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, Rachel F. Tyndale, PhDb
aTobacco Use Research Center, Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania;bCentre for Addiction and Mental Health, Department of Pharmacology, University of Toronto, Toronto, Ontario, Canada;cLombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC
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.
ABSTRACT
OBJECTIVES.The objectives of our study were to evaluate whether genetic variation in nicotine metabolic inactivation accounted for the emergence of nicotine depen-dence 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 forCYP2A6alleles 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 (indi-viduals with no detectedCYP2A6variants) 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 howCYP2A6 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 ado-lescents and inform future work aimed at understanding the complex interplay between genetic, social, and psychological factors in adolescent smoking behavior.
www.pediatrics.org/cgi/doi/10.1542/ peds.2006-1583
doi:10.1542/peds.2006-1583
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
A
DOLESCENTS 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 ex-periment with cigarettes and those who did not.1–3 Arecent study also showed that these initial smoking re-actions can predict the development of nicotine depen-dence.4
Individual differences in response to smoking and the emergence of nicotine dependence may be partially ex-plained by genetic factors. The heritability of nicotine dependence has been well documented.5–7Genetic
sus-ceptibility 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. Ap-proximately 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 char-acterized 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 ofDiagnostic and Sta-tistical Manual of Mental Disorders, Fourth Edition– defined nicotine dependence in adults with polymorphisms in theCYP2A6 gene.14,15Slower metabolizers (SMs), those
with genetic variants predictingⱕ50% 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 depen-dence. Adolescents who metabolize nicotine faster com-pared 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 depen-dence. One recent study assessed whether theCYP2A6
genotype predicted risk of nicotine dependence defined byInternational Classification of Diseases, 10th Revisionfrom early to midadolescence. O’Loughlin et al17 found no
association between CYP2A6 and initial responses to smoking, and contrary to expectation, the risk of becom-ing nicotine dependent was almost 3 times higher among adolescents with at least 1 fully inactiveCYP2A6
variant (SMs,⬍50% of the activity of NMs) than ado-lescents 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 adoles-cents with the wild-typeCYP2A6genotype (NMs) would progress in nicotine dependence faster than adolescents with a CYP2A6 genetic variant (SMs). We further hy-pothesized that ISEs (pleasant and unpleasant initial ex-periences) would mediate this effect.
METHODS
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 determi-nants 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 ros-ters 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-dis-abled 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 re-sponse. 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 in-teraction 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
Participation in the study also required student as-sent. Fifteen students declined participation. Another 13 students failed to participate in the baseline administra-tion 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 non-white (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), respec-tively. University institutional review board approval of the study protocol was obtained.
the analyses were limited to adolescents of European ancestry (n⫽714). Of the 714 adolescents, 326 adoles-cents 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 phe-notypes.22If genetic variation in nicotine metabolic
in-activation accounts for the emergence of nicotine depen-dence, then biological exposure is necessary for the genetic effects to be expressed. Never-smokers may dif-fer in numerous ways from those who have been ex-posed 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-depen-dence scores at baseline (scores greater than the median of 2) were removed. The primary variables of interest were nicotine dependence,CYP2A6genotype, and pleas-ant and unpleaspleas-ant 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 ances-try.
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 fre-quently used, valid, and reliable measures of adolescent smoking history, household and peer smoking, and al-cohol 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 instruc-tions, emphasizing confidentiality to promote honest re-sponding, 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-re-port measures of smoking behavior and substance use in adolescents, particularly in nontreatment contexts in which confidentiality is emphasized.24,25Although a
spe-cific 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-chloro-form techniques. Genotyping was perphenol-chloro-formed by using previously described 2-step allele-specific polymerase chain reaction assays.14 TheCYP2A6alleles investigated
lead to either a decrease (CYP2A6*9andCYP2A6*12) or loss (CYP2A6*2andCYP2A6*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
FIGURE 1
water instead of DNA. Assays were previously validat-ed,14and 20% were repeated indicating a negligible
dis-cordance 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 smok-ing.23,30–32Because 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 de-pendence 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.33Thus, 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) ac-cording to the impact of theCYP2A6alleles on nicotine metabolism. NMs (100% activity) included adolescents with no detectedCYP2A6variants. Intermediate metabo-lizers (75% activity) included adolescents who had 1 copy of eitherCYP2A6*9orCYP2A6*12. The SMs (ⱕ50% activity) included adolescents with 1 or 2 copies of the inactive variants (CYP2A6*2andCYP2A6*4) or 2 copies of decreased activity variants CYP2A6*9 and/or
CYP2A6*12. Because the average levels of nicotine de-pendence 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,ⱕ75% activity) for sample-size pur-poses.
