 |
|
Effect of Team Sport Participation on Genetic Predisposition to Adolescent Smoking Progression
Janet Audrain-McGovern, PhD;
Daniel Rodriguez, PhD;
E. Paul Wileyto, PhD;
Kathryn H. Schmitz, PhD, MPH;
Peter G. Shields, MD
Arch Gen Psychiatry. 2006;63:433-441.
ABSTRACT
| |
Context There is much to be learned about why some adolescents progress to a regular smoking habit and others do not.
Objective To evaluate whether (1) team sport participation buffers the effect of having 2 smoking risk genotypes (the dopamine reuptake transporter [SLC6A3] and the dopamine D2 receptor [DRD2]) or 1 of these risk genotypes vs having none on adolescent smoking progression and (2) the buffering effects of team sports were due to physical activity associated with team sport participation.
Design Longitudinal cohort study. Survey data were collected annually from grade 9 to the end of grade 12. Self-report measures included smoking, team sport participation, physical activity, depression, smoking exposure, and alcohol and marijuana use. DNA was collected via buccal swabs. Data were analyzed using latent growth modeling.
Setting Five public high schools in Virginia.
Participants A total of 361 students of European ancestry.
Main Outcome Measure Smoking progression.
Results For adolescents participating in at least 1 team sport, but not for adolescents with no team sport participation, physical activity had a significant negative effect on smoking progression (z = –3.85, P<.001;  21,N = 361 = 6.73, P = .009). In addition, having 1 (z = 2.69; P = .007) and 2 (z = 2.22; P = .03) smoking risk genotypes had a positive effect on physical activity. These represented significant between-group effects (21,N = 361 = 6.29, P = .01; 21,N = 361 = 3.81, P = .05, respectively). Thus, having 1 or more smoking risk genotypes was related to higher levels of physical activity, which, in turn, was related to lower levels of smoking progression for adolescents participating in at least 1 team sport but not for adolescents with no team sport participation.
Conclusions This study provides the first evidence of an interaction between environmental influences and specific genes on adolescent smoking and may promote an understanding of important protective relationships in the environment.
INTRODUCTION
Experimentation with cigarette smoking usually begins in adolescence. Some, although not all, adolescents who experiment with cigarettes will progress to a regular smoking habit.1-4 A critical question is why do some adolescents progress to a regular smoking habit and others do not progress beyond experimentation?
Environmental or behavioral factors, such as physical activity, seem to account for some of the variability in adolescent smoking progression. More physically active adolescents are less likely to smoke5-8 and are half as likely to initiate smoking or to progress along the uptake continuum.9 Furthermore, adolescents with decreasing or erratic team sport participation patterns are almost 3 times more likely to become regular smokers than adolescents with consistently high team sport participation.10 These findings suggest that physical activity may be protective against adolescent smoking.
Individual differences in genetic susceptibility may also account, in part, for the variability in adolescent smoking progression. The heritability of smoking initiation and persistence has been well documented11-14 and may be explained, in part, by genetic variation in the dopamine pathway.15 For example, studies have linked smoking behavior in adults with polymorphisms in the dopamine D2 receptor gene (DRD2)16-18 and the dopamine reuptake transporter gene (SLC6A3).19-20 There has been little research to evaluate specific genetic contributions to smoking acquisition in adolescents. A recent study21 found that adolescents were almost 2 times more likely to progress to smoking for every DRD2-A1 allele present and that this effect was accentuated by depression symptoms.
Twin studies indicate that the heritability of smoking can vary by environmental factors. For example, the importance of genetic effects on the risk of smoking initiation tends to be greater for males vs females,22-23 whereas the genetic effects on the risk of becoming a smoker are greater for older vs younger adolescents.24 Although studies have evaluated differences in the heritability of smoking based on environmental factors, to date, to our knowledge, no studies have evaluated the effects of interactions between the environment and specific genetic polymorphisms on smoking.
Although not yet investigated, sports participation may buffer the effects of genetic predisposition on smoking progression in adolescents. Evidence from animal models suggests that brain dopamine concentrations are increased as a function of exercise,25-28 but more research in humans is needed.29 Increases in dopamine levels caused by other behaviors, including physical activity and food intake, may be similar to smoking-associated increases in dopamine levels.30-33 The ability of these behaviors to increase dopamine levels may make them substitutable. This may partially explain why physical activity decreases nicotine withdrawal symptoms and bolsters smoking cessation rates34-36 and why food reward and food intake increase after smoking cessation.37-38 Although the notion of substituting one rewarding behavior for another lends itself to the investigation of protective gene-environment interactions, there has been a lack of attention to environmental or behavioral factors that may buffer the effects of genes on smoking acquisition. Evaluating protective gene-environment interactions may inform the next generation of adolescent smoking prevention and intervention efforts.39-41
The present study sought to elucidate the interaction between behavioral and genetic influences on adolescent smoking. This study evaluated whether team sport participation interacted with polymorphisms in both SLC6A3 and DRD2 to affect adolescent smoking progression. We chose to investigate the role of these 2 dopamine genotypes on adolescent smoking acquisition because they have received the most attention in adult smoking practices.42 We hypothesized that team sport participation would buffer the effects of having 1 or 2 smoking risk genotypes on adolescent smoking progression (vs having no risk genotypes) and that physical activity would be the mechanism explaining the buffering effect (ie, moderated-mediated effect).
METHODS
STUDY POPULATION
Participants consisted of 361 students in 9th grade 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 health habits. Of these 361 adolescents, 177 (49%) were male and 184 (51%) 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. Students were ineligible to participate if they had a special classroom placement. The cohort was formed in the 9th grade and was followed until the end of the 12th grade.
Based on the cohort selection criteria, a total of 2120 students (89%) were eligible to participate. The parents of 1533 students (72%) provided a response. Of these 1533 parents, 1151 (75%) 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 x 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%).43
Participation in the study also required student assent. Fifteen students declined participation. An additional 13 students did not participate in the baseline administration owing to absence. The final baseline sample size (year 2000) was 1123 of the 2120 eligible students. The rates of participation at the 3 spring follow-ups in the 10th (2001), 11th (2002), and 12th (2003) grades were approximately 96% (n = 1081), 93% (n = 1043), and 89% (n = 1005), respectively. The Georgetown University and University of Pennsylvania institutional review boards approved the study protocol.
