Published online November 27, 2006
PEDIATRICS Vol. 119 No. 1 January 2007, pp. e264-e274 (doi:10.1542/peds.2006-1583)
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ARTICLE

The Role of CYP2A6 in the Emergence of Nicotine Dependence in Adolescents

Janet Audrain-McGovern, PhDa, Nael Al Koudsi, BScb, Daniel Rodriguez, PhDa, E. Paul Wileyto, PhDa, Peter G. Shields, MDc and Rachel F. Tyndale, PhDb

a Tobacco Use Research Center, Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
b Centre for Addiction and Mental Health, Department of Pharmacology, University of Toronto, Toronto, Ontario, Canada
c Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC


   ABSTRACT
TOP
 ABSTRACT
METHODS
 RESULTS
 DISCUSSION
 REFERENCES
 
OBJECTIVES. The objectives of our study were to evaluate whether genetic variation in nicotine metabolic inactivation accounted for the emergence of nicotine dependence from mid- to late adolescence and whether initial smoking experiences mediated this effect.

METHODS. Participants were 222 adolescents of European ancestry who participated in a longitudinal cohort study of the biobehavioral determinants of adolescent smoking. Survey data were collected annually from grade 9 to the end of grade 12. Self-report measures included nicotine dependence, smoking, age first smoked, initial smoking experiences, peer and household member smoking, and alcohol and marijuana use. DNA collected via buccal swabs was assessed for CYP2A6 alleles that are common in white people and are demonstrated to decrease enzymatic function (CYP2A6*2, *4, *9, *12).

RESULTS. Latent growth-curve modeling indicated that normal metabolizers (individuals with no detected CYP2A6 variants) progressed in nicotine dependence at a faster rate and that these increases in nicotine dependence leveled off more slowly compared with slower metabolizers (individuals with CYP2A6 variants). Initial smoking experiences did not account for how CYP2A6 genetic variation impacts nicotine dependence.

CONCLUSIONS. These findings may help to promote a better understanding of the biology of smoking behavior and the emergence of nicotine dependence in adolescents and inform future work aimed at understanding the complex interplay between genetic, social, and psychological factors in adolescent smoking behavior.

Key Words: adolescent smoking • nicotine metabolism • CYP2A6

Abbreviations: ISE—initial smoking experience • SM—slower metabolizer of nicotine • NM—normal metabolizer of nicotine • mFTQ—modified Fagerstrom Tolerance Questionnaire • YRBS—Youth Risk Behavior Survey • LGM—latent growth-curve modeling • CFI—comparative-fit index • RMSEA—root-mean-square error of approximation • SRMR—standardized root-mean residual • HW—Hardy-Weinberg

Adolescents differ in the initial responsivity to both the rewarding and aversive effects of cigarette smoking. Adolescents who become nicotine dependent may be more responsive to the rewarding effects of smoking. Research indicates that pleasant emotional and physiologic effects (eg, enjoyed it, felt high, dizzy versus coughing, feeling sick) of the initial smoking experiences (ISEs) discriminated adolescents who continued to experiment with cigarettes and those who did not.1–3 A recent study also showed that these initial smoking reactions can predict the development of nicotine dependence.4

Individual differences in response to smoking and the emergence of nicotine dependence may be partially explained by genetic factors. The heritability of nicotine dependence has been well documented.5–7 Genetic susceptibility to drug dependence is thought to reflect, in part, variability in drug metabolism.8 Thus, genes that are involved in the metabolic inactivation of nicotine, such as CYP2A6, might be important in understanding which adolescents progress in nicotine dependence. Approximately 80% of nicotine consumed via cigarette smoking is removed from the body via inactivation to cotinine; the CYP2A6 gene encodes a hepatic enzyme that mediates ~90% to 100% of this metabolism to cotinine.9–12 The CYP2A6 gene is highly polymorphic, with many genetic variants identified to date. However, only a small number of these variants have been characterized for their impact on enzymatic activity in vivo or their frequencies among different ethnic groups (www.imm.ki.SE/CYPalleles/cyp2a6.htm).12,13 Studies have linked smoking rate and risk of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition–defined nicotine dependence in adults with polymorphisms in the CYP2A6 gene.14,15 Slower metabolizers (SMs), those with genetic variants predicting ≤50% of the activity of normal metabolizers (NMs), smoke fewer cigarettes and are less likely to be current smokers.14–16

