Dataset Information

Nicotine Dependence among Current Cigarette Smokers Who Use E-Cigarettes and Cannabis.

ABSTRACT:

Background

Co-use of tobacco and cannabis and dual use of cigarettes and e-cigarettes are very common among young adults. However, it is unclear whether co-use of cigarettes, e-cigarettes, and/or cannabis is associated with higher levels of nicotine dependence than cigarette-only use. We investigated the relationship between cigarette/nicotine dependence and co-use of tobacco and cannabis among 4 groups of cigarette smokers aged 18-35: cigarette-only smokers, cigarette-e-cigarette (CIG-ECIG) co-users, cigarette-cannabis (CIG-CAN) co-users, and cigarette-e-cigarette-cannabis (CIG-ECIG-CAN) co-users.

Methods

Data were from a 2018 cross-sectional survey based on a national convenience sample of smokers aged 18-35 (n = 315). Cigarette/nicotine dependence was measured by the Fagerstrom Test of Nicotine Dependence (FTND) and e-cigarette dependence was measured by the Penn State E-cigarette Dependence Index. Bivariate analyses examined sociodemographic and tobacco/other substance use characteristics by co-use status and multivariable linear regression assessed the relationship between co-use and nicotine dependence.

Results

In the sample, 27.6% were cigarette-only smokers, 24.8% were CIG-ECIG, 27.6% were CIG-CAN, and 20.0% were CIG-ECIG-CAN co-users. Significant differences were observed in sociodemographic and tobacco/other substance use characteristics by co-use status. E-cigarette co-users had low e-cigarette dependence, but moderate FTND scores. In adjusted analyses, only CIG-ECIG co-use was associated with higher FTND scores compared to cigarette-only smoking. However, CIG-ECIG and CIG-ECIG-CAN co-use were associated with higher FTND scores compared to CIG-CAN co-use.

Conclusions

Co-use of cigarettes and e-cigarettes was associated with greater nicotine dependence among smokers aged 18-35. Additional research is needed to understand the underlying mechanisms of these relationships and inform prevention efforts.

SUBMITTER: Jones DM

PROVIDER: S-EPMC10249428 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Publications

Nicotine Dependence among Current Cigarette Smokers Who Use E-Cigarettes and Cannabis.

Jones Dina M DM Guy Mignonne C MC Fairman Brian J BJ Soule Eric E Eissenberg Thomas T Fagan Pebbles P

Substance use & misuse 20230227 5

<h4>Background</h4>Co-use of tobacco and cannabis and dual use of cigarettes and e-cigarettes are very common among young adults. However, it is unclear whether co-use of cigarettes, e-cigarettes, and/or cannabis is associated with higher levels of nicotine dependence than cigarette-only use. We investigated the relationship between cigarette/nicotine dependence and co-use of tobacco and cannabis among 4 groups of cigarette smokers aged 18-35: cigarette-only smokers, cigarette-e-cigarette (CIG-E ...[more]

PMID: 36852436

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Project description:IntroductionHow nicotine dependence will be affected when current smokers initiate electronic cigarette (e-cigarette) use to reduce cigarette smoking is unknown. This study evaluated cigarette, e-cigarette, and total nicotine dependence more than 6 months among smokers reducing cigarette consumption by replacing with e-cigarettes.Aims and methodsAdult cigarette smokers were randomized to one of four conditions (36 mg/ml e-cigarette, 8 mg/ml e-cigarette, 0 mg/ml e-cigarette, or cigarette-substitute [CS] [provided at no cost]) and instructed to reduce their cigarette smoking by 75% at 1 month. Participants completed follow-up at 1, 3, and 6 months. The Penn State Nicotine Dependence Index (PSNDI) measured dependence on cigarettes (PSCDI) and e-cigarettes (PSECDI). Urine cotinine measured total nicotine exposure. Linear mixed effects models for each outcome were conducted and included interaction terms between visit and condition.ResultsParticipants (n = 520) were 58.8% female, 67.3% White, and 48.0 years old. At baseline, the median number of cigarettes smoked per day was 17.3 and the mean PSCDI score was 13.4, with no significant differences between conditions. Participants in the e-cigarette conditions reported significantly lower PSCDI scores, compared with baseline, and with the CS condition at all follow-up visits. Those in the 36 mg/ml e-cigarette condition reported greater PSECDI scores at 6 months, compared with baseline and the 0 mg/ml and 8 mg/ml conditions. At all follow-up visits, there were no differences in total nicotine exposure compared to baseline, nor between any conditions.ConclusionsE-cigarette use was associated with reduced cigarette dependence, compared to the CS, without significant increases in total nicotine exposure.ImplicationsInitiation of electronic cigarette use while continuing to smoke could potentially increase nicotine dependence. In this randomized trial aimed at helping smokers to reduce their cigarette intake, we found that use of an e-cigarette was associated with a reduction in cigarette dependence and an increase in e-cigarette dependence (in the condition with the highest nicotine concentration only), with no long term increase in total nicotine dependence or nicotine exposure.

