In this study, we investigated the association between substance use (i.e. cigarettes, alcohol and cannabis) and COMT gene methylation in the MB‐COMT promoter [previously studied by (Xu et al. 2010 )], as well as the S‐COMT promoter (not studied previously). We used DNA from a large general population sample of adolescents (14–18 years). Given the lack of studies so far, we carefully hypothesized that COMT gene methylation will not only be associated with tobacco and alcohol use but also with cannabis use. Given the seemingly contradictory findings on COMT genotype and COMT gene methylation (increased activity vs. lower expression of COMT in substance users), an interplay between the two may be present, with indirect oppositional effects on dopamine levels. Therefore, we explored whether the association between COMT gene methylation and substance use depended on the COMT Val 108/158 Met polymorphism.

While COMT genotypes influence COMT activity, epigenetic modifications (e.g. DNA methylation) of the COMT gene may affect gene expression. Indeed, increased COMT gene methylation was associated with decreased gene expression (Abdolmaleky et al. 2006 ; Sasaki et al. 2003 ), but very little is known about the association between COMT gene methylation and substance use. In the only general population study we know of, nicotine dependence was related to higher MB‐COMT promoter methylation, suggesting lower COMT gene activity and thus less dopamine degradation in smokers (Xu et al. 2010 ). In schizophrenia patients, alcohol use was associated with increased MB‐COMT promoter methylation (Abdolmaleky et al. 2006 ). Although studies on genetic variation suggest COMT hyperactivity in substance users, these first epigenetic results indicate lower COMT gene activity in substance users. No studies have yet investigated the relationship between cannabis use and COMT gene methylation.

The COMT gene (chr:22, q11.21; Grossman et al. 1992 ) encodes two different protein isoforms, each with its own promoter (Tenhunen et al. 1994 ): the membrane‐bound isoform ( MB‐COMT , 271 amino acids), and the soluble isoform ( S‐COMT , 221 amino acids). The functional Val 108/158 Met single nucleotide polymorphism (SNP) in the COMT gene, rs4680, has been associated with altered COMT activity (Lachman et al. 1996 ). The Val/Val genotype results in a threefold to fourfold increase in COMT activity and was more prevalent in substance users (Beuten et al. 2006 ; Horowitz et al. 2000 ; Li et al. 2004 ; Redden et al. 2005 ; Vandenbergh et al. 1997 ), albeit not consistently (Tammimaki & Mannisto 2010 ). These findings indicate a higher COMT activity, hence faster dopamine degradation, in substance users, which arguably is associated with a drive for constant activation of the reward system.

Substance use (i.e. alcohol, cigarettes or cannabis) often starts in adolescence. Prolonged use can lead to poor health, and detrimental social and economic outcomes (Macleod et al. 2004 ). The dopaminergic reward system plays an important role in substance use and addiction (Robinson & Berridge 1993 ). Frequent substance use is associated with altered dopamine levels in the brain reward system (Wanat et al. 2009 ). Catechol‐ O ‐methyltransferase (COMT) degrades dopamine, and variations in COMT expression and activity could modify reward system functioning, thereby influencing vulnerability to substance use.

As Xu et al. demonstrated, CpG‐site‐specific associations of the MB‐COMT promoter with nicotine dependence (for overlap with CpG units in this study, see Table S1/Fig. S1, Supporting Information), we tested whether methylation rates differed between substance‐use categories for individual CpG units using multinomial logistic regression, with non‐substance users as reference category. For these exploratory analyses, we adjusted for multiple testing using the Bonferroni method. The new P ‐value regarded as significant was 0.0045.

To test whether substance use was associated with methylation rates of the MB‐COMT or S‐COMT promoter regions, we used multinomial logistic regression analyses. The group of non‐substance users was used as reference group in all analyses. In addition, we tested whether the interaction between the COMT Val 108/158 Met genotype and methylation was significantly associated with substance use. If this was the case, analyses were stratified by COMT genotype.