To limit potential bias due to ethnic admixture, the analyses were limited to adolescents of European ancestry (n = 714). Of the 714 adolescents, 413 had at least 1 puff of a cigarette. We included only those adolescents who had at least 1 puff of a cigarette because never smokers would not have had the opportunity for the genetic predisposition to nicotine reward to be expressed.44-46 Separating never smokers from those who have smoked has been considered an important step in refining smoking phenotypes.47 Fifteen adolescents with rare SLC6A3 alleles (ie, other than 9- or 10-repeat alleles) were excluded from the analyses. Approximately 37 adolescents had missing data on at least 1 covariate. The primary variables of interest were smoking, team sport participation, physical activity, and the SLC6A3 and DRD2 genotypes. Depression, smoking exposure, alcohol use, marijuana use, and sex served as controlling variables. The data presented herein are based on 361 adolescents of European ancestry with "all available data" (a pairwise missing data strategy used when data are missing at random that capitalizes on the data available for each wave for each participant) for these variables, although this method does not include participants with data missing on the covariates.
PROCEDURES
Data were collected on-site in a classroom common to all students. A member of the research team distributed the survey. Each student received a survey with a subject identification number. 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 contained only an identification number. A member of the research team (J.A.-M.) read aloud a set of instructions, emphasizing confidentiality to promote honest responding, and encouraged questions if survey items were not clear. Surveys took approximately 30 minutes to complete.
Biological samples were collected using buccal swabs as previously described,19-20 and DNA was extracted using standard phenol-chloroform techniques. Genotyping was performed as in previous studies.48 The assays were validated by confirming a polymorphic inheritance pattern in 7 human family lines that encompassed 3 generations (data not shown; National Institute of General Medical Sciences Human Genetic Mutant Cell Repository, Coriell Institute, Camden, NJ). Quality control procedures included positive and negative controls with each assay and independent repeated genotyping for 20% of the results. The discordant rate was less than 5%, and ambiguous or discordant results were considered missing data.
MEASURES
Smoking Status and Progression
An ordered categorical variable was generated from responses to a series of standard epidemiologic questions regarding smoking, including, "Have you ever tried or experimented with cigarette smoking, even a few puffs?" Have you smoked at least 1 whole cigarette?" "Have you smoked a cigarette in the past 30 days?" "How many cigarettes have you smoked in your lifetime?"49-54
Based on participant responses to these items, adolescents were categorized as (1) never smokers (never having smoked a cigarette, not even a puff), (2) puffers (not ever having smoked a whole cigarette), (3) experimenters (smoked  1 whole cigarette but <100 total in a lifetime), (4) current smokers (smoked on <20 of the past 30 days and >100 cigarettes in a lifetime), and (5) frequent smokers (smoked on 20 of the past 30 days and >100 cigarettes in a lifetime). These categories for smoking status have been used in previous studies.9, 55 The smoking progression variable was designed to capture any progression from never to frequent by assessing smoking at each wave. Adolescents who reported not smoking in the past 30 days but having more than 100 cigarettes in a lifetime were classified as experimenters (n = 2).
Genotype
Genotyping was performed as in previous studies.19-21 The SLC6A3 genotype was classified as the number of 10-repeat alleles (0, 1, or 2), and the DRD2 genotype was classified as the number of A1 alleles (0, 1, or 2).16, 21
Physical Activity and Team Sport Participation
Physical activity was assessed with 3 items from the Youth Risk Behavior Survey.49 These items assessed intensity, duration, and frequency of physical activity, including vigorous, moderate, and strengthening/toning activity. Physical activity was treated as a latent variable with the 3 indicators (ie, vigorous, moderate, and strengthening/toning activity). Team sport participation was assessed with 1 item from the Youth Risk Behavior Survey.49 The item requested the number of teams on which the individual played during the past 12 months.
COVARIATES
Depression symptoms were assessed using the Center for Epidemiological Studies Depression inventory at baseline.56 The 20 items on this inventory are rated along a 4-point Likert scale to indicate symptom frequency during the past week (0 = rarely or none of the time and 3 = most of the time).
Exposure to smoking from family members and peers was evaluated as in previous studies.57-58 Overall exposure was characterized as no exposure, family exposure only, peer exposure only, and both family and peer exposure.58
Past-month alcohol use was assessed with an item that asked, "During the past 30 days, on how many days did you have at least 1 drink (not just a sip) of alcohol?"49 Marijuana use was assessed with an item that asked, "During your life, how many times have you used marijuana?"49 The response options ranged from 1 (0 days or times) to 7 (100 days or times).
STATISTICAL ANALYSES
Univariate statistics were used to characterize the study variables, and bivariate statistics were used to create the correlation matrix. We design-coded risk genotypes with reference cell coding, with the 2 design variables representing 1 and 2 risk genotypes59; zero risk genotypes was the reference. We used latent growth curve modeling (growth modeling from a latent variable perspective60-61) to analyze for moderated and mediated effects in the longitudinal data. The latent variables represent baseline (initial level) and slope (ie, trend) and allow for the assessment of average trajectory shape (eg, linear or quadratic), developmental heterogeneity, and whether select covariates predict initial level and trends. Latent growth modeling also permits centering the level at any wave, permitting testing of the effects of covariates on a specific year's level while maintaining the longitudinal nature of the study.61 In the present study, we centered level at grade 12. Thus, our measure of smoking progression by the end of 12th grade represents the accumulation of 4 years of smoking experience in a single latent variable.61-62 To test for a team sport participation (0 vs 1 teams) by smoking risk–genotype interaction, we used multigroup modeling, dividing the sample by the number of team sports. Interactions occurred if differences in specific effects were significant between groups. We used a 2 difference test to assess between-group differences.60 The mediating role of physical activity was evaluated by computing specific indirect effects, with delta method standard errors and associated z values.
