Influence of catechol-O-methyltransferase enzyme gene polymorphism on alcohol and tobacco consumption in North Indian treatment seeking population.

BACKGROUND: The co-occurrence of alcohol and tobacco dependence is frequently witnessed in treatment settings. It is a challenge for clinicians to treat such patients due to their powerful biological association. AIM: The study is aimed to assess the relationship of Catechol-O-methyltransferase (COMT) Val158Met polymorphism with substance intake among individuals who are dependent on both alcohol and tobacco.
MATERIALS AND METHODS: A cross-sectional study involving patients coming to the outpatient department was planned. Brief information on their sociodemographic and substance use profile was recorded. Genotyping of COMT Val158Met was carried out using established polymerase chain reaction-restriction fragment length polymorphism method. The COMT genotyping was classified based on the presence or absence of Met allele using the dominant model. Descriptive statistics, Chi-square test, Mann-Whitney test, and Binary logistic regression analysis were performed to analyze the data.
RESULTS: The study included 104 alcohol and nicotine co-dependent subjects. More than eighty percent of the participants were educated above secondary level, married, and employed. The allele frequencies of met and Val were found to be 0.23 and 0.77, respectively. Forty percent of the participants reported tobacco-related health problems. The odds of consuming alcohol and nicotine were four times high among Met allele carriers. While the Fagerström test for nicotine dependence and heaviness of smoking index scores were up to four and eight times higher among met allele (odds ratio 4.3 and 8.9, respectively).
CONCLUSION: Patients carrying Met allele are reported to consume higher amounts of alcohol and tobacco and were likely to score high among measures of nicotine dependence. Thus met allele carriers needs additional attention for a successful treatment outcome. Copyright:

INTRODUCTION

Alcohol and tobacco are the two most commonly used licit substances which contribute to the global burden of diseases.[1] Tobacco use among alcohol-dependent (AD) people is very common and it has a major effect on morbidity.[2] In India, alcohol is associated with significant health and economic impact.[3] Interestingly, compared to the global average, fewer people consume alcohol in India but a disproportionate number suffer from alcohol use disorders. The recent Indian national survey indicates that among the age group of 10–75 years, about 14.6% (16 crore) of the general population uses alcohol, while about 2.7% (2.9 crore) individuals use alcohol in a dependent pattern.[4]

The use of alcohol and tobacco are co-morbid with potent biological associations between their co-dependence.[5] ADs are three times more vulnerable to use tobacco while nicotine dependents (ND) are four times more vulnerable to become AD as compared to the general population.[6] Similarly, AD smokers may find it more difficult to quit smoking than AD nonsmokers.[7] This co-occurrence poses a clinical dilemma whether both substance problems should be treated together or separately, as quitting one substance may negatively impact the intervention of the other.[8] The comorbid AD and ND is a complex process influenced by several psychosocial, environmental, and genetic factors.[9]

Both alcohol and nicotine mediate their action through the brain dopamine reward pathway. Catechol-O-methyltransferase (COMT) metabolizes dopamine and minimizes its load in the postsynaptic neurons. COMT gene contains a well-studied single-nucleotide polymorphism that results in amino acid change valine to methionine at codon 158. The presence of the met allele results in lower metabolic activity and stability of the enzyme.[10] The lower rates of metabolism for the Met allele result in higher synaptic dopamine levels following neurotransmitter release.[11]

Literature review illustrates the association of Met allele with a relatively higher risk for alcohol use.[1213] While another study observed Met allele to be protective towards ND in African-American or European-American population.[14] Some recent Indian studies highlighted the risk conferred by COMT polymorphism (Met allele) toward AD.[1516] Converging evidence suggests Met allele individuals with reduced COMT activity were at higher risk to develop AD. Early reports from a similar setting found an association of ankyrin repeats and kinase domain containing 1 gene polymorphism with higher consumption of alcohol and nicotine.[17] The available literature pointed toward a dearth of information over the influence of COMT gene Val158Met on alcohol and tobacco consumption in the Indian population. This study was planned to investigate the influence of COMT gene polymorphism Val158Met among North Indian Treatment seeking alcohol and nicotine-dependent participants.

MATERIALS AND METHODS

Study subjects and design

A cross-sectional study was conducted at a leading drug dependence treatment center in North India. Patients seeking treatment from the out-patient department, primarily for alcohol use disorder during the year (2013–2014) were included in the study. Inclusion criteria were (i) Males between 18 and 65 years from North India (ii) Fulfilling Diagnostics and Statistical Manual of Mental Disorder IV criteria for ND and AD.[18](iii) Use of a minimum of 10 cigarettes/day in the past 12 months (iv) Not current poly-substance user (other than tobacco and alcohol) (v) No major physical or mental health problem (vi) Willing to participate in the study. Written informed consent was obtained from all the participants. The study protocol was approved by the institutional ethical committee.

After inclusion, the following data were recorded from each subject using self-report (i) Socio-demographic profile including age, sex, marital status, employment, and education (ii) Substance use detail, i.e., alcohol and tobacco use duration and amount (iii) Other details such as medical history and medical comorbidity.

Tobacco and alcohol consumption

Information on the average amount of alcohol and tobacco consumed per day was recorded from all the participants. Consumption of alcohol was collected as the usual amount (milliliters) and type of beverage. The standard measure of alcohol (grams) was calculated as per the reference from Indian beverages.[19] Similarly, tobacco use was reported based on the consumption of cigarettes/beedi (numbers) or smokeless tobacco (pouches) per day. The standard measure of tobacco (milligram) was calculated as per reference on Indian tobacco products.[20] The heaviness of smoking index (HSI) and fagerström test for nicotine dependence (FTND) scores were used to measure the severity of nicotine dependence.[21]

DNA extraction and catechol-O-methyltransferase genotyping

Genomic DNA extraction was carried from 2 ml of EDTA coated venous blood using blood DNA extraction kit (Qiagen, Valencia, CA, USA). COMT genotyping (rs4680) was done using polymerase chain reaction (PCR) restriction fragment length polymorphism published approach.[22] The following primer sequences were used: Forward (5'-TACTGTGGCTACTCAGCTGTGC-3') and reverse (5'-GTGAACGTGGTGTGAACACC-3'). The presence of G or A encoding a valine or methionine at codon 158 was recognized by digestion with restriction endonuclease enzyme (Nuclear Inclusion a III). The digested PCR products were resolved on agarose gel stained with ethidium bromide. Genotype profiles obtained from this were analyzed along with the clinical data obtained from each participant. The researcher performing genotyping of the samples was blinded about the clinical data of the subjects.

Statistical analysis

All the clinical and genotype data were recorded into a Microsoft Excel spreadsheet and the analysis was performed with SPSS version 16 (SPSS Inc., Chicago, IL, USA). The COMT genotyping was classified as the presence (Met/Met or Mel/Val) or absence of Met allele (Val/Val) using the dominant model. Genotypes were tested for Hardy–Weinberg equilibrium using the Chi-square test for differences in the observed and expected frequencies. Demographic and clinical profile were tested for any significant difference based on the allele variants (Met vs. Val) using independent sample t-tests for age and Chi-square test for socioeconomic status and nicotine-related health problems. To present substance use profile the Mann–Whitney test was used due to nonnormal distribution of variables. To test the hypothesis of whether Met allele predicts substance use, binary logistic regression analysis was performed taking each dependent variable (Alcohol and nicotine consumed and FTND and HSI score) one at a time, as per our earlier reports.[17] The independent variable considered was the presence and absence of Met allele as risk and reference allele, respectively (Met 1 and Val 0). This provided estimates of the odds of risk by adjusting with respect to the duration of use.

RESULTS

Genotype and demographic profile

A total of 104 alcohol and nicotine-dependent males were included in the study with a mean age of 37.74 year (standard deviation 9.94). Majority of the participants (more than 80%) were educated, married, and employed with income groups varying from low to medium. The COMT (Met158) genotyping resulted in the presence of Met allele among 79 (76%) (Met/Val: 56 and Met/Met: 23) participants while 25 (24%) had Val allele (Val/Val). The Met and Val allele frequencies were found to be 0.23 and 0.77, respectively. The socio-demographic profile among Met and Val allele revealed no significant differences. The self-reported nicotine-related health problems are present in about 40% in both groups and the differences were nonsignificant [Table 1].

Table 1

Demographic and clinical profile based on the catechol-O-methyltransferase (Val158Met) allele variants (n=104)

Characteristics	Met absence (Val/Val) (n=25), n (%)	Met present (Met/Met or Val/Met) (n=79), n (%)	P (Chi-square test)
Age (years), mean±SD	38.3±11.48	37.6±9.5	0.495†
Marital status
Single	3 (12)	11 (14)	0.822
Married	22 (88)	67 (86)
Education
Illiterate	1 (4)	7 (9)	0.390
Literate	24 (96)	72 (91)
Employment
Employed	25 (100)	68 (86)	0.049
Unemployed	0	11 (14)
Monthly income (₹)
None	1 (4)	16 (20)	0.278
Low-medium (<5000 and above)	24 (96)	63 (80)
Nicotine related health problems
Absent	16 (64)	46 (59)	0.885
Present	9 (36)	33 (41)

†Independent sample t-test. SD Standard deviation

Substance use and dependence measure by catechol-O-methyltransferase (Met158) genotyping

The self-reported alcohol and nicotine use and nicotine dependence profile based on COMT (Met158) genotyping is shown in Table 2. The age of onset of alcohol use was 20.0 (median) years among both the allele (P = 0.589). Duration of alcohol use was marginally increased in the Met group with the quartile range varying from 5 to 17 years. Consumption of self-reported alcohol and nicotine use was significantly higher in the Met carrier group (P = 0.001). The nicotine dependence scores, i.e., FTND and HSI indicate a significant increase in the Met group as compared to the Val allele group (P = 0.001).