There has been little research to evaluate the role of CYP2A6 in the etiology of adolescent nicotine dependence. Adolescents who metabolize nicotine faster compared with those who metabolize nicotine slower might experience more pleasurable effects of smoking and fewer aversive effects, which, in turn, may increase the likelihood of subsequent smoking and nicotine dependence. One recent study assessed whether the CYP2A6 genotype predicted risk of nicotine dependence defined by International Classification of Diseases, 10th Revision from early to midadolescence. O'Loughlin et al17 found no association between CYP2A6 and initial responses to smoking, and contrary to expectation, the risk of becoming nicotine dependent was almost 3 times higher among adolescents with at least 1 fully inactive CYP2A6 variant (SMs, <50% of the activity of NMs) than adolescents with the wild-type genotype (NMs).17

In this study we sought to evaluate whether genetic variation in nicotine metabolism played a role in the emergence of nicotine dependence from mid- to late adolescence. Specifically, we hypothesized that adolescents with the wild-type CYP2A6 genotype (NMs) would progress in nicotine dependence faster than adolescents with a CYP2A6 genetic variant (SMs). We further hypothesized that ISEs (pleasant and unpleasant initial experiences) would mediate this effect.


   METHODS
TOP
 ABSTRACT
 METHODS
RESULTS
 DISCUSSION
 REFERENCES
 
Participants and Procedures
Participants were 222 9th-grade high school students of European ancestry who were enrolled in 1 of 5 public high schools in Virginia. These adolescents participated in a longitudinal cohort study of biobehavioral determinants of adolescent smoking. Of these 222 adolescents, 113 (51%) were male and 109 (49%) were female.

This sample is a subset of a larger cohort that was drawn from 2393 students identified through class rosters at the beginning of 9th grade and followed until the end of 12th grade. Figure 1 provides a summary of the sample derivation for the larger cohort study as well as the subset of participants that comprised the present study. Students were ineligible to participate if they had a special classroom placement (eg, were learning-disabled or English was their second language). On the basis of the cohort selection criteria, a total of 2120 (89%) students were eligible to participate. Of the 2120 eligible students, 1533 (72%) parents provided a response. Of these 1533 students, 1151 (75%) parents consented to their teen's participation in the study, yielding an overall consent rate of 54%. An analysis of differences between parents who consented and those who did not consent to their teen's participation in the study revealed a race-by-education interaction. The interaction indicated that the likelihood of consent was significantly greater for white parents with more than a high school education than for those with a high school education or less (89% vs 77%).18


Figure 1
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  		FIGURE 1 Adolescent cohort study.

 
Participation in the study also required student assent. Fifteen students declined participation. Another 13 students failed to participate in the baseline administration because of absence. The final baseline sample size (year 2000) was 1123 of the 2120 eligible students. Approximately 65% of the adolescents enrolled were white (of European ancestry), and ~35% were nonwhite (black, Asian, Hispanic, or "other"). The rates of participation at the 3 spring follow-ups in the 10th (2001), 11th (2002), and 12th (2003) grades were ~96% (1081), 93% (1043), and 89% (1005), respectively. University institutional review board approval of the study protocol was obtained.

To limit potential bias resulting from ethnic admixture (ie, allelic frequencies differing as a result of race and not the phenotype or outcome under investigation), the analyses were limited to adolescents of European ancestry (n = 714). Of the 714 adolescents, 326 adolescents smoked at least 1 whole cigarette either before the baseline assessment (9th grade) or during the follow-up period (end of 12th grade). We only included those adolescents who smoked at least 1 cigarette, because never-smokers would not have had the opportunity for the genetic predisposition involving genetically variable nicotine metabolism to be expressed.14,19–21 Separating never-smokers from those who have smoked has been considered an important step in refining smoking phenotypes.22 If genetic variation in nicotine metabolic inactivation accounts for the emergence of nicotine dependence, then biological exposure is necessary for the genetic effects to be expressed. Never-smokers may differ in numerous ways from those who have been exposed to nicotine through smoking. Approximately 31 adolescents had missing data on at least 1 covariate, and 62 adolescents had insufficient DNA for genotyping. Eleven adolescents who had higher nicotine-dependence scores at baseline (scores greater than the median of 2) were removed. The primary variables of interest were nicotine dependence, CYP2A6 genotype, and pleasant and unpleasant initial smoking reactions. Age first smoked, baseline smoking, alcohol use, marijuana use, peer and household member smoking, and gender served as controlling variables. The data presented herein are based on 222 adolescents of European ancestry.