Initial Smoking Experiences
ISEs were measured by the 7-item Early Smoking Expe-riences Scale.34Pleasurable 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
sensa-tions measured by this scale have been validated.35
Con-vergent and discriminant validity has been demon-strated 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 adoles-cents.4,36Negative sensations associated with ISEs have
been shown to protect against subsequent dependence.4
Covariates
Baseline Smoking
An ordered categorical variable was generated from re-sponses 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 house-hold smokes cigarettes, such as mother, father, and/or siblings. This variable was dichotomized because of non-normality 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?”37The response options were 0 (0
days or times), 1 (1 day or time), and 2 (⬎1 toⱖ100 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 devel-opmental trends (eg, linear, quadratic).43,44The factors
are random effects. Therefore, LGM permits the estima-tion of developmental heterogeneity in initial status and the rate of change from baseline across time43 and the
there were 4 annual repeated measurements of nicotine dependence, spanning ages 14 to 18 years. This ap-proach 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 Mplus4.1 (Mu-the´n & Mu(Mu-the´n, Los Angeles, CA) for all multivariate modeling. Mplusis a statistical software package for con-ducting growth modeling from a latent variable frame-work.
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. Mplusprovides this op-tion 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.45Those with
miss-ing data did not differ from those without missmiss-ing data on the covariates and on the dependent variable of nicotine dependence (P⬎.05). We log-transformed nic-otine dependence to correct for univariate nonnormal-ity.
Model fit was evaluated with model2, comparative-fit index (CFI), root-mean-square error of approxima-tion (RMSEA), and standardized root-mean residual (SRMR). Suggested criteria for model fit are nonsignifi-cant model2, CFI⬎0.95, RMSEA⬍0.05 to 0.08, and SRMR ⬍ 0.08.46–48 An RMSEA value of 0 represents
exact model fit.48Mplusprovides a 95% RMSEA
confi-dence interval, and for single-group models it provides a
P value for the probability that the RMSEA value is
⬍.05.46
RESULTS
Descriptive Statistics
Distributions for the categorical covariates appear in Ta-ble 1. Means and SDs and bivariate correlations for the 4 repeated measures of nicotine dependence (log-trans-formed) and covariates appear in Table 2.
CYP2A6 Allele Frequencies and Genotype Groupings
The allele frequencies ofCYP2A6*2(5.3% [490 alleles]),
CYP2A6*4 (0.6% [478 alleles]), CYP2A6*9 (6.1% [494 alleles]), andCYP2A6*12(1.9% [482 alleles]) were sim-ilar to previously reported allele frequencies in an ado-lescent and adult white populations.14,17TheCYP2A6
ge-notype distributions did not deviate significantly from Hardy-Weinberg (HW) equilibrium. Of the 222 individ-uals, 164 (74%) were NMs and 58 (26%) were SMs.
Model Fit
Measurement Model
The single-group measurement model, absent covari-ates, fit reasonably well with linear and quadratic trends
(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 (0 ⫽ 0.11; z⫽ 5.17;P ⬍ .0001). The linear trend was also significant (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 vari-ance.
Full Model
The full LGM with covariates fit the data well with linear and quadratic trends (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 covari-ates and standardized path coefficients for the significant paths.
The effect ofCYP2A6Genotype on Nicotine Dependence
Baseline Nicotine Dependence
Parameter estimates, SEs, andzvalues 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 ef-fects on baseline nicotine dependence. Greater lifetime
TABLE 1 Proportions for Categorical Covariates
%
Gender
Female 49
Male 51
CYP2A6
NMs 74
SMs 26
9th-grade smoking
Current/frequent 3
Never/puffer/experimenter 97
Household members smoke
No 69
Yes 31
9th-grade marijuana use
Used more than once 17
Used once 12
Never used 71
9th-grade alcohol use
Had⬎1 drink 53
Had 1 drink 20
Never had a drink 27
Age first smoked
⬎13 y 39
13 y 25
marijuana use at 9th grade was associated with higher nicotine dependence at baseline (⫽.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 ( ⫽ .04; z ⫽ 4.37; P ⬍ .0001). In addi-tion, having a pleasant ISE was associated with higher
baseline nicotine dependence (⫽ .02; z⫽ 1.98;P ⫽
.048). Finally, the effect of age first smoking approached significance (⫽ ⫺.04;z⫽ ⫺1.76;P⫽.078), suggest-ing that the younger an adolescent was at smoksuggest-ing onset, the higher the baseline level of nicotine depen-dence.
