Abstract
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.
Introduction
Tobacco use remains the leading cause of preventable death in the United States (US).1 Cigarette smoke contains at least 70 known carcinogens.2 Despite the detrimental health effects, 14% of US adults continue to smoke.3 Approximately 9% of daily tobacco smokers also smoke cannabis daily4 and 40%–54% of cannabis users reported smoking cigarettes in the past 30 days.5 Given the high prevalence of tobacco and cannabis couse, it is important to understand what additive toxicant exposure cousers may be experiencing. In addition, the potential confounding effects from cannabis use are instructive when examining biomarkers of tobacco exposure in smokers.
Although the health effects of tobacco are well known, there is still some debate about the effects of cannabis use. Cannabis smoke has been shown to have numerous toxicants and carcinogens including acrylonitrile (a potential carcinogen),6,7 carbon monoxide (a cardiovascular toxin),6 and 1,3,-butadiene (a carcinogen).6,7 Only two published studies have examined cannabis smoke metabolites in humans.7,8 In one study, researchers examined cannabis smokers, cigarette smokers (CS), and nonusers of either.7 Cannabis smokers demonstrated significantly higher levels of polycyclic aromatic hydrocarbons (PAHs) and volatile organic compounds (VOCs) than nonusers, and similar or lower levels than CS; however, cannabis smokers versus CS were not statistically compared. A follow-up study examined the effects of heaviness of cannabis smoking (ie, joints per day/month/year and never use) on PAH levels. Results indicated cannabis smokers who smoked two or more joints per day demonstrated higher levels of 2-hydroxynaphthalene, 2-hydroxyfluorene, and 1-hydroxypyrene than less frequent cannabis smokers.8 These studies suggest that cannabis smokers are exposed to harmful toxicants. Consequently, cousers of cannabis and tobacco may be exposed to even higher levels of toxicants and carcinogens than CS.
There is a dearth of research examining the differential harm experienced by CS and cousers. A recent review of the additive health risks of couse found only 10 articles related to toxicant exposure of cannabis and tobacco couse.9 With the lack of available research, this review was largely inconclusive about the potential health effects of couse (compared to tobacco only use) based on toxicant exposure. Additionally, much of the literature contains methodological issues such as inconsistent definitions of couse across studies (eg, daily cannabis use vs. past month cannabis use), small sample sizes, and lack of control for important covariates (eg, time since last use). Together, these limitations highlight the complexity of this area of research and demonstrate a need for research examining couse of tobacco cigarettes and cannabis that stringently defines use groups.
The current study is a secondary analysis of baseline data from a multisite randomized clinical trial that examined the effects of different approaches to reducing nicotine content in cigarettes on smoking behavior and exposure biomarkers. Specifically, levels of exposure to harmful chemicals were examined between tobacco CS and cousers of tobacco cigarettes and cannabis (cousers) during a 2-week baseline period when participants smoked their usual brand of cigarettes ad libitum prior to randomization to experimental study cigarettes.
Materials and Methods
Parent Study Overview
This study used baseline data of participants from a randomized, parallel, double-blind trial10 in which participants (N = 1250) were assigned to receive either reduced nicotine cigarettes (with different schedules of nicotine reduction) or usual nicotine content cigarettes. Participants were recruited from 10 sites across the US. Participants completed 2 weeks of baseline smoking in which they smoked their usual brand cigarettes, followed by 20 weeks in which they smoked experimental cigarettes. All procedures were approved by each site’s institutional review board. Full details of the protocol are published in the original article.10
Participants
Eligible participants were adults (legal age to purchase cigarettes depending on state); current smokers (≥5 cigarettes/day); who were uninterested in quitting smoking in the next 30 days. Eligible participants also had (1) an exhaled carbon monoxide level of 8 ppm or more or a urinary cotinine level of 1000 ng/mL or more (NicAlert = 6); (2) used other tobacco products 9 days or less in the past 30 days; (3) nonexclusive use of roll-your-own cigarettes; (4) no use of reduced nicotine content cigarettes in the past 3 years; (5) stable mental and physical health with no serious psychiatric or medical conditions; (6) negative urine screen for all other illegal drugs except cannabis; and (7) were not breastfeeding, pregnant, or planning to become pregnant.