Project description:Nicotine dependence and smoking quantity are both robustly associated with the CHRNA5-A3-B4 gene cluster in the 15q25 region, and SNP rs16969968 in particular. The purpose of this paper is to use structural equation modeling techniques (SEM) to disentangle the complex pattern of relationships between rs16969968, nicotine quantity (as measured by the number of cigarettes an individual smokes per day; CPD) and nicotine dependence (as measured by the Fagerström Test for Nicotine Dependence; FTND). CPD is an indicator, but also a potential cause, of FTND, complicating the interpretation of associations between these constructs and requires a more detailed investigation than standard GWAS or general linear regression models can provide. FTND items and genotypes were collected in four samples, with a combined sample size of 5,373 respondents. A mega-analysis was conducted using a multiple group SEM approach to test competing hypotheses regarding the relationships between the SNP rs16969968, FTND and CPD. In the best fitting model, the FTND items loaded onto two correlated factors. The first, labeled "maintenance," assesses the motivation to maintain constant levels of nicotine through out the day. The second was labeled "urgency" as its items concern the urgency to restore nicotine levels after abstinence. We focus our attention on the "maintenance" factor, of which CPD was an indicator. The best fitting model included a negative feedback loop between the Maintenance factor and CPD. Accordingly, the motivation to maintain higher levels of nicotine increased the quantity of nicotine consumed, which subsequently decreases the maintenance motivation. The fact that the Maintenance-CPD feedback model fits the data best implies that there are at least two biological pathways that lead from rs16969968 to smoking behaviors. The model is consistent with a supply and demand system, which allows individuals to achieve a homeostatic equilibrium for their nicotine concentration.

Project description:INTRODUCTION:Cannabis and tobacco couse is common and could expose users to higher levels of toxicants. No studies have examined biomarkers of toxicant exposure in cousers of cannabis and cigarettes, compared with cigarette smokers (CS). AIMS AND METHODS:Adult daily CS were recruited from 10 US sites for a study of reduced nicotine cigarettes. In this analysis of baseline data, participants were categorized as either cousers of cannabis and tobacco (cousers; N = 167; urine positive for 11-nor-9-carboxy-Δ 9-tetrahydrocannnabinol and self-reported cannabis use ≥1×/week), or CS (N = 911; negative urine and no self-reported cannabis use). Participants who did not meet either definition (N = 172) were excluded. Self-reported tobacco and cannabis use and tobacco and/or combustion-related biomarkers of exposure were compared between groups. RESULTS:Compared to CS, cousers were younger (couser Mage = 38.96, SD = 13.01; CS Mage = 47.22, SD = 12.72; p < .001) and more likely to be male (cousers = 67.7%, CS = 51.9%, p < .001). There were no group differences in self-reported cigarettes/day, total nicotine equivalents, or breath carbon monoxide, but cousers had greater use of non-cigarette tobacco products. Compared to CS, cousers had higher concentrations of 3-hydroxypropylmercapturic acid, 2-cyanoethylmercapturic acid, S-phenylmercapturic acid, 3-hydroxy-1-methylpropylmercapturic acid (ps < .05), and phenanthrene tetraol (p < .001). No biomarkers were affected by number of cannabis use days/week or days since last cannabis use during baseline (ps > .05). CONCLUSIONS:Cousers had higher concentrations of biomarkers of exposure than CS, but similar number of cigarettes per day and nicotine exposure. Additional studies are needed to determine whether cannabis and/or alternative tobacco products are driving the increased toxicant exposure. IMPLICATIONS:Cousers of cannabis and tobacco appear to be exposed to greater levels of harmful chemicals (ie, volatile organic compounds and polycyclic aromatic hydrocarbons), but similar levels of nicotine as CS. It is unclear if the higher levels of toxicant exposure in cousers are due to cannabis use or the increased use of alternative tobacco products compared with CS. It is important for studies examining biomarkers of exposure among CS to account for cannabis use as it may have a significant impact on outcomes. Additionally, further research is needed examining exposure to harmful chemicals among cannabis users.

Project description:IntroductionStudies suggest tobacco and cannabis co-users may experience greater toxicant exposure than exclusive cigarette (ET) smokers. No study has systematically tested differences in toxicant exposure among co-users, exclusive cannabis (ECa) smokers, and ET smokers.Aims and methodsAdult daily cigarette smokers and/or weekly cannabis smokers completed two laboratory visits. Co-users (n = 19) tested positive for urinary 11-nor-9-carboxy-Δ 9-tetrahydrocannabinol (THCCOOH), self-reported cannabis use ≥1 per week, and smoked ≥5 cigarettes per day (CPD). ET smokers (n = 18) denied past month cannabis use, tested negative for urinary THCCOOH and smoked ≥5 CPD. ECa smokers (n = 16) tested positive for urinary THCCOOH, self-reported cannabis use ≥1 per week, and denied past month tobacco use (NicAlert <3). Self-reported tobacco and cannabis use were collected at both visits. First morning urinary tobacco and combustion-related biomarkers of exposure were compared following a cannabis or tobacco smoking session (visit 2).ResultsCo-users and ET smokers had higher levels of exhaled carbon monoxide, total nicotine equivalents, metabolites of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNAL), and all four measured mercapturic acids (measures of volatile organic compounds) than ECa smokers (ps < .005). ET smokers (geometric mean = 7220.2 pmol/mg) had higher levels of 2-hydroxypropylmercapturic acid than co-users (geometric mean = 5348.7 adjusted p = .009). Phenanthrene tetraol did not differ by group (p > .05).ConclusionsCo-users and ET smokers demonstrated comparable levels of biomarkers of exposure to harmful constituents despite smoking similar amounts of tobacco. ECa smokers demonstrated lower levels of toxicant exposure for most biomarkers.ImplicationsAlthough ECa smokers are exposed to significantly lower levels of harmful constituents compared with co-users and exclusive cigarette smokers, this group is still exposed to higher levels of toxicants than observed in studies of nonsmokers. Additionally, these three groups were exposed to similar levels of phenanthrene tetraol. It is important to account for cannabis use in studies examining biomarkers of exposure among cigarette smokers. Additionally, further research is needed examining exposure to harmful chemicals among various types of cannabis and tobacco users.

Dataset Information

Nicotine Dependence among Current Cigarette Smokers Who Use E-Cigarettes and Cannabis.

Background

Methods

Results

Conclusions

Publications

Nicotine Dependence among Current Cigarette Smokers Who Use E-Cigarettes and Cannabis.

Similar Datasets

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