FIGURE 2
Latent growth-curve model of the role ofCYP2A6in 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.aP⬍.06;bP⬍.05;cP⬍.01.
TABLE 2 Bivariate Correlations for All Measured Variables in the Model
1 2 3 4 5 6 7 8 9 10 11 12 13 14
1 Pleasant ISE 1.00
2 Negative ISE ⫺0.16 1.00
3 Log ND 9 0.25 0.03 1.00
4 Log ND 10 0.17 0.01 0.26 1.00
5 Log ND 11 0.40 ⫺0.15 0.25 0.45 1.00
6 Log ND 12 0.17 ⫺0.08 0.17 0.41 0.53 1.00
7 Female ⫺0.11 0.03 0.01 ⫺0.12 ⫺0.06 ⫺0.07 1.00
8 Household smoking ⫺0.09 ⫺0.01 0.06 0.11 0.08 0.17 0.02 1.00
9 9th-grade smoking 0.16 0.08 0.14 0.23 0.27 0.15 ⫺0.11 0.13 1.00
10 Age first smoked ⫺0.19 ⫺0.04 ⫺0.25 ⫺0.18 ⫺0.15 ⫺0.14 0.13 ⫺0.21 ⫺0.20 1.00
11 9th-grade marijuana use 0.13 0.09 0.29 0.28 0.22 0.20 ⫺0.15 0.08 ⫺0.23 ⫺0.28 1.00
12 9th-grade alcohol use 0.14 0.05 0.18 0.09 0.12 0.05 0.00 0.05 ⫺0.02 ⫺0.22 0.30 1.00
13 No. of friends smoking 0.10 0.03 0.36 0.27 0.22 0.13 ⫺0.02 0.06 0.22 ⫺0.16 0.29 0.14 1.00
14 CYP2A6 0.07 0.02 0.01 0.13 0.00 ⫺0.06 0.05 0.00 ⫺0.09 ⫺0.03 ⫺0.09 ⫺0.06 0.09 1.00
Mean 6.09 7.21 0.11 0.24 0.41 0.49 0.49 0.31 0.03 1.02 0.46 1.26 1.75 0.74
SD 2.24 2.53 0.30 0.48 0.59 0.58 0.50 0.46 0.16 0.87 0.77 0.86 2.02 0.44
Nicotine-Dependence Linear Trend
There was a significant effect for the CYP2A6genotype on linear trend (⫽.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 (⫽ .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 forCYP2A6 geno-type on the quadratic trend (⫽ ⫺.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 (⫽ ⫺.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 theCYP2A6genotype and nic-otine 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 (accel-eration) 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 equiv-alent to slope or rate of growth across time. NMs in-creased 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 unpleas-ant, did not explain how CYP2A6genetic variation im-pacts 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 re-sulting from our analysis, and the population size wasN ⫽222. For the effect of theCYP2A6genotype 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 subpopu-lations, 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 polymor-phisms (a list of single-nucleotide polymorpolymor-phisms is available on request). Our 42 random single-nucleotide polymorphisms were at HW equilibrium according to the GENHW routine in Stata (Stata Corp, College Sta-tion, TX). Structure results indicated a single population. The average probability of assignment to subpopulation
TABLE 3 Linear Regression Coefficients, SEs, andz-Test Statistics (Nⴝ222)
Predictors Dependent Latent and Measured Variables
Baseline ND ND Linear Trend ND Quadratic Trend Pleasant ISE Negative ISE
␥ SE z ␥ SE z ␥ SE z ␥ SE z ␥ SE z
Female 0.03 0.04 0.82 ⫺0.08 0.07 ⫺1.12 0.02 0.02 0.92
Household smoking 0.01 0.04 0.22 0.02 0.08 0.28 0.01 0.03 0.44
Smoking 0.00 0.12 ⫺0.02 0.70 0.24 2.91a ⫺0.21 0.08 ⫺2.70b 1.42 0.95 1.50 0.71 1.12 0.63
Age first smoked ⫺0.04 0.02 ⫺1.70c 0.05 0.05 0.93 ⫺0.01 0.02 ⫺0.92 ⫺0.38 0.32 ⫺1.21 ⫺0.14 0.38 ⫺0.38
Marijuana use 0.06 0.03 2.16b 0.04 0.05 0.73 ⫺0.01 0.02 ⫺0.54 0.20 0.25 0.81 0.23 0.29 0.78
Alcohol use 0.01 0.02 0.51 0.01 0.05 0.16 ⫺0.01 0.02 ⫺0.41 0.45 0.29 1.57 0.18 0.35 0.53
Friends smoking 0.04 0.01 4.37a 0.00 0.02 0.10 0.00 0.01 ⫺0.50
Pleasant ISE 0.02 0.01 2.00b 0.03 0.02 1.33 ⫺0.01 0.01 ⫺1.08
Negative ISE 0.01 0.01 0.58 ⫺0.02 0.02 ⫺1.03 0.00 0.01 0.58
CYP2A6 0.00 0.04 0.02 0.17 0.09 1.96b ⫺0.07 0.03 ⫺2.42b 0.25 0.50 0.51 0.09 0.60 0.14
ND indicates nicotine dependence.
