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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Cancer Epidemiol Biomarkers Prev. Author manuscript; available in PMC Feb 1, 2012.
Published in final edited form as:
PMCID: PMC3077576
NIHMSID: NIHMS260488
Nicotine metabolite ratio predicts smoking topography and carcinogen biomarker level
Andrew A. Strasser,1 Neal L. Benowitz,2 Angela G. Pinto,1 Kathy Z. Tang,1 Stephen S. Hecht,3 Steve G. Carmella,3 Rachel F. Tyndale,4 and Caryn E. Lerman1
1Department of Psychiatry and Abramson Cancer Center, University of Pennsylvania, Philadelphia, Pennsylvania
2Division of Clinical Pharmacology and Experimental Therapeutics, University of California San Francisco, San Francisco, California
3Masonic Cancer Center, University of Minnesota, Minneapolis, Minnesota
4Centre for Addiction and Mental Health, and Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario
CORRESPONDING AUTHOR: Andrew A. Strasser, Department of Psychiatry and Abramson Cancer Center, University of Pennsylvania, 3535 Market Street; Suite 4100; Philadelphia, PA 19104-3309 USA; phone (215) 746-5788; fax (215) 746-7140 ; strasse3/at/mail.med.upenn.edu
Background
Variability in smoking behavior is partly attributable to heritable individual differences in nicotine clearance rates. This can be assessed as the ratio of the metabolites cotinine (COT) and 3'-hydroxycotinine (3HC) (referred to as the nicotine metabolism ratio, NMR). We hypothesized that faster NMR would be associated with greater cigarette puff volume and higher levels of total NNAL, a carcinogen biomarker.
Methods
Current smokers (n=109) smoked one of their preferred brand cigarettes through a smoking topography device and provided specimens for NMR and total NNAL assays.
Results
Faster nicotine metabolizers (third and fourth quartiles versus first quartile) based on the NMR exhibited significantly greater total puff volume and total NNAL; the total puff volume by daily cigarette consumption interaction was a significant predictor of total NNAL level.
Conclusion
A heritable biomarker of nicotine clearance predicts total cigarette puff volume and total NNAL.
Impact
If validated, the NMR could contribute to smoking risk assessment in epidemiological studies and potentially in clinical practice.
The substantial variability in smoking behavior is attributable, in part, to heritable individual differences in nicotine clearance rates (1). Nicotine, the primary addictive compound in tobacco, is metabolized to cotinine (COT), and then to 3'-hydroxycotinine (3HC), predominantly by the hepatic CYP2A6 enzyme (2, 3). Smokers can extract varying levels of nicotine by altering their smoking topography (e.g., puff volume, number of puffs) (46), which in turn can affect level of toxin exposure (68).
To provide a non-invasive assessment of CYP2A6 activity, a phenotypic marker has been characterized (9). The ratio of 3HC/COT, referred to as the nicotine metabolite ratio (NMR), reflects CYP2A6 genetic variation as well as environmental factors influencing CYP2A6 activity and therefore nicotine clearance in vivo (10). The NMR is highly reproducible and independent of time since last cigarette (9, 11). Faster metabolizers of nicotine have higher smoking rates (12), and therefore may have increased risk for lung cancer (13, 14). We hypothesized that smokers with higher NMRs (faster nicotine metabolism) will exhibit: (1) increased total puff volume, reflecting efforts to extract more nicotine from their cigarettes, and (2) increased total NNAL levels, reflecting the effect of total puff volume on toxin exposure. If confirmed, the NMR would be of value in assessment of risk from cigarette smoking.
To test this hypothesis, 109 smokers of ≥10 cigarettes per day and ages 18–65 years were recruited from participants in a nicotine replacement therapy trial (for exclusion criteria see 15). The study protocol was approved by the Institutional Review Board of the University of Pennsylvania and all other analytical sites.
Participants were recruited from April 2005 to February 2006. Following informed consent and prior to initiating treatment, they completed measures of demographics, smoking history, current cigarette brand, and nicotine dependence level (Fagerstrom Test of Nicotine Dependence; FTND;16). They provided a 15ml blood sample for analysis of nicotine metabolites, assayed at the University of California, San Francisco via liquid chromatography with tandem mass spectrometry (9). Genotyping for CYP2A6 variants was performed at the Centre for Addiction and Mental Health (Toronto, Canada) as previously described (17). A 30ml urine sample was provided and assayed for total 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), the sum of NNAL and its glucuronides, at the University of Minnesota per standard procedures (18).