Survey data were collected on-site during a classroom common to all students. A member of the research team distributed the survey. The surveys comprised frequently used, valid, and reliable measures of adolescent smoking history, household and peer smoking, and alcohol and marijuana use. The surveys were completed in the classroom. The survey contained a front page with the student's name. The front page was removed when the survey was given to the student. The completed survey only contained an identification number. A member of the research team read aloud a set of instructions, emphasizing confidentiality to promote honest responding, and encouraged questions if survey items were not clear. Teachers or school administrators were not involved in the data collection (to promote honest responding).23 Research supports the validity of self-report measures of smoking behavior and substance use in adolescents, particularly in nontreatment contexts in which confidentiality is emphasized.24,25 Although a specific reading level was not determined, as indicated above, adolescents with a special classroom placement were ineligible to participate. The surveys took ~30 minutes to complete.

Buccal cells were collected as described previously,26,27 and DNA was extracted with standard phenol-chloroform techniques. Genotyping was performed by using previously described 2-step allele-specific polymerase chain reaction assays.14 The CYP2A6 alleles investigated lead to either a decrease (CYP2A6*9 and CYP2A6*12) or loss (CYP2A6*2 and CYP2A6*4) of CYP2A6 function and occur at relatively high frequencies in white people. Positive controls included heterozygote and homozygote samples for each variant, and negative controls included water instead of DNA. Assays were previously validated,14 and 20% were repeated indicating a negligible discordance rate.

Measures
Nicotine Dependence
Nicotine dependence was measured with a modified version of the Fagerstrom Tolerance Questionnaire (mFTQ) for adolescents.28,29 This 7-item measure has been used frequently in studies of adolescent smoking.23,30–32 Because nicotine dependence is a continuous variable, adolescents progressed in nicotine dependence when they reached a score of 1 (low level of nicotine dependence) on the mFTQ and could progress to a score of 9 (high level of nicotine dependence). Nicotine dependence was measured at every data-collection wave.

Nicotine dependence was conceived of as a process existing on a continuum and not a state whereby an adolescent was placed in a category reflecting a static end product of regular smoking.33 Thus, our statistical model evaluated the rate at which an adolescent progressed to a score of 1 on the mFTQ and the rate at which the mFTQ score increased to a score of 9 (acceleration) and decreased (deceleration) across time.

Genotype Groupings
Individuals were categorized initially into 3 main groups (normal, intermediate, and slowest metabolizers) according to the impact of the CYP2A6 alleles on nicotine metabolism. NMs (100% activity) included adolescents with no detected CYP2A6 variants. Intermediate metabolizers (75% activity) included adolescents who had 1 copy of either CYP2A6*9 or CYP2A6*12. The SMs (≤50% activity) included adolescents with 1 or 2 copies of the inactive variants (CYP2A6*2 and CYP2A6*4) or 2 copies of decreased activity variants CYP2A6*9 and/or CYP2A6*12. Because the average levels of nicotine dependence for intermediate metabolizers fell between the average values for the NMs and the SMs across time, the intermediate and slowest metabolizers were combined into 1 group (SMs, ≤75% activity) for sample-size purposes.

Initial Smoking Experiences
ISEs were measured by the 7-item Early Smoking Experiences Scale.34 Pleasurable and unpleasurable sensations were rated on a 4-point scale (1, none; 4, intense), including rush or buzz, relaxation, nausea, and cough. Nicotine-dependent individuals tend to have more pleasant effects associated with their initial exposure to smoking.34 Retrospective reports of pleasurable sensations measured by this scale have been validated.35 Convergent and discriminant validity has been demonstrated for adolescent populations.30 Pleasurable ISEs adapted from the Early Smoking Experiences Scale have been shown to be predictive of a more regular smoking habit and subsequent nicotine dependence in adolescents.4,36 Negative sensations associated with ISEs have been shown to protect against subsequent dependence.4

Covariates
Baseline Smoking
An ordered categorical variable was generated from responses to a series of standard epidemiologic questions regarding smoking.24,25,37,38 On the basis of participant responses to these items, adolescents were categorized as a (1) never-smoker (never having smoked a cigarette, not even a puff), (2) puffer (not ever having smoked a whole cigarette), (3) experimenter (smoked at least 1 whole cigarette but <100 cigarettes total in a lifetime), or (4) current smoker (smoked on at least 1 of the past 30 days and >100 cigarettes in a lifetime).39,40

Age First Smoked
Adolescents were asked, "How old were you when you smoked your first whole cigarette?" The question was based on an item from the Youth Risk Behavior Survey (YRBS).37

Friends Smoking
Adolescents were asked if their best friend smokes and how many of their other 4 best male and 4 best female friends currently smoke, which yielded an estimate of smoking among their 9 best friends.41,42

Household Member Smoking
Adolescents were asked if any member of their household smokes cigarettes, such as mother, father, and/or siblings. This variable was dichotomized because of nonnormality of the responses (0, no; 1, yes).

Alcohol and Marijuana Use
Lifetime alcohol and marijuana use was assessed with items that asked, "During you life, on how many days have you had at least one drink (not just a sip) of alcohol?" and "During your life, how many times have you used marijuana?"37 The response options were 0 (0 days or times), 1 (1 day or time), and 2 (>1 to ≥100 days or times).

Statistical Analysis
Statistical analysis used latent growth-curve modeling (LGM).43 LGM is a multivariate method that models repeated measures of an observed variable on latent variables (factors) representing baseline level and developmental trends (eg, linear, quadratic).43,44 The factors are random effects. Therefore, LGM permits the estimation of developmental heterogeneity in initial status and the rate of change from baseline across time43 and the regression of factors on select covariates. In this study, there were 4 annual repeated measurements of nicotine dependence, spanning ages 14 to 18 years. This approach considered individual growth and, thus, did not assume that all adolescents start at the same level of nicotine dependence at baseline and progress in nicotine dependence at the same rate. We used Mplus 4.1 (Muthén & Muthén, Los Angeles, CA) for all multivariate modeling. Mplus is a statistical software package for conducting growth modeling from a latent variable framework.

The multivariate modeling used all available data, a missing-data strategy used when data are missing at random and capitalizes on the data that are available for each wave for each participant. Mplus provides this option for latent variable modeling with missing data with maximum-likelihood estimation of the mean, variance, and covariance parameters, when requested, using the expectation maximization algorithm.45 Those with missing data did not differ from those without missing data on the covariates and on the dependent variable of nicotine dependence (P > .05). We log-transformed nicotine dependence to correct for univariate nonnormality.

Model fit was evaluated with model {chi}2, comparative-fit index (CFI), root-mean-square error of approximation (RMSEA), and standardized root-mean residual (SRMR). Suggested criteria for model fit are nonsignificant model {chi}2, CFI > 0.95, RMSEA < 0.05 to 0.08, and SRMR < 0.08.46–48 An RMSEA value of 0 represents exact model fit.48 Mplus provides a 95% RMSEA confidence interval, and for single-group models it provides a P value for the probability that the RMSEA value is <.05.46


   RESULTS
TOP
 ABSTRACT
 METHODS
 RESULTS
DISCUSSION
 REFERENCES
 
Descriptive Statistics
Distributions for the categorical covariates appear in Table 1. Means and SDs and bivariate correlations for the 4 repeated measures of nicotine dependence (log-transformed) and covariates appear in Table 2.


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  		TABLE 1 Proportions for Categorical Covariates

 

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  		TABLE 2 Bivariate Correlations for All Measured Variables in the Model

 
CYP2A6 Allele Frequencies and Genotype Groupings
The allele frequencies of CYP2A6*2 (5.3% [490 alleles]), CYP2A6*4 (0.6% [478 alleles]), CYP2A6*9 (6.1% [494 alleles]), and CYP2A6*12 (1.9% [482 alleles]) were similar to previously reported allele frequencies in an adolescent and adult white populations.14,17 The CYP2A6 genotype distributions did not deviate significantly from Hardy-Weinberg (HW) equilibrium. Of the 222 individuals, 164 (74%) were NMs and 58 (26%) were SMs.