1 was .50, with the entire range of assignment probabil-ities from .48 to .53.
DISCUSSION
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). Con-trary to our hypothesis, ISEs did not account for how
CYP2A6genetic 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 andCYP2A6variation. A faster rate of acquisi-tion 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.50In
addi-tion, 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).33In the present study, among those
adoles-cents 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 inacti-vate nicotine faster and may smoke more to titrate nic-otine to a preferred level.14,15Thus, 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 ac-count 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.14,16SMs may be better able
to quit successfully, resulting in shorter durations of smoking.14,51In late adolescence, SMs level off in
nico-tine 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 relation-ship betweenCYP2A6 genotype and progression in
nic-otine 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 rela-tionship between CYP2A6 genetic variation and emer-gence of nicotine dependence, or the mediated relation-ship 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 previ-ously, are present for 90% of the first opportunities to smoke cigarettes.3 Peer presence may prompt
adoles-cents to experiment further despite initial negative reac-tions to cigarette smoking. In addition, if the ISE also involved other substance use such as alcohol or mari-juana, 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, nonphar-macologic and pharnonphar-macologic factors associated with the initial smoking episode may be important in explaining smoking progression and the emergence of nicotine de-pendence.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 asso-ciated with nicotine dependence at baseline. Thus, those adolescents who were smoking more regularly at base-line retrospectively reported more pleasurable experi-ences.
Our findings contrast with a previous report of the relationship between theCYP2A6genotype and the odds of becoming nicotine dependent from early to midado-lescence.17 This may be related to different measures of
nicotine dependence (mFTQ versusInternational Classifi-cation of Diseases, 10th Revisioncriteria), which may cap-ture differing aspects of nicotine dependence, particu-larly among adolescents who have low levels of dependence and are smoking at low rates.32It may also
have been influenced by the age of the cohort partici-pants (14 –18 vs 12–16 years) and the fact that we com-bined those with reduced nicotine inactivation (less than
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 previ-ous study.17Consistent 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 theCYP2A6genotype 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.56However, the interpretation of these data are
un-clear; 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 individu-als with no smoking and, therefore, no nicotine expo-sure.14,19–21
Consistent with our findings, O’Loughlin et al17found
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 sig-nificant relationship between the ratio of 3-hydroxyco-tinine to co3-hydroxyco-tinine, a validated measure of CYP2A6 activ-ity,57and levels of smoking indicating that SMs smoked
fewer cigarettes per day.58In contrast, Huang et al56did
not find a significant effect of theCYP2A6 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 adoles-cent 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-dence phenotype, the conception of nicotine depen-dence as a continuum rather than a category, and the analysis of the potentially biasing effects of ethnic ad-mixture as an alternative explanation for the study find-ings.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 ver-ification of smoking status is important in smoking-ces-sation intervention studies, such measures are not typi-cally implemented in epidemiologic studies, because adolescent self-reports have been determined to be valid and sufficient, especially when confidentiality is as-sured.25,61–63In 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 representa-tive of all adolescents in the United States, the sample is nationally and locally representative on basic demo-graphic characteristics, and the sample smoking rates are regionally and locally comparable to those found in na-tional 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.70,71In 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
tradi-tional approach for the assessment of nicotine depen-dence has limitations with respect to capturing the emergence of nicotine dependence in adolescents.72,73
However, at present, the limited research on the acqui-sition of or the changes in nicotine dependence across time has not highlighted an epidemiologic instrument that adequately captures the process of nicotine depen-dence.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 sug-gests 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 ofCYP2A6genetic 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
addi-tion, future research is needed to better understand how differences in nicotine metabolism influence other sys-tems involved in nicotine dependence (eg, nicotinic ace-tylcholine receptors desensitization and upregulation). This line of inquiry may inform youth smoking-preven-tion and intervensmoking-preven-tion efforts and reduce smoking-re-lated morbidity and mortality.
