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. Author manuscript; available in PMC: 2015 Jun 1.
Published in final edited form as: Addiction. 2014 Mar 17;109(6):965–976. doi: 10.1111/add.12512

Clinical features of methamphetamine-induced paranoia and preliminary genetic association with DBH −1021C→T in a Thai treatment cohort

Rasmon Kalayasiri a,*, Viroj Verachai b, Joel Gelernter c, Apiwat Mutirangura d, Robert T Malison c
PMCID: PMC4018411  NIHMSID: NIHMS569993  PMID: 24521142

Abstract

Aims

To explore clinical features of methamphetamine-induced paranoia (MIP) and associations between MIP and a genetic polymorphism in dopamine β-hydroxylase (DBH −1021C→T).

Design

Retrospective analysis of clinical presentation and genetic association by chi-square test and logistic regression analysis.

Setting

A Thai substance abuse treatment center

Participants

727 Methamphetamine-dependent (MD) individuals

Measures

Clinical: Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA) and the Methamphetamine Experience Questionnaire (MEQ). Genetic: DBH −1021C→T.

Findings

Forty percent of individuals (289 of 727) with MD had MIP. Within-binge latency to MIP onset occurred more rapidly in the most recent compared with initial MIP episode (p=0.02), despite unchanging intake (p=0.89). Individuals with MIP were significantly less likely to carry lower (TT/CT) compared with higher (CC) activity genotypes (34% vs 43%; χ21=5, p=0.03). DBH effects were confirmed (OR=0.7, p=0.04) after controlling for associated clinical variables (MD severity, OR=3.4, p<0.001; antisocial personality disorder, OR=2.2, p<0.001; alcohol dependence, OR=1.4, p=0.05; and nicotine dependence, OR=1.4, p=0.06). TT/CT carriers were more likely to initiate cigarette smoking (OR=3.9, p=0.003) and probably less likely to be dependent on alcohol (OR=0.6, p=0.05).

Conclusions

Among methamphetamine-dependent individuals, paranoia appears to occur increasingly rapidly in the course of a session of methamphetamine use. Severity of methamphetamine dependence and antisocial personality disorder predicts methamphetamine-induced paranoia. The genetic polymorphism in dopamine β-hydroxylase is associated with methamphetamine-induced paranoia and influences smoking initiation.

Keywords: methamphetamine, paranoia, psychosis, smoking, DBH, gene

Introduction

In 2011, an estimated 14–53 million people used amphetamine-type stimulants (ATS) globally, making ATS the second most popular illicit substances in the world after cannabis (1). In Thailand, methamphetamine (MA) or “yaba” is the most prevalent ATS by far (2), and recent reports show annual prevalence rates of >1% among 15–64 year olds (rates, higher than worldwide averages; 0.7%) (1). Given the high prevalence, Thailand is a potentially informative location for studying both the environmental and genetic risk factors for ATS use and its complications (35) in humans.

Paranoia, defined here operationally as an irrational distrust or fear of others despite the absence of realistic potential for harm (6), occurs in 46–76% of experienced MA users (713). It is the primary symptom associated with MA-psychosis (1417), and it can extend beyond states of acute intoxication, lasting anywhere from several days to months, and may even persist in some cases (13, 18, 19). Previous studies suggest that there is a heritable component (13, 20), and several genetic markers have been suggested to be associated with the trait (13, 2123). Most commonly, methamphetamine-induced paranoia (MIP) is a short-lived phenotype that is expressed during MA intoxication and resembles other forms of stimulant-induced suspiciousness (e.g., cocaine-induced paranoia or CIP) (11, 24, 25).

Previously, we reported a genetic association between CIP and a functional polymorphism in the dopamine β-hydroxylase (DβH) gene (26). To our knowledge, genetic risk factors for MIP in humans have not been previously reported. We therefore pursued a candidate gene study of MIP in a large cohort rigorously characterized for clinical features and other environmental risk factors associated with the trait. Finding genetic risk factors for MIP, a MA-induced psychosis spectrum trait, may shed light on potential vulnerability or protective factors for other psychotic illnesses.

DβH is the sole enzyme responsible for converting dopamine (DA) to norepinephrine (NE) in humans. Measures of DβH enzyme activity and/or the genetic markers linked to its activity have been previously reported to be associated with substance- and nonsubstance-related psychosis (2735) and substance (i.e., alcohol, nicotine) use or dependence (3538). Prior work has demonstrated that 50% of the variance in plasma enzyme activity is explained by a putative functional polymorphism (−1021 C→T) in the DβH gene (39). Since its identification, this single nucleotide polymorphism (SNP) has been found to be associated with a number of neuropsychological phenotypes, including those related to substance use traits. For example, the low-activity T allele was associated with impulsive personality styles (40), fewer cigarettes smoked per day (41), and CIP in a human laboratory paradigm of cocaine self-administration (26). Such findings motivated our exploration of clinical and genetic risk factors for MIP in a reasonably large cohort (N=727).

Methods

Subjects

Thai-speaking MA users age 18 years or above were recruited from Thanyarak Institute, a substance dependence treatment center in Central Thailand where they were hospitalized for four-months of MA rehabilitation treatment between 2007 and 2011. The study ran as part of an ongoing genetic study of MIP which included data from a smaller, overlapping studies of MIP risk factors (N=96) (7) and inhalant use (n = 456) in MA users (42). Exclusion criteria were similar across studies, including 1) lifetime use of MA < 11 instances; 2) history of primary psychotic disorders; 3) brain disease(s) (i.e., epilepsy, stroke, brain trauma). In addition, only subjects with MA dependence (N = 727 out of 990 MA users) were included in the current study. All subjects underwent voluntary written informed consent prior to their research participation and were compensated (500 baht, or roughly US$15), per IRB-approved protocol.

Diagnostic assessments were performed during each subject’s rehabilitation period using a Thai version of the Semi-Structured Assessment for Drug Dependence and Alcoholism (SSADDA) (43, 44). Additional information on clinical manifestations of MA use, including MIP, was obtained retrospectively using the Thai language version of the Methamphetamine Experience Questionnaire (MEQ) (7). Both instruments were implemented as computerized versions (see below) and conducted by interviewers (all with bachelors degrees in psychology or higher) certified for their use based on a standard training protocol (ten SSADDA training interviews followed by two qualifying/examination interviews). Interviews were subjected to a rigorous quality control process, including editing and cross-editing by interviewers and review by the principal investigator (RK). The study was approved by The Faculty of Medicine, Chulalongkorn University Institutional Review Board (Med Chula IRB), The Ethical Review Committee for Research in Human Subjects, Thailand Ministry of Public Health, and the Research Committee, Thanyarak Institute on Drug Abuse.

Assessments

The SSADDA is a comprehensive semi-structured diagnostic interview used in genetic studies of substance dependence and related phenotypes (44). Our group developed a Thai version of the SSADDA, which was translated, back-translated, and validated in genetic studies of opioid dependence in Northern Thailand, where it was shown to have both high inter-instrument validity (κ = 0.97) and inter-rater reliability (κ = 0.97) (43). Demographic, diagnostic (i.e., antisocial personality disorder or ASPD), attention deficit hyperactivity disorder or ADHD, anxiety disorders) and substance-use data (i.e., onset, duration, amount and frequency during period of heaviest use) are available in sections on tobacco, alcohol, MA (cocaine in the English version), opioids, and other substances (i.e., cannabis, solvents, other stimulants). The diagnosis of substance dependence is based on the Diagnostic Statistical Manual for Mental Disorder 4th revision (DSM-IV). In the present study, we used number of DSM-IV criteria met for MA dependence (range between 3–7 criteria) to determine severity of MA dependence (MD).

In the current study, we evaluated the concurrent validity of a SSADDA diagnosis of MA dependence with respect to that established by the Mini International Neuropsychiatric Inventory (M.I.N.I) - lifetime Thai version (43) in 79 MA users by kappa statistics. We also assessed the instrument’s inter-rater reliability, examining the agreement between interviewers according to the number of DSM-IV criteria met for MA dependence (intraclass correlation) and a diagnosis of MIP (kappa statistic).

The Thai MEQ (7) was adapted from the Yale Cocaine Experience Questionnaire (Yale CEQ), substituting “yaba” (the common term for MA) for “cocaine” (6), and was used to explore paranoid experiences during MA use (7). The presence or absence of MIP was evaluated based on a specific probing algorithm beginning with a thorough description of the trait, followed by the two criteria questions (“Have you ever had a paranoid experience?” and “Have you ever had a paranoid experience while using yaba?”). Affirmative responses to both questions define the MIP phenotype, which has been shown previously to have high reliability across instruments (κ = 0.87) (Thai SSADDA and Thai MEQ) (7). Clinical features of MIP (age of onset, accompanying psychotic symptoms, behavioral response to MIP experience) were obtained from the MEQ by retrospective interview, as were four additional variables relating to the onset and progression of the trait (i.e., amount of MA use, latency to MIP within a binge, latency to staying awake, and MIP duration).

Genotyping

Genotyping was done at the Center of Excellence in Molecular Genetics of Cancer and Human Diseases, Department of Anatomy, Faculty of Medicine, Chulalongkorn University. DNA was extracted from 10 milliliters of whole blood using a ZR Genomic DNA I kit (Silica Bead Format) (Zymo Research, Irving, CA). A restriction fragment length polymorphism (RFLP) method was used to obtain genotypic data at DBH −1021C→T (rs1611115) for each subject, as described elsewhere (45). In brief, DBH −1021C→T was amplified by polymerase chain reaction (PCR) method. The PCR product was digested using HhaI. The digested product was visualized using an 8% acrylamide gel, size fractionated to identify the T and C alleles. About 10% of assays in all genotyping plates were repeated for quality control. Of 727 DNA samples, DBH data for 55 (7.6%) could not be obtained. Genotypic data were double-scored by two independent researchers. Deviation from Hardy Weinberg equilibrium expectations was assessed in the total cohort and diagnostic subgroups.

Data analysis

Clinical characteristics of MIP were explored both through descriptive statistics and visual inspection of each variable’s underlying distribution within the populations. Non-normally distributed continuous variables were categorized prior to analyses (age, MA pills per day, MA use duration). MA use, and characteristics of MIP (including onset and course), were compared among MA-dependent individuals with MIP using a McNemar test. In addition, demographic, diagnostic, and MA use variables were compared between MA-dependent individuals with and without MIP by using two-tailed chi-square or unpaired t-test and were entered in the binary logistic regression analysis of MIP in an exploratory manner to identify clinical variables possibly associated with the risk for MIP.

In genetic association analyses, subjects were excluded if three or four grandparents were of non-Thai (e.g., Chinese) ancestry. MIP and clinical features of MIP were first explored according to genotypic group (TT vs. CT vs. CC) by two-tailed, 2x3 heterogeneity chi-square test. C- and T – allele frