Next Article in Journal
Important Roles of Key Genes and Transcription Factors in Flower Color Differences of Nicotiana alata
Next Article in Special Issue
Gnas Promoter Hypermethylation in the Basolateral Amygdala Regulates Reconsolidation of Morphine Reward Memory in Rats
Previous Article in Journal
The Terpene Mini-Path, a New Promising Alternative for Terpenoids Bio-Production
Previous Article in Special Issue
Association of PIP4K2A Polymorphisms with Alcohol Use Disorder
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 12
Issue 12
10.3390/genes12121977
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Analysis of Relationships between DAT1 Polymorphism Variants, Personality Dimensions, and Anxiety in New Psychoactive Substance (Designer Drug) (NPS) Users

by
Jolanta Chmielowiec
1,
Krzysztof Chmielowiec
1,
Jolanta Masiak
2,
Tomasz Pawłowski
3,
Dariusz Larysz
4 and
Anna Grzywacz
5,*
1
Department of Hygiene and Epidemiology, Collegium Medicum, University of Zielona Góra, 28 Zyty St., 65-046 Zielona Góra, Poland
2
Second Department of Psychiatry and Psychiatric Rehabilitation, Medical University of Lublin, Głuska St., 20-059 Lublin, Poland
3
Division of Psychotherapy and Psychosomatic Medicine, Wroclaw Medical University, Wyb. L. Pasteura 10 St., 50-367 Wroclaw, Poland
4
109 Military Hospital with Cutpatient Cinic in Szczecin, Piotra Skargi 9-11 St., 70-965 Szczecin, Poland
5
Independent Laboratory of Health Promotion, Pomeranian Medical University in Szczecin, 11 Chlapowskiego St., 70-204 Szczecin, Poland
*
Author to whom correspondence should be addressed.
Genes 2021, 12(12), 1977; https://doi.org/10.3390/genes12121977
Submission received: 22 November 2021 / Revised: 6 December 2021 / Accepted: 7 December 2021 / Published: 10 December 2021
(This article belongs to the Special Issue Genetics and Genomics of Addiction)
Browse Figures
Versions Notes

Abstract

:
The use of ‘new psychoactive substances’ appears to be increasingly common. The aim of this study was to examine biological and personality determinants in individuals who choose to use these substances, which may help in the prevention and treatment of psychoactive substance use disorders. The study group consisted of 374 male volunteers; all were users of ‘new psychoactive substances’ (NPS). The NPS users were recruited after they had abstained—for at least 3 months—from any substance of abuse in addiction treatment facilities. The NPS patients and the control subjects were examined by a psychiatrist using the Mini-International Neuropsychiatric Interview (M.I.N.I.), the NEO Five-Factor Personality Inventory (NEO-FFI), and the State-Trait Anxiety Inventory (STAI) scales. The real-time PCR method was used for genotyping. When we compared the controls with the study group, statistically significant interactions were found between DAT1 polymorphism, neuroticism, and NPS use. NPS use and DAT1 polymorphism were associated with a higher level of neuroticism on the NEO-FFI scale. The study group of NPS users showed a higher severity of anxiety symptoms, both in terms of trait and state, compared to the control group. The results may support the idea that neuroticism and anxiety correlate strongly with coping motives for using NPS.