Cousers of cigarettes and cannabis (n = 167) were defined as individuals who: (1) had a positive urine toxicology test for recent exposure to delta-9-tetrahydrocannabinol (THC) at screening; (2) reported smoking cannabis an average of once/week for the past month and during the baseline period; and (3) reported smoking cannabis at least once during the last 7 days prior to the second urine collection for biomarker analysis. CS (n = 911) were defined as individuals who: (1) tested negative for THC exposure at screening and (2) did not report cannabis use during the past month prior to their orientation visit and during baseline. Participants were excluded from the present analysis if they: (1) had a negative urine THC, but endorsed use of cannabis during the prior month (n = 17); (2) tested positive for THC, but used cannabis less than once/week during baseline period (n = 121; ie, insufficient cannabis use); (3) tested positive for THC, but did not report cannabis use (n = 27); or (4) were prescribed medical cannabis and it was uncertain if cannabis was smoked (n = 7).
Procedures
Study volunteers were initially screened by telephone and then scheduled for an orientation visit during which written informed consent was obtained and further screening conducted. Participants were told that the purpose of the study was to examine how the rate of changing nicotine doses in their cigarettes over time affects their smoking behavior. During the in-person screening session, participants completed a number of self-report and researcher administered measures, as well as physiological measures; these are detailed below.
Measures
The current study is restricted to analysis of data across 3 weeks: an orientation visit and two baseline visits.
Orientation Visit
Sociodemographic Characteristics.
At orientation, participants reported their sex, age, race, ethnicity, educational attainment, and current employment status.
Tobacco Use Characteristics.
Participants reported the number of cigarettes that they smoked per day, the number of years they had been smoking, whether they typically smoked menthol cigarettes, and their preferred brand of cigarette (as biomarkers of exposure may differ across brands).11 Participants also completed the Fagerström Test for Nicotine Dependence, which assessed levels of nicotine dependence.12 Participants self-reported any past 30-day use of the following tobacco products: cigars, cigarillos, little cigars, cigar paper (used for smoking cannabis; aka “blunts”), pipes, chewing tobacco, snuff, snus, e-cigarettes, hookah, dissolvable tobacco products, and bidis or clove cigarettes. Participants also provided a saliva sample which was analyzed to derive their nicotine metabolite ratio (free 3′-hydroxycotinine:free cotinine); nicotine metabolite ratio reflects the rate of nicotine metabolism.
Cannabis Use Characteristics.
As part of screening procedures, participants provided a urine toxicology test for the presence of illicit substances, including the THC metabolite 11-nor-9-carboxy-Δ 9-tetrahydrocannnabinol (THCCOOH). A rapid immunoassay urine “dipstick” test (Confirm Biosciences©) with a cutoff of 50 ng/mL was used. Participants also completed the Timeline Follow-Back assessment to report past 30-day cannabis use.
Two Baseline Visits
Tobacco and Cannabis Use Characteristics.
Participants completed a Timeline Follow-Back interview assessing use of cigars, cigarillos, little cigars, pipes, chewing tobacco, snuff, snus, e-cigarettes, hookah, dissolvable tobacco products, bidis or clove cigarettes, and cannabis since their last visit.13 Participants who reported using cannabis also reported whether they smoked cannabis in the form of a blunt. This tobacco and cannabis use information was collected at both baseline visits.
Biomarkers of Smoke and Nicotine Exposure.
Biomarkers of smoke exposure included expired breath carbon monoxide and urinary phenanthrene tetraol (PheT), an indicator of exposure to PAHs. Additionally, the following urinary mercapturic acids, measurements of exposure to volatile organic compounds, were analyzed: 3-hydroxypropylmercapturic acid (measuring acrolein exposure); 2-cyanoethylmercapturic acid (acrylonitrile exposure); S-phenylmercapturic acid (benzene exposure); 2-hydroxypropylmercapturic acid (propylene oxide exposure); 3-hydroxy-1-methylpropylmercapturic acid (crotonaldehyde, 2-methylacrolein, and methylvinyl ketone exposure).14,15 Biomarkers of tobacco exposure included urinary total nicotine equivalents (TNEs) and metabolites of a tobacco-specific nitrosamine, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK; total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL)). Urinary biomarkers were analyzed at the second baseline visit only.
Biomarker analysis was conducted as described in our previous work for NNAL,16 PheT,16 3-hydroxypropylmercapturic acid,17 3-hydroxy-1-methylpropylmercapturic acid,17 2-cyanoethylmercapturic acid,17 2-hydroxypropylmercapturic acid,18 and S-phenylmercapturic acid.18
Statistical Analyses
The continuous demographic and smoking characteristics were summarized with means and standard deviations, and compared using two-sample t tests. Categorical factors were summarized with frequencies and percentages and were compared using the chi-square test. A composite “any other tobacco product use” variable was created (excluding blunt papers) and analyzed rather than use of separate analyses for individual tobacco product categories given the small subsamples’ sizes.
Because biomarker values were right skewed, they were log-transformed and summarized using the geometric mean (GM) and 95% confidence interval (CI). Biomarkers were tested for significance using two-sample t tests. All biomarkers were adjusted for creatinine which accounts for variations in urine dilutions between participants. Multiple regression models were also completed to calculate the adjusted ratio of GMs for each biomarker, adjusting for age, sex, race (white/black/other), smoking duration, Fagerström Test for Nicotine Dependence, blunt use, nonblunt other tobacco use, and cigarette brand. These covariates were identified given their statistically significant variation between groups. Separate, identical multiple regression models were repeated among cousers comparing blunt users and nonblunt users. These analyses adjusted for age, race (white/black/other), smoking duration, cigarettes per day, log total nicotine equivalent, menthol use, number of cannabis use days during baseline, and cigarette brand as they differed significantly between cousers who did and did not use blunts.
The effect of average cannabis use days per week across both baseline weeks and number of days since last cannabis use on the biomarkers was also evaluated in the multiple regression models among cousers only. All analyses were completed using R Version 3.5.1. p values less than .05 were considered statistically significant.
Results
Participants
Table 1 shows demographic and tobacco/cannabis smoking characteristics of cousers and CS. Cousers were younger (couser Mage = 38.96, SD = 13.01; CS Mage = 47.22, SD = 12.72; p < .001), more likely to be male (cousers = 67.7%, CS = 51.9%, p < .001), reported fewer years of smoking (couser M = 21.27, SD = 13.42; CS M = 29.01, SD = 13.03; p < .001), and had lower nicotine dependence as assessed by the Fagerström Test for Nicotine Dependence (couser M = 5.1, SD = 2.1; CS M = 5.4, SD = 2.1; p < .05). Cousers had higher rates of non-cigarette tobacco product use compared to CS (cousers = 13.1%; CS = 4.3%, p < .001; see Supplementary Table 1, available at Nicotine and Tobacco Research online for a between group differences by other tobacco product). Cousers were more likely to report using Natural American Spirit (9.6%) and Camel (18.6%) brand cigarettes compared to CS (4.1% and 9.3%, respectively). Cousers reported smoking cannabis an average of 4.8 (SD = 2.3) days a week during baseline.
Table 1.