ACKNOWLEDGMENTS
Scholar-ship Program, and Canadian Institutes of Health Re-search Strategic Training Program in Tobacco ReRe-search (Mr Koudsi), and a Canada Research Chair in Pharma-cogenetics (Dr Tyndale)
REFERENCES
1. Hirschman RS, Leventhal H, Glynn K. The development of smoking behavior: conceptualization and supportive cross-sectional survey data.J Appl Soc Psychol.1984;14:184 –206 2. Friedman LS, Lichtenstein E, Biglan A. Smoking onset among
teens: an empirical analysis of initial situations.Addict Behav.
1985;10:1–13
3. Curry SJ, Peterson AV Jr, Mann SL. Investigation of first op-portunities to use cigarettes and smokeless tobacco.Health Educ Res.1989;4:27–34
4. DiFranza JR, Savageau JA, Fletcher K, et al. Recollections and repercussions of the first inhaled cigarette.Addict Behav.2004; 29:261–72
5. Sullivan P, Kendler K. The genetic epidemiology of smoking.
Nicotine Tob Res.1999;1(suppl 2):S51–S57; discussion S69 –S70 6. Kendler KS, Neale MC, Sullivan P, Corey LA, Gardner CO, Prescott CA. A population-based twin study in women of smoking initiation and nicotine dependence.Psychol Med.1999; 29:299 –308
7. Sullivan PF, Neale BM, van den Oord E, et al. Candidate genes for nicotine dependence via linkage, epistasis, and bioinfor-matics.Am J Med Genet B Neuropsychiatr Genet.2004;126:23–36 8. Crabbe JC. Genetic contributions to addiction.Annu Rev
Psy-chol.2002;53:435– 462
9. Benowitz NL, Jacob P 3rd. Metabolism of nicotine to cotinine studied by a dual stable isotope method.Clin Pharmacol Ther.
1994;56:483– 493
10. Messina ES, Tyndale RF, Sellers EM. A major role for CYP2A6 in nicotine C-oxidation by human liver microsomes.J Pharma-col Exp Ther.1997;282:1608 –1614
11. Nakajima M, Yamamoto T, Nunoya K, et al. Characterization of CYP2A6 involved in 3⬘-hydroxylation of cotinine in human liver microsomes.J Pharmacol Exp Ther.1996;277:1010 –1015 12. Xu C, Rao YS, Xu B, et al. An in vivo pilot study characterizing
the new CYP2A6*7, *8, and *10 alleles.Biochem Biophys Res Commun.2002;290:318 –324
13. Malaiyandi V, Sellers EM, Tyndale RF. Implications of CYP2A6 genetic variation for smoking behaviors and nicotine depen-dence.Clin Pharmacol Ther.2005;77:145–158
14. Schoedel KA, Hoffmann EB, Rao Y, Sellers EM, Tyndale RF. Ethnic variation in CYP2A6 and association of genetically slow nicotine metabolism and smoking in adult Caucasians.