1. Introduction

The term ‘new psychoactive substance’ was defined by the European Union as a new narcotic or psychotropic drug, in pure form or in a preparation, that is not scheduled under the Single Convention on Narcotic Drugs of 1961 or the Convention on Psychotropic Substances of 1971, but which may pose a public health threat comparable to that posed by substances listed in those conventions (Council of the European Union decision 2005/387/JHA) [1].
The U.S. Drug Enforcement Administration (DEA) recognizes seven types of de-signer drugs: cannabinoids, phenethylamines, phencyclidines or arylcyclohexamines, tryptamines, piperazines, pipradrols, N-ring systems [2]. The speed at which new psychoactive substances have been introduced to the market has stabilized in recent years.
However, each year, the EU Early Warning System detects more than 50 new psychoactive substances, which increases the pool of previously reported new psychoactive substances. These substances resemble long-known psychoactive substances used without medical prescription, but they are not controlled under international drug laws. At the end of 2019, the EMCDDA (European Monitoring Centre for Drugs and Drug Addiction) monitored over 790 new psychoactive substances, 53 of which were first reported in Europe in 2019.
The most common types of new psychoactive substances in Europe are synthetic cannabinoids and cathinones (77% of all types reported in 2018). Synthetic drug use in Europe appears to be increasingly common, and new patterns of consumption present similar features in each European country. These patterns of consumption appear to have established across the European Union. New psychoactive substances are also used as a tool for managing chronic pain [3,4], as well as in social isolation associated with the COVID-19 pandemic [5,6].
In the existing research on personality and addictions, one of the most widely used personality models is the Big Five—a dimensional personality model. This model is based on trait-related personality conceptualizations and encompasses the following personality domains: extraversion, neuroticism, openness to new experiences, agreeableness, and conscientiousness [7]. Although it has been sufficiently confirmed by numerous research studies that personality traits influence the development, duration, and prognosis of substance use disorders [8], the data on associations between personality dimensions and the use of new psychoactive substances are limited.
A study conducted by Cohen et al. showed that NPS users exhibited a higher level of neuroticism and a lower level of agreeableness and extraversion than cannabis users and non-users. NPS users had lower scores for conscientiousness than natural cannabis users and non-users [9].
A study of polydrug users showed a low level of conscientiousness and a low level of agreeableness. High trait impulsivity and poor self-control were reported to constitute an additional risk to NPS and polydrug use severity and distinguished those seeking treatment from those who use NPS recreationally [10]. Polydrug users and alcohol users showed a high level of neuroticism and a low level of openness to new experiences in both groups, compared to healthy controls [11]. Studies of addiction confirmed higher levels of neuroticism in the examined individuals who had a diagnosis of substance and behavioral addictions [12,13,14,15,16], and severe neuroticism was reported to be associated with ‘self-medication’ of unpleasant or negative emotional states [17]. Mensen et al. [18] conducted a study in which synthetic cannabinoid users were compared to natural cannabis users; synthetic cannabinoid users had higher scores for anxiety and phobic anxiety. Studies analyzing gene variants examined in NPS patients are very limited. Pehlivan et al. [19] examined variants of the COMT (rs4680), CNR2 (rs2501432), CNR2 (rs2229579), UCP2 (rs659366), and IL-17 (rs763780) genes in patients with synthetic cannabinoid use disorder: the CNR2 (rs2229579) and the UCP2 (rs659366) variants were associated with synthetic cannabinoid use disorder. Pehlivan et al. [20] examined the relationship of global methylation with cannabinoid use disorder or synthetic cannabinoid use disorder, as well as methylation of the NR3C1 gene promotor and the NR3C1 BclI polymorphism. The existence of previous polysubstance abuse significantly influenced genotype distribution between the two groups, and global DNA methylation may have been associated with synthetic cannabinoid use disorder. Oyaci et al. [21] analyzed the association between cannabinoid use disorder/synthetic cannabinoid use disorder and methylation of the MB-COMT (membrane-bound catechol-O-methyltransferase) promotor or the DRD2 gene in terms of gene variants; MB-COMT promoter methylation is believed to be associated with CUD/SCUD (cannabinoid use disorder/synthetic cannabinoid use disorder), but methylation of the DRD2 gene is not. A common feature of addictive substances is their capacity to reinforce behavior and act as a powerful reward. There are numerous studies which have confirmed the critical role of DA in the rewarding effects of drugs of abuse [22,23,24]. The dopamine transporter (DAT) is a 12-membrane domain Na+/Cl−-dependent transport protein. It has a major role in dopaminergic neurotransmission, mediates the active reuptake of dopamine into the presynaptic neuron [25], and therefore regulates synaptic dopamine concentrations and the continuity of dopaminergic activity [26]. DAT may be involved in the pathophysiology of substance use disorder. The dopamine transporter gene (DAT1/gene symbol: SLC6A3), located on chromosome 5p15.3, encloses 15 exons ranging approximately 53 kb. As DAT is coded by a single-copy gene (SLC6A3), with its expression mostly restricted to the dopaminergic system, it points to a limited ability to be compensated for by other gene products. Polymorphisms in this gene may have a potentially large impact [27]. A variable number tandem repeat (VNTR) sequence in the 3′ untranslated region is considered to have a functional polymorphism regulating expression of the DAT1 gene [28]. The DAT1 gene consists of a polymorphic 40-base pair (bp) variable number tandem repeat (VNTR) in an untranslated region. Variants with 3 to 13 repeats of the 40 bp sequence have been identified, but the most often found are variants with 9 or 10 repeats, which are associated with high and low dopamine availability. Studies examining associations between DAT1 polymorphisms and personality traits, as measured by the TCI, by Kim et al. in a Korean population found no associations in healthy Americans [29,30]. There are reports of an association between DAT1 VNTR and novelty seeking, but they contradict each other [31,32]. Van Gestel et al. showed that in a Belgian cohort, particularly in females, novelty seeking was associated with DAT1 polymorphisms [33].
The main aim of this study was to investigate associations between DAT1 polymorphism variants, personality dimensions, and anxiety in users of new psychoactive substances (designer drugs) (NPS).

2. Materials and Methods

2.1. Subjects

The study group consisted of 374 male volunteers: users of ‘new psychoactive substances’ (NPS) (n = 73; mean age = 25.67, SD = 6.18, minimum = 17.00, maximum = 50.00), and healthy controls (n = 301; mean age = 22.13, SD = 4.57, minimum = 17.00, maximum = 50.00). Approval for the study was obtained from the Bioethics Committee of the Pomeranian Medical University in Szczecin (KB-0012/106/16). Informed written consent was received from the participants of the study. The study was conducted in the Independent Laboratory of Health Promotion. The users of ‘new psychoactive substances’ (NPS) were recruited after they had abstained for at least 3 months from any substance of abuse in addiction treatment facilities. The ‘new psychoactive substance’ (NPS) patients and the control subjects were examined by a psychiatrist using the Mini-International Neuropsychiatric Interview (M.I.N.I.), the NEO Five-Factor Personality Inventory (NEO-FFI), and the State-Trait Anxiety Inventory (STAI) scales.
The STAI is a questionnaire used to assess anxiety as a trait (A-Trait), which may be described as a permanent and enduring disposition to experience worries, stress, and discomfort, or as a state (A-state), including discomfort, fear, and the arousal of the autonomic nervous system occurring temporarily in relation to a particular situation. The NEO Personality Inventory (NEO Five-Factor Inventory, NEO-FFI) includes six dimensions for each of the five traits—extraversion (positive emotion, warmth, gregariousness, activity, excitement seeking, assertiveness), agreeableness (tender-mindedness, trust, altruism, straightforwardness, compliance, modesty), openness to experience (fantasy, feelings, esthetics, actions, values, ideas), conscientiousness (deliberation, competence, dutifulness, order, achievement striving, self-discipline), neuroticism (anxiety, vulnerability to stress, hostility, self-consciousness, impulsiveness, depression) [34].
The results of the NEO-FFI and STAI inventories were given as sten scores. Conversion of the raw score into the sten scale was performed according to the Polish norms. For adults, it was assumed that sten values of 1–2 were very low scores; values of 3–4 were low scores; values of 5–6 were average scores; values of 7–8 were high scores; and values of 9–10 were very high scores.

2.2. Genotyping

Tubes with EDTA (anticoagulant) were used to collect blood for the genetic assays. Genomic DNA from blood leukocytes was obtained using the High Pure Polymerase Chain Reaction (PCR) Template Preparation extraction kit (Roche Diagnostics, Mannheim, Germany). The process of extraction was conducted in accordance with the manufacturer’s instructions. The samples of DNA extracted in this way were stored at 4 °C until further analysis was performed.
Venous blood collected according to standard procedures was the source of genomic DNA. The PCR method was used to genotype the samples. The DAT1 genotypes were grouped according to the presence of 9- or 10-repeat variants. Genotyping was performed using the PCR-VNTR method with primers: F: 50-TGT GGT GTA GGG AAC GGC CTG Ag 30, R: 50-CTT CCT GGA GGT CAC GGC TCA AGG 30, in a final volume of 25 L PCR mix per reaction, with l00 ng genomic DNA, 10 pmol of primers, 50 mM KCL, 10 mM TrisHCl, 1.5 mM MgCl2, 200 M dATP, dCTP, dTTP, and dGTP, and 0.8 U of the Tag polymerase. Conditions for the reaction: 5 min of initial denaturation at 94 °C; cycling: 55 s of denaturation at 94 °C, 50 s of primer hybridization at 55 °C, and 1 min of elongation at 72 °C, repeated in 30 cycles, with 10 min of final elongation at 72 °C. The amplified products were visualized using ethidium bromide-stained gel electrophoresis (3% agarose) and UV photography. The product lengths were 450 bp for 10-repeat alleles and 410 bp for 9-repeat alleles.

2.3. Statistical Analysis

Concordance between the genotype frequency distribution and Hardy–Weinberg equilibrium (HWE) was tested using the HWE software (http://www.oege.org/software/hwe-mr-calc.html, accessed on 20 March 2021). The relationships between DAT1 variants, users of ‘new psychoactive substances’ (NPS), the control subjects, and the NEO Five-Factor Inventory (NEO-FFI) scores were analyzed using a multivariate analysis of factor effects ANOVA (NEO-FFI/scale STAI/× genetic feature × control and ‘new psychoactive substances’ (NPS) × (genetic feature × control and NPS)). The condition of homogeneity of variance was satisfied (Levene test p > 0.05). The variables did not present a normal distribution. The NEO Five-Factor Inventory scores (neuroticism, extraversion, openness, agreeableness, conscientiousness) were measured and compared using the Mann–Whitney U test. The DAT1 genotype frequencies between healthy control subjects and users of ‘new psychoactive substances’ (NPS) were tested using the chi-square test. All computations were performed using STATISTICA 13 (Tibco Software Inc, Palo Alto, CA, USA) for Windows (Microsoft Corporation, Redmond, WA, USA).

3. Results

These frequency distributions accorded with the HWE both for the NPS users and control subjects (Table 1).
DAT1 genotype frequencies in the studied sample did not differ between NPS users and control subjects (Table 2).
Compared to the control group, no statistically significant difference in the frequency of the genotype variants for the DAT1 gene was found in the subjects using NPS (9/9 0.03 vs. 9/9 0.06, 9/10 0.33 vs. 9/10 0.39, 10/10 0.65 vs. 10/10 0.56, χ2 = 2.48, p = 0.289), and no statistically significant difference in the frequency for the DAT1 alleles was found between the NPS users and the control group (9 0.19 vs. 9 0.25, 10 0.81 vs. 10 0.75, χ2 = 2.37, p = 0.124).
Compared to the controls, the study group subjects were observed to have significantly higher scores (Table 3) on the STAI state scale (M 6.08 vs. M 4.69, p < 0.001), the STAI trait scale (M 7.34 vs. M 5.16, p < 0.001), and the NEO Five-Factor Inventory neuroticism scale (M 6.92 vs. M 4.67, p < 0.001).
Compared to the controls, the study group had significantly lower scores (Table 3) on the NEO Five-Factor Inventory extraversion scale (M 5.78 vs. M 6.37, p = 0.0345) and the NEO Five-Factor Inventory agreeableness scale (M 4.67 vs. M 5.60, p = 0.0005).
The results of the 2 × 3 factorial ANOVA of the NEO Five-Factor Personality Inventory (NEO-FFI) and the State-Trait Anxiety Inventory (STAI) sten scales, and the scale of NEO-FFI, are summarized in Table 4. We found a significant result when comparing the NEO-FFI extraversion scale and DAT1 polymorphisms (F2, 368 = 3.24, p = 0.040), accounting for 1.7% of the variance. For the interactions, we found a significant result when comparing the groups (NPS users vs. controls) for the NEO-FFI neuroticism scale and DAT1 polymorphisms (Figure 1, F2, 368 = 4.23, p = 0,015), accounting for 2.2% of the variance. The results of the post hoc test are included in Table 5. For the interactions, we found a significant result when comparing the groups (NPS users vs. controls) for the NEO-FFI extraversion scale and DAT1 polymorphisms (Figure 2, F2, 368 = 3.81, p = 0.023), accounting for 2.0% of the variance. Post hoc analysis is shown in Table 4. The results of the post hoc test are included in Table 5.