Model Fit
Measurement Model
The single-group measurement model, absent covariates, fit reasonably well with linear and quadratic trends ({chi}42(n=222) = 8.5, P = .07; CFI = 0.97; RMSEA = 0.07 [95% confidence limits: 0, 0.14], P = .24; SRMR = .05), although the lower RMSEA 95% confidence limit was 0 and the upper limit was 0.14, indicating the possibility of fit from perfect to less than adequate. The baseline level was significant ({eta}0 = 0.11; z = 5.17; P < .0001). The linear trend was also significant ({eta}0 = 0.16; z = 4.51; P < .0001), although the quadratic trend was not (P > .05). The variances for baseline level and the linear trend were both significant (P < .05). The quadratic trend was fixed to 0 to eliminate a nonsignificant negative variance.

Full Model
The full LGM with covariates fit the data well with linear and quadratic trends ({chi}172(n=222) = 18.24, P = .374; CFI = 1.00; RMSEA = 0.02 [95% confidence limits: 0, 0.07], P = .84; SRMR = 0.03). In addition, the upper RMSEA 95% confidence limit decreased to an adequate level (0.07). Figure 2 is the structural model including covariates and standardized path coefficients for the significant paths.


Figure 2
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  		FIGURE 2 Latent growth-curve model of the role of CYP2A6 in the emergence of nicotine dependence in adolescents. Note that the repeated observed measure of nicotine dependence was log-transformed to correct for univariate nonnormality. The values represent standardized regression coefficients. Circles represent latent variables (factors), and rectangles represent observed (measured) variables. The arrow representing the factor loading (0) from the 2 trend factors to 9th-grade log nicotine dependence is omitted for simplicity. Only significant paths are shown; thus, the nonsignificant covariates (gender, household smoking, lifetime alcohol use, and negative ISEs) are not shown. a P < .06; b P < .05; c P < .01.

 
The effect of CYP2A6 Genotype on Nicotine Dependence
Baseline Nicotine Dependence
Parameter estimates, SEs, and z values appear in Table 3. Parameter estimates reflect a change in the dependent variable for a unit change in the predictor variable, and the z value indicates the likelihood that the change is significant. Four predictor variables had significant effects on baseline nicotine dependence. Greater lifetime marijuana use at 9th grade was associated with higher nicotine dependence at baseline (ß = .06; z = 2.16; P = .031). The more friends one had in 9th grade that smoked, the higher the level of nicotine dependence at baseline (ß = .04; z = 4.37; P < .0001). In addition, having a pleasant ISE was associated with higher baseline nicotine dependence (ß = .02; z = 1.98; P = .048). Finally, the effect of age first smoking approached significance (ß = –.04; z = –1.76; P = .078), suggesting that the younger an adolescent was at smoking onset, the higher the baseline level of nicotine dependence.


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  		TABLE 3 Linear Regression Coefficients, SEs, and z-Test Statistics (N = 222)

 
Nicotine-Dependence Linear Trend
There was a significant effect for the CYP2A6 genotype on linear trend (ß = .17; z = 1.97; P = .048), such that NMs had faster acceleration in nicotine dependence than SMs. Current smoking had a significant and positive effect on linear trend (ß = .71; z = 2.96; P = .0031), indicating that having smoked in the past month at baseline (9th grade) resulted in an increased acceleration in nicotine dependence across the 4 waves.

Nicotine-Dependence Quadratic Trend
There was a significant negative effect for CYP2A6 genotype on the quadratic trend (ß = –.07; z = –2.41; P = .016), such that NMs had slower deceleration in nicotine dependence after 10th grade than SMs. The effect of the quadratic trend materializes, independent of the linear trend, only after the second wave (see the factor loadings in Fig 2). Current smokers at grade 9 (smoking at least 1 cigarette in the past month) had slower deceleration in nicotine dependence (ß = –.21; z = –2.67; P = .008) than adolescents who had not yet smoked or had not smoked a cigarette in the past month.

