Association of cytochrome P450 2A6 polymorphism, anxiety, and environmental factors with cigarette smoking by Thai adults
Article Category: Brief communication (Original)
Published Online: Jan 31, 2017
Page range: 147 - 154
DOI: https://doi.org/10.5372/1905-7415.1002.476
Keywords
© 2016 Napakkawat Buathong, Rasmon Kalayasiri, Pajaree Chaliyavilaskul, Khwanruan Phetnoi, Thanapoom Ratananupong
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.
Cigarette smoking is a leading cause of morbidity and mortality worldwide. Several studies have shown that many factors including genetic variation, psychological, and environmental factors are associated with cigarette smoking and nicotine dependence [1].
Genetic variation directly involved in the metabolism of drugs such as rs1051730 genetic variants in the nicotine acetylcholine receptor gene cluster (
Psychological factors, especially anxiety and depression, are related to a greater level of smoking, cigarette consumption, and degree of nicotine dependence [10, 11]. The relationships between anxiety, depression, cigarette consumption, and nicotine dependence have been explained either by nicotine intake causing an increase in the chances of developing anxiety and depression, or that anxiety and depression may induce smoking behavior [10, 12, 13]. The prevalence of cigarette consumption among persons with anxiety or mood disorders may be higher than individuals without psychiatric problems [14-16]. The finding from the Thai National Survey on mental health in 2008 reported that the prevalence of anxiety and major depressive disorder in the Thai population were 1.7% and 2.2% respectively. However, the prevalence of anxiety and major depressive disorders among Thais with illicit drug use and alcohol abuse were 1.8%– 16.5% and 1.3%–17.7%, respectively [17]. Thus, the prevalence of anxiety and depression is increased in Thai populations with substance use. Other psychosocial factors may be related to cigarette use. The role of a smoking household member, or a peer or colleague smoking, have been demonstrated to be environmental factors that highly influence smoking behavior and nicotine dependence [18, 19]. However, to our knowledge, studies that determine the effects and associations of genetic variation, anxiety, and other environment factors on cigarette smoking are still largely lacking. Therefore, the objectives of this study were to determine the associations between genetic polymorphisms of CYP2A6, anxiety, and environment factors with cigarette consumption and nicotine dependence, and to examine the associations between genetic polymorphisms of CYP2A6 and anxiety, genetic polymorphisms of CYP2A6 and environmental and other related factors among Thai adult smokers.
After approval by the Ethics Committee and the Institutional Review Board of the Faculty of Medicine, Chulalongkorn University (approval No. 367/2014), we conducted a cross-sectional study at King Chulalongkorn Memorial Hospital, Thailand between October 2014 and June 2015. We recruited 127 participants from Thai adult smokers who visited for a check-up at the Preventive and Social Medicine Clinic. The inclusion criteria were smokers who were
willing to disclose their smoking behavior, aged from 18 to 60 years, having daily cigarette use and had smoked more than 100 cigarettes in the past 6 months. Exclusion criteria were subjects who had used any medication that may induce or inhibit CYP2A6 activity within 14 days of the study. The present study did not include pregnant women, or women with oral or injected contraception, or individuals with kidney or liver disease. All participants provided their written informed consent to participate.
All participants completed questionnaires to determine demographic data, smoking behavior in the past month (to control recall bias), environmental factors regarding smoking household members and colleagues, The Fagerstr m Test for Nicotine Dependence, and The Thai Hospital Anxiety and Depression Scale for evaluation of anxiety and depression. The Fagerstr m Test for Nicotine Dependence [20, 21] is a widely used 6 item self-report instrument to measure the degree of nicotine dependence. The total scores range from 0-10, with higher scores indicating a higher degree of dependence. In this study, the scores 0-3, 4-5, and 6-10 indicated low, moderate, and high dependence status, respectively. The Thai Hospital Anxiety and Depression Scale (The Thai-HADS), a reliable and valid instrument for screening anxiety and depression in both Thai patients and general populations were applied [22-24]. The test is composed of 14 items (each item scored 0-3). Seven items relate to depressive symptoms and the rest of 7 items relate to anxiety symptoms. A cut-off point of ≥11 was interpreted as a clinical case of both depression and anxiety. The sensitivity of anxiety and depression subscales of the Thai HADS are 100% and 85.71%, respectively, while the specificity is 86.0% for anxiety, and 91.3% for depression. Both subscales also showed good internal consistencies with a Cronbach’s alpha coefficient of 0.85 for the anxiety subscale and 0.83 for the depression subscale.