In a smoking-approved ventilated room, participants smoked one of their own brand cigarettes under ad libitum conditions, using a smoking topography device (Clinical Research Support System; Borgwaldt, Richmond VA) validated in prior research (19, 20). Participants were asked to refrain from smoking for one hour prior to their laboratory smoking session, and were generally compliant (mean 64.3 minutes; SD=35.4, range 42–190). Total puff volume, defined as the sum of all puffs taken, was a priori selected as the outcome measure for analysis (21, 22).
NMR values were positively skewed (+1.8) and have positive kurtosis (+4.8) consistent with previously results, and was therefore log-transformed (15, 17). NMR quartiles were created (15, 23) and CYP2A6 genotypes were coded as described previously (17). NMR quartiles, previously determined from Received Operator Characteristic analyses, have been used to characterize smokers' response to transdermal nicotine treatment and bupropion (15; 17; 24), and therefore were used in this study to assist comparisons to previous research that utilized NMR. Hypotheses were tested using analysis of covariance (ANCOVA) where total puff volume and total NNAL were the outcome measures and NMR quartile was the between group factor. Fisher's post-hoc analyses were used to identify quartile differences. Regression analysis was used to examine the association between log-transformed NMR and total puff volume, as well as with NNAL. Stepwise regression analysis was used to examine the association between smoking behavior (daily cigarette consumption, total puff volume, and their product as an index of daily puff volume) and total NNAL levels, retaining covariates at p<.2.
The participant sample was 59% male, 96% Caucasian with an average age of 45.4 (SD=10.8) years old. On average, they had been smoking for 29.3 years (SD=11.2), smoked an average of 20.5 cigarettes per day (SD=8.4), with an average nicotine dependence score of 4.9 (SD=2.1). Most participants smoked a Light brand (55%) and non-menthol (71%) cigarette. This study sample is similar to the full clinical study sample, [n=568; (see 15)], but with a significantly greater proportion of Caucasians (84% in the full sample). Of the 142 who attended these initial intake sessions, 131 (92%) agreed to complete a smoking topography assessment with their own brand cigarettes; 14 withdrew prior to having assays completed, 5 had contaminated urine samples, and 3 had failed topography assessment. Topography session completers did not differ from non-completers on demographic or smoking variables.
Mean plasma NMR value was 0.395 (SD=0.20; range 0.012–1.246). Mean, standard deviation, lower and upper limits for quartiles were: 1) 0.192 (SD=0.06; 0.010–0.259); 2) 0.313 (SD=0.03; 0.260–0.357); 3) 0.410 (SD=.036; 0.358–0.477); 4) 0.668 (SD=0.21; 0.478–1.246) (additional data see Table 1). Females were more likely to be in the highest NMR (faster metabolism; p=.02), consistent with prior reports that women metabolize nicotine faster than men (25). NMR was non-significantly higher among participants who were older (p=.10), had higher nicotine dependence levels (p=.09), and who smoked non-menthol cigarettes (p=.10). Thus, these variables were included as covariates.
Table 1
Table 1
Mean (Standard Deviation), unless noted otherwise. Note: Reg.=Regular cigarette; Lt.=Light cigarette; U-Lt=Ultra-Light cigarette, based on Federal Trade Commission (FTC) cigarette classifications used at the time data was collected (FTC, 2000).
Mean total puff volume was 785.1 ml (SD=284.9; range 247.4–1776.1). There was an overall association of NMR quartiles with total puff volume (F=2.62, p=.05); smokers in the third quartile (p=.042) and fourth quartile (p=.016) exhibited significantly higher total puff volumes than those in the first quartile (Table 1; Figure 1a). Faster metabolizers by CYP2A6 (*1/*1, n=89) genotype also had higher puff volumes than slower metabolizers (defined as any of *2, *4, *9, *12 variants, n=19); means were 816.8 ml (SD=292.0) vs. 643.3 ml (SD=206.9), respectively (F=6.04, p=.02) (results the same when the 5 non-European ancestry subjects were excluded).