Model fit was evaluated using 2 tests, the comparative fit index (CFI), root-mean-squared error of approximation (RMSEA), and standardized root-mean-square residual (SRMR). Suggested values are a nonsignificant 2, a CFI less than 0.95, an RMSEA less than 0.05 to 0.08 (including the upper limit of the confidence interval), and an SRMR below 0.08.63-65 Finally, we evaluated the power of the results using a Monte Carlo analysis with 500 replications, using model parameter estimates resulting from our analysis as the population values. All multivariate analyses were conducted using Mplus 3.13 software.66
RESULTS
Of the 361 participants, 253 (70%) had no DRD2-A1 alleles (A2/A2), 94 (26%) had 1 DRD2-A1 allele (A1/A2), and 14 (4%) had 2 DRD2-A1 alleles (A1/A1). With respect to SLC6A3, 35 participants (10%) had no SLC6A3 10-repeat alleles (9/9), 127 (35%) had 1 SLC6A3 10-repeat allele (9/10), and 199 (55%) had 2 SLC6A3 10-repeat alleles (10/10). Neither the SLC6A3 nor the DRD2 alleles departed significantly from Hardy-Weinberg (HW) equilibrium (P<.08 and P<.90, respectively). The DRD2 and SLC6A3 genotype data were summarized into 3 categories of increasing risk for smoking: 107 participants (30%) had zero risk genotypes for smoking (DRD2 A2/A2 and SLC6A3 9/10 or 9/9), 200 participants (55%) had 1 risk genotype for smoking (DRD2 A2/A2 and SLC6A3 10/10 or DRD2 A1/* and SLC6A3 9/10, 9/9), and 53 participants (15%) had 2 risk genotypes for smoking (DRD2 A1/* and SLC6A3 10/10).
The average amount of physical activity per week was 3.00, which corresponded to 2 days of physical activity of at least 20 to 30 minutes' duration (SD = 1.85). The number of adolescents participating in team sports during the past 12 months was 118 on 0 teams (33%), 85 on 1 team (23%), 72 on 2 teams (20%), and 86 on 3 or more teams (24%). The mean (SD) Center for Epidemiological Studies Depression inventory score was 13.28 (9.47). Approximately 27% of the sample progressed in their smoking (any progression) during the 3-year follow-up period; 30% progressed to a regular habit (ie, current or frequent smoking).
LATENT GROWTH MODEL OF SMOKING PROGRESSION BY THE END OF GRADE 12
A 2-group latent growth model with linear and quadratic trends was fit to the data. The Figure presents the model, with standardized regression coefficients for key regression paths. To ensure proper model fit, all the factors (ie, physical activity, smoking level, and the linear and quadratic trends) were regressed on the covariates. This model fit the data well (271,N = 361 = 84.29, P = .13, CFI = 0.99, SRMR = 0.03, RMSEA = 0.03 [95% confidence interval, 0.00-0.06]). Three error variances were constrained to zero owing to nonsignificant negative error variances. No post hoc modifications were made to this model to improve fit. Table 1 provides the correlation matrices for both groups.
|
|
|
|
Figure. Two-group latent growth model for adolescent smoking progression by the end of 12th grade. Values above the horizontal dividing line are for adolescents participating in 1 or more team sports; values below the dividing line are for adolescents with no team sport participation. Zero smoking risk genotypes, DRD2 A2/A2 and SLC6A3 9/10 or 9/9; 1 smoking risk genotype, DRD2 A1/* and SLC6A3 9/10, 9/9 or DRD2 A2/A2 and SLC6A3 10/10; and 2 smoking risk genotypes, DRD2 A1/* and SLC6A3 10/10. PA indicates physical activity; asterisk, significant within-group effect; and dagger, significant between-group effect.
|
|
|
|
|
|
|
Table 1. Bivariate Correlation for All Measured Variables in the Model by Team Sport Participation*
|
|
|
LONGITUDINAL ANALYSIS OF BUFFERING EFFECTS
Table 2 provides regression coefficients with standard errors and z test statistics for the effects of smoking progression by the end of 12th grade and physical activity by team sport participation. For adolescents participating in at least 1 team sport, having 1 (z = 2.69; P = .007) and 2 (z = 2.22; P = .03) risk genotypes had a positive effect on physical activity. For adolescents with no team sport participation, neither risk genotype had a significant effect on physical activity. This difference in the effect of smoking risk genotype on physical activity between the groups was significant for 1 and 2 smoking risk genotypes (21,N = 361 = 6.29; P = .01 and 21,N = 361 = 3.81; P = .05, respectively). Thus, having 1 or more smoking risk genotypes was related to higher levels of physical activity for adolescents participating in at least 1 team sport but not for adolescents with no team sport participation. Physical activity, in turn, had a significant negative effect on smoking (z = –3.85; P<.001), but only for adolescents with team sport participation. This between-group difference in the effect of physical activity on smoking was significant (21,N = 361 = 6.73; P = .009). This result suggests that the protective effects of physical activity on smoking progression by the end of 12th grade are specific to adolescents with team sport participation.
|
|
|
|
Table 2. Linear Regression Coefficients, Standard Errors, and z Test Statistics for 361 Adolescents*
|
|
|
For adolescents involved in at least 1 team sport, neither smoking risk genotype had a significant direct effect on smoking (P>.10); the effect was indirect through physical activity. The indirect effect of 1 and 2 smoking risk genotypes on smoking progression through physical activity was significant (1 risk genotype: z = –2.20; P = .03 and 2 risk genotypes: z = –1.94; P = .05). There was not a significant indirect effect of 1 and 2 smoking risk genotypes on smoking progression through physical activity for adolescents without team sport participation, although a direct effect approached significance (z = 1.75; P = .08 and z = 1.67; P = .09, respectively), indicating an increased risk of smoking progression by the end of 12th grade for adolescents with 1 and 2 risk genotypes.
Thus, having 1 or more smoking risk genotypes was related to higher levels of physical activity, which, in turn, was related to lower levels of smoking progression by the end of 12th grade for adolescents participating in at least 1 team sport but not for adolescents with no team sport participation.
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 2-group latent growth model. Monte Carlo analyses assess the power of a sample to detect specific effects based on repeated samplings from a population with known parameters.67 In the present case, the population parameters were those resulting from our analysis, and the population size was 361 adolescents. For the effect of physical activity on smoking in the group of adolescents participating in 1 or more team sports, the power was 0.99. For the effect of 1 and 2 smoking risk genotypes on physical activity in the same group, the power was 0.75 and 0.66, respectively.
ANALYSIS OF POPULATION SUBSTRUCTURE
The sample was examined for evidence of population stratification using the STRUCTURE program.68 STRUCTURE is a clustering program that uses genotypes that may be out of HW equilibrium overall and that attempts to identify subpopulations that are at HW equilibrium internally. Based on 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 using class probabilities. A stratified population would separate into a bimodal distribution of assignment probabilities, with values close to 0 or 1. Data for the analysis were genotypes of 42 randomly selected biallelic single-nucleotide polymorphisms (list available on request).