Pharmacogenetics.2004;14:615– 626
15. Rao Y, Hoffmann E, Zia M, et al. Duplications and defects in the CYP2A6 gene: identification, genotyping, and in vivo ef-fects on smoking.Mol Pharmacol.2000;58:747–755
16. Malaiyandi V, Lerman C, Benowitz NL, Jepson C, Patterson F, Tyndale RF. Impact of CYP2A6 genotype on pretreatment smoking behaviour and nicotine levels from and usage of nicotine replacement therapy. Mol Psychiatry. 2006;11: 400 – 409
17. O’Loughlin J, Paradis G, Kim W, et al. Genetically decreased CYP2A6 and the risk of tobacco dependence: a prospective study of novice smokers.Tob Control.2004;13:422– 428 18. Audrain J, Tercyak KP, Goldman P, Bush A. Recruiting
ado-lescents into genetic studies of smoking behavior.Cancer Epi-demiol Biomarkers Prev.2002;11:249 –252
19. Kozlowski L. Rehabilitating a genetic perspective in the study of tobacco and alcohol use.Br J Addict.1991;86:517–520 20. Vandenbergh D, Bennett C, Grant M, et al. Smoking status and
the human dopamine transporter variable number of tandem repeats (VNTR) polymorphism: failure to replicate and finding that never-smokers may be different.Nicotine Tob Res.2002;4: 333–340
21. Kozlowski LT, Harford MR. On the significance of never using a drug: an example from cigarette smoking.J Abnorm Psychol.
1976;85:433– 434
22. Vandenbergh DJ, Kozlowski LT, Bennett CJ, et al. DAT’s not all, but it may be more than we realize.Nicotine Tob Res.2002;4: 251–252
23. Rojas N, Killen J, Haydel K, Robinson T. Nicotine dependence among adolescent smokers.Arch Pediatr Adolesc Med.1998;152: 151–156
24. Patrick DL, Cheadle A, Thompson DC, et al. The validity of self-reported smoking: a review and meta-analysis.Am J Public Health.1994;84:1086 –1093
25. Botvin GJ, Botvin EM. Adolescent tobacco, alcohol, and drug abuse: prevention strategies, empirical findings, and assess-ment issues.J Dev Behav Pediatr.1992;13:290 –301
26. Lum A, Le Marchand L. A simple mouthwash method for obtaining genomic DNA in molecular epidemiological studies.
Cancer Epidemiol Biomarkers Prev.1998;7:719 –724
27. Harty LC, Caporaso NE, Hayes RB, et al. Alcohol dehydroge-nase 3 genotype and risk of oral cavity and pharyngeal cancers.
J Natl Cancer Inst.1997;89:1698 –1705
28. Prokhorov A, Pallonen U, Fava J, Ding L, Niaura R. Measuring nicotine dependence among high-risk adolescent smokers.
Addict Behav.1996;21:117–127
29. Prokhorov AV, Koehly LM, Pallonen U, Hudman KS. Adoles-cent nicotine dependence measured by the modified Fager-strom Tolerance Questionnaire at two time points. J Child Adolesc Subst Abuse.1998;7:35– 46
30. Rodriguez D, Audrain-McGovern J. Construct validity analysis of the early smoking experience questionnaire for adolescents.
Addict Behav.2004;29:1053–1057
31. Wellman RJ, Difranza JR, Pbert L, et al. A comparison of the psychometric properties of the Hooked on Nicotine Checklist and the modified Fagerstrom Tolerance Questionnaire.Addict Behav.2006;31:486 – 495
32. Kandel D, Schaffran C, Griesler P, Samuolis J, Davies M, Ga-lanti R. On the measurement of nicotine dependence in adolescence: comparisons of the mFTQ and a DSM-IV-based scale.J Pediatr Psychol.2005;30:319 –332
33. Tiffany ST, Conklin CA, Shiffman S, Clayton RR. What can dependence theories tell us about assessing the emergence of tobacco dependence?Addiction.2004;99(suppl 1):78 – 86 34. Pomerleau O, Pomerleau C, Namenek R. Early experiences
with tobacco among women smokers, ex-smokers, and never-smokers.Addiction.1998;93:595–599
35. Pomerleau OF, Pomerleau CS, Mehringer AM, Snedecor SM, Cameron OG. Validation of retrospective reports of early ex-periences with smoking.Addict Behav.2005;30:607– 611 36. Chen X, Stacy A, Zheng H, et al. Sensations from initial
expo-sure to nicotine predicting adolescent smoking in China: a potential measure of vulnerability to nicotine.Nicotine Tob Res.
2003;5:455– 463
37. Grunbaum JA, Kann L, Kinchen SA, et al. Youth risk behavior surveillance: United States, 2003 [published corrections appear inMMWR Morb Mortal Wkly Rep. 2004;53:536 andMMWR Morb Mortal Wkly Rep. 2005;54:608].MMWR Surveill Summ. 2004;53: 1–96
38. Brener ND, Kann L, McManus T, Kinchen SA, Sundberg EC, Ross JG. Reliability of the 1999 youth risk behavior survey questionnaire.J Adolesc Health.2002;31:336 –342
39. Audrain-McGovern J, Rodriguez D, Moss HB. Smoking pro-gression and physical activity.Cancer Epidemiol Biomarkers Prev.