Figure 1
Figure 1
Figure 1
Association of the NMR with total puff volume and NNAL. (A) NMR quartiles versus total puff volume (ml). Results are mean values +/− standard error (F=2.62, p=.05). Post-hoc comparisons indicate a significant difference between first and third (more ...)
Mean total NNAL was 1.47 pmol/mg creatinine (SD=0.79; range 0.10–4.2). There was a significant main effect of the NMR (F=3.59, p=.02); smokers in the third quartile (p=.001) and fourth quartile (p=.033) had higher total NNAL levels than those in the first quartile (Table 1; Figure 1b). A similar, non-significant difference was seen in genotypic fast versus slow metabolizers (1.54 pmol/mg creatinine [SD=.92] vs. 1.34 pmol/mg creatinine [SD=.68]), p=.35). Results were unchanged when the nicotine dependence (FTND) covariate was replaced with smoking rate.
Linear regression analysis indicated a positive association between log-transformed NMR and total puff volume (beta=321.2, t=2.35, p=.024, R2 =.051); and log-transformed NMR and total NNAL (beta=.831, t=2.41, p=.02, R2 =.052). Stepwise regression analysis indicated that the total puff volume by daily cigarette consumption product was positively associated with total NNAL level (beta=2.538×10−5, t=2.94, p=.004), controlling for sex (p=.02), years smoking (p=.04), and menthol (p=.16); the overall model was significant [F(4,104)=4.85, p=.001, R2=.16).
This study is the first to show that a heritable biomarker of nicotine clearance, the NMR, predicts total cigarette puff volume and overall carcinogen exposure based on total NNAL. Compared to the slowest metabolizers (first quartile), smokers in the third and fourth quartiles exhibited 23% and 28% increases in cigarette puff volume respectively, and total NNAL levels that are 61% and 53% higher, respectively. These results are consistent with our prior study (26) and current results showing increased puff volume among fast metabolizers by CYP2A6 genotype. Results potentially could be interpreted as faster metabolizers take greater puffs to obtain a desired, greater level of nicotine, or that slow metabolizers smoke less intensely to avoid excessive or toxic nicotine levels. We suggest that it is more likely that faster metabolizers smoke more to obtain a desired level of nicotine, as there is little support for slow metabolizers reporting nausea when learning to smoke (27) and in high dose transdermal nicotine studies, continued smoking did not lead to signs of nicotine toxicity (28).
The availability of a phenotypic measure of CYP2A6 activity, such as the NMR, is useful as it captures both genetic, such as yet unidentified alleles or other genes, and environmental influences, such as estrogen levels (25), on CYP2A6 activity and nicotine clearance and NMR can be measured noninvasively (e.g., saliva) without additional drug administration (29, 30).
Cigarette smoking causes the majority of lung cancer cases (31) and total NNAL is a biomarker for one of the most prevalent systemic lung carcinogens in tobacco (32,33). Yet, there is substantial variability in lung cancer risk at a given level of smoking (34, 35). This may be attributable to individual differences in the amount of toxin exposure per cigarette, as well as in activation in carcinogens (13). Because lung cancer can take years to develop, the identification of practical biomarkers to improve risk assessment would be of great value.
There are some limitations of this study. Although puff volume is reliable and stable (19, 20), repeated assessments, rather than the single assessment here, may better reflect daily smoking patterns. Participants in this study were restricted to treatment-seeking smokers who smoked >10 cigarettes per day, and may not represent the general smoking population. One might postulate that since slow metabolizers have lower daily cigarette consumption, a high percentage may have not met study inclusion criteria (26). Therefore, these findings should also be replicated in a population-based sample. Lastly, a more comprehensive biomarker panel that includes not only the NMR, but also information on cigarette brand features (e.g., filter ventilation; 6), and diurnal variations in smoking patterns (36) may provide a more refined assessment of risk from smoking. Such measures could potentially enhance risk assessment in epidemiological studies and, if replicated, could be translated in the future to clinical practice.
Acknowledgments
Funding This research was supported by grants from the National Cancer Institute and the National Institutes on Drug Abuse at the National Institutes of Health: P50 CA/DA84718, CA143187 (CL), U01 DA020830 (NB, RT, CL), R01 CA120594 (AS) and R01 CA130961 (AS); and Canadian Institutes of Health Research: MOP86471 (RFT).
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