We tested HW equilibrium for each of the 42 single nucleotide polymorphisms using the GENHW routine in Stata (StataCorp, College Station, Tex). The routine generates a 2 value for each genotype. Our 42 random single nucleotide polymorphisms were at HW equilibrium. Results from the STRUCTURE program indicated a single population, as the probability of assignment to a subpopulation was clearly not bimodal. Having determined ahead of time to attempt to identify 2 subpopulations, the average probability of assignment to subpopulation 1 was 0.500, with the entire range of assignment probabilities from 0.476 to 0.527.
COMMENT
The present study provides the first evidence of interacting effects of environmental influences with specific genetic variants on adolescent smoking progression. Among adolescents participating in at least 1 team sport, having 1 or 2 smoking risk genotypes was related to higher levels of physical activity, which, in turn, was related to lower levels of smoking progression by the end of 12th grade. These effects were not present for adolescents with no team sport participation. Thus, team sport participation buffered the impact of smoking risk genotypes on adolescent smoking progression. The protective effects can be explained, in part, by the physical activity associated with team sport participation.
The interacting effects of genetic predisposition and team sport participation on adolescent smoking progression may be best explained in the context of the existing neurobiological research. The A1 allele of the DRD2 gene has been associated with altered receptor density and binding characteristics and thus less endogenous dopamine.69-71 The 10-repeat allele of SLC6A3 has also been associated with greater gene expression72 and greater dopamine transporter reuptake protein levels, which results in greater clearance and less bioavailability of dopamine.73 Thus, it is possible that adolescents who carry the A1 allele of the DRD2 gene or the 10-repeat allele of the SLC6A3 gene may achieve greater reward from nicotine's effects on dopamine activity because they have less dopamine activity. Greater reward from the nicotine in cigarettes may promote further experimentation and progression to regular smoking. Consistent with this hypothesis are findings indicating that individuals with both the SLC6A3-9 and the DRD2 A2 genotypes are significantly less likely to be smokers,19 and if they do smoke to have higher smoking abstinence rates and longer latency to smoking relapse.74
Evidence from animal models suggests that similar to nicotine in cigarettes, exercise increases brain dopamine concentrations and DRD2 receptor binding,25-28,75-76 which may help explain the reinforcing aspects of physical activity and why it may be protective against smoking progression. Research with humans has implicated the DRD2 gene with annual levels of physical activity in women.77 It is possible, as found in the present study, that specific genetic effects on smoking may also impact physical activity. A recent study78 of adult male twins found that physical activity and smoking shared a common genetic component.
There are 2 possible explanations for the finding that team sport participation protects adolescents with 1 and 2 risk genotypes from progressing in their smoking. One mechanism, physical activity, was evaluated in the present study. This biologically based explanation suggests that the physical activity involved in team sport, or the physical activity habits of those involved in team sport, may increase dopamine levels in the brains of adolescents with genotypes associated with less available dopamine. Thus, the physical activity may provide a reward and make smoking less rewarding, or it may reduce the need for alternative methods to increase dopamine levels, such as smoking. The model evaluating physical activity as a mechanism accounted for 37% of the variance in smoking progression by the end of 12th grade, which suggests that there are other mechanisms that explain this relationship.
A more socially based explanation is that team sport participation has a component of social reinforcement derived from coaches and teammates. In addition, there are behavioral norms and rules for the athletic subculture (ie, athletes do not smoke and smoking is inconsistent with athletic performance) to which an adolescent may conform. These norms may also decrease smoking among teammates, which may protect against peer smoking influences.79-80 These social influences may offset the genetic propensity to smoke. Both of these explanations are consistent with research showing that erratic and declining team sport participation is associated with the adoption of a regular smoking habit.10
Regarding adolescent smoking prevention, almost 25% of adolescents are regular smokers, and smoking prevalence increases across adolescence.3-4 Whereas smoking prevalence increases, physical activity and team sport involvement decreases across adolescence. In fact, research has consistently shown that levels of physical activity from all sources, including team sports, declines 26% to 37% during adolescence and that physical inactivity tracks into adulthood.8, 10, 81-86 Almost 20% of adolescents have both the DRD2 A1/* and SLC6A3 10/10 smoking risk genotypes, and at least 30% have 1 of these smoking risk genotypes. Thus, a significant subset of adolescents who would probably progress to a regular smoking habit could avoid the acquisition of a smoking habit through involvement in team sports, even 1 team sport a year. Successful adolescent smoking prevention efforts may focus on helping adolescents identify team sports to participate in and promoting consistent participation.
As the first investigation of the interacting effects of environmental factors and specific genes on adolescent smoking progression, this study has strengths and weaknesses. Strengths include the collection of DNA and behavioral data from a large sample of adolescents, the use of more refined longitudinal smoking phenotypes, and analysis of the potentially biasing effects of ethnic admixture as an alternative explanation for the study findings.15, 45, 87-88 Disparate findings regarding the neurobiological features of DRD2 and SLC6A3 or their association with smoking phenotypes in adults20, 89-94 may be related, in part, to these methodological factors. It is important to point out that the functional significance of the 2 polymorphisms evaluated in the present study is not known. Thus, these polymorphisms may be in linkage disequilibrium with other functional variants, or it may be that variants downstream of the DRD2 and SLC6A3 loci could be affected by these polymorphisms.95
One potential limitation of this study is the parental consent rate for adolescent participation. Seventy-five percent of parents who responded provided consent, and the differences between those who provided consent and those who declined were relatively small and few.43 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,96-98 and the sample smoking rates are regionally and locally comparable with those found in national surveys. For example, data from our 2003 survey indicated that 10% are daily smokers compared with approximately 9% in the 2003 Youth Risk Behavior Survey and approximately 15% in the 2003 Monitoring the Future Survey.3-4,98 In addition, 15% of the adolescents in our sample were current smokers compared with 13% in the 2003 Youth Risk Behavior Survey.
Another potential limitation is that an unmeasured factor associated with team sport participation and smoking accounted for the genotype interaction. Although we controlled for several key variables that could account for this association (eg, depression, family and peer smoking, alcohol use, and marijuana use), we cannot assume that another variable that we did not control for explained the interaction. Finally, there were insufficient numbers of adolescents in other racial or ethnic groups (eg, African American, Asian American, and Hispanic) to conduct analyses stratified by race.
