Abstract

Introduction:

Common variation in the CHRNA5–CHRNA3–CHRNB4 gene region is robustly associated with smoking quantity. Conversely, the association between one of the most significant single nucleotide polymorphisms (SNPs; rs1051730 within the CHRNA3 gene) with perceived difficulty or willingness to quit smoking among current smokers is unknown.

Methods:

Cross-sectional study including current smokers, 502 women, and 552 men. Heaviness of smoking index (HSI), difficulty, attempting, and intention to quit smoking were assessed by questionnaire.

Results:

The rs1051730 SNP was associated with increased HSI (age, gender, and education-adjusted mean ± SE: 2.6 ± 0.1, 2.2 ± 0.1, and 2.0 ± 0.1 for AA, AG, and GG genotypes, respectively, p < .01). Multivariate logistic regression adjusting for gender, age, education, leisure-time physical activity, and personal history of cardiovascular or lung disease showed rs1051730 to be associated with higher smoking dependence (odds ratio [OR] and 95% CI for each additional A-allele: 1.38 [1.11–1.72] for smoking more than 20 cigarette equivalents/day; 1.31 [1.00–1.71] for an HSI ≥5 and 1.32 [1.05–1.65] for smoking 5 min after waking up) and borderline associated with difficulty to quit (OR = 1.29 [0.98–1.70]), but this relationship was no longer significant after adjusting for nicotine dependence. Also, no relationship was found with willingness (OR = 1.03 [0.85–1.26]), attempt (OR = 1.00 [0.83–1.20]), or preparation (OR = 0.95 [0.38–2.38]) to quit. Similar findings were obtained for other SNPs, but their effect on nicotine dependence was no longer significant after adjusting for rs1051730.

Conclusions:

These data confirm the effect of rs1051730 on nicotine dependence but failed to find any relationship with difficulty, willingness, and motivation to quit.

Introduction

Cigarette smoking is the most important modifiable risk factor for premature death in the world causing 5.4 million deaths every year (World Health Organization, 2008). Recent studies have shown that both nicotine dependence as assessed by the Fagerström Test for Nicotine Dependence (FTND; Beuten, Ma, Lou, Payne, & Li, 2007; Breitling, Dahmen, Mittelstrass, Rujescu, et al., 2009; Li et al., 2006, 2009) and successful smoking cessation (Breitling, Dahmen, Mittelstrass, Illig, et al., 2009; Drgon et al., 2009; Uhl et al., 2008) are modulated by genetic factors, with a heritability of more than 50% (Li, 2003). The CHRNA5CHRNA3CHRNB4 region of chromosome 15 has been shown to be significantly related with increased tobacco consumption (Caporaso et al., 2009; Li et al., 2010; Pillai et al., 2010), but the exact identification of the polymorphisms associated with increased tobacco consumption is hampered by the strong linkage disequilibrium between single nucleotide polymorphisms (SNPs) of this region (Caporaso et al., 2009; Li et al., 2010). Recently, two SNPs rs55853698, located within the promoter region of CHRNA5 and rs6495308 in CHRNA3, have been shown to be associated with smoking quantity in a meta-analysis that included the current study (Liu et al., 2010). rs55853698 is highly correlated with rs1051730 in Europeans (r2 > .96; Liu et al., 2010). The rs1051730 SNP lies within the nicotinic acetylcholine receptor alpha 3 subunit gene (CHRNA3) and is associated with increased tobacco consumption (Thorgeirsson et al., 2008). The rs1051730 SNP has also been related with lung disease (Pillai et al., 2009). Contrary to the dopamine receptor genes (Cinciripini et al., 2004; Laucht et al., 2008), it is currently unknown whether this CHRNA5 SNP is also related to perceived difficulty and willingness to quit smoking among current smokers.

Hence, we used the data from a population-based cross-sectional study (CoLaus study) to assess the relationships between the rs1051730 SNP and intention and perceived difficulty to quit smoking among current smokers.