Testing for Mediation Effects
We tested whether pleasant or unpleasant ISEs mediated the relationship between the CYP2A6 genotype and nicotine dependence. CYP2A6 did not have a significant effect on either pleasant or unpleasant ISEs, negating the possibility of mediation.

In summary, there was a significant increase (acceleration) in nicotine dependence from 9th to 11th grade (linear trend), which then was followed by a leveling off (deceleration) of nicotine dependence scores from 11th to 12th grade (quadratic trend). The term trend is equivalent to slope or rate of growth across time. NMs increased in their nicotine dependence scores at a faster rate than SMs from 9th to 11th grade. NMs also leveled off in their nicotine dependence at a slower rate than SMs from 11th to 12th grade. ISEs, pleasant or unpleasant, did not explain how CYP2A6 genetic variation impacts nicotine dependence.

Statistical Power to Detect Effects
To test the statistical power of these results, we ran a Monte Carlo analysis based on the results of the LGM. Monte Carlo analyses assess the power of a sample to detect specific effects on the basis of repeated samplings from a population with known parameters.45 In the present case, the population parameters were those resulting from our analysis, and the population size was N = 222. For the effect of the CYP2A6 genotype on nicotine dependence, the power was .60 for the linear trend, and .80 for the quadratic trend.

Analysis of Population Substructure
The sample was examined for evidence of population stratification by using the Structure clustering program (University of Chicago, Chicago IL [http://pritch.bsd.uchicago.edu/software.html]), which uses genotypes that may be out of HW equilibrium overall and attempts to identify subpopulations that are at HW equilibrium internally.49 On the basis of the hypothesis that the sample population was not 1 population but 2 subpopulations, the program attempted to classify individuals as belonging to one population or the other by using class probabilities. Data for the analysis were genotypes of 42 randomly selected biallelic single-nucleotide polymorphisms (a list of single-nucleotide polymorphisms is available on request). Our 42 random single-nucleotide polymorphisms were at HW equilibrium according to the GENHW routine in Stata (Stata Corp, College Station, TX). Structure results indicated a single population. The average probability of assignment to subpopulation 1 was .50, with the entire range of assignment probabilities from .48 to .53.


   DISCUSSION
TOP
 ABSTRACT
 METHODS
 RESULTS
 DISCUSSION
REFERENCES
 
In this study we sought to evaluate whether genetic variation in nicotine metabolism played a role in the emergence of nicotine dependence from mid- to late adolescence. We hypothesized that NMs would progress in nicotine dependence faster than SMs and that this effect would be mediated by ISEs. Consistent with our hypotheses, NMs did show a faster rate of progression in nicotine dependence (significant linear trend), and these increases in nicotine dependence leveled off more slowly compared with SMs (significant quadratic trend). Contrary to our hypothesis, ISEs did not account for how CYP2A6 genetic variation impacts nicotine dependence.

The finding that adolescent NMs progress in nicotine dependence at a faster rate than SMs can be discussed within the context of animal self-administration studies, the role of learning in the etiology of drug dependence, and research on the relationship between adult smoking practices and CYP2A6 variation. A faster rate of acquisition might be associated with stronger dependence on nicotine. Animal research indicates that addiction-prone rat strains have a faster rate of drug self-administration acquisition than addiction-resistant rat strains.50 In addition, models of drug dependence assume that repetitive drug use is a learned behavior, strengthened over time and over repeated exposure to the drug (eg, number of cigarettes).33 In the present study, among those adolescents with even low levels of nicotine dependence, NMs smoked significantly more cigarettes than SMs at grade 12 (73 vs 32 cigarettes per week; P = .04). NMs inactivate nicotine faster and may smoke more to titrate nicotine to a preferred level.14,15 Thus, faster metabolism is compensated for by smoking more cigarettes, which, in turn, is associated with more learning trials. Therefore, NMs not only accelerated in nicotine dependence at a faster rate, but the habit may be more ingrained because they also smoked more cigarettes. This process may account for the path from smoking experimentation to a nicotine-dependent smoking habit among NMs.