Table 2

Association between catechol-O-methyltransferase (Val158Met) allele variants and substance use and dependence profile

Variables	Median (quartile)		P (Mann-Whitney U-test)
	Met absence (n=25)	Met presence (n=79)
Age of onset (years)	20.0 (16.0-25.0)	20.0 (15.0-22.0)	0.589
Duration of alcohol consumption (years)	15.0 (3.0-11.0)	10.0 (5.0-17.0)	0.045
Nicotine consumed (mg)	92.7 (77.2-126.0)	135.0 (104.0-220.0)	0.001
Alcohol consumed (g)	154.8 (77.4-154.8)	322.5 (154.8-322.5)	0.001
FTND	5.0 (4.0-5.0)	6.0 (6.0-7.0)	0.001
HSI	0.0 (0.0-1.0)	2.0 (1.0-3.0)	0.001

FTND Fagerström test for nicotine dependence; HIS Heaviness of smoking index

All the variables like substance use and dependence scores found significant using the Mann-Whitney test were entered in the linear regression model. The odds of consuming higher amounts of alcohol and nicotine were four times high if the individual is carrying a Met allele (odds ratio [OR] 4.0 and 3.9, respectively). While the FTND and HSI scores were up to four and nine times higher with the presence of Met allele (OR 4.3 and 8.9 respectively) [Table 3].

Table 3

Univariate logistic regression analysis for dependent variables based on catechol-O-methyltransferase (Val158Met) allele variants

Variable†	β	SE	P	Ex (B) (OR)
Nicotine consumed	1.372	0.519	0.008	3.94
Alcohol consumed	1.399	0.530	0.008	4.05
FTND	1.478	0.346	0.001	4.38
HSI	2.189	0.659	0.001	8.93

†Adjusted for the duration of use. SE Standard error; OR Odds ratio; FTND Fagerström test for nicotine dependence; HIS Heaviness of smoking index

DISCUSSION

This study examined the possible association of COMT enzyme gene polymorphism (Val158Met) among alcohol and nicotine-dependent treatment seekers from North India. The socio-demographic profile indicates that eighty percent of the participants were educated below the secondary level, married, and employed with low or medium monthly income. This demographic profile was found to be similar to the earlier reports.[1723]

It was interesting to note that tobacco-related health problems were observed in about forty percent of the participants in both Met and Val allele groups without any significant difference. The account of self-reported health problems is subjective and is not corroborated clinically. Earlier evidence indicates the presence of nicotine-related health problems in 40% of the alcohol and nicotine co-dependent participants suggesting a sub-set of patients requiring additional attention.

Results of COMT (Val158Met) genotyping revealed the frequencies of Met and Val allele to be 0.23 and 0.77, respectively. Met and Val allele frequencies as observed earlier in AD populations from North Indians[1516] and European Caucasians[12] were in accordance with the present study. The substance use profile indicated the consumption of higher amounts of alcohol and nicotine among Met carrier (OR 4.0 and 3.9, respectively) as compared to Val carriers. Met allele leads to higher weekly consumption in social drinkers among.[24] and implicit drinking behavior among undergraduates students,[13] while a recent study advocates co-occurrence of COMT Met and BDNF Val allele to predict the risk of increased alcohol consumption among AD population.[12] The presence of COMT Met variants results in the expression of lower enzyme activity[25] leading to higher dopamine levels within the brain. It has been suggested that individuals with Met allele may experience a long-lasting and effective release of dopamine in the brain, thus experiencing a more rewarding effect after alcohol and nicotine use.

The nicotine dependence scores FTND and HSI were suggested to have a genetic basis, both measures the severity of physical dependence and predicts cessation rate.[26] The presence of the Met allele was associated with increased FTND and HSI scores (OR 4.3 and 8.9 respectively). Higher FTND and HSI scores were observed earlier among nicotine users alone[26] or with co-dominance of alcohol use.[1723] These results build the growing evidence base about genetic vulnerability underlying concurrent alcohol and tobacco dependence.

The limitation of the present study was that it was conducted among a treatment-seeking population making generalization difficult. The use of alcohol and tobacco was assessed only by the self-report, which may be associated with recall bias. The sample size was rather modest and hence the future studies may be conducted with more participants from diverse settings.

CONCLUSION

Treatment-seeking men carrying Met allele of COMT gene reported consuming higher amounts of alcohol and tobacco. They are likely to score higher on measures of nicotine dependence. The comorbid alcohol and nicotine dependence is a commonly observed problem in a treatment setting. Thus for a successful treatment outcome individuals with the Met allele need additional attention while planning an intervention.

Financial support and sponsorship

This study was supported by National Drug Dependence Treatment Centre, All India Institute of Medical Sciences, Delhi.

Conflicts of interest

There are no conflicts of interest.