Methods

Recruitment Process and Inclusion Criteria

The design of the CoLaus study has been described previously (Firmann et al., 2008). The CoLaus study is a population-based study aimed to investigate the epidemiology and genetic determinants of cardiovascular risk factors and metabolic syndrome. All participants were asked to attend the outpatient clinic at the Centre Hospitalier Universitaire Vaudois, Lausanne, in the morning after an overnight fast. Data were collected by trained field interviewers during a single visit lasting about 60 min. The Institutional Review Board in Lausanne approved the study protocol, and signed informed consent was obtained from participants. Information on demographic data, socioeconomic and marital status, lifestyle factors, personal and family history of disease, and cardiovascular risk factors and treatment was collected. Lung disease was considered in presence of a personal or a family history of emphysema and/or chronic bronchitis. Educational level was categorized into basic, apprenticeship, secondary, and university. Participants were considered as physically active if they reported practicing at least 2 hr of leisure-time physical activity per week.

Smoking Status

In a subgroup of the initial CoLaus study, current smokers were invited to respond to a questionnaire regarding perceived difficulty and intention to quit smoking. The questions included (a) perceived difficulty to stop smoking (four possible answers ranging from “very difficult” to “very easy,” further grouped into “difficult” and “not difficult”); (b) the number of attempts to quit smoking during the last twelve months, further categorized into “attempted” and “did not attempt,” and (c) intention to quit smoking (four possible answers ranging from “yes, definitely” to “not at all,” further categorized into “yes” and “no”). If a positive answer was given to Question c, motivation to stop smoking was measured using a modified algorithm (Prochaska & DiClemente, 1992) of the state of change construct as suggested previously (Thyrian et al., 2008; Wewers, Stillman, Hartman, & Shopland, 2003). Briefly, the groups were defined as follows: (a) precontemplation: not interested in quitting smoking in the next six months, (b) contemplation: interested in quitting smoking in the next six months but not the next thirty days, and (c) preparation: interested in quitting smoking in the next thirty days.

Nicotine dependence was assessed by the FTND, briefly, the subject’s current amount of smoked tobacco (number of cigarettes, cigarillos, cigars, or pipes per day) and the heaviness of smoking index (HSI; Heatherton, Kozlowski, Frecker, Rickert, & Robinson, 1989; Hyland et al., 2006). Both markers have been used in previous genetic analyses of nicotine dependence (Beuten, Ma, Payne, et al., 2007; Li et al., 2009). Cigarette equivalents were assessed as described previously (Section de Santé Office Fédéral de la Statistique, 2008): one cigarillo or one pipe = 2.5 cigarettes and one cigar = 5 cigarettes. Subjects were considered as “heavy” smokers if they smoked more than 20 cigarette equivalents (one packet)/day. The HSI is the sum of two categorical measures: number of cigarettes smoked per day (codes 0: 0–10 cigarettes/day; 1: 11–20; 2: 21–30; and 3: 31+) and time to first cigarette after waking (coded: 0: 61+ min; 1: 31–60 min; 2: 6–30 min; and 3: 5 min or less). Values for HSI range from 0 to 6 and were categorized into low + medium (0–4) and high (5–6) according to the literature (Chaiton, Cohen, McDonald, & Bondy, 2007). Time to first cigarette was also categorized into “<5 min” and “6 min or more” to identify the most dependent smokers. For simplicity, in the rest of the paper by the term “nicotine,” we refer to FTND-derived nicotine.

Genotyping and Quality Controls

Nuclear DNA was extracted from whole blood for whole genome scan analysis using Nucleon genomic DNA extraction kit (Tepnel life sciences, Manchester, UK) according to the manufacturer’s recommendations. Genotyping was performed using the Affymetrix GeneChip Human Mapping 500K array set as recommended by the manufacturer. Genotypes were called using BRLMM (Affymetrix, 2006). Duplicate individuals, and first- and second-degree relatives, were identified by computing genomic identity-by-descent coefficients using PLINK (Purcell et al., 2007). The younger individual from each duplicate or relative pair was removed.

In this study, we decided to focus on the SNP (rs1051730) thath showed the highest relationship with smoking quantity (Liu et al., 2010). As a control, we also assessed the effects of other SNPs spanning the CHRNA5–CHRNA3–CHRNB4 gene region and present in the Affymetrix chip (rs11636732, rs481134, rs951266, rs514743, rs6495308 rs8192475, rs950776, rs11072768, rs7166158, and rs8043123). Conversely, it was not possible to assess the association with two SNPs (rs16969968 and rs588765 of CHRNA5) that have been shown to be associated with smoking quantity (Saccone et al., 2010).