40. Audrain-McGovern J, Rodriguez D, Tercyak KP, Cuevas J, Rodgers K, Patterson F. Identifying and characterizing adoles-cent smoking trajectories. Cancer Epidemiol Biomarkers Prev.
2004;13:2023–2034
41. Distefan JM, Gilpin EA, Choi WS, Pierce JP. Parental influences predict adolescent smoking in the United States, 1989 –1993.
J Adolesc Health.1998;22:466 – 474
42. Choi WS, Pierce JP, Gilpin EA, Farkas AJ, Berry CC. Which adolescent experimenters progress to established smoking in the United States.Am J Prev Med.1997;13:385–391
43. Muthe´n BO. Beyond SEM: general latent variable modeling.
Behaviormetrika.2002;29:81–117
44. Duncan TE, Duncan SC, Strycker LA, Li F, Alpert A.An Intro-duction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications. Mahwah, NJ: Lawrence Erlbaum Associates; 1999
45. Muthe´n BO.Mplus Technical Appendices. Los Angeles, CA: Mu-the´n & MuMu-the´n; 1998 –2004
46. Muthe´n LK, Muthe´n BO.Mplus User’s Guide. Los Angeles, CA: Muthe´n & Muthe´n; 2001
47. Loehlin JC.Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis. Mahwah, NJ: Lawrence Erl-baum Associates; 2004
48. Curran PJ, Bollen KA, Chen F, Paxton P, Kirby JB. Finite sampling properties of the point estimates and confidence in-tervals of the RMSEA.Sociol Methods Res.2003;32:208 –252 49. Pritchard JK, Stephens M, Donnelly P. Inference of population
structure using multilocus genotype data.Genetics.2000;155: 945–959
50. Ranaldi R, Bauco P, McCormick S, Cools AR, Wise RA. Equal sensitivity to cocaine reward in prone and addiction-resistant rat genotypes.Behav Pharmacol.2001;12:527–534 51. Gu DF, Hinks LJ, Morton NE, Day IN. The use of long PCR to
confirm three common alleles at the CYP2A6locus and the relationship between genotype and smoking habit.Ann Hum Genet.2000;64:383–390
52. Lerman C, Tyndale RF, Patterson F, et al. Nicotine metabolite ratio predicts the efficacy of transdermal nicotine for smoking cessation.Clin Pharmacol Ther.2006;79:600 – 608
53. Dani JA, Harris RA. Nicotine addiction and comorbidity with alcohol abuse and mental illness. Nat Neurosci. 2005;8: 1465–1470
54. Eissenberg T, Balster R. Initial tobacco use episodes in children and adolescents: current knowledge, future direction. Drug Alcohol Depend.2000;59(suppl 1):S41–S60
55. Riedel BW, Blitstein JL, Robinson LA, Murray DM, Klesges RC. The reliability and predictive value of adolescents’ reports of initial reactions to smoking.Nicotine Tob Res.2003;5:553–559 56. Huang S, Cook DG, Hinks LJ, et al. CYP2A6, MAOA, DBH,
DRD4, and5HT2Agenotypes, smoking behaviour and cotinine levels in 1518 UK adolescents.Pharmacogenet Genomics.2005; 15:839 – 850
57. Dempsey D, Tutka P, Jacob P 3rd, et al. Nicotine metabolite ratio as an index of cytochrome P450 2A6 metabolic activity.