Demographic and Smoking Characteristics (n = 1078)
| Characteristics | Cousers (n = 167) | Cigarette smokers (n = 911) | All (n = 1078) | p |
|---|---|---|---|---|
| Age, mean (SD), years | 38.96 (13.01) | 47.22 (12.72) | 45.94 (13.10) | <.001 |
| Male, No. (%) | 113 (67.7%) | 473 (51.9%) | 586 (54.4%) | <.001 |
| Race, No. (%) | .001 | |||
| White | 113 (68.5%) | 541 (60.2%) | 654 (61.5%) | |
| Black | 33 (20.0%) | 297 (33.0%) | 330 (31.0%) | |
| Other | 19 (11.5%) | 61 (3.8%) | 80 (7.4%) | |
| Hispanic, No. (%) | 9 (5.4%) | 45 (4.9%) | 54 (5.0%) | .807 |
| Education, No. (%) | .095 | |||
| <High school | 12 (7.2%) | 78 (8.6%) | 90 (8.4%) | |
| High school | 44 (26.3%) | 309 (33.9%) | 353 (32.7%) | |
| >High school | 111 (66.5%) | 524 (57.5%) | 635 (58.9%) | |
| Employment, No. (%) | .133 | |||
| Employed (full-time or part-time) | 76 (45.5%) | 403 (44.2%) | 479 (44.4%) | |
| Unemployed | 41 (24.6%) | 187 (20.5%) | 228 (21.2%) | |
| Disability | 10 (6.0%) | 108 (11.9%) | 118 (10.9%) | |
| Other | 40 (24.0%) | 213 (23.4%) | 253 (23.5%) | |
| Cigarettes per day, mean (SD) | 17.10 (8.28) | 17.21 (8.60) | 17.19 (8.54) | .873 |
| Years of smoking, mean (SD) | 21.27 (13.42) | 29.01 (13.09) | 27.8 (13.4) | <.001 |
| CO, ppm, mean (SD) | 20.2 (10.3) | 19.1 (9.3) | 19.3 (9.5) | .297 |
| TNE, nmol/mg creatinine, GM (95% CI) | 54.9 (49.8, 60.6) | 59.1 (56.5, 61.7) | 58.4 (56.1, 60.8) | .190 |
| NMR, mean (SD)a | 0.40 (0.22) | 0.39 (0.24) | 0.39 (0.24) | .744 |
| FTND,b mean (SD) | 5.1 (2.1) | 5.4 (2.1) | 5.4 (2.1) | .041 |
| Menthol cigarettes, # (%) | 70 (41.9%) | 444 (48.7%) | 514 (47.7%) | .105 |
| Other tobacco products, not including blunts, # (%) | 22 (13.1%) | 39 (4.3%) | 61 (5.7%) | <.001 |
| Blunt use, # (%) | 34 (20.4%) | 0 (0%) | 34 (3.2%) | <.001 |
| Cannabis use days (SD) during baseline | 4.8 (2.3) | — | — | — |
| Days since last cannabis use at urine collection | 1.1 (2.2) | — | — | — |
| Brand, No. (%) | <.001 | |||
| American Spirit | 16 (9.6%) | 37 (4.1%) | 53 (4.9%) | .005 |
| Camel | 31 (18.6%) | 85 (9.3%) | 116 (10.8%) | <.001 |
| Marlboro | 44 (26.3%) | 228 (25.0%) | 272 (25.2%) | .700 |
| Newport | 38 (22.8%) | 263 (28.9%) | 301 (27.9%) | .111 |
| Pall Mall | 11 (6.6%) | 85 (9.3%) | 96 (8.9%) | .302 |
| Missing | 27 (16.2%) | 213 (23.4%) | 240 (22.3%) | .043 |
CO = carbon monoxide; CPD = cigarettes per day; FTND = Fagerström Test for Nicotine Dependence; NMR = nicotine metabolite ratio; TNE = total nicotine equivalent.
aNicotine metabolite ratio (3′-hydroxycotinine:cotinine) reflects the rate of nicotine metabolism, which has been associated with amount of smoking, smoking intensity, and smoking cessation success19.
bFTND with CPD.