Statistical Analysis

Statistical analyses were conducted using SAS v. 9.2 (SAS, Cary, NC, USA) for Windows. Results were expressed as mean ± SD or as number of participants and percentage. Bivariate comparisons were performed using Student’s t test or chi-square test for continuous and discrete variables, respectively. The associations between SNPs and binary variables were also assessed by the Cochran–Armitage test for trend. Multivariate analysis of the effect of genotypes on quantitative variables was conducted using a general linear model adjusting for gender, age, and education, and the results were expressed as adjusted mean ± SE. Multivariate logistic regression adjusting for gender, age, education, leisure-time physical activity, and personal history of cardiovascular or lung disease was used to assess the independent contributions of each SNP on qualitative variables, and the results were expressed as odds ratio (OR) and 95% CI. As most SNPs studied were in linkage disequilibrium with rs1051730, a further conditioning on rs1051730 was performed. Statistical significance was considered for nominal p < .05.

Sample size and power were calculated using Quanto software (Gauderman, 2002; available at http://hydra.usc.edu/gxe/). Power analyses using the following settings: unmatched case–control (1:5), gene only, dominant inheritance mode, allele frequency 0.38 (for the A-allele), two-sided test, type 1 error rate 0.05, sample size 159 (AA homozygote), and the observed ORs (1.03 for willingness and 0.95 for preparation to quit) showed a 5%–6% power. Sample sizes for 80% power with the same settings were 15,023 and 45,750 to consider as significant an OR of 1.03 and 0.95, respectively. Changing to a 1:1 unmatched sample gave relatively similar results: a statistical power between 6% and 7%, and sample sizes of 25,070 and 76,190, respectively.

Results

The questionnaire was distributed to 1,385 current smokers of which 1,054 (77.5%, 502 women and 552 men, mean age 51.3 ± 9.9 years) had genetic data and provided complete information on difficulty and intention to quit. No between-gender differences were found for the distribution of rs1051730 genotypes (Supplementary Table 1). Also, no differences were found between current smokers included and not included regarding gender, age, educational level, leisure-time physical activity, personal history of cardiovascular or lung disease, HSI, difficulty, willingness, and motivation to quit smoking; conversely, included participants reported a higher frequency of previous quitting attempts and also tended to smoke more, although no difference was found for the prevalence of heavy smokers (>20 cigarette equivalents/day, see Supplementary Table 2).

The rs1051730 SNP was associated with smoking amount, FTND-derived nicotine dependence (as assessed by HSI or time to first cigarette), and difficulty to quit, while no relationship was found with willingness, attempt, or motivation to quit (Table 1). Multivariate logistic regression adjusting for gender, age, education, leisure-time physical activity, and personal history of cardiovascular or lung disease showed rs1051730 to be associated with higher FTND-derived nicotine dependence (OR and 95% CI for each additional A-allele: 1.38 [1.11–1.72] for smoking more than 20 cigarette equivalents/day, 1.31 [1.00–1.71] for a HSI ≥5, and 1.32 [1.05–1.65] for smoking 5 min after waking up) and borderline associated with difficulty to quit (OR = 1.29 [0.98–1.70]), while no relationship was found with willingness (OR = 1.03 [0.85–1.26]), attempt (OR = 1.00 [0.83–1.20]), or preparation (OR = 0.95 [0.38–2.38]) to quit (Table 2). Further adjustment on smoking amount (≤or >20 cigarette equivalents/day or HIS < and ≥5) or nicotine dependence (smoking ≤ or >5 min after waking up) did not change the results (not shown).

Table 1

Binary Associations of the rs1051730 Single Nucleotide Polymorphism With Difficulty and Willingness to Quit Smoking

 AA (N = 159) AG (n = 493) GG (N = 402) p Value 
Smoking amounta,19.2 ± 1.0 17.5 ± 0.6 15.5 ± 0.7 .002 
Smoking amounta (%) 
    1–20/day 114 (71.7) 384 (77.9) 331 (82.3) .02 
    >20/day 45 (28.3) 109 (22.1) 71 (17.7) .005c 
HSIb 2.6 ± 0.1 2.2 ± 0.1 2.0 ± 0.1 .004 
HSI classes (%) 
    Low–medium (0–4) 132 (83.0) 431 (87.4) 361 (89.8) .09 
    High (5–6) 27 (17.0) 62 (12.6) 41 (10.2) .03c 
Time to first cigarette (%) 
    <5 min 43 (27.0) 95 (19.3) 66 (16.4) .02 
    6+ min 116 (73.0) 398 (80.7) 336 (83.6) .007c 
Perceived difficulty quitting smoking (%) 
    Very difficult + difficult 150 (94.3) 419 (85.0) 346 (86.1) .009 
    Very easy + easy 9 (5.7) 74 (15.0) 56 (13.9) .05c 
Willingness to quit (%) 
    Yes 116 (73.0) 350 (71.0) 281 (69.9) .77 
    No 43 (27.0) 143 (29.0) 121 (30.1) .48c 
Attempted to quit last twelve months (%) 
    No 55 (34.6) 165 (33.5) 140 (34.8) .90 
    Yes 104 (65.4) 328 (66.5) 262 (65.2) .85c 
Motivation to quit (%)d N = 116 N = 350 N = 281  
    Preparation 15 (12.9) 72 (20.6) 59 (21.0)  
    Contemplation 55 (47.4) 143 (40.9) 114 (40.6) .38 
    Precontemplation 46 (39.7) 135 (38.6) 108 (38.4)  
 AA (N = 159) AG (n = 493) GG (N = 402) p Value 
Smoking amounta,19.2 ± 1.0 17.5 ± 0.6 15.5 ± 0.7 .002 
Smoking amounta (%) 
    1–20/day 114 (71.7) 384 (77.9) 331 (82.3) .02 
    >20/day 45 (28.3) 109 (22.1) 71 (17.7) .005c 
HSIb 2.6 ± 0.1 2.2 ± 0.1 2.0 ± 0.1 .004 
HSI classes (%) 
    Low–medium (0–4) 132 (83.0) 431 (87.4) 361 (89.8) .09 
    High (5–6) 27 (17.0) 62 (12.6) 41 (10.2) .03c 
Time to first cigarette (%) 
    <5 min 43 (27.0) 95 (19.3) 66 (16.4) .02 
    6+ min 116 (73.0) 398 (80.7) 336 (83.6) .007c 
Perceived difficulty quitting smoking (%) 
    Very difficult + difficult 150 (94.3) 419 (85.0) 346 (86.1) .009 
    Very easy + easy 9 (5.7) 74 (15.0) 56 (13.9) .05c 
Willingness to quit (%) 
    Yes 116 (73.0) 350 (71.0) 281 (69.9) .77 
    No 43 (27.0) 143 (29.0) 121 (30.1) .48c 
Attempted to quit last twelve months (%) 
    No 55 (34.6) 165 (33.5) 140 (34.8) .90 
    Yes 104 (65.4) 328 (66.5) 262 (65.2) .85c 
Motivation to quit (%)d N = 116 N = 350 N = 281  
    Preparation 15 (12.9) 72 (20.6) 59 (21.0)  
    Contemplation 55 (47.4) 143 (40.9) 114 (40.6) .38 
    Precontemplation 46 (39.7) 135 (38.6) 108 (38.4)  

Note. Results are expressed as number of subjects and (column percentage). Statistical analysis by general linear model (quantitative data), chi-square, and two-sided Cochran–Armitage test for trend (qualitative data). HSI = Heaviness of Smoking Index.

a

In cigarette equivalents.

b

Adjusted for gender, age, and education.

c

Statistical analysis by two-sided Cochran–Armitage test for trend (qualitative data).

d

Among participants willing to quit only.

Table 2

Multivariate Analysis of the Association of the rs1051730 Single Nucleotide Polymorphism With Difficulty and Willingness to Quit Smoking

 GG (N = 402) AG (n = 493) AA (N = 159) p Value for trend 
Smoking amount >20/daya 1 (ref.) 1.44 (1.01–2.03) 1.87 (1.19–2.94) .005 
HSI >4 1 (ref.) 1.34 (0.87–2.06) 1.70 (0.99–2.93) .05 
Time to first cigarette <5 min 1 (ref.) 1.29 (0.90–1.85) 1.75 (1.11–2.77) .02 
Perceived difficulty quitting smoking 1 (ref.) 0.93 (0.63–1.36) 2.58 (1.23–5.40) .07 
Willingness to quit 1 (ref.) 1.04 (0.77–1.40) 1.07 (0.70–1.62) .74 
Attempted to quit last twelve months 1 (ref.) 1.04 (0.79–1.38) 0.97 (0.66–1.43) .98 
Preparation to quitb 1 (ref.) 0.95 (0.65–1.39) 0.56 (0.31–1.03) .11 
 GG (N = 402) AG (n = 493) AA (N = 159) p Value for trend 
Smoking amount >20/daya 1 (ref.) 1.44 (1.01–2.03) 1.87 (1.19–2.94) .005 
HSI >4 1 (ref.) 1.34 (0.87–2.06) 1.70 (0.99–2.93) .05 
Time to first cigarette <5 min 1 (ref.) 1.29 (0.90–1.85) 1.75 (1.11–2.77) .02 
Perceived difficulty quitting smoking 1 (ref.) 0.93 (0.63–1.36) 2.58 (1.23–5.40) .07 
Willingness to quit 1 (ref.) 1.04 (0.77–1.40) 1.07 (0.70–1.62) .74 
Attempted to quit last twelve months 1 (ref.) 1.04 (0.79–1.38) 0.97 (0.66–1.43) .98 
Preparation to quitb 1 (ref.) 0.95 (0.65–1.39) 0.56 (0.31–1.03) .11 

Note. Multivariate analysis by logistic regression adjusting for gender, age group (35–44, 45–54, 55–64, and 65+), educational level (basic; apprenticeship; secondary, and university), leisure-time physical activity (yes/no), and personal history of cardiovascular or lung disease (yes/no). HSI = Heaviness of Smoking Index.

a

In cigarette equivalents.

b

Among participants willing to quit only (n = 797). Results are expressed as odds ratio and (95% CI).

Among the other SNPs assessed, two (rs6495308 and rs951266) were also significantly related with smoking amount, HSI, and perceived difficulty quitting smoking (Supplementary Tables 35), and the latter relationship remained statistically significant after adjusting on smoking amount (≤or >20 cigarette equivalents/day or HIS < and ≥5) or FTND-derived nicotine dependence (smoking ≤ or >5 min after waking up; not shown). Conversely, all relationships were no longer significant after conditioning on rs1051730; also, neither rs6495308 nor rs951266 was related with willingness or motivation to quit smoking (Supplementary Tables 34). These two SNPs (rs6495308 and rs951266) were in linkage disequilibrium with rs1051730 (Supplementary Table 5).

Finally, no association with reported lung disease was found, although borderline significant (p < .09) trends were found for rs481134 and rs514743 (Supplementary Table 6).

Discussion

There is few data regarding the genetics of perceived difficulty and willingness to quit smoking. This cross-sectional population-based study thus provides important information regarding this topic. Furthermore, the ongoing follow-up of the entire CoLaus cohort will enable a better assessment of the genetic factors associated with successful quitting.

The rs1051730 SNP was significantly related with all markers of FTND-derived nicotine dependence, a finding already reported (Liu et al., 2010). Furthermore, the ORs observed for nicotine dependence (between 1.31 and 1.38 for each additional A-allele) were comparable to those published previously (Thorgeirsson et al., 2008), albeit lower values have been reported (Breitling, Dahmen, Mittelstrass, Illig, et al., 2009). Overall, our results confirm the importance of the CHRNA5–CHRNA3–CHRNB4 gene region regarding nicotine dependence (Breitling, Dahmen, Mittelstrass, Rujescu, et al., 2009; Li et al., 2009).

The rs1051730 SNP was also associated with difficulty to quit, a somewhat expected finding as difficulty to quit increases with nicotine dependence (Hyland et al., 2006). This SNP was not retained in previous studies assessing the genetics of successful quitting (Drgon et al., 2009; Uhl et al., 2008), an indirect marker of difficulty to quit. A possible explanation is that, contrary to the other studies that assessed successful quitting, only difficulty to quit (but not successful quitting) was assessed in this study. Other likely explanations are the fact that difficulty to quit was self-reported (and thus likely presenting with some variability) and that the relationship between rs1051730 and difficulty quitting smoking might be mediated by nicotine dependence. Indeed, after adjusting for FTND-derived nicotine dependence, the relationship between rs1051730 and difficulty quitting smoking became nonsignificant (OR = 1.22 [0.92–1.62] and 1.26 [0.96–1.67] after adjusting for >20 cigarette equivalents and HSI >4, respectively). Taken together, these findings suggest that rs1051730 is associated with difficulty to quit due to its effect on nicotine dependence.