Blood samples were collected into ethylenedia-minetetraacetic acid containing tubes and transported on dry ice to a -80 °C freezer within 3 h of collection. Genomic DNA was extracted using a Purelink Genomic DNA mini kit (Invitrogen, USA). The genotyping of CYP2A6*9 alleles was assessed using real-time PCR with Stepone software, version 2.2, and an Applied Biosystems 7500 Real Time PCR System. The genotyping of CYP2A6*1A, CYP2A6*1B, and CYP2A6*4 alleles were determined using a restriction fragment length polymorphism method (PCR-RFLP) [25].
CYP2A6 genotypes were categorized into 4 groups: ultrarapid metabolizers (UM) including persons with more than 2 CYP2A6*1x2A or CYP2A61x2B alleles; extensive metabolizers (EM) included those having 2 CYP2A6*1A/x1A or CYP2A6*1A/x1B or CYP2A6* 1A/x1B alleles; intermediate metabolizers (IM) included those having either one CYP2A6*1A/ x4C, CYP2A6*1B/x4C, CYP2A6*1A/x*9, or CYP2A6*1B/x*9; and poor metabolizers (PM) including participants having 2 copies of the inactive variants (CYP2A6*4C) or having one or 2 CYP2A6*4C/x*9, CYP2A6*9A/x10 variants [9].
The frequency of categorical variables was analyzed using descriptive variables. Mean and standard deviation (SD) were used to describe continuous variables. Univariate analysis for associations of independent variables with nicotine dependence and cigarette consumption were analyzed using an independent sample
We recruited 127 Thai adult smokers (96% men) with a mean age (SD) = 37.5 (9.99) years, range 18-59 years. Baseline data regarding socioeconomic status are presented in
Comparisons of demographic characteristics according to daily cigarette consumption and nicotine dependence (Fagerstr m Test for Nicotine Dependence score)
| Cigarette consumption | Nicotine dependence | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| n | Mean SD | Statistic | (df) | † | Mean SD | Statistic (df) | † | ||||
| Male | 122 | 11.8 | 6.87 | –0.64 | 0.522 | 3.6 | 2.58 | –3.83 | 0.006 | ||
| Female | 5 | 13.8 | 6.87 | 5.4 | 0.89 | ||||||
| Primary school | 31 | 14.4 | 9.39 | χ2 | 2.11 | 0.347 | 4.2 | 2.87 | 1.77 | 0.18 | |
| Secondary/vocational | 77 | 11.0 | 5.84 | 3.4 | 2.54 | ||||||
| Bachelor’sdegree/higher | 19 | 11.4 | 4.85 | 4.2 | 1.93 | ||||||
| Single/divorced/widowed | 65 | 11.6 | 5.84 | –0.42 | 0.677 | 3.7 | 2.53 | 0.07 | 0.95 | ||
| Married | 62 | 12.1 | 7.82 | 3.7 | 2.61 | ||||||
| Employee/ laborer | 108 | 12.3 | 7.01 | 1.67 | 0.193 | 3.6 | 2.53 | 0.30 | 0.74 | ||
| Government and state enterprise 10 | 10.0 | 5.03 | 4.3 | 2.87 | |||||||
| Own business | 9 | 8.6 | 5.96 | 3.7 | 2.79 | ||||||
| ≤10,000 | 50 | 13.1 | 7.22 | 1.65 | 0.196 | 4.1 | 2.64 | 2.95 | 0.06 | ||
| 10,001–15,000 | 36 | 10.4 | 6.05 | 2.8 | 2.47 | ||||||
| >15,000 | 41 | 11.7 | 6.95 | 4.0 | 2.43 | ||||||
| No | 37 | 13.6 | 6.92 | 1.84 | 0.068 | 4.1 | 2.35 | 1.25 | 0.21 | ||
| Yes | 90 | 11.2 | 6.74 | 3.5 | 2.64 | ||||||
| No | 33 | 11.2 | 5.75 | –0.64 | 0.526 | 3.6 | 2.81 | –0.15 | 0.88 | ||
| Yes | 94 | 12.1 | 7.22 | 3.7 | 2.48 | ||||||
df = Degrees of freedom. †
Prevalence of anxiety was 13% and depression was 9% among adult smokers. Most participants exposed to a household member smoking (85%) or colleague smoking (94%) in their environment.
Based on CYP2A6 genotype, there were UM (28%); (mean (SD) cigarette consumption = 13.14 (7.97)), EM (32.3%); (mean (SD) cigarette consumption = 13.02 (7.65)), IM (27.6%); (mean cigarette consumption 10.77 (4.71)) and PM (12.6%); (mean cigarette consumption = 8.50 (4.66)). Because the average cigarette consumption per day between UM and EM groups were not different, UM and EM were combined for inferential statistical analysis.
CYP2A6 genotype, anxiety, depression, environmental factors according to daily amount of cigarette consumption and severity of nicotine dependence
| Cigarette consumption | Nicotine dependence | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| n | Mean | SD | Statistic | (df) | † | Mean SD | Statistic (df) | † | ||||
| No | 19 | 9.8 | 5.29 | –1.40 | 0.164 | 2.2 | 2.25 | –2.81 | 0.006 | |||
| Yes | 108 | 12.2 | 7.06 | 4.0 | 2.53 | |||||||
| No | 10 | 15.2 | 10.35 | 1.09 | 0.303 | 4.1 | 2.64 | 0.52 | 0.60 | |||
| Yes | 117 | 11.6 | 6.46 | 3.7 | 2.56 | |||||||
| No | 111 | 11.6 | 6.46 | –1.00 | 0.331 | 3.4 | 2.46 | –3.96 | <0.001 | |||
| Yes | 16 | 13.9 | 9.15 | 5.9 | 2.11 | |||||||
| No | 116 | 12.2 | 6.88 | 1.98 | 0.05 | 3.7 | 2.56 | –0.17 | 0.87 | |||
| Yes | 11 | 8.0 | 5.42 | 3.8 | 2.75 | |||||||
| UM/EM | 76 | 13.1 | 7.75 | c2 | 6.09 | 0.048 | 3.9 | 2.53 | 0.13 | |||
| IM | 35 | 10.8 | 4.72 | 3.7 | 2.66 | |||||||
| PM | 16 | 8.5 | 4.66 | 2.5 | 2.28 | |||||||
df = Degrees of freedom. †
Means, standard deviation, and bivariate correlation of the study variables
| Mean | SD | 1 Age | 2 Duration of smoking habit | 3 Anxiety | 4 Depression | 5 Cigarette Nicotine | 6 consumption dependence | |
|---|---|---|---|---|---|---|---|---|
| 37.5 | 9.99 | |||||||
| 20.5 | 9.98 | |||||||
| 6.5 | 3.43 | |||||||
| 5.1 | 3.45 | |||||||
| 11.9 | 2.56 | |||||||
| 3.7 | 2.56 | |||||||
*Bivariate correlation analysis was performed using a Spearman correlation
Predictors of cigarette consumption and nicotine dependence
| Cigarette consumption | Nicotine dependence | |||||||
|---|---|---|---|---|---|---|---|---|
| SE( | 95%CI ( | SE( | 95%CI ( | |||||
| Female sex | –0.626 | 3.057 | –6.68, 5.43 | 0.838 | 0.756 | 1.022 | –1.27, 2.78 | 0.461 |
| Age Age and anxiety variables as a continuous data into the model. | 0.155 | 0.061 | 0.03, 0.276 | 0.012 | 0.036 | 0.020 | –0.005, 0.08 | 0.082 |
| Smoking household member Dummy variable (reference with “no”) | 2.047 | 1.669 | –1.26, 5.35 | 0.222 | 1.208 | 0.558 | 0.10, 2.31 | 0.032 |
| Anxiety | 0.168 | 0.177 | –0.18, 0.52 | 0.344 | 0.309 | 0.059 | 0.19, 0.43 | <0.001 |
| UM/EM Dummy variable (reference with “Poor metabolizers”) | 3.929 | 1.821 | 0.32, 7.53 | 0.033 | 0.977 | 0.609 | –0.23, 2.18 | 0.111 |
| IM Dummy variable (reference with “Poor metabolizers”) | 2.127 | 1.988 | –1.81, 6.06 | 0.287 | 0.970 | 0.665 | –0.35, 2.29 | 0.147 |
Model 2 was adjusted for gender, age, and smoking household member. | ||||||||
| Anxiety and UM/EM Dummy variable (reference with “Poor metabolizers”) | 0.363 | 0.167 | 0.03, 0.69 | 0.031 | 0.258 | 0.057 | 0.15, 0.37 | <0.001 |
| Anxiety and IM Dummy variable (reference with “Poor metabolizers”) | 0.049 | 0.20 | –0.35, 0.44 | 0.808 | 0.285 | 0.068 | 0.15, 0.42 | <0.001 |
Model 3 was adjusted for gender, age, and anxiety. | ||||||||
| Smoking household member | ||||||||
| Smoking and UM/EM Dummy variable (reference with “Poor metabolizers”) | 4.127 | 1.408 | 1.34, 6.14 | 0.004 | 1.427 | 0.471 | 0.49, 2.36 | 0.003 |
| Smoking household member | ||||||||
| Smoking and IM Dummy variable (reference with “Poor metabolizers”) | 1.574 | 1.686 | –1.76, 4.91 | 0.352 | 1.293 | 0.563 | 0.18, 2.41 | 0.024 |
Model 4 was adjusted for gender, smoking household member, and anxiety. | ||||||||
| Age and UM/EM Dummy variable (reference with “Poor metabolizers”) | 0.149 | 0.039 | 0.07, 0.23 | <0.001 | 0.037 | 0.013 | 0.01, 0.06 | 0.005 |
| Age and IM Dummy variable (reference with “Poor metabolizers”) | 0.082 | 0.046 | –0.01, 0.17 | 0.071 | 0.031 | 0.015 | 0.001, 0.06 | 0.043 |
UM/EM, ultrarapid/extensive metabolizers; IM, intermediate metabolizers.