Table 2a shows GM and between group differences of biomarkers of exposure. Compared to CS, cousers had higher levels of 3-hydroxypropylmercapturic acid (cousers adjusted GM = 6.79, CI = 5.83–7.90; CS adjusted GM = 5.77, CI = 4.92–6.76, p = .019), 2-cyanoethylmercapturic acid (cousers adjusted GM = 0.97, CI = 0.82–1.15; CS adjusted GM = 0.55, CI = 0.42–0.65, p < .001), 3-hydroxy-1-methylpropylmercapturic acid (cousers adjusted GM = 4.45, CI = 3.95–5.01; CS adjusted GM = 4.00, CI = 3.54–4.53, p = .049), S-phenylmercapturic acid (cousers adjusted GM = 3.58, CI = 2.84–4.51; CS adjusted GM = 2.65, CI = 2.08–2.38, p = .005), and PheT (cousers adjusted GM = 2.67, CI = 2.27–3.14; CS adjusted GM = 1.86, CI = 1.57–2.20, p < .001). There were no significant differences between groups on total nicotine equivalent, NNAL, and 2-hydroxypropylmercapturic acid.
Table 2.
Geometric Means and Between Group Differences of Biomarkers of Exposure and Effect
| (a) Cousers versus cigarette smokers | |||||||
|---|---|---|---|---|---|---|---|
| Parent compound or indication | Biomarkers | Cousers (n = 167) Unadjusted GM (95% CI) |
Cigarette smokers (n = 911) Unadjusted GM (95% CI) |
p (unadjusted)a | Cousers (n = 167) Adj. GM (95% CI) |
Cigarette smokers (n = 911) Adj. GM (95% CI) |
p (adjusted)b |
| NNK | NNAL, pmol/mg | 0.97 (0.85, 1.11) | 1.36 (1.28, 1.44) | <.001 | 0.83 (0.70, 0.98) | 0.88 (0.74, 1.04) | .467 |
| Acrolein | 3-HPMA, nmol/mg | 7.02 (6.24, 7.89) | 6.78 (6.47, 7.09) | .586 | 6.79 (5.83, 7.90) | 5.77 (4.92, 6.76) | .019 |
| Acrylonitrile | CEMA, nmol/mg | 0.96 (0.86, 1.08) | 0.63 (0.60, 0.66) | <.001 | 0.97 (0.82, 1.15) | 0.55 (0.42, 0.65) | <.001 |
| Propylene oxide | 2-HPMA, nmol/mg | 0.65 (0.48, 0.73) | 0.65 (0.62, 0.69) | .952 | 0.69 (0.58, 0.83) | 0.66 (0.55, 0.80) | .626 |
| Crotonaldehyde | HMPMA, nmol/mg | 3.95 (3.53, 4.43) | 4.00 (3.82, 4.18) | .864 | 3.99 (3.44, 4.62) | 3.52 (3.01, 4.10) | .060 |
| Benzene | SPMA, pmol/mg | 3.98 (3.39, 4.67) | 3.50 (3.26, 3.75) | .154 | 3.58 (2.84, 4.51) | 2.65 (2.08, 3.38) | .005 |
| Phenanthrene | PheT, pmol/mg | 3.15 (2.80, 3.56) | 2.18 (2.08, 2.29) | <.001 | 2.67 (2.27, 3.14) | 1.86 (1.57, 2.20) | <.001 |
| (b) Blunt users versus nonblunt cousers | |||||||
|---|---|---|---|---|---|---|---|
| Parent compound or indication | Biomarkers | Blunt users (n = 34) Unadjusted GM (95% CI) |
Nonblunt cousers (n = 133) Unadjusted GM (95% CI) |
p (unadjusted)a | Blunt users (n = 34) Adj. GM (95% CI) |
Nonblunt cousers (n = 133) Adj. GM (95% CI) |
p (adjusted)c |
| NNK | NNAL, pmol/mg | 0.66 (0.51, 0.85) | 1.07 (0.92, 1.25) | .002 | 0.83 (0.64, 1.09) | 0.95 (0.79, 1.14) | .359 |
| Acrolein | 3-HPMA, nmol/mg | 4.90 (3.82, 6.27) | 7.70 (6.77, 8.74) | .003 | 7.46 (5.84, 9.52) | 9.25 (7.86, 10.87) | .088 |
| Acrylonitrile | CEMA, nmol/mg | 0.73 (0.56, 0.95) | 1.03 (0.91, 1.17) | .025 | 1.03 (0.79, 1.34) | 1.16 (0.97, 1.39) | .360 |
| Propylene oxide | 2-HPMA, nmol/mg | 0.54 (0.40, 0.73) | 0.68 (0.59, 0.77) | .178 | 0.62 (0.44, 0.89) | 0.63 (0.50, 0.80) | .922 |
| Crotonaldehyde | HMPMA, nmol/mg | 2.77 (2.14, 3.59) | 4.33 (3.83, 4.90) | .004 | 4.29 (3.39, 5.42) | 5.29 (4.53, 6.18) | .084 |
| Benzene | SPMA, pmol/mg | 2.61 (1.75, 3.90) | 4.43 (3.74, 5.25) | .022 | 2.75 (1.81, 4.18) | 3.77 (2.85, 4.98) | .143 |
| Phenanthrene | PheT, pmol/mg | 2.19 (1.72, 2.78) | 3.46 (3.03, 3.96) | .002 | 2.78 (2.03, 3.81) | 2.98 (2.42, 3.67) | .674 |
2-HPMA = 2-hydroxypropylmercapturic acid; 3-HPMA = 3-hydroxypropylmercapturic acid; CEMA = 2-cyanoethylmercapturic acid; CI = confidence interval; CPD = cigarettes per day; FTND = Fagerström Test for Nicotine Dependence; GM = geometric mean; HMPMA = 3-hydroxy-1-methylpropylmercapturic acid; PheT = phenanthrene tetraol; SPMA = S-phenylmercapturic acid; TNE = total nicotine equivalent.
a p value from two-sample t test.
b p value from use group coefficient in regression model adjusting for age, gender, race (white/black/other), smoking duration, FTND, blunt use, nonblunt other tobacco product use, and cigarette brand.
c p value from use group coefficient in regression model adjusting for age, race (white/black/other), smoking duration, CPD, log TNE, menthol use, number of cannabis use days during baseline, and cigarette brand.
Table 2b shows GM and between group differences (nonblunt using cousers and blunt-using cousers) of biomarkers of exposure. After adjusting for variables that were significantly different at baseline, there were no significant differences between groups (ps > .05).
In a separate analysis among cousers only, none of the biomarkers were significantly associated with the number of days since last cannabis use (ps > .05). No biomarkers were affected by number of cannabis use days/week during baseline (ps > .05).
Discussion
This is the first study to compare levels of exposure to harmful chemicals between CS and cousers of tobacco cigarettes and cannabis. Compared to CS, cousers tended to be younger, male, and use more non-cigarette tobacco products. Cousers also demonstrated higher levels of exposure to acrolein, acrylonitrile, crotonaldehyde, benzene, and PAHs (as measured by PheT), after controlling for key demographic, smoking history, blunt use, other tobacco use, and current smoking patterns. This may be due to the increased exposure to combustion products as a result of cannabis smoking given that cigarette smoking patterns and nicotine and NNK (specific to tobacco) exposures did not differ between CS and cousers. Additionally, our analyses comparing cousers who did and did not use blunts demonstrated no significant differences in biomarkers of exposure after adjusting for significant covariates.
In a separate analysis among cousers, frequency of cannabis used (as measured by number of days using per week) and recency of cannabis use (as measured by days since last cannabis use) did not affect biomarkers. One previous study showed that additional PAHs (ie, 2-hydroxynaphthalene, 2-hydroxyfluorene, and 1-hydroxypyrene) were significantly higher in exclusive daily cannabis smokers who smoked at least two joints per day compared to less frequent cannabis smokers.8 We did not have an adequate sample size of cousers to evaluate the impact amount and/or frequency of cannabis use has on biomarkers of exposure. It is likely that amount of use per day (rather than frequency per week) also influences biomarkers of exposure.
Although all cousers included in this analysis smoked cannabis, we do not know if they exclusively smoked cannabis. It is possible that study participants used cannabis via other methods (eg, edibles or vaporization) clouding a relation between frequency of cannabis use and biomarkers of exposure. Future research is needed to examine the potential relationships between quantity and frequency of cannabis smoking, methods of cannabis use, and cannabis use topography, with toxicant exposures. For example, daily cigar smokers are exposed to similar levels of some combustion-related toxicants as daily CS,20 but it is unclear to what extent blunt users (cigar paper) are exposed to varying levels of toxicants compared to those who use cannabis via vaporizers, pipes, or joints.
Cousers were also more likely to report using Natural American Spirit and Camel brand cigarettes, which was statistically controlled for in our analyses. According to results from the Population Assessment of Tobacco and Health data, smoking cannabis is associated with an increased odds of smoking Natural American Spirit cigarettes.21 Smokers of Natural American Spirit cigarettes have demonstrated lower levels of NNAL, NNN, 3-hydroxypropylmercapturic acid, and 2-cyanoethylmercapturic acid.22 Subsamples’ sizes by cigarette brand were too small in the current study to make inferences regarding the impact of brand selection on cousers exposure to toxicants, a possible topic for future study.
In addition to needing more precise assessment of amount, frequency, and type of cannabis use, and analysis of cigarette brands used among cousers, there were several other limitations. First, the present study had eligibility criteria of smoking a minimum of five cigarettes per day (parent study), using other tobacco products no more than 9 days per month (parent study) and smoking cannabis at least once per week (present paper). Thus, the current study cannot generalize to less frequent users of either substance as well as more frequent users of other tobacco products (eg, blunts). Second, urine drug analysis for THC exposure was not conducted during the baseline phase when urine samples for biomarker analysis were collected; therefore, it is possible that some CS were using cannabis and did not report it. Third, most of the biomarkers of exposure to cannabis have an elimination half-life of 9–12 h.23–25 Despite these limitations, this study provides important information regarding the increased exposure to toxicants experienced by cousers of cannabis and tobacco.
In summary, cousers of cannabis and tobacco in this study appear to be exposed to greater levels of harmful chemicals (ie, volatile organic compounds and PAHs). However, this study’s smokers demonstrated little differences in exposure to tobacco evidenced by similar daily cigarette use patterns and exposure to nicotine and NNK. Thus, it is important for studies examining biomarkers of exposure among CS to account for cannabis use as it appears to have a significant impact on outcomes. Additionally, further research is needed regarding cannabis users’ exposure to harmful chemicals so that users can be educated about the potential negative health effects from its use.
Funding
This study was funded by the National Institutes of Health (NIH) and Food and Drug Administration grant U54DA031659 (DH, ED), NIH T32DA007097 (EM), NIDA K01DA043413 (LRP), NIH P30CA077598 (NR, XL). The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the NIH or the FDA.
Declaration of Interests
None declared.
Supplementary Material
Acknowledgments
Study data were collected and managed using REDCap electronic data capture tools hosted at University of Minnesota—Twin Cities (Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture [REDCap]—a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform. 2009;42(2):377–381). REDCap (Research Electronic Data Capture) is a secure, web-based application designed to support data capture for research studies, providing (1) an intuitive interface for validated data entry; (2) audit trails for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages; and (4) procedures for importing data from external sources.
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