No relationship was found between rs1051730 and willingness or motivation to quit smoking. These findings are in agreement with other studies that showed no genetic effect on smoking cessation probability (Breitling, Dahmen, Mittelstrass, Illig, et al., 2009). Although our study sample was underpowered to detect a possible genetic effect, the values of the ORs for willingness and motivation to quit were rather small and most likely devoid of any clinical interest for the assessment of successful smoking cessation. Overall, there is no indication that intention to quit is related to chromosome 15 n-acetycholine receptors (nAChR) variants.

In agreement with a previous study (Liu et al., 2010), rs6495308 was associated with increased smoking amount. In this study, rs6495308 was also associated with FTND-derived nicotine dependence, but these relationships were no longer significant after conditioning on rs1051730. Hence, and contrary to a previous study (Liu et al., 2010), we could not demonstrate an independent effect of rs6495308 on smoking amount or on FTND-derived nicotine dependence. Regarding SNP rs951266, the most likely explanation for its associations with smoking amount and FTND-derived nicotine dependence is its linkage disequilibrium with rs1051730; indeed, conditioning on rs1051730 suppressed or considerably reduced all observed associations (Supplementary Table 4b). Finally, the fact that neither rs6495308 nor rs951266 was related with willingness or motivation to quit smoking further strengthens the likelihood that these conditions are not mediated by genetic factors.

Contrary to a previous study (Pillai et al., 2009), no relationship was found between rs1051730 and reported lung disease (p value = .43), although other SNPs (rs481134 and rs514743) showed a borderline trend. Possible explanations for this lack of relationship include the fact that lung disease was self-reported and the small number of subjects reporting lung disease, which considerably reduced statistical power. Hence, further studies are needed to better assess the relationship between CHRNA5–CHRNA3–CHRNB4 gene markers and lung disease.

This study has some limitations worth noting. First, only subjects of Caucasian origin were included in this study, and inference should be done accordingly, as demographic and ethnic differences regarding intention and easiness to quit smoking have been reported (Shiffman, Brockwell, Pillitteri, & Gitchell, 2008). Second, questions might arise whether a population sample drawn in Lausanne is representative of the whole country. Still, a considerable proportion of the Lausanne population is non-Swiss or comes from other cantons: In 2006, of the 128,231 Lausanne inhabitants, 38% were non-Swiss, 30% came from other cantons (including Italian and German-speaking cantons), and only 32% were actually from the Vaud canton. Third, we had no genotype data for the two SNPs (rs16969968 and rs588765) of CHRNA5 that have been shown to be associated with smoking quantity (Saccone et al., 2010), and thus, no confirmatory analysis could be performed. Finally, it should be noted that a nominal significance of 0.05 was used and that correcting for multiple comparisons would considerably reduce the significance level. For instance, considering 10 tests for each SNP (13 overall), the corrected significance level would be 0.05/(13 × 10) = 4 × 104, and most relationships would not be statistically significant with the current sample size, which might provide a low statistical power.

In summary, our data confirm the effect of rs1051730 on FTND-derived nicotine dependence. Conversely, there was no indication that self-reported ability to quit has genetic influences from chromosome 15 nAChR variants that are independent of the heritable (and chromosome 15 nAChR variant associated) influences on features of FTND-derived nicotine dependence.

Supplementary Material

Supplementary Tables 16 can be found online at http://www.ntr.oxfordjournals.org

Funding

The CoLaus study was supported by research grants from GlaxoSmithKline and from the Faculty of Biology and Medicine of Lausanne, Switzerland, and is currently supported by Swiss National Science Foundation (grant no: 33CSCO-122661).

Declaration of Interests

None declared.

We thank Vincent Mooser from GlaxoSmithKline for helpful comments. We also thank Yolande Barreau, Anne-Lise Bastian, Binasa Ramic, Martine Moranville, Martine Baumer, Marcy Sagette, Jeanne Ecoffey, and Sylvie Mermoud for data collection.

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