Descriptives were computed and analysis of variation (anova) was performed to compare S‐COMT/MB‐COMT promoter methylation rates between different genotypes. We used multinomial logistic regression to study the association between COMT genotype and substance use, using the Met/Met genotype (with the lowest enzyme activity) as the reference category. To avoid loss of power when comparing different substance‐use categories, we did not limit our sample to individuals with methylation data, but we used the genotype data of the 1411 TRAILS subjects.

The COMT Val 108/158 Met SNP (rs4680) genotyping was performed on the Illumina BeadStation 500 platform (Illumina, Inc., San Diego, CA, USA) using Golden Gate assay and array technology [for details, see (Nijmeijer et al. 2010 ; Stavrakakis et al. 2013 )]. Data on the Val 108/158 Met genotype (Val/Val, Val/Met or Met/Met) was available for 1411 of the TRAILS subjects, of whom 452 had complete data on both genotype and methylation rates (Table 1 ). The lower number available for methylation analyses resulted from a pre‐selection of subjects (see above). The genotyping call rate for rs4680 was 100%. A χ 2 test confirmed that rs4680 was in Hardy–Weinberg equilibrium ( P = 0.92).

All samples were analyzed in triplicate and for each CpG unit, methylation rates of the triplicates were averaged (van der Knaap et al. 2014 ). Samples with an SD of ≥10% between replicates were removed for analysis. CpG units with ≥25% missing values were not included in the analyses (two CpG units, CpGU2 and CpGU3, in the S‐COMT promoter and one CpG unit, CpGU16, in the MB‐COMT promoter). We accounted for mass change in CpG units by SNPs (only when minor allele frequency >5%) by removing CpG units containing SNPs from analyses [one CpG unit ( S‐COMT promoter, CpGU7)], and by removing units with the same mass as non‐CpG units containing SNPs or other CpG units containing SNPs (none in our sample). In total, 11 CpG units were available for the MB‐COMT promoter region and 5 CpG units were available for the S‐COMT promoter region.

DNA methylation rates were analyzed using the EpiTYPER method from Sequenom. Bisulfite conversion was followed by PCR (polymerase chain reaction) amplification, reverse transcription and base‐specific cleavage. Fragments were analyzed on a mass spectrometer (Sequenom EpiTYPER, San Diego, CA, USA). Bisulfite conversion of DNA was performed using EZ‐96 DNA Methylation Kit (Shallow; Zymo Research, CA, USA), according to the manufacturers' protocol. Polymerase chain reaction, reverse transcription, cleavage and mass spectrometry were performed in triplicate, according to EpiTYPER protocol. The mass signal patterns generated were translated to quantitative methylation rates for different CpG units by the MassARRAY EpiTYPER analyzer software from Sequenom (v1.0, build1.0.6.88; Sequenom, Inc., San Diego, CA, USA). Fragments with CpG dinucleotides are referred to as CpG units. One CpG unit can contain one or more CpG dinucleotides. CpG units with a mass outside the range of the mass spectrometer, or with overlap in mass of another CpG unit, could not be analyzed ( MB‐COMT : seven CpG units, S‐COMT : six CpG units).

Substance use was assessed with a self‐report questionnaire at T3, which was filled out at school or at the subjects' home. Confidentiality of the study was important and adolescents were reassured that their parents or teachers would not have access to the information they provided. Smoking was assessed with the question: ‘How many cigarettes did you smoke in the past 4 weeks?’ Adolescents who had not smoked in the past 4 weeks were categorized as non‐smokers. Adolescents who had smoked less than one cigarette a day in the past 4 weeks were categorized as non‐daily smokers and those who had smoked one or more cigarettes per day as daily smokers (Harakeh et al. 2012 ). Cannabis use was assessed with the question: ‘How many times have you used weed (marijuana) or hash in the past 4 weeks?’ Adolescents who had not used cannabis in the past 4 weeks were categorized as non‐users. Adolescents who had used up to four times were categorized as low‐frequent users and those who used more than four times as high‐frequent users (Creemers et al. 2011 ). Alcohol use was assessed with the question: ‘How many times have you had alcohol in the past 4 weeks? By this, we mean the number of occasions, such as going to a party, going out or an evening at home’. Adolescents who reported that they had not drunk alcohol in the past 4 weeks were categorized as abstainers. Adolescents who reported drinking were categorized into two groups: those who had drunk alcohol up to 9 times were defined as low‐frequent users and those who had drunk alcohol 10 times or more were defined as high‐frequent users (Creemers et al. 2011 ).

This study was part of the TRacking Adolescents' Individual Lives Survey (TRAILS), a prospective population study in which Dutch preadolescents ( N = 2230) are followed into adulthood. Assessment waves, involving interviews, biological measures and validated questionnaires, are conducted biennially or triennially, and five assessment waves have been completed so far. This study involves data collected during the third assessment wave, which took place from September 2005 to December 2007 ( N = 1816, mean age 16.3 years, SD = 0.71). Written consent was obtained from each subject and their parents at every assessment wave. The study was approved by the Dutch Central Medical Ethics Committee (CCMO), and all subjects received compensation for their participation. A detailed description of sampling and methods can be found in Huisman et al. ( 2008 ) and Ormel et al. ( 2012 ). In short, the assessment at T3 included an extensive experimental session, in which 715 adolescents participated (focus sample, response rate 96.1%). Adolescents with a higher risk of mental health problems had a greater chance of being selected for the experimental session. Risk was defined based on T1 measures of temperament (high frustration and fearfulness, low effortful control), lifetime parental psychopathology, and living in a single‐parent family. In total, 66.0% of the focus sample had at least one of the above‐described risk factors; the remaining 34.0% were selected randomly from the ‘low‐risk’ TRAILS participants. Although ‘high‐risk’ adolescents were slightly oversampled, the sample included the total range of mental health problems present in a community population of adolescents. T3 also involved a blood draw. Selection for methylation analyses ( N = 475) was based on the participation in the extensive experimental session, availability of a blood sample with sufficient DNA concentration, Dutch ethnicity and we randomly excluded one of each sibling pair. This selection of 475 adolescents did not differ significantly ( P > 0.05) from the TRAILS focus sample ( N = 715) with regard to sex, socioeconomic status and age. Following dropout after methylation analyses (for further explanation, see section on DNA methylation ), we obtained MB‐COMT promoter methylation rates for 458 subjects and S‐COMT promoter methylation rates for 463 subjects.

When included in our model, the interaction term ‘COMT genotype × MB‐COMT promoter methylation ’ was associated with cannabis use (Table 3 ). Therefore, we stratified the analyses for methylation and cannabis use by COMT Val 108/158 Met genotype. In adolescents with the Met/Met genotype, methylation rates were associated with lower odds of high‐frequent cannabis use (OR = 0.25, 95% CI = 0.08; 0.82, P = 0.02). The interaction term ‘COMT genotype × MB‐COMT promoter methylation ’ was not associated with smoking or alcohol use, and we also did not find any significant association between the interaction term ‘COMT genotype × S‐COMT promoter methylation ’ and substance use.

In the individual CpG unit analyses (Fig. 3 ), we found that MB‐COMT promoter methylation in CpG unit 3 (OR = 1.17, 95% CI = 1.05;1.30, P = 0.004) and CpG unit 9 (OR = 1.64, 95% CI = 1.06; 2.52, P = 0.03) was associated with non‐daily smoking, although only the effect for MB‐COMT ‐promoter CpGU3 remained significant after correction for multiple testing. There were no associations between methylation rates of single CpG units and cannabis use. For alcohol use, we found that MB‐COMT promoter methylation was associated with low‐frequent alcohol use in CpGU5 (OR = 0.76, 95% CI = 0.58; 0.995, P = 0.046) and in CpGU19 (OR = 0.79, 95% CI = 0.64; 0.98, P = 0.03). These associations were not significant after correction for multiple testing. No effects were found for S‐COMT promoter methylation.

As shown in Table 2 , MB‐COMT promoter methylation was associated with non‐daily smoking, but not with daily smoking. MB‐COMT or S‐COMT promoter methylation were not associated with cannabis use or alcohol use (Table 2 ). Because of differences in S‐COMT promoter methylation according to COMT genotype (described above, Fig. 2 ), we added genotype as a covariate in the analyses with S‐COMT promoter methylation. This did not change the relationships between S‐COMT promoter methylation and substance use.

Val/Val adolescents were less likely to be low‐frequent cannabis users [odds ratio (OR) = 0.57, confidence interval (CI) = 0.32; 1.01, P = 0.06] or high‐frequent cannabis users (OR = 0.45, CI = 0.21; 0.96, P = 0.04) than non‐users, compared with adolescents with the Met/Met genotype . The Val/Met genotype did not have significantly different odds of cannabis use compared with the Val/Val genotype. There was no significant association between COMT genotype and smoking or alcohol use.

Discussion

This is the first study in which the association between COMT gene methylation and adolescents' substance use is analyzed. We found higher methylation rates in non‐daily smokers compared with non‐smokers and daily smokers. Also, in adolescents homozygous for the Met allele, methylation was associated with lower odds for cannabis use.

Higher rates of MB‐COMT promoter methylation were associated with non‐daily smoking in adolescents. It is difficult to explain why no association with daily smoking was found. We could speculate that specific, currently unknown, regulation mechanisms are at work linking methylation of the MB‐COMT promoter with non‐daily smoking, which represents a more controlled form of smoking in adolescents. In the study by (Xu et al. 2010), no association between daily smoking and overall MB‐COMT promoter methylation was found. However, differences in methylation between their daily smokers and controls became apparent when testing individual CpG sites: methylation rates were higher in daily smokers compared with non‐smokers at CpG sites ‐193 and ‐39, which correspond with CpGU3 and CpGU12 in this study (Table S1/Fig. S1). Interestingly, in the unit specific analyses for smoking, we also found a higher methylation of CpGU3, but specific for non‐daily smoking. We did not find differences in methylation of CpGU12 between the smoking groups. It is possible that during adolescence the relationship between methylation and smoking status is different from that later in life. The rate of methylation in the MB‐COMT promoter in this study was relatively low compared with the methylation rates reported by (Xu et al. 2010), possibly due to chronic heavy smoking. Or, as DNA methylation rates increase with age (Horvath 2013), particularly in CpG islands (Johansson et al. 2013), differences in methylation rates may reflect differences in age between the samples (∼16‐year‐old adolescents vs. ∼45‐year‐old adults). Our findings and those from Xu et al. show that COMT gene methylation is associated with smoking status, but also raise many questions concerning the exact relationship. As these are the first studies in this area, this should not be surprising and obviously further research is necessary to gain insight into smoking habits and COMT gene methylation. Longitudinal studies with repeated measures of both methylation and smoking habits will be necessary to further increase our understanding of how both interrelate.

Although other studies have found relationships between methylation of genes in the dopamine system and alcohol dependence, e.g. higher rates of methylation in the dopamine transporter gene (Hillemacher et al. 2009) and methylation of monoamine oxidase‐A (Philibert et al. 2008), we did not find a relationship between mean MB‐COMT or S‐COMT promoter methylation and alcohol use in adolescents. Neither did we find associations between methylation and alcohol use in our unit‐specific analyses. We are not aware of any other study relating methylation of the COMT gene to alcohol use in adolescents.

In this study, the Val/Val variant was associated with lower odds of high‐frequent cannabis use in adolescents. A recent meta‐analysis of the association between the COMT Val108/158Met polymorphism and substance use identified the Val‐allele as risk factor for smoking and for cannabis use (Tammimaki & Mannisto 2010), but the populations studied were highly heterogeneous. Adolescence is a phase in which novelty‐seeking, impulsivity and peer behavior may play a major role in the initiation of substance use (Brook et al. 2011; Marschall‐Levesque et al. 2014; Teichman et al. 1989). In line with this theory are findings from studies that have linked the Met/Met variant to increased novelty‐seeking, which could drive cannabis use in adolescents (Demetrovics et al. 2010; Golimbet et al. 2007; Hosak et al. 2006). Another study associated the Val/Val variant with novelty‐seeking (Lang et al. 2007). The relationship might be dependent on genetic variations in other genes in addition to the COMT polymorphism (Strobel et al. 2003) and might be moderated by personality, stress or other environmental factors.

We found that adolescents with the Met/Met genotype and higher methylation rates had a lower risk for cannabis use. Hence, there seems to be an interaction between the COMT Val108/158Met polymorphism and MB‐COMT promoter methylation rates in relation to cannabis use. This is a novel finding which is in line with the anhedonia hypothesis of substance use (Wise 1978).The combination of the low enzyme activity (Met/Met genotype) and reduced expression of the enzyme (higher methylation rates) might result in higher levels of dopamine through diminished dopamine degradation. It is known that low brain dopamine levels result in an under‐active reward system, accompanied by anhedonia. Substance use could be explained as an attempt to alleviate this unfavorable anhedonic state (Markou & Koob 1991; Volkow et al. 2010). Arguably, individuals with a combination of the low enzyme activity Met/Met genotype have high dopamine levels – and do not have an anhedonic state – which might be preventive for substance use.

A strength of this study is the measurement of both genetic and epigenetic variations of the COMT gene, which provides a more complete picture of the role of COMT in substance use. In addition, we analyzed several types of substance use that are highly prevalent in adolescence and assessed recent use to minimize recall bias, thereby gaining reliable measures for substance use. Some limitations of our study have to be noted as well. The cross‐sectional nature of this study prevented us from investigating whether methylation is a consequence of substance use, or predisposes an individual to drug‐seeking behavior, a question with no definitive answer in the literature thus far (Nielsen et al. 2012). To this end, repeated measurements of methylation status are needed. This paper includes a multiplicity of comparisons, which increases the risk of obtaining chance findings. This is especially relevant for the analyses of the single CpG unit data. To minimize this risk, we applied a Bonferroni correction. However, for the analyses including methylation data, genotypes and substance use, we did not correct for multiple testing. Therefore, we were cautious with interpreting our findings and would like to emphasize that replication is warranted. It should be noted that we studied adolescents who have had a relatively short exposure to substance use. Associations may be stronger in adults who have developed a substance addiction earlier in life or have a more intense and longer history of use. Adolescents in this study may still be experimenting with drugs, and this may be motivated by different brain mechanisms than drug addiction. We were interested in methylation of the COMT gene in the brain, but as this is impossible to determine in a cohort study of adolescents, we used DNA from blood cells to determine methylation rates. This is probably a valid approach as identical methylation patterns for the COMT gene in blood and the brain were reported previously (Murphy et al. 2005), which indicates that COMT gene methylation in blood may be used as a proxy for COMT gene methylation in brain.

To conclude, we showed that methylation of the MB‐COMT promoter was associated with non‐daily smoking in adolescents. This study further suggests that epigenetics, in combination with the COMT Val108/158Met polymorphism, could be associated with cannabis use during adolescence. Maybe through altering COMT activity and gene expression, and thereby influencing the dopamine metabolism in the brain. However, this finding warrants replication in other populations, including adults and individuals who are addicted to substances. The findings of the study may also provide a first step in the prevention of substance‐use disorders. Epigenetic modifications may prove to be useful biomarkers to identify susceptibility or vulnerability for substance use, and, in time, our findings may even contribute to the development or improvement of effective behavioral or pharmacological interventions for substance‐use disorders. In order to obtain more insight into the mechanisms involved in substance use and abuse, it may be helpful to include both genetic and epigenetic factors.