Despite these potential limitations, the present study provides the first evidence of an interaction between environmental influences and specific genes on adolescent smoking acquisition. Although replication of these findings is necessary, these results, coupled with previous research,9-10 provide more support for the role of team sport participation and physical activity in adolescent smoking prevention and may provide an increased understanding of why these behaviors may be protective.
Future research may include investigation of the social factors important to the buffering effect of team sport participation on the impact of genes on adolescent smoking progression. Furthermore, positron emission tomography studies of the immediate impact of physical activity on dopamine release in the brain that consider the role of habitual activity and smoking status/history on this relationship are also warranted.25, 27-29,99 Because adolescent smoking often results in long-term smoking in adulthood, the medical and economic impact of preventing and reducing youth smoking could be significant.
AUTHOR INFORMATION
Correspondence: Janet Audrain-McGovern, PhD, Department of Psychiatry, University of Pennsylvania, 3535 Market St, Suite 4100, Philadelphia, PA 19104 (audrain{at}mail.med.upenn.edu).
Submitted for Publication: May 20, 2005; final revision received October 5, 2005; accepted October 25, 2005.
Funding/Support: This study was supported by Transdisciplinary Tobacco Use Research Center grant P50 84718 from the National Cancer Institute and the National Institute on Drug Abuse, Bethesda, Md.
Author Affiliations: Tobacco Use Research Center, Department of Psychiatry (Drs McGovern, Rodriguez, and Wileyto), and Department of Biostatistics and Epidemiology (Dr Schmitz), University of Pennsylvania, Philadelphia; and Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC (Dr Shields).
REFERENCES
| |
1. USDHHS. Preventing tobacco use among young people: a report of the Surgeon General. MMWR Recomm Rep. 1994;43(RR-4):1-10.
PUBMED
2. Chassin L, Presson CC, Rose JS, Sherman SJ. The natural history of cigarette smoking from adolescence to adulthood: demographic predictors of continuity and change. Health Psychol. 1996;15:478-484.
FULL TEXT
|
ISI
| PUBMED
3. Centers for Disease Control and Prevention. Surveillance summaries. MMWR Surveill Summ. 2004;53(SS-2).4. Johnston LD, O'Malley PM, Bachman JG, Schulenberg JE. Monitoring the Future: National Results on Adolescent Drug Use: Overview of Key Findings, 2003. Bethesda, Md: National Institution on Drug Abuse; 2004. NIH publication 04-5506.5. Pate RR, Heath GW, Dowda M, Trost SG. Association between physical activity and other health behaviors in a representative sample of U.S. adolescents. Am J Public Health. 1996;86:1577-1581.
FREE FULL TEXT
6. Thorlindsson T, Vilhjalmsson R. Factors related to cigarette smoking and alcohol use among adolescents. Adolescence. 1991;26:399-418.
ISI
| PUBMED
7. Abrams K, Skolnik N, Diamond JJ. Patterns and correlates of tobacco use among suburban Philadelphia 6th- through 12th-grade students. Fam Med. 1999;31:128-132.
PUBMED
8. Raitakari OT, Porkka KV, Taimela S, Telama R, Rasanen L, Viikari JS. Effects of persistent physical activity and inactivity on coronary risk factors in children and young adults: the Cardiovascular Risk in Young Finns Study. Am J Epidemiol. 1994;140:195-205.
FREE FULL TEXT
9. Audrain-McGovern J, Rodriguez D, Moss HB. Smoking progression and physical activity. Cancer Epidemiol Biomarkers Prev. 2003;12(11, pt1):1121-1129.
PUBMED
10. Rodriguez D, Audrain-McGovern J. Team sport participation and smoking: analysis with general growth mixture modeling. J Pediatr Psychol. 2004;29:299-308.
FREE FULL TEXT
11. Heath AC, Martin NG. Genetic models for the natural history of smoking: evidence for a genetic influence on smoking persistence. Addict Behav. 1993;18:19-34.
FULL TEXT
|
ISI
| PUBMED
12. Heath AC, Kirk KM, Meyer JM, Martin NG. Genetic and social determinants of initiation and age at onset of smoking in Australian twins. Behav Genet. 1999;29:395-407.
FULL TEXT
|
ISI
| PUBMED
13. 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.
FULL TEXT
|
ISI
| PUBMED
14. Madden PA, Heath AC, Pedersen NL, Kaprio J, Koskenvuo MJ, Martin NG. The genetics of smoking persistance in men and women: a multicultural study. Behav Genet. 1999;29:423-431.
FULL TEXT
|
ISI
| PUBMED
15. 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.
PUBMED
16. Comings DE, Ferry L, Bradshaw-Robinson S, Burchette R, Chiu C, Muhleman D. The dopamine D2 receptor (DRD2) gene: a genetic risk factor in smoking. Pharmacogenetics. 1996;6:73-79.
FULL TEXT
|
ISI
| PUBMED
17. Noble EP, St Jeor ST, Ritchie T, Syndulko K, St Jeor SC, Fitch RJ, Brunner RL, Sparkes RS. D2 dopamine receptor gene and cigarette smoking: a reward gene? Med Hypotheses. 1994;42:257-260.
FULL TEXT
|
ISI
| PUBMED
18. Spitz MR, Shi H, Yang F, Hudmon KS, Jiang H, Chanberlain RM, Amos CI, Wan Y, Cinciripini P, Hong WK, Wu X. Case-control study of the D2 dopamine receptor gene and smoking status in lung cancer patients. J Natl Cancer Inst. 1998;90:358-363.
FREE FULL TEXT
19. Lerman C, Audrain J, Main D, Boyd N, Caporaso N, Bowman E, Lockshin B, Boyd NR, Shields PG. Evidence suggesting the role of specific genetic factors in cigarette smoking. Health Psychol. 1999;18:14-20.
FULL TEXT
|
ISI
| PUBMED
20. Sabol SZ, Nelson ML, Fisher C, Gunzerath L, Brody CL, Hu S, Sirota LA, Marcus SE, Greenberg BD, Lucas FR 4th, Benjamin J, Murphy DL, Hamer DH. A genetic association for cigarette smoking behavior. Health Psychol. 1999;18:7-13.
FULL TEXT
|
ISI
| PUBMED
21. Audrain-McGovern J, Lerman C, Wileyto EP, Rodriguez D, Shields PG. Interacting effects of genetic predisposition and depression on adolescent smoking progression. Am J Psychiatry. 2004;161:1224-1230.
FREE FULL TEXT
22. Han C, McGue MK, Iacono WG. Lifetime tobacco, alcohol and other substance use in adolescent Minnesota twins: univariate and multivariate behavioral genetic analyses. Addiction. 1999;94:981-993.
FULL TEXT
|
ISI
| PUBMED
23. Hopper JL, White VM, Macaskill GT, Hill DJ, Clifford CA. Alcohol use, smoking habits and the Adult Eysenck Personality Questionnaire in adolescent Australian twins [corrected] Acta Genet Med Gemellol (Roma). 1992;41:311-324. [published correction appears in Acta Genet Med Gemellol (Roma). 1992;41:311-324].
PUBMED
24. Koopmans JR, van Doornen LJ, Boomsma DI. Association between alcohol use and smoking in adolescent and young adult twins: a bivariate genetic analysis. Alcohol Clin Exp Res. 1997;21:537-546.
ISI
| PUBMED
25. Meeusen R, Smolders I, Sarre S, de Meirleir K, Keizer H, Serneels M, Ebinger G, Michotte Y. Endurance training effects on neurotransmitter release in rat striatum: an in vivo microdialysis study. Acta Physiol Scand. 1997;159:335-341.
FULL TEXT
|
ISI
| PUBMED
26. Sutoo DE, Akiyama K. The mechanism by which exercise modifies brain function. Physiol Behav. 1996;60:177-181.
FULL TEXT
| PUBMED
27. Wilson WM, Marsden CA. Extracellular dopamine in the nucleus accumbens of the rat during treadmill running. Acta Physiol Scand. 1995;155:465-466.
FULL TEXT
|
ISI
| PUBMED
28. Hattori S, Naoi M, Nishino H. Striatal dopamine turnover during treadmill running in the rat: relation to the speed of running. Brain Res Bull. 1994;35:41-49.
FULL TEXT
|
ISI
| PUBMED
29. Wang GJ, Volkow ND, Fowler JS, Franceschi D, Logan J, Pappas NR, Wong CT, Netusil N. PET studies of the effects of aerobic exercise on human striatal dopamine release. J Nucl Med. 2000;41:1352-1356.
FREE FULL TEXT
30. Brody AL, Olmstead RE, London ED, Farahi J, Meyer JH, Grossman P, Lee GS, Huang J, Hahn EL, Mandelkern MA. Smoking-induced ventral striatum dopamine release. Am J Psychiatry. 2004;161:1211-1218.
FREE FULL TEXT
31. Wang GJ, Volkow ND, Thanos PK, Fowler JS. Similarity between obesity and drug addiction as assessed by neurofunctional imaging: a concept review. J Addict Dis. 2004;23:39-53.
ISI
| PUBMED
32. Wang GJ, Volkow ND, Fowler JS. The role of dopamine in motivation for food in humans: implications for obesity. Expert Opin Ther Targets. 2002;6:601-609.
FULL TEXT
| PUBMED
33. Wang GJ, Volkow ND, Logan J, Pappas NR, Wong CT, Zhu W, Netusil N, Fowler JS. Brain dopamine and obesity. Lancet. 2001;357:354-357.
FULL TEXT
|
ISI
| PUBMED
34. Marcus BH, Albrecht AE, King TK, Parisi AF, Pinto BM, Roberts M, Niaura RS, Abrams DB. The efficacy of exercise as an aid for smoking cessation in women: a randomized controlled trial. Arch Intern Med. 1999;159:1229-1234.
FREE FULL TEXT
35. Ussher M, Nunziata P, Cropley M, West R. Effect of a short bout of exercise on tobacco withdrawal symptoms and desire to smoke. Psychopharmacology (Berl). 2001;158:66-72.
FULL TEXT
| PUBMED
36. Daniel J, Cropley M, Ussher M, West R. Acute effects of a short bout of moderate versus light intensity exercise versus inactivity on tobacco withdrawal symptoms in sedentary smokers. Psychopharmacology (Berl). 2004;174:320-326.
PUBMED
37. Lerman C, Berrettini W, Pinto A, Patterson F, Crystal-Mansour S, Wileyto EP, Restine SL, Leonard DG, Shields PG, Epstein LH. Changes in food reward following smoking cessation: a pharmacogenetic investigation. Psychopharmacology (Berl). 2004;174:571-577.
PUBMED
38. Epstein LH, Wright SM, Paluch RA, Leddy JJ, Hawk LW Jr, Jaroni JL, Saad FG, Crystal-Mansour S, Shields PG, Lerman C. Relation between food reinforcement and dopamine genotypes and its effect on food intake in smokers. Am J Clin Nutr. 2004;80:82-88.
FREE FULL TEXT
39. Swan GE, Hudmon KS, Jack LM, Hemberger K, Carmelli D, Khroyan TV, Ring HZ, Hops H, Andrews JA, Tildesley E, McBride D, Benowitz N, Webster C, Wilhelmsen KC, Feiler HS, Koenig B, Caron L, Illes J, Cheng LS. Environmental and genetic determinants of tobacco use: methodology for a multidisciplinary, longitudinal family-based investigation. Cancer Epidemiol Biomarkers Prev. 2003;12:994-1005.
FREE FULL TEXT
40. Swan GE. Implications of genetic epidemiology for the prevention of tobacco use. Nicotine Tob Res. 1999;1(suppl 1):S49-S56.
ABSTRACT
41. Clayton RR, Scutchfield FD, Wyatt SW. Hutchinson Smoking Prevention Project: a new gold standard in prevention science requires new transdisciplinary thinking. J Natl Cancer Inst. 2000;92:1964-1965.
FREE FULL TEXT
42. Munafo M, Clark T, Johnstone E, Murphy M, Walton R. The genetic basis for smoking behavior: a systematic review and meta-analysis. Nicotine Tob Res. 2004;6:583-597.
FULL TEXT
|
ISI
| PUBMED
43. Audrain J, Tercyak KP, Goldman P, Bush A. Recruiting adolescents into genetic studies of smoking behavior. Cancer Epidemiol Biomarkers Prev. 2002;11:249-252.
FREE FULL TEXT
44. Kozlowski LT. Rehabilitating a genetic perspective in the study of tobacco and alcohol use. Br J Addict. 1991;86:517-520.
FULL TEXT
|
ISI
| PUBMED
45. Vandenbergh DJ, Bennett C, Grant M, Strasser A, O'Connor R, Stauffer R, Vogler GP, Kozlowski LT. 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.