Clin Pharmacol Ther.2004;76:64 –72
58. Moolchan ET, Franken FH, Jaszyna-Gasior M. Adolescent nic-otine metabolism: ethnoracial differences among dependent smokers.Ethn Dis.2006;16:239 –243
59. Lerman C, Berrettini W. Elucidating the role of genetic factors in smoking behavior and nicotine dependence.Am J Med Genet B Neuropsychiatr Genet.2003;118:48 –54
60. Sullivan PF, Eaves LJ, Kendler KS, Neale MC. Genetic case-control association studies in neuropsychiatry.Arch Gen Psychi-atry.2001;58:1015–1024
61. Williams CL, Eng A, Botvin GJ, Wynder EL. Validation of students’ self-reported cigarette smoking status with plasma cotinine levels.Am J Public Health.1979;69:1272–1274 62. Velicer WF, Prochaska JO, Rossi JS, Snow MG. Assessing
out-come in smoking cessation studies. Psychol Bull. 1992;111: 23– 41
63. Wills TA, Cleary SD. The validity of self-reports of smoking: analyses by race/ethnicity in a school sample of urban adoles-cents.Am J Public Health.1997;87:56 – 61
64. SRNT Subcommittee on Biochemical Verification. Biochemical verification of tobacco use and cessation.Nicotine Tob Res.2002: 4,149 –159
65. Abroms L, Simons-Morton B, Haynie DL, Chen R. Psychosocial predictors of smoking trajectories during middle and high school.Addiction.2005;100:852– 861
66. Dolcini MM, Adler NE, Lee P, Bauman KE. An assessment of the validity of adolescent self-reported smoking using three biological indicators.Nicotine Tob Res.2003;5:473– 483 67. US Census Bureau. State and county quick facts: Fairfax
County, Virginia. Available at: http://quickfacts.census.gov/ qfd/states/51/51059.html. Accessed February 8, 2005 68. US Census Bureau. State and county quick facts: Virginia.
Available at: http://quickfacts.census.gov/qfd/states/ 51000.html. Accessed August 29, 2003
69. Developmental Research & Programs: Fairfax County, VA. Fairfax county youth survey report, Communities That Care. 2001. Available at: www.fairfaxcounty.gov/demogrph/pdf/ youth2001.pdf. Accessed March 3, 2002
70. Johnston LD, O’Malley PM, Bachman JG, Schulenberg JE.
Monitoring the Future National Results on Adolescent Drug Use: Overview of Key Findings, 2003. National Institute of Drug Abuse, ed. Bethesda, MD: National Institute of Drug Abuse; 2004 71. Centers for Disease Control and Prevention. Surveillance
sum-maries.MMWR Morb Mortal Wkly Rep.2004;53(SS-2):1–96 72. Colby SM, Tiffany ST, Shiffman S, Niaura RS. Measuring
nic-otine dependence among youth: a review of available ap-proaches and instruments.Drug Alcohol Depend.2000;59(suppl 1):S23–S39
73. Colby SM, Tiffany ST, Shiffman S, Niaura RS. Are adolescent smokers dependent on nicotine? A review of the evidence.
Drug Alcohol Depend.2000;59(suppl 1):S83–S95
74. Glautier S. Measures and models of nicotine dependence: pos-itive reinforcement.Addiction.2004;99(suppl 1):30 –50 75. Karp I, O’Loughlin J, Hanley J, Tyndale RF, Paradis G. Risk
DOI: 10.1542/peds.2006-1583 originally published online November 27, 2006;
2007;119;e264
Pediatrics
G. Shields and Rachel F. Tyndale
Janet Audrain-McGovern, Nael Al Koudsi, Daniel Rodriguez, E. Paul Wileyto, Peter
in the Emergence of Nicotine Dependence in Adolescents
CYP2A6
The Role of
Services
Updated Information &
http://pediatrics.aappublications.org/content/119/1/e264 including high resolution figures, can be found at:
References
http://pediatrics.aappublications.org/content/119/1/e264#BIBL This article cites 65 articles, 10 of which you can access for free at:
Subspecialty Collections
http://www.aappublications.org/cgi/collection/smoking_sub Smoking
http://www.aappublications.org/cgi/collection/substance_abuse_sub Substance Use
following collection(s):
This article, along with others on similar topics, appears in the
Permissions & Licensing
http://www.aappublications.org/site/misc/Permissions.xhtml in its entirety can be found online at:
Information about reproducing this article in parts (figures, tables) or
Reprints
DOI: 10.1542/peds.2006-1583 originally published online November 27, 2006;
2007;119;e264
Pediatrics
G. Shields and Rachel F. Tyndale
Janet Audrain-McGovern, Nael Al Koudsi, Daniel Rodriguez, E. Paul Wileyto, Peter
in the Emergence of Nicotine Dependence in Adolescents
CYP2A6
The Role of
http://pediatrics.aappublications.org/content/119/1/e264
located on the World Wide Web at:
The online version of this article, along with updated information and services, is
by the American Academy of Pediatrics. All rights reserved. Print ISSN: 1073-0397.