In model 1, the predictors for cigarette consumption were age and UM/EM. Older age was significantly associated with greater cigarette consumption. UM/EM was also associated with greater cigarette consumption. Model 2 presented the associations of gene and anxiety on cigarette consumption after adjusting for sex, age, and a smoking household member. UM/EM and anxiety were associated with greater cigarette consumption. In Model 3, after adjusting for sex, age and anxiety, UM/EM, and a smoking household member, were associated with greater cigarette consumption. In Model 4, after adjusting for sex, a smoking household member, and anxiety, UM/EM, and age were associated with greater cigarette consumption.
In Model 1, the predictors for a greater degree of nicotine dependence score were age, anxiety, and a smoking household member. By contrast, metabolizer groups were not significantly associated with the degree of nicotine dependence. However, in Model 2 where the associations of genes and anxiety on the degree of nicotine dependence were examined, after adjusting for sex, age, and smoking household member, UM/EM and anxiety, and IM and anxiety were associated with a greater nicotine dependence score. In Model 3, after adjusting for sex, age, and anxiety, UM/EM and smoking household member, and IM and smoking household member were associated with an increased in nicotine dependence score. In Model 4, after adjusting for sex, smoking household member, and anxiety, UM/EM and age were associated with a greater degree of nicotine dependence indicated by a higher nicotine dependence score.
We found that the proportion of UM/EM in participants was 60%. This is higher than that of the previous study in Thailand, where the prevalence of UM/EM group in Thai smokers was 37.5% [26]. This higher proportion of UM/EM in Thai Adult smokers in our study probably reflects that all of our participants were current smokers, while data of the previous study was collected on general population. Associations between UM/EM and cigarette consumption were found. This finding is consistent with a previous study that found that the nicotine and cotinine clearance in UM/EM smokers is faster than that by IM or PM and might lead to higher cigarette consumption [6-9, 27]. However, CYP2A6 polymorphisms were not a predictor of the degree of nicotine dependence.
Previous studies described that female sex is associated with a higher rate of nicotine biotransformation compared with male sex, leading to higher rate of withdrawal symptoms [28, 29]. Consistent with this, our study found that female smokers had an average degree of nicotine dependence score that was higher than that for male smokers.
To our knowledge, the associations between UM/ EM and age, anxiety, and environmental factors were associated with cigarette consumption and nicotine dependence. The effect of the associations between UM/EM and a smoking household member was the strongest predictor for greater cigarette consumption and the degree of nicotine dependence. This might be the consequence of genetic variations and the individual habits of smokers in their family environment. Environmental factors highly influence the initiation of smoking by adolescents and lead to persistent smoking and resulting nicotine dependence [19, 30]. Determining associations between genetic variation and environmental factors is important knowledge for health care professionals in all areas. Prevention of cigarette smoking in a home environment can reduce the number of new smokers and might also be a strategies for smokers who are attempting to quit smoking. Apart from environmental factors, the associations between UM/EM and anxiety were also associated with cigarette use, as similarly found in a previous study that described associations between anxiety and nicotine dependence. The relationship between anxiety, depression, cigarette consumption, and nicotine dependence were studied as co-occurring mental health disorders. Anxiety and depression may be associated with an increase in cigarette consumption as a self-medicating behavior in order to cope with their stress [10, 12, 13]. Moreover, one study found that cigarette consumption may play a role in the onset of mental illness [31].
Information on the association of genes, anxiety, and their interactions may be of benefit for selecting available treatments in smoking cessation programs in regard to individual CYP2A6 genotypes. A urine testing method to determine CYP2A6 genotypes may be practical for physicians in the clinic in considering the benefit of various treatments for smokers. Nonetheless, health care providers should still consider the important role of psychological counseling, relaxation techniques, or cognitive behavior therapy to help their smoking patients to cope with anxiety associated with nicotine dependence.
Our participants were current smokers who visited for their annual medical check-up. This means they might have a physical or health status that differs from smokers in other settings. The relatively small sample size may also have led to difficulty in identifying some predictors of cigarette use. In addition, we used a candidate gene approach in this study and only one gene was selected. However, we might be able to generalize the results of the association between their CYP2A6 genotype and cigarette smoking to the Thai population. A further study with more intensive genotyping including genome-wide associations study may provide better insight into the influence of genetic variation on nicotine use and dependence.