These findings might clarify why both SMs and NMs can become nicotine dependent, yet the SMs represent a smaller portion of those adults who present for formal smoking-cessation treatment.14,16 SMs may be better able to quit successfully, resulting in shorter durations of smoking.14,51 In late adolescence, SMs level off in nicotine dependence faster than NMs. This could explain why SMs are half as likely to be smokers in adulthood, and if they do smoke, they smoke fewer cigarettes.14,15 Data also suggest that SMs are more successful than NMs at quitting when using the nicotine patch, likely because of their higher levels of plasma nicotine.52

The hypothesis that ISEs would mediate the relationship between CYP2A6 genotype and progression in nicotine dependence was not supported. There are several plausible reasons why a mediated effect was not found. Quite simply, these ISEs may not account for the relationship between CYP2A6 genetic variation and emergence of nicotine dependence, or the mediated relationship is more complex than modeled. It is also possible that the context of initial use of cigarettes influences an adolescent's reactions to the physiologic and emotional reactions to smoking. Research indicates that others, usually of the same gender who have smoked previously, are present for 90% of the first opportunities to smoke cigarettes.3 Peer presence may prompt adolescents to experiment further despite initial negative reactions to cigarette smoking. In addition, if the ISE also involved other substance use such as alcohol or marijuana, the likelihood of continued experimentation may have been influenced irrespective of the reactions to smoking.2,53 Friedman et al2 found that experimenters who continued in their smoking did not experience fewer unpleasant reactions to smoking. Thus, nonpharmacologic and pharmacologic factors associated with the initial smoking episode may be important in explaining smoking progression and the emergence of nicotine dependence.54 Although we controlled for peer smoking, household smoking, and alcohol and marijuana use in the present model, we did not measure the context (eg, presence of others, use of another substance) of the ISE. Finally, it is possible that recall of ISEs is compromised by current smoking status.55 Although we prospectively captured the first episode in over half of the sample, the model did show that pleasant ISEs were positively associated with nicotine dependence at baseline. Thus, those adolescents who were smoking more regularly at baseline retrospectively reported more pleasurable experiences.

Our findings contrast with a previous report of the relationship between the CYP2A6 genotype and the odds of becoming nicotine dependent from early to midadolescence.17 This may be related to different measures of nicotine dependence (mFTQ versus International Classification of Diseases, 10th Revision criteria), which may capture differing aspects of nicotine dependence, particularly among adolescents who have low levels of dependence and are smoking at low rates.32 It may also have been influenced by the age of the cohort participants (14–18 vs 12–16 years) and the fact that we combined those with reduced nicotine inactivation (less than ~75%) into 1 group versus 2 groups because of the small sample size of those with the slowest inactivation of nicotine (≤50%). Although our studies had similar numbers of adolescents with the slowest inactivation of nicotine (≤50%), power analyses indicated that power was insufficient to conduct analyses on this group separately. Nonetheless, similar trends were observed when slow and intermediate metabolizers were grouped together or analyzed separately. In this study, combining the slowest and the intermediate groups together was possible because of the gene-dose effect; however, this was not observed for nicotine dependence in the previous study.17 Consistent with our findings, O'Loughlin et al17 did not find that ISEs mediated the effect between CYP2A6 and the odds of becoming nicotine dependent. One other recent study of smoking in English youth found no significant impact of the CYP2A6 genotype on risk for being a current or ex-smoker relative to being a never-smoker at 13 to 15 years of age and at 18 years of age.56 However, the interpretation of these data are unclear; one might argue that never-smokers are a poor comparison group because there is no chance for the impact of nicotine metabolism to affect risk in individuals with no smoking and, therefore, no nicotine exposure.14,19–21

Consistent with our findings, O'Loughlin et al17 found a trend for higher levels of smoking among dependent NMs compared with SM groups. Similarly, another study in white and black adolescents found that a significant relationship between the ratio of 3-hydroxycotinine to cotinine, a validated measure of CYP2A6 activity,57 and levels of smoking indicating that SMs smoked fewer cigarettes per day.58 In contrast, Huang et al56 did not find a significant effect of the CYP2A6 genotype on levels of smoking, although this was assessed in all smokers rather than in those who were dependent. As previously shown in adults and again here, the genotype only alters smoking levels in those who are dependent smokers,14 which was not assessed in the Huang et al56 study.