ABSTRACT
46. Kozlowski LT, Harford MR. On the significance of never using a drug: an example from cigarette smoking. J Abnorm Psychol. 1976;85:433-434.
FULL TEXT
|
ISI
| PUBMED
47. Vandenbergh DJ, Kozlowski LT, Bennett CJ, Grant MD, Strasser AA, O'Connor R, Stauffer RL, Vogler GP. DAT's not all, but it may be more than we realize [comment]. Nicotine Tob Res. 2002;4:251-252.
FULL TEXT
| PUBMED
48. Harty LC, Caporaso NE, Hayes RB, Winn DM, Bravo-Otero E, Blot WJ, Kleinman DV, Brown LM, Armenian HK, Fraumeni JF Jr, Shields PG. Alcohol dehydrogenase 3 genotype and risk of oral cavity and pharyngeal cancers. J Natl Cancer Inst. 1997;89:1698-1705.
ABSTRACT
49. Grunbaum JA, Kann L, Kinchen S, Ross J, Hawkins J, Lowry R, Harris WA, McManus T, Chyen D, Collins J. Youth risk behavior surveillance–United States, 2003. MMWR Surveill Summ. 2004;53:1-96.
PUBMED
50. Mayhew KP, Flay BR, Mott JA. Stages in the development of adolescent smoking. Drug Alcohol Depend. 2000;59:S61-S81.
FULL TEXT
|
ISI
| PUBMED
51. Brener ND, Collins JL, Kann L, Warren CW, Williams BI. Reliability of the Youth Risk Behavior Survey Questionnaire. Am J Epidemiol. 1995;141:575-580.
FREE FULL TEXT
52. 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.
FULL TEXT
|
ISI
| PUBMED
53. Patrick DL, Cheadle A, Thompson DC, Diehr P, Koepsell T, Kinne S. The validity of self-reported smoking: a review and meta-analysis. Am J Public Health. 1994;84:1086-1093.
FREE FULL TEXT
54. Botvin GJ, Botvin EM. Adolescent tobacco, alcohol, and drug abuse: prevention strategies, empirical findings, and assessment issues. J Dev Behav Pediatr. 1992;13:290-301.
ISI
| PUBMED
55. Audrain-McGovern J, Rodriguez D, Tercyak KP, Cuevas J, Rodgers K, Patterson F. Identifying and characterizing adolescent smoking trajectories. Cancer Epidemiol Biomarkers Prev. 2004;13:2023-2034.
FREE FULL TEXT
56. Radloff LS. The CES-D Scale: a new self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1:385-401.
FULL TEXT
57. Conrad KM, Flay BR, Hill D. Why children start smoking cigarettes: predictors of onset. Br J Addict. 1992;87:1711-1724.
FULL TEXT
|
ISI
| PUBMED
58. 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.
ISI
| PUBMED
59. Hosmer DW, Lemeshow S. Applied Logistic Regression. 2nd ed. New York, NY: John Wiley & Sons Inc; 2000.60. Duncan TE, Duncan SC, Strycker LA, Li F, Alpert A. An Introduction to Latent Variable Growth Curve Modeling: Concepts, Issues, and Applications. Mahwah, NJ: Lawrence Erlbaum Associates; 1999.61. Muthén BO. Methodological issues in random coefficient growth modeling using a latent variable framework: applications to the development of heavy drinking ages 18-37. In: Rose JS, Presson CC, Sherman SJ, eds. Multivariate Applications in Substance Use Research. Mahwah, NJ: Lawrence Erlbaum Associates; 2000:113-140.62. Muthén BO. Beyond SEM: general latent variable modeling. Behaviormetrika. 2002;29:81-117.
FULL TEXT
63. Loehlin JC. Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis . Mahwah, NJ: Lawrence Erlbaum Associates; 2004.64. Hu L, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives Structural Equation Modeling. 1999;6:1-55.
65. Muthén LK, Muthén BO. Mplus User's Guide. Los Angeles, Calif: Muthén & Muthén; 2001.66. Muthén LK, Muthén BO. Mplus User's Guide. 3rd ed. Los Angeles, Calif: Muthén & Muthén; 2004.67. Muthén BO, Muthén LK. How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling. 2002;4:599-620.
FULL TEXT
68. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155:945-959.
FREE FULL TEXT
69. Thompson J, Thomas N, Singleton A, Piggott M, Lloyd S, Perry EK, Ferrier IN, Court JA. D2 dopamine receptor gene (DRD2) Taq1 A polymorphism: reduced dopamine D2 receptor binding in the human striatum associated with the A1 allele. Pharmacogenetics. 1997;7:479-484.
FULL TEXT
|
ISI
| PUBMED
70. Ritchie T, Noble EP. [3H]naloxone binding in the human brain: alcoholism and the TaqI A D2 dopamine receptor polymorphism. Brain Res. 1996;718:193-197.
FULL TEXT
|
ISI
| PUBMED
71. Noble EP, Blum K, Ritchie T, Montgomery A, Sheridan PJ. Allelic association of the D2 dopamine receptor gene with receptor-binding characteristics in alcoholism. Arch Gen Psychiatry. 1991;48:648-654.
FREE FULL TEXT
72. Fuke S, Suo S, Takahashi N, Koike H, Sasagawa N, Ishiura S. The VNTR polymorphism of the human dopamine transporter (DAT1) gene affects gene expression. Pharmacogenomics J. 2001;1:152-156.
PUBMED
73. Heinz A, Goldman D, Jones D, Palmour R, Hommer D, Gorey J, Lee KS, Linnoila M, Weinberger DR. Genotype influences in vivo dopamine transporter availability in human striatum. Neuropsychopharmacology. 2000;22:133-139.
FULL TEXT
|
ISI
| PUBMED
74. Lerman C, Shields PG, Wileyto EP, Audrain J, Hawk LH Jr, Pinto A, Kucharski S, Krishnan S, Niaura R, Epstein LH. Effects of dopamine transporter and receptor polymorphisms on smoking cessation in a bupropion clinical trial. Health Psychol. 2003;22:541-548.
FULL TEXT
|
ISI
| PUBMED
75. Mazzeo RS. Catecholamine responses to acute and chronic exercise. Med Sci Sports Exerc. 1991;23:839-845.
ISI
| PUBMED
76. MacRae PG, Spirduso WW, Cartee GD, Farrar RP, Wilcox RE. Endurance training effects on striatal D2 dopamine receptor binding and striatal dopamine metabolite levels. Neurosci Lett. 1987;79:138-144.
