Assessment of the effects of CNR1, FAAH and MGLL gene variations on the synthetic cannabinoid use disorder

Introduction

Synthetic cannabinoids (SC) are human-made, mind-altering, hallucinogenic substances that mimic the effects of Δ⁹-tetrahydrocannabinol (Δ⁹-THC), the main psychoactive ingredient of the Cannabis plant. These chemicals are the largest group of new psychoactive substances (NPS) that are being monitored by the European Union Early Warning System (EWS) [1]. SC has become a global threat since it began to replace cannabis, which continues to be the most widely used illicit drug worldwide [2].

Recently, SC is becoming common among young individuals in Turkey. According to the Turkish Drug Report published by the Turkish Monitoring Centre for Drugs and Drug Addiction (TUBIM), there has been a dramatic increase in the number of events associated with SC in recent years, and SC use is considered within the frame of “High-Risk Drug Use (HRDU).” SC was detected in 59.9 and 45.8 % of drug-related mortality in 2017 and 2018, respectively, in Turkey [3].

SC are known as “Bonzai” or “Jamaika” in Turkey, “Spice” in Europe, and “K2” in the United States [4]. These substances have gained a reputation among users due to their intensified psychostimulant effects than cannabis. Furthermore, they have been mislabeled as legal alternatives; as a result, some users use them specifically for this reason [5]. Another handicap is the increasing number of SC derivatives (more than 280), making monitoring difficult [6]. SC’s detailed pharmacological and toxicological effects have not been fully elucidated yet. But, similar to Δ⁹-THC, SC binds to cannabinoid receptors in the endocannabinoid system [7].

The endocannabinoid system (ECS) is a complex neuromodulator signaling system implicated in various physiological processes such as mood, pain, and appetite. Moreover, ECS is thought to play a critical role in substance abuse and addiction, e.g., modulating the rewarding effects of cannabinoid and non-cannabinoid drugs [8]. Although dopaminergic and opioidergic pathways constitute the main components of the classical reward pathway, ECS signaling is also involved in this process, particularly via interaction with dopaminergic and opioidergic circuits [9].

Currently, two cannabinoid receptors that mediate ECS signaling are well-characterized: Cannabinoid receptor 1 (CB₁) and Cannabinoid receptor 2 (CB₂). CB₁ is mainly distributed throughout brain regions important for reward function, including the hippocampus, striatum, and cerebral cortex, while CB₂ is expressed in immune cells, spleen, and hematopoietic cells [8]. CB₁ is encoded by the CNR1 gene located on human chromosome 6 q14-15. CB₁ receptors modulate dopaminergic activity in the midbrain associated with substance abuse, depression, and anxiety. Increasing evidence showed that functional polymorphisms in the CNR1 gene are associated with substance abuse [10]. Lately, it has been demonstrated that CB₂ is also expressed in neuronal and glial cells in physiological and pathological conditions and might also be associated with substance abuse [11].

Fatty acid amide hydrolase (FAAH) is a mammalian enzyme that inactivates various endogenous ligands of CB₁, primarily anandamide (AEA). Studies on animal models suggest that FAAH inhibition changes the brain’s dopamine levels [12]. These findings suggest that various mutations or variations in the FAAH gene alter the brain’s reward function by disrupting endocannabinoid signaling and modulating substance abuse risk [13].

Monoacylglycerol lipase (MGLL) is an ECS enzyme primarily responsible for 2-arachydonoylglycerol (2-AG) degradation. Studies have found that the increased MGLL function and decreased 2-AG levels are associated with higher stress, which might lead to stress-induced anxiety and, ultimately, cannabis use behavior [14]. In line with these data, MGLL inhibition in rats alleviates cannabis, opioid, and nicotine withdrawal symptoms [15], [16], [17]. Moreover, the 3rd chromosome region where the MGLL gene is located was found to be associated with cannabis use disorder [18].

Since the substance use disorder (SUD) occurs as a result of complex gene-environment interaction, there is an increase in the number of studies investigating the role of single nucleotide polymorphisms (SNPs) of ECS genes in alcohol, opioid, cannabis, and methamphetamine addiction [19], [20], [21]. However, the number of studies investigating the inherited risk of SC addiction is extremely scarce, with only a few available [22]. Accordingly, it is important to understand the genetic underpinnings contributing to an individual’s vulnerability to SC use disorder. We hereby aim to determine whether there is a genetic contribution of CNR1 rs1049353, FAAH rs324420, and MGLL rs604300 gene variants to the risk of SC use disorder in the Turkish population.

Materials and methods

Results

Characteristics of the study population

The demographic characteristics of the study population are presented in Table 1. The data showed significant gender differences between the patient and control groups (p<0.05). While 80 % of subjects were male and 20 % female in the control group, 98 % were male and 2 % female of patients. Since there were only 2 female individuals in the patient group, allelic and genotypic comparisons by gender could not be performed.