As one of the first investigations of the impact of CYP2A6 genetic variation on the emergence of adolescent nicotine dependence, our study has both strengths and weaknesses. Strengths include the collection of DNA and behavioral data from a large sample of adolescents, the use of a more refined longitudinal nicotine-dependence phenotype, the conception of nicotine dependence as a continuum rather than a category, and the analysis of the potentially biasing effects of ethnic admixture as an alternative explanation for the study findings.33,59,60

Although not a limitation of the current study, it is important to note that we did not incorporate biomarker validation of smoking status. Although biochemical verification of smoking status is important in smoking-cessation intervention studies, such measures are not typically implemented in epidemiologic studies, because adolescent self-reports have been determined to be valid and sufficient, especially when confidentiality is assured.25,61–63 In addition, the standard cotinine cutoff of 15 ng/mL cannot validate cotinine levels consistent with definitions of being an adolescent current smoker (eg, 1 cigarette in the past 30 days).64–66

One potential limitation of this study is the parental consent rate for adolescent participation. Seventy-five percent of those parents who responded did provide consent, and the difference between those who provided consent and those who declined was small.18 However, some caution is warranted in generalizing the results of this study. Although the sample may not be representative of all adolescents in the United States, the sample is nationally and locally representative on basic demographic characteristics, and the sample smoking rates are regionally and locally comparable to those found in national surveys.67–69 For example, data from our 2003 survey indicated that 10% were daily smokers compared with ~9% in the 2003 YRBS and ~15% in the 2003 Monitoring the Future Survey.70,71 In addition, 15% of the adolescents in our sample were current smokers compared with 13% in the 2003 YRBS survey.

Another potential limitation is that our measure of nicotine dependence, the mFTQ, was adapted from an adult assessment of nicotine dependence.28 This traditional approach for the assessment of nicotine dependence has limitations with respect to capturing the emergence of nicotine dependence in adolescents.72,73 However, at present, the limited research on the acquisition of or the changes in nicotine dependence across time has not highlighted an epidemiologic instrument that adequately captures the process of nicotine dependence.33,74 Finally, there were insufficient numbers of adolescents in other racial groups to conduct analyses stratified by race, and the sample size of our study suggests that this investigation may be considered a pilot study.

Despite these potential limitations, our findings help explain variability in adolescent nicotine dependence and may provide clues to why some carry a smoking habit into adulthood and others do not. Future research may include investigation of environmental factors that modify the effect of CYP2A6 genetic variation on nicotine dependence. That is, it would be valuable to identify factors that either promote nicotine dependence in SMs (vulnerability interaction) and protect against nicotine dependence in NMs (buffering interaction).75 In addition, future research is needed to better understand how differences in nicotine metabolism influence other systems involved in nicotine dependence (eg, nicotinic acetylcholine receptors desensitization and upregulation). This line of inquiry may inform youth smoking-prevention and intervention efforts and reduce smoking-related morbidity and mortality.


   ACKNOWLEDGMENTS
 
This study was supported by Transdisciplinary Tobacco Use Research Center grants P50 84718 and NCI RO1 CA109250 from the National Cancer Institute and the National Institute on Drug Abuse (to Dr Audrain-McGovern), Canadian Institutes of Health Research grant MOP-53248 (to Dr Tyndale), the Centre for Addiction and Mental Health, Ontario Graduate Scholarship Program, and Canadian Institutes of Health Research Strategic Training Program in Tobacco Research (Mr Koudsi), and a Canada Research Chair in Pharmacogenetics (Dr Tyndale)


   FOOTNOTES
 
Accepted Aug 9, 2006.

Address correspondence to Janet Audrain-McGovern, PhD, Department of Psychiatry, University of Pennsylvania, 3535 Market St, Suite 4100, Philadelphia, PA 19104. E-mail: audrain{at}mail.med.upenn.edu

Financial Disclosure: Dr Tyndale holds shares in Nicogen Inc, a company focused on creating novel smoking-cessation treatments; no funding for this study was received from Nicogen, and no benefit to the company was obtained. The other authors have indicated they have no financial relationships relevant to this article to disclose.


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