FULL TEXT
|
ISI
| PUBMED
77. Simonen RL, Rankinen T, Perusse L, Leon AS, Skinner JS, Wilmore JH, Rao DC, Bouchard C. A dopamine D2 receptor gene polymorphism and physical activity in two family studies. Physiol Behav. 2003;78:751-757.
FULL TEXT
| PUBMED
78. Simonen R, Levalahti E, Kaprio J, Videman T, Battie MC. Multivariate genetic analysis of lifetime exercise and environmental factors. Med Sci Sports Exerc. 2004;36:1559-1566.
ISI
| PUBMED
79. Melnick MJ, Miller KE, Sabo DF, Farrell MP, Barnes GM. Tobacco use among high school athletes and nonathletes: results of the 1997 Youth Risk Behavior Survey. Adolescence. 2001;36:727-747.
ISI
| PUBMED
80. Audrain-McGovern J, Rodriguez D, Tercyak K, Neuner G, Moss HB. The impact of self-control indices on peer smoking and adolescent smoking progression. J Pediatr Psychol. 2006;31:139-151.
FREE FULL TEXT
81. Dovey SM, Reeder AI, Chalmers DJ. Continuity and change in sporting and leisure time physical activities during adolescence. Br J Sports Med. 1998;32:53-57.
FREE FULL TEXT
82. Boreham C, Twisk J, van Mechelen W, Savage M, Strain J, Cran G. Relationships between the development of biological risk factors for coronary heart disease and lifestyle parameters during adolescence: the Northern Ireland Young Hearts Project. Public Health. 1999;113:7-12.
FULL TEXT
|
ISI
| PUBMED
83. van Mechelen W, Twisk JW, Post GB, Snel J, Kemper HC. Physical activity of young people: the Amsterdam Longitudinal Growth and Health Study. Med Sci Sports Exerc. 2000;32:1610-1616.
ISI
| PUBMED
84. Telama R, Yang X. Decline of physical activity from youth to young adulthood in Finland. Med Sci Sports Exerc. 2000;32:1617-1622.
ISI
| PUBMED
85. Aaron DJ, Storti KL, Robertson RJ, Kriska AM, LaPorte RE. Longitudinal study of the number and choice of leisure time physical activities from mid to late adolescence: implications for school curricula and community recreation programs. Arch Pediatr Adolesc Med. 2002;156:1075-1080.
FREE FULL TEXT
86. Kimm SY, Glynn NW, Kriska AM, Barton BA, Kronsberg SS, Daniels SR, Crawford PB, Sabry ZI, Liu K. Decline in physical activity in black girls and white girls during adolescence. N Engl J Med. 2002;347:709-715.
FREE FULL TEXT
87. Lerman C, Swan G. Non-replication of genetic association studies: is DAT all folks [comment]? Nicotine Tob Res. 2002;4:247-249.
FULL TEXT
| PUBMED
88. Sullivan PF, Eaves LJ, Kendler KS, Neale MC. Genetic case-control association studies in neuropsychiatry. Arch Gen Psychiatry. 2001;58:1015-1024.
FREE FULL TEXT
89. Jacobsen LK, Staley JK, Zoghbi SS, Seibyl JP, Kosten TR, Innis RB, Gelernter J. Prediction of dopamine transporter binding availability by genotype: a preliminary report. Am J Psychiatry. 2000;157:1700-1703.
FREE FULL TEXT
90. Martinez D, Gelernter J, Abi-Dargham A, van Dyck CH, Kegeles L, Innis R, Laruelle M. The variable number of tandem repeats polymorphism of the dopamine transporter gene is not associated with significant change in dopamine transporter phenotype in humans. Neuropsychopharmacology. 2001;24:553-560.
FULL TEXT
|
ISI
| PUBMED
91. Miller GM, Madras BK. Polymorphisms in the 3'-untranslated region of human and monkey dopamine transporter genes affect reporter gene expression. Mol Psychiatry. 2002;7:44-55.
FULL TEXT
|
ISI
| PUBMED
92. Bierut LJ, Rice JP, Edenberg HJ, Goate A, Foroud T, Cloninger CR, Begleiter H, Conneally PM, Crowe RR, Hesselbrock V, Li TK, Nurnberger JI Jr, Porjesz B, Schuckit MA, Reich T. Family-based study of the association of the dopamine D2 receptor gene (DRD2) with habitual smoking. Am J Med Genet. 2000;90:299-302.
FULL TEXT
|
ISI
| PUBMED
93. Jorm AF, Henderson AS, Jacomb PA, Christensen H, Korten AE, Rodgers B, Tan X, Easteal S. Association of smoking and personality with a polymorphism of the dopamine transporter gene: results from a community survey. Am J Med Genet. 2000;96:331-334.
FULL TEXT
|
ISI
| PUBMED
94. Laruelle M, Gelernter J, Innis RB. D2 receptors binding potential is not affected by Taq1 polymorphism at the D2 receptor gene. Mol Psychiatry. 1998;3:261-265.
FULL TEXT
|
ISI
| PUBMED
95. Neville MJ, Johnstone EC, Walton RT. Identification and characterization of ANKK1: a novel kinase gene closely linked to DRD2 on chromosome band 11q23.1. Hum Mutat. 2004;23:540-545.
FULL TEXT
|
ISI
| PUBMED
96. US Census Bureau. National report: 2001a. Available at: http://quickfacts.census.gov/qfd/states. Accessed August 29, 2003.97. US Census Bureau. State report: 2001b. Available at: http://quickfacts.census.gov/qfd/states/51/51059.html. Accessed February 8, 2005.98. Developmental Research and Programs. Communities That Care: 2001 Youth Survey Report, Fairfax County, Virginia. Available at: http://www.co.fairfax.va.us/comm/demogrph/pdf/Youth2001.pdf. Accessed March 3, 2002.99. Koepp MJ, Gunn RN, Lawrence AD, Cunningham VJ, Dagher A, Jones T, Brooks DJ, Bench CJ, Grasby PM. Evidence for striatal dopamine release during a video game. Nature. 1998;393:266-268.
FULL TEXT
| PUBMED
 CiteULike  Connotea  Del.icio.us  Digg  Reddit  Technorati  Twitter
What's this?
|