Table 1:

Demographic characteristics of the study population.

		Patient		Control		p-Value
		n	%	n	%
Gender	Male	98	98.0	80	80.0	<0.001
	Female	2	2.0	20	20.0
Age categories	18–28	64	64.0	63	63.0	0.445
	29–39	29	29.0	25	25.0
	40 and older	7	7.0	12	12.0
Education	Elementary	14	14.0	6	6.0	<0.001
	Middle	34	34.0	4	4.0
	High	36	36.0	13	13.0
	Graduate/Postgraduate	16	16.0	77	77.0
Smoking	Yes	99	99.0	100	100.0	0.698
	No	1	1.0	0	0.0

77 % of the control group consists of subjects who have graduated from higher education (bachelor, master’s, or doctorate). The education levels of the patient group were as follows: 14 % primary school, 34 % secondary school, 36 % high school, and 16 % higher education. Nearly all the subjects in the study population were smokers. Therefore, the patient and control groups had no significant difference in smoking habits (p>0.05).

No significant difference was observed in the median (minimum-maximum) age values of the patient and control subjects (Table 2, p=0.128). The patient and control groups showed a similar distribution in terms of age categories (Table 1, p=0.445). According to face-to-face interviews, the mean age of onset of SC use was 20.09 ± 5.38 in the patient group.

Table 2:

Other measurable characteristics of the study population.

	Patient		Control		p-Value
	Mean	Median (Min.-Max.)	Mean	Median (Min.-Max.)
Age, years	27.9	26.5 (18.0–43.0)	29.0	27.0 (18.0–65.0)	0.128
BW, kg	76	73 (52–124)	76	74 (46–115)	0.059
Height, m	1.76	1.75 (1.60–1.98)	1.74	1.75 (1.56–1.98)	0.497
BMI	24.65	24.39 (16.41–38.94)	24.98	24.32 (14.17–42.97)	0.479

1. BW, body weight; BMI, body mass index; kg, kilogram; m, meter.

As shown in Table 2, there were no significant differences in body weight, height, and body mass index (BMI) parameters between the patient and control groups (p>0.05).

The patient group was divided into 3 sub-groups according to their frequency of SC use: every day, 1–2 times a week, and 1–2 times a month. 82 % of the patients reported that they use SC every day, 16 % 1–2 times a week, and 2 % once a month. The frequency of daily use of SC in the 18–28 aged subjects is significantly higher than that of other age categories. However, no correlation was found between the age categories and the frequency of SC use (Table 3). Since the number of subjects who reported using SC once a month was only 2, they were excluded from this analysis.

Table 3:

Frequency of SC use among the age categories.

	Daily, n (%)	1-2 times a week, n (%)	Total
18–28	57 (90.5)	6 (9.5)	63 (100.0)
29–39	19 (67.9)	9 (32.1)	28 (100.0)
40 and older	6 (85.7)	1 (14.3)	7 (100.0)
Total	82	16	98a

1. r=−0.041, p=0.689, ^a2 missing values.

The information about other substances used by patients was obtained through face-to-face interviews and the data is presented in Figure 1. It is revealed that 88 % of subjects have had polysubstance use. Nicotine was the most common co-used substance, with a rate of 99 %; second was cannabis and/or skunk, with a rate of 66 %.

Figure 1:

Percentage of subjects using other substances in the patient group.

Discussion

SC is a chemically diverse group of NPS produced under laboratory conditions. SC, also known as cannabinoid receptor agonists, was initially developed to study the structure and function of cannabinoid receptors (CB₁ and CB₂). However, the activity of these compounds in the central nervous system poses greater risks for abuse and progression of SUD.

SUD, or substance addiction for the severe state, is a chronic, relapsing, multifactorial brain disease that is mediated by complex gene-environment interaction. It is well-known that susceptibility to SUD differs individually and affects the early stages of addictive processes. Individual differences and genetic vulnerability may drive a person from recreational use to an addicted state characterized by incentive motivation for drug use and loss of self-control [32]. Environmental factors such as stress, traumas, peer pressure, or familial disorganization are usually referred to in substance addiction, but it is estimated that 40–60 % of genetics play a role in overall risk [33]. Determining genetic risk factors and identifying vulnerable sub-populations may contribute to establishing novel prevention, diagnosis, and treatment strategies.

Research on addiction genetics mostly focused on cannabis, the natural cannabinoid, so far. However, the number of studies investigating the inherited risk of SC use is limited [22]. We have met no study investigating the contribution of ECS genes to the risk of SC use disorder. This is the first study to assess the influence of CNR1 rs1049353, FAAH rs324420, and MGLL rs604300 polymorphisms on the risk of SC use disorder. Our results showed no significant differences in genotype distributions or allelic frequencies between the two groups for all studied SNPs.

Numerous studies have investigated the association between CNR1 rs1049353 intragenic gene polymorphism and alcohol, opioid, heroin, and cannabis use disorders [21, 25, 27, 34, 35]. However, the findings have been equivocal. While several studies have found no significant association between CNR1 rs1049353 polymorphism and SUD, some have reported an increased risk in A allele carriers or G allele carriers and G/G genotype. It is difficult to conclude from studies due to the differences in study design, such as the heterogeneity of study populations, different ethnicities, types of substances (e.g., alcohol, cannabis, cocaine, opioid), inclusion/exclusion criteria, and addiction phenotypes (impulsivity, addiction severity, craving, etc.). Furthermore, underlying neurobiological mechanisms and the pathophysiology may differ for a specific substance type.

Our findings confirm a previous study conducted on the Turkish population. The authors stated that CNR1 rs1049353 polymorphism does not contribute to cannabinoid use disorder in Turkish subjects [24].

Recent studies have suggested that the FAAH gene is associated with substance abuse involving the rs324420 SNP region in exon 3. The FAAH rs324420 polymorphism is a common missense variation of the FAAH gene that causes cytosine conversion at position 385 to adenine (385C→A) [31]. The proline residue is converted to threonine (Pro129→Thr), resulting in a phenotypic variant of FAAH that exhibits normal catalytic properties but is more susceptible to proteolytic degradation. Ultimately, FAAH Pro129→Thr variation results in lower enzymatic activity compared to wild-type and may cause functional abnormalities in the ECS, alter the brain reward function, and contribute to the risk of substance abuse and SUD.

Allele frequencies and genotype distributions of FAAH rs324420 polymorphism were similar between the patient and control subjects in this study. In parallel with the findings of our study, Proudnikov et al. 2010 have found no significant relationship between heroin addiction and FAAH polymorphism [35]. However, Flanagan et al. (2006) found that FAAH rs324420 mutant A allele is over-represented in patients compared to controls in the Caucasian population. This FAAH polymorphism has also been shown to be associated with cannabis use disorder and related phenotypes [36], [37], [38]. Melroy et al., 2016 have found that FAAH polymorphism is significantly associated with DSM-5 cannabis use disorder in Mexican-Americans, and minor A allele was related to increased risk [39]. FAAH rs324420 polymorphism may contribute to the risk of methamphetamine dependence [40]. The risk was higher in homozygous A allele carriers compared with homozygous C carriers plus heterozygous. A recent study has confirmed that FAAH rs324420 polymorphism is significantly associated with methamphetamine dependence in the Chinese Han population [19]. Sim et al. (2013) have suggested that the A allele is functionally recessive, in line with the findings of Sipe et al. (2002). This result indicates that the A allele shows a higher risk when in homozygous form. However, the homozygous A genotype was found in similar numbers in our study’s patient and control groups (1 and 3 %, respectively). We may not have adequately tested its effect on the risk of SC use disorder due to the low frequency of the homozygous A genotype. The association of the FAAH rs324420 polymorphism with SC use disorder in the Turkish population is suggested to be repeated in a larger sample size.

We did not find an association between MGLL rs604300 polymorphism and SC use disorder in this study. Carey et al. (2015) found that the interaction of rs604300 polymorphism with childhood stress factors causes epigenetic modulation of the MGLL gene and is associated with cannabis addiction symptoms [14]. It is known that the ECS and stressful life events have interacted. It has been reported that dysregulation of ECS components may affect learning and memory, trigger substance-seeking behavior (relapse), and makes a person vulnerable to addiction [41, 42]. However, further research on the MGLL rs604300 SNP region is needed.

Our results revealed a lack of association between CNR1 rs1049353, FAAH rs324420, MGLL rs604300 SNPs, and the risk of SC use disorder. As in other psychiatric diseases, genetic predisposition to SUD and/or addiction is complicated since SUD is polygenic, meaning many susceptibility genes and gene variants contribute to addiction risk. However, each gene variant has only a small individual effect on disease risk. The more candidates for risky genes for a disease, the harder it is to detect any of them. Environmental risk factors magnify this difficulty. Moreover, it is hard to find such an association with a small sample size, which could be a limitation of our study. Consequently, additional studies with extended sample sizes must confirm or disprove our results.

CNR1 rs1049353, FAAH rs324420, and MGLL rs604300 SNPs might be related to impulsivity, addiction severity, abstinence, relapse, craving, and withdrawal symptoms. Further research is needed to identify the influence of genetic susceptibility to SC addiction endophenotypes.