite lower average daily nicotine intake in those who quit smoking. This finding is consistent with a prior study of primarily white people who smoke cigarettes, in which NMR was not associated with abstinence (Lerman et al., 2006). However, slower metabolizers in studies of primarily white and Black individuals have found that slower metabolizers benefit more from nicotine patch than do faster metabolizers (Lerman et al., 2015; Lerman et al., 2006; Schnoll et al., 2009). Research has not yet examined differences in pharmaceutical smoking cessation treatment response by NMR in Alaska Native people who smoke. Because Alaska Native people in this study did not appear to titrate their nicotine intake to their metabolism, we may expect a positive response to combination (i.e., slow-acting and fast-acting) NRT or varenicline. Combination NRT helps maintain a steady nicotine level and manage acute cravings, while varenicline blocks the acute rewarding effects of nicotine.

Study limitations include reliance on self-reported smoking behaviors, which may not fully capture smoking characteristics. For example, while cigarettes per day did not differ by NMR, there may have been differences in smoking topography, such that faster metabolizers took in more nicotine per cigarette (Zhu, Binnington, et al., 2013). Alaska Native heritage was self-identified, and participants may have differed in the strength of their Alaska Native ancestry, resulting in a genetically heterogeneous sample. It is unclear whether smoking topography differed by NMR because urine total nicotine equivalents, the gold standard for daily intake of nicotine, were not measured. The molar sum of 3HC and cotinine (i.e., a proxy for daily nicotine intake) was found to significantly correlate only with cigarettes per day and was not associated with other measured participant characteristics in this sample. Nonetheless, future research should measure total nicotine equivalents in Alaska Native individuals who smoke for a more comprehensive view of titration of smoking by NMR. Future research could also match white and AN individuals on cigarettes per day to determine whether relationships between NMR and smoking are race-specific. Substance use was coded according to whether participants had engaged in any cannabis use or binge drinking in the past month. A more fine-grained measure, capturing recency and quantity of use, may have provided more nuanced results.

Conclusions

Research on the associations between nicotine metabolism, smoking characteristics, and cessation has primarily focused on white adults. Relative to white people who smoke, Black and Alaska Native people tend to smoke fewer cigarettes per day, and the present study suggests that like Black individuals, Alaska Native people with slower nicotine metabolism may not titrate their nicotine intake accordingly. Consistent with clinical practice guidelines, behavioral counseling with cessation medications are recommended for treatment of Alaska Native adults who smoke cigarettes, including a combination of short-acting and long-acting nicotine replacement therapy, bupropion, and varenicline.

Public Significance.

Research on the associations between nicotine metabolism, smoking characteristics, and cessation has primarily focused on white adults. The present study suggests that like Black adults who smoke cigarettes, Alaska Native adults with slower nicotine metabolism may not change their nicotine intake according to how quickly they metabolize nicotine.

Disclosures and Acknowledgements

Research funding was provided by the National Heart, Lung, and Blood Institute #R01HL117736 both in a parent award and three Diversity Supplement awards. Preparation of this manuscript was supported by the Tobacco-Related Disease Research Program (#28FT-0015). The funding sources had no role other than financial support.

Dr. Vogel contributed to conceptualization, formal analysis, and writing (original draft). Dr. Benowitz contributed to conceptualization, funding acquisition, investigation, resources, and writing (reviewing and editing). Dr. Skan and Dr. Schnellbaecher contributed to investigation and writing (reviewing and editing). Dr. Prochaska contributed to conceptualization, funding acquisition, investigation, supervision, and writing (reviewing and editing). All authors have read and approved the final manuscript.

Drs. Benowitz and Prochaska have served as expert witnesses against the tobacco companies in lawsuits for which they have received fees for the work and have provided consultation to pharmaceutical and technology companies that make medications and other treatments for quitting smoking. Drs. Vogel, Skan, and Schnellbaecher have no disclosures to report.

We acknowledge with great appreciation the tribal representatives who make up the Norton Sound Health Corporation’s (NSHC) Research Ethics Review Board (RERB). The RERB has been central in approving, guiding, and overseeing this research. In addition, we appreciate Reba Lean, NSHC CEO Angela Gorn, RD, MBA, and Steven Daniel, MD for their support with study recruitment and study presence in the community. We recognize the dedicated efforts of the HEALTHH recruitment and counseling team members including Nicole Anzai, Maria Crouch, Nicole Jeffery, Colleen Johnson, Mariah Knox, Anne Michalek, and Derek Searcy and our Data Safety Monitoring Board Members (DSMB) Drs. Tina Woods, Teresa LaFromboise, and Andrew Pipe. We thank the Clinical Pharmacology Laboratory at Zuckerberg San Francisco General Hospital for analyzing plasma samples.

Footnotes

Clinical trial registration: #NCT02137902

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