4. Discussion

Compared to the controls, we found no statistically significant differences in the genotype frequency between DAT1 polymorphism variants, in the NPS users. This result may arise because new psychoactive substances are used by different groups, including people who take drugs recreationally, and because NPS users are a very heterogeneous group in terms of the substances taken. Designer drugs interact pharmacologically with various monoaminergic targets. Stimulants such as pipradrols and pyrovalerone cathinones inhibit the transport of dopamine; noradrenaline, amphetamines, and methamphetamine-like cathinones induce the release of these monoamines; entactogens such as phenylpiperazines, aminoindanes, and MDMA-like cathinones can enhance serotonin hallucin release; and in turn, ecstasy, tryptamines, and hallucinogenic phenethylamines are direct agonists at serotonergic 5-HT2A receptors [35]. A similar result was obtained in studies conducted on more homogeneous populations. In a study by Tzeng and colleagues [32] on a large cohort of Chinese male subjects diagnosed with amphetamine use disorder avoidance, they did not find an association between the DAT1 gene and amphetamine dependence.
The main findings of our study are that interactions between DAT1 polymorphism, neuroticism, and NPS use were found. Neuroticism is a personality dimension that involves emotional reactivity [36,37,38,39]. Individuals whose scores are high for neuroticism are more sensitive to negative mood states and have excessive vulnerability to environmental stressors. Neuroticism is thought to be genetically determined, with an estimated heritability of 40 to 60%, and may be involved in the susceptibility, onset, and course of mental disorders, including substance use disorder. In the existing literature, we found no papers which focused on analysis of personality dimensions of people who use various NPS, which are different from synthetic cannabinoids, meaning we can only compare our results with the results of research conducted on people with a diagnosis of SUD, which differs from NPS or synthetic cannabinoid use disorder. In a previous study [31], we showed that in the examined group of patients diagnosed with substance use disorder, which is different to NPS use disorder, no main effects of DAT1 polymorphisms were found for any personality dimensions assessed using the NEO-FFI, but the main effects of DAT1 polymorphisms approximated to the statistical significance for the agreeableness scale. Meta-analyses disclosed that high neuroticism and low conscientiousness were linked with substance use disorders [40]. Terracciano et al. [8] showed that low scores of conscientiousness, and high scores of neuroticism, were associated with the use of drugs. In this research, no correlation between the personality traits of extraversion, openness, conscientiousness, and agreeableness and DAT1 polymorphism was found. In the group of NPS users, higher levels of neuroticism were found compared to the control group, whereas scores for extraversion and agreeableness were lower. People with low scores for agreeableness tend to be distant, unfriendly, and uncooperative, whereas people with low scores on extraversion are introverted. Introverted people are less involved in social activities; they are quiet and keep to themselves.
Analysis of the DAT1 gene and the personality traits measured using the TPQ test was also performed by Tzeng and colleagues [36] on a large cohort of Chinese male subjects diagnosed with amphetamine use disorder. Novelty seeking and harm avoidance scores were higher in the case group than in the controls, but the analyzed polymorphism did not influence the scores. The explanation of these differences can be found in ethnic differences; the minor allele frequencies of the promoter and the 3′ VNTR polymorphisms are lower in Asians than in European Caucasian and Brazilian populations [36,41,42,43,44]. Kazantseva et al. [45] suggested that the 3′ VNTR and rs27072 of DAT1 were associated with personality trait persistence in healthy Caucasian individuals.
A survey of the personality profile in alcohol, nicotine, cannabis, and gambling disorder in 3785 twins and siblings from the Australian Twin Registry showed that high neuroticism, low agreeableness, and low conscientiousness were associated with all four addictive disorders. In this research, neuroticism, agreeableness, and conscientiousness were associated with the general propensity for developing an addictive disorder and may, in part, explain their co-occurrence. However, they may be more broadly associated with the propensity for any psychiatric disorder [46]. Similarly, in our results, SUD was associated in the existing research with neuroticism and was also significantly associated with low extraversion and low agreeableness [40]. Compared to the control group, a higher severity of anxiety symptoms—both in terms of trait and state—was found in the group of NPS users. Numerous previous studies confirmed that personality features—anxiety and emotional distress—are associated with an early onset of drug use [47]. Neuroticism and anxiety were reported as correlating most strongly with coping motives for substance use [48]. It was also confirmed that females are more prone to using drugs in order to cope with negative emotions, such as anxiety [49,50]. Low anxiety sensitivity predicted increased motives for alcohol use, whereas high anxiety sensitivity predicted adjustment motives for alcohol and cannabis use, and high trait anxiety predicted coping motives for alcohol and cigarette use [51].

5. Conclusions

Conclusively, in this research, statistically significant interactions between neuroticism, the use of new psychoactive substances, and DAT1 polymorphism were found. NPS users with 9/10 genotype variants had the highest levels of neuroticism compared to the control group. These associations reveal that psychological factors combined with genetic data enable a better understanding of the pathogenesis of addiction. The results may support the idea that neuroticism and anxiety correlate strongly with coping motives for using NPS.

Author Contributions

Conceptualization, A.G., J.C. and K.C.; methodology, J.C.; software, K.C.; validation, J.C. and K.C.; formal analysis, A.G. and J.C.; investigation, J.M.; resources, J.C.; data curation, J.M. and J.C.; writing—original draft preparation, J.M., T.P., A.G., K.C. and J.C.; writing—review and editing, J.M., T.P., A.G., K.C., D.L. and J.C.; visualization, K.C.; supervision, A.G.; project administration, A.G. and J.C.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Science Center, Poland (grant number UMO-2015/19/B/NZ7/03691).

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki principles and approved by the Bioethics Committee of the Pomeranian Medical University in Szczecin (KB-0012/106/16).

Informed Consent Statement

All subjects provided signed informed consent for participating in the research.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Council Decision 2005/387/JHA. Official Journal of the European Union.
  2. European Monitoring Centre for Drugs and Drug Addiction. European Drug Report 2020: Key Issues Summary; Publications Office of the European Union: Luxembourg, 2020. [Google Scholar]
  3. Berger, A.A.; Keefe, J.; Winnick, A.; Gilbert, E.; Eskander, J.P.; Yazdi, C.; Kaye, A.D.; Viswanath, O.; Urits, I. Cannabis and cannabidiol (CBD) for the treatment of fibromyalgia. Best Pr. Res. Clin. Anaesthesiol. 2020, 34, 617–631. [Google Scholar] [CrossRef] [PubMed]
  4. Andrews, N.J.; Phillips, A.J. Community nurses’ support for patients with fibromyalgia who use cannabis to manage pain. Br. J. Community Nurs. 2021, 26, 92–98. [Google Scholar] [CrossRef]
  5. Di Trana, A.; Carlier, J.; Berretta, P.; Zaami, S.; Ricci, G. Consequences of COVID-19 lockdown on the misuse and marketing of addictive substances and new psychoactive substances. Front. Psychiatry 2020, 11, 584462. [Google Scholar] [CrossRef] [PubMed]
  6. Zaami, S.; Marinelli, E.; Varì, M.R. New trends of substance abuse during COVID-19 pandemic: An international perspective. Front. Psychiatry 2020, 11, 700. [Google Scholar] [CrossRef]
  7. Costa, P.T.; McCrae, R.R. Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI) professional manual. Psychol. Assess. Resour. 1992. [Google Scholar]
  8. Terracciano, A.; Löckenhoff, C.E.; Crum, R.M.; Bienvenu, O.J.; Costa, P.T., Jr. Five-Factor Model personality profiles of drug users. BMC Psychiatry 2008, 8, 22. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Cohen, K.; Rosenzweig, S.; Rosca, P.; Pinhasov, A.; Weizman, A.; Weinstein, A. Personality Traits and Psychotic Proneness Among Chronic Synthetic Cannabinoid Users. Front. Psychiatry 2020, 11, 355. [Google Scholar] [CrossRef]
  10. Savulich, G.; Bowden-Jones, O.; Stephenson, R.; Brühl, A.B.; Ersche, K.D.; Robbins, T.W.; Sahakian, B.J. “Hot” and “Cold” cognition in users of club drugs/novel psychoactive substances. Front. Psychiatry 2021, 12, 333. [Google Scholar] [CrossRef] [PubMed]
  11. Dalkner, N.; Unterrainer, H.-F.; Neubauer, A. Differences in big five personality traits between alcohol and polydrug abus-ers: Implications for treatment in the therapeutic community. Int. J. Ment. Health Addict. 2013, 11, 682–692. [Google Scholar]
  12. Benotsch, E.G.; Jeffers, A.J.; Snipes, D.J.; Martin, A.M.; Koester, S. The five factor model of personality and the non-medical use of prescription drugs: Associations in a young adult sample. Pers. Individ. Differ. 2013, 55, 852–855. [Google Scholar] [CrossRef]
  13. Zilberman, N.; Yadid, G.; Efrati, Y.; Neumark, Y.; Rassovsky, Y. Personality profiles of substance and behavioral addic-tions. Addict. Behav. 2018, 82, 174–181. [Google Scholar] [CrossRef]
  14. Przepiorka, A.; Blachnio, A.; Cudo, A. Relationships between morningness, Big Five personality traits, and problematic Internet use in young adult university students: Mediating role of depression. Chrono Int. 2020, 38, 248–259. [Google Scholar] [CrossRef]
  15. Ramirez, V.; Wiers, C.E.; Wang, G.; Volkow, N.D. Personality traits in substance use disorders and obesity when compared to healthy controls. Addiction 2020, 115, 2130–2139. [Google Scholar] [CrossRef]
  16. Zilberman, N.; Yadid, G.; Efrati, Y.; Rassovsky, Y. Who becomes addicted and to what? Psychosocial predictors of sub-stance and behavioral addictive disorders. Psychiatry Res. 2020, 291, 113221. [Google Scholar] [CrossRef]
  17. Suh, J.J.; Ruffins, S.; Robins, C.E.; Albanese, M.J.; Khantzian, E.J. Self-medication hypothesis: Connecting affective experience and drug choice. Psychoanal. Psychol. 2008, 25, 518–532. [Google Scholar] [CrossRef]
  18. Mensen, V.T.; Vreeker, A.; Nordgren, J.; Atkinson, A.; de la Torre, R.; Farré, M.; Ramaekers, J.G.; Brunt, T.M. Psychopathological symptoms associated with synthetic cannabinoid use: A comparison with natural cannabis. Psychopharmacology 2019, 236, 2677–2685. [Google Scholar] [CrossRef] [Green Version]
  19. Pehlivan, S.; Aytac, H.M.; Kurnaz, S.; Pehlivan, M.; Aydin, P.C. Evaluation of COMT (rs4680), CNR2 (rs2501432), CNR2 (rs2229579), UCP2 (rs659366), and IL-17 (rs763780) gene variants in synthetic cannabinoid use disorder patients. J. Addict. Dis. 2020, 38, 495–505. [Google Scholar] [CrossRef] [PubMed]
  20. Pehlivan, S.; Aytac, H.M.; Aydin, P.C.; Nursal, A.F.; Pehlivan, M. Global and glucocorticoid receptor gene-specific (NR3C1) DNA methylation analysis in patients with cannabinoid or synthetic cannabinoid use disorder. Psychiatry Res. 2021, 298, 113774. [Google Scholar] [CrossRef] [PubMed]
  21. Oyaci, Y.; Aytac, H.M.; Pasin, O.; Aydin, P.C.; Pehlivan, S. Detection of altered methylation of MB-COMT promotor and DRD2 gene in cannabinoid or synthetic cannabinoid use disorder regarding gene variants and clinical parameters. J. Addict. Dis. 2021, 1–19. [Google Scholar] [CrossRef]
  22. Koob, G.F. Dopamine, addiction and reward. Semin. Neurosci. 1992, 4, 139–148. [Google Scholar] [CrossRef]
  23. Di Chiara, G.; Bassareo, V. Reward system and addiction: What dopamine does and doesn’t do. Curr. Opin. Pharmacol. 2007, 7, 69–76. [Google Scholar] [CrossRef]
  24. Volkow, N.D.; Michaelides, M.; Baler, R. The neuroscience of drug reward and addiction. Physiol. Rev. 2019, 99, 2115–2140. [Google Scholar] [CrossRef]
  25. Bannon, M.J.; Michelhaugh, S.K.; Wang, J.; Sacchetti, P. The human dopamine transporter gene: Gene organization, transcriptional regulation, and potential involvement in neuropsychiatric disorders. Eur. Neuropsychopharmacol. 2001, 11, 449–455. [Google Scholar] [CrossRef]
  26. Greenwood, T.; Kelsoe, J.R. Promoter and intronic variants affect the transcriptional regulation of the human dopamine transporter gene. Genomics 2003, 82, 511–520. [Google Scholar] [CrossRef]
  27. Fuke, S.; Suo, S.; Takahashi, N.; Koike, H.; Sasagawa, N.; Ishiura, S. The VNTR polymorphism of the human dopamine transporter (DAT1) gene affects gene expression. Pharmacogenom. J. 2001, 1, 152–156. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Hahn, M.K.; Blakely, R.D. Monoamine transporter gene structure and polymorphisms in relation to psychiatric and other complex disorders. Pharmacogenom. J. 2002, 2, 217–235. [Google Scholar] [CrossRef] [Green Version]
  29. Kim, S.J.; Kim, Y.S.; Kim, C.-H.; Lee, H.S. Lack of association between polymorphisms of the dopamine receptor D4 and dopamine transporter genes and personality traits in a Korean population. Yonsei Med. J. 2006, 47, 787–792. [Google Scholar] [CrossRef] [Green Version]
  30. Kim, S.J.; Kim, Y.S.; Lee, H.S.; Kim, S.Y.; Kim, C.-H. An interaction between the serotonin transporter promoter region and dopamine transporter polymorphisms contributes to harm avoidance and reward dependence traits in normal healthy subjects. J. Neural Transm. 2006, 113, 877–886. [Google Scholar] [CrossRef]
  31. Chmielowiec, J.; Chmielowiec, K.; Suchanecka, A.; Trybek, G.; Mroczek, B.; Małecka, I.; Grzywacz, A. Associations be-tween the dopamine D4 receptor and DAT1 dopamine transporter genes polymorphisms and personality traits in addicted patients. Int. J. Environ. Res. Public Health 2018, 15, 2076. [Google Scholar] [CrossRef] [Green Version]
  32. Tzeng, N.-S.; Lu, R.-B.; Yeh, H.-W.; Yeh, Y.-W.; Huang, C.-C.; Yen, C.-H.; Kuo, S.-C.; Chen, C.-Y.; Chang, H.-A.; Ho, P.-S.; et al. The dopamine transporter gene may not contribute to susceptibility and the specific personality traits of amphetamine dependence. Drug Alcohol Depend. 2015, 149, 100–107. [Google Scholar] [CrossRef]
  33. Van Gestel, S.; Forsgren, T.; Claes, S.; Del Favero, J.; Van Duijn, C.M.; Sluijs, S.; Nilsson, L.-G.; Adolfsson, R.; Van Broeckhoven, C. Epistatic effect of genes from the dopamine and serotonin systems on the temperament traits of Novelty Seeking and Harm Avoidance. Mol. Psychiatry 2002, 7, 448–450. [Google Scholar] [CrossRef] [Green Version]
  34. Costa, P.; McCrae, R.R. The Revised NEO Personality Inventory (NEO-PI-R); Sage Publications Inc.: Thousand Oaks, CA, USA, 2008; Volume 2, pp. 179–198. [Google Scholar]
  35. Liechti, M. Novel psychoactive substances (designer drugs): Overview and pharmacology of modulators of monoamine signaling. Swiss. Med. Wkly. 2015, 145, w14043. [Google Scholar] [CrossRef] [PubMed]
  36. Chmielowiec, K.; Chmielowiec, J.; Masiak, J.; Czekaj, M.; Krawczyk, P.; Soroka, E.; ´Smiarowska, M.; Musiał, W.; Pawłowski, T.; Grzywacz, A. DRD4Exon 3 Gene Polymorphisms in Patients Diagnosed withPolysubstance Use Disorder and Co-Occurrence of a Depressive Episode. Genes 2021, 12, 1834. [Google Scholar] [CrossRef] [PubMed]
  37. Calboli, F.C.F.; Tozzi, F.; Galwey, N.W.; Antoniades, A.; Mooser, V.; Preisig, M.; Vollenweider, P.; Waterworth, D.; Waeber, G.; Johnson, M.R.; et al. A genome-wide association study of neuroticism in a population-based sample. PLoS ONE 2010, 5, e11504. [Google Scholar] [CrossRef] [Green Version]
  38. Hettema, J.M.; Prescott, C.A.; Kendler, K.S. Genetic and environmental sources of covariation between generalized anxiety disorder and neuroticism. Am. J. Psychiatry 2004, 161, 1581–1587. [Google Scholar] [CrossRef]
  39. van den Oord, E.J.; Kuo, P.H.; Hartmann, A.M.; Webb, B.T.; Möller, H.J.; Hettema, J.M.; Giegling, I.; Bukszár, J.; Rujescu, D. Genomewide association analysis followed by a rep-lication study implicates a novel candidate gene for neuroticism. Arch. Gen. Psychiatry 2008, 65, 1062–1071. [Google Scholar] [CrossRef] [Green Version]
  40. Kotov, R.; Gamez, W.; Schmidt, F.; Watson, D. Linking “big” personality traits to anxiety, depressive, and substance use disorders: A meta-analysis. Psychol. Bull. 2010, 136, 768–821. [Google Scholar] [CrossRef] [PubMed]
  41. Michałowska-Sawczyn, M.; Lachowicz, M.; Grzywacz, A.; Suchanecka, A.; Chmielowiec, J.; Chmielowiec, K.; Chycki, J.; Trybek, G.; Żmijewski, P.; Cięszczyk, P. Analysis of Polymorphic Variants of the Dopamine Transporter (DAT1) Gene Polymorphism and Personality Traits Among Athletes. J. Hum. Kinet. 2020, 72, 79–89. [Google Scholar] [CrossRef] [Green Version]
  42. Huang, S.-Y.; Chen, H.-K.; Ma, K.-H.; Shy, M.-J.; Chen, J.-H.; Lin, W.-C.; Lu, R.-B. Association of promoter variants of human dopamine transporter gene with schizophrenia in Han Chinese. Schizophr. Res. 2010, 116, 68–74. [Google Scholar] [CrossRef]
  43. de Azeredo, L.A.; Rovaris, D.L.; Mota, N.R.; Polina, E.R.; Marques, F.Z.; Contini, V.; Vitola, E.S.; Belmonte-de-Abreu, P.; Rohde, L.A.; Grevet, E.H.; et al. Further evidence for the association between a polymorphism in the promoter region of SLC6A3/DAT1 and ADHD: Findings from a sample of adults. Eur. Arch. Psychiatry Clin. Neurosci. 2014, 264, 401–408. [Google Scholar] [CrossRef]
  44. Vereczkei, A.; Demetrovics, Z.; Szekely, A.; Sarkozy, P.; Antal, P.; Szilagyi, A.; Sasvari-Szekely, M.; Barta, C. Multi-variate analysis of dopaminergic gene variants as risk factors of heroin dependence. PLoS ONE 2013, 8, e66592. [Google Scholar] [CrossRef] [PubMed]
  45. Kazantseva, A.; Gaysina, D.; Malykh, S.; Khusnutdinova, E. Theroleofdopamine transporter (SLC6A3) and dopamine D2 re-ceptor/ankyrin repeat and kinase domain containing 1 (DRD2/ANKK1) gene polymorphisms in personality traits. Prog. Neuropsychopharmacol. Biol. Psychiatry 2011, 35, 1033–1040. [Google Scholar] [CrossRef]
  46. Dash, G.F.; Slutske, W.S.; Martin, N.G.; Statham, D.J.; Agrawal, A.; Lynskey, M.T. Big Five personality traits and alcohol, nicotine, cannabis, and gambling disorder comorbidity. Psychol. Addict. Behav. 2019, 33, 420–429. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. DordiNejad, F.G.; Shiran, M.A.G. Personality traits and drug usage among addicts. Lit. Inform. Comput. Educ. J. 2011, 2, 402–405. [Google Scholar] [CrossRef]
  48. Kuntsche, E.; Knibbe, R.; Gmel, G.; Engels, R. Who drinks and why? A review of socio-demographic, personality, and contextual issues behind the drinking motives in young people. Addict. Behav. 2006, 31, 1844–1857. [Google Scholar] [CrossRef]
  49. Newcomb, M.D.; Chou, C.-P.; Bentler, P.M.; Huba, G. Cognitive motivations for drug use among adolescents: Longitu-dinal tests of gender differences and predictors of change in drug use. J. Couns. Psychol. 1988, 35, 426. [Google Scholar] [CrossRef]
  50. Stewart, S.H.; Karp, J.; Pihl, R.O.; Peterson, J. Anxiety sensitivity and self-reported reasons for drug use. J. Subst. Abus. 1997, 9, 223–240. [Google Scholar] [CrossRef]
  51. Comeau, N.; Stewart, S.H.; Loba, P. The relations of trait anxiety, anxiety sensitivity, and sensation seeking to adolescents’ motivations for alcohol, cigarette, and marijuana use. Addict. Behav. 2001, 26, 803–825. [Google Scholar] [CrossRef]
Genes 12 01977 g001 550
Figure 1. Interaction between users of ‘new psychoactive substances’ (NPS)/controls, DAT1 polymorphisms, and the NEO-FFI neuroticism scale. M, mean; SE, standard error.
Figure 1. Interaction between users of ‘new psychoactive substances’ (NPS)/controls, DAT1 polymorphisms, and the NEO-FFI neuroticism scale. M, mean; SE, standard error.
Genes 12 01977 g001
Genes 12 01977 g002 550
Figure 2. Interaction between users of ‘new psychoactive substances’ (NPS)/controls, DAT1 polymorphisms, and the NEO-FFI extraversion scale. M, mean; SE, standard error.
Figure 2. Interaction between users of ‘new psychoactive substances’ (NPS)/controls, DAT1 polymorphisms, and the NEO-FFI extraversion scale. M, mean; SE, standard error.
Genes 12 01977 g002
Table
Table 1. Hardy–Weinberg law for the NPS users and control subjects.
Table 1. Hardy–Weinberg law for the NPS users and control subjects.
Hardy–Weinberg Equilibrium Calculator Including Analysis for Ascertainment BiasObserved (Expected) Test χ2
χ2p-Value
DAT1 NPS users9/92 (2.68)9 allele freq = 0.19
10 allele freq = 0.81		0.268>0.05
9/1024 (22.63)
10/1047 (47.68)
DAT1 control subjects 9/919 (19.19)9 allele freq = 0.25
10 allele freq = 0.75		0.003>0.05
9/10114 (113.62)
10/10168 (168.19)
p—statistical significance in χ2 test.
Table
Table 2. Frequency of genotypes of the DAT1 gene polymorphisms in the NPS users and controls.
Table 2. Frequency of genotypes of the DAT1 gene polymorphisms in the NPS users and controls.
GroupDAT1
Genotype Alleles
9/9
n (%)		9/10
n (%)		10/10
n (%)		9
n (%)		10
n (%)
NPS users2244728118
n = 73(0.03)(0.33)(0.65)(0.19)(0.81)
Control19114168152450
n = 301(0.06)(0.39)(0.56)(0.25)(0.75)
χ22.482.37
p-value0.2890.124
p—statistical significance in χ2 test; N—number of subjects.
Table
Table 3. STAI and NEO Five-Factor Inventory results (sten scale), shown as mean ± standard deviation for the healthy controls and the NPS users; significance of the difference was assessed by the Mann–Whitney U test.
Table 3. STAI and NEO Five-Factor Inventory results (sten scale), shown as mean ± standard deviation for the healthy controls and the NPS users; significance of the difference was assessed by the Mann–Whitney U test.
STAI/NEO Five-Factor Inventory/NPS Users
(n = 73)		Control
(n = 301)		Zp-Value
STAI state/scale6.08 ± 2.234.69 ± 2.144.6140.0000 *
STAI trait/scale7.34 ± 2.215.16 ± 2.186.8890.0000 *
Neuroticism/scale6.92 ± 2.394.67 ± 2.017.1600.0000 *
Extraversion/scale5.78 ± 2.166.37 ± 1.98−2.1130.0345 *
Openness/scale4.84 ± 1.954.53 ± 1.611.2480.2118
Agreeableness/scale4.67 ± 2.065.60 ± 2.09−3.4460.0005 *
Conscientiousness/scale5.82 ± 2.266.08 ± 2.15−0.7470.4550
p—statistical significance, Mann–Whitney U test; N—number of subjects, M ± SD, mean ± standard deviation. * Significant differences between groups.
Table
Table 4. Differences in DAT1 and the NEO Five-Factor Inventory scale or the STAI scale, between healthy control subjects and NPS users.
Table 4. Differences in DAT1 and the NEO Five-Factor Inventory scale or the STAI scale, between healthy control subjects and NPS users.
STAI/NEO Five-Factor Inventory DAT1ANOVA
NPS Users (n = 73)Control
(n = 301)		9/9
(n = 21)		9/10
(n = 138)		10/10
(n = 215)		FactorF (p-Value)ɳ2Power (alfa = 0.05)
STAI state/scale6.08 ± 2.234.69 ± 2.144.38 ± 2.255.05 ± 2.214.96 ± 2.24interceptF1, 368 = 341.56 (p < 0.0001 *)0.4811.000
NPS users/controlF1, 368 = 6.30 (p = 0.012 *)0.0170.707
DAT1F2, 368 = 1.32 (p = 0.267)0.0070.285
NPS users/control × DAT1F2, 368 = 1.23 (p = 0.291)0.0070.269
STAI trait/scale7.34 ± 2.215.16 ± 2.185.00 ± 2.075.74 ± 2.365.54 ± 2.36interceptF1, 368 = 457,68 (p < 0.0001 *)0.5541.000
NPS users/controlF1, 368 = 15.07 (p = 0.0001 *)0.0390.972
DAT1F2, 368 = 1.22 (p = 0.295)0.0070.266
NPS users/control × DAT1F2,368 = 0.39 (p = 0.673)0.0020.114
Neuroticism/scale6.92 ± 2.394.67 ± 2.014.48 ± 2.115.12 ± 2.365.16 ± 2.22interceptF1, 368 = 415.23 (p < 0.0001 *)0.5301.000
NPS users/controlF1, 368 = 14.02 (p = 0.0002 *)0.0370.962
DAT1F2,36 8 = 2.62 (p = 0.073)0.0140.521
NPS users/control × DAT1F2, 368 = 4.23 (p = 0.015 *)0.0220.739
Extraversion/scale5.78 ± 2.166.37 ± 1.986.52 ± 2.066.19 ± 2.046.33 ± 2.01interceptF1, 368 = 599.92 (p < 0.0001 *)0.6201.000
NPS users/controlF1, 368 = 0.240 (p = 0.624)0.0010.078
DAT1F2, 368 = 3.24 (p = 0.040 *)0.0170.615
NPS users/control × DAT1F2, 368 = 3.81 (p = 0.023 *)0.0200.691
Openness/scale4.84 ± 1.954.53 ± 1.614.29 ± 1.524.49 ± 1.784.68 ± 1.63interceptF1, 368 = 409.43 (p < 0.0001 *)0.5271.000
NPS users/controlF1, 368 = 0.055 (p = 0.814)0.00010.056
DAT1F2, 368 = 0.57 (p = 0.564)0.0030.145
NPS users/control × DAT1F2, 368 = 0.14 (p = 0.872)0.0010.071
Agreeableness/scale4.67 ± 2.065.60 ± 2.094.81 ± 1.635.30 ± 2.185.55 ± 2.10interceptF1, 368 = 356,66 (p < 0.0001 *)0.4921.000
NPS users/controlF1, 368 = 0.24 (p = 0.623)0.0010.078
DAT1F2, 368 = 0.68 (p = 0.507)0.0040.164
NPS users/control × DAT1F2, 368 = 1.15 (p = 0.314)0.0060.254
Conscientiousness/scale5.82 ± 2.266.08 ± 2.156.62 ± 2.336.04 ± 2.105.96 ± 2.20interceptF1, 368 = 452.91 (p < 0.0001 *)0.5521.000
NPS users/controlF1, 368 = 0.20 (p = 0.654)0.00050.073
DAT1F2, 368 = 0.38 (p = 0.686)0.0020.111
NPS users/control × DAT1F2, 368 = 0.44 (p = 0.638)0.0020.123
* Significant differences between groups.
Table
Table 5. Post hoc analysis of interactions between users of ‘new psychoactive substances’ (NPS)/controls, DAT1 polymorphisms, and the NEO-FFI neuroticism/extraversion scale.
Table 5. Post hoc analysis of interactions between users of ‘new psychoactive substances’ (NPS)/controls, DAT1 polymorphisms, and the NEO-FFI neuroticism/extraversion scale.
DAT1 and NEO-FFI Neuroticism Scale
{1}
M = 7.87		{2}
M = 5.50		{3}
M = 6.49		{4}
M = 4.54		{5}
M = 4.37		{6}
M = 4.78
NPS DAT1 9/10 {1} 0.12070.0081 *0.0000 *0.0000 *0.0000 *
NPS DAT1 9/9 {2} 0.50930.51860.46360.6286
NPS DAT1 10/10 {3} 0.0000 *0.0002 *0.0000 *
control DAT1 9/10 {4} 0.73310.3373
control DAT1 9/9 {5} 0.4065
control DAT1 10/10 {6}
DAT1 and NEO-FFI Extraversion Scale
{1}
M = 5.12		{2}
M = 8.50		{3}
M = 6.23		{4}
M = 6.41		{5}
M = 6.32		{6}
M = 6.35
NPS DAT1 9/10 {1} 0.02260.02790.00440.05360.0053
NPS DAT1 9/9 {2} 0.11790.14470.14310.1322
NPS DAT1 10/10 {3} 0.60790.88070.7231
control DAT1 9/10 {4} 0.84590.8016
control DAT1 9/9 {5} 0.9418
control DAT1 10/10 {6}
* Significant statistical differences; M—mean; {1–6}—the various polymorphisms of the DAT1 gene in NPS and control.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Chmielowiec, J.; Chmielowiec, K.; Masiak, J.; Pawłowski, T.; Larysz, D.; Grzywacz, A. Analysis of Relationships between DAT1 Polymorphism Variants, Personality Dimensions, and Anxiety in New Psychoactive Substance (Designer Drug) (NPS) Users. Genes 2021, 12, 1977. https://doi.org/10.3390/genes12121977

AMA Style

Chmielowiec J, Chmielowiec K, Masiak J, Pawłowski T, Larysz D, Grzywacz A. Analysis of Relationships between DAT1 Polymorphism Variants, Personality Dimensions, and Anxiety in New Psychoactive Substance (Designer Drug) (NPS) Users. Genes. 2021; 12(12):1977. https://doi.org/10.3390/genes12121977

Chicago/Turabian Style

Chmielowiec, Jolanta, Krzysztof Chmielowiec, Jolanta Masiak, Tomasz Pawłowski, Dariusz Larysz, and Anna Grzywacz. 2021. "Analysis of Relationships between DAT1 Polymorphism Variants, Personality Dimensions, and Anxiety in New Psychoactive Substance (Designer Drug) (NPS) Users" Genes 12, no. 12: 1977. https://doi.org/10.3390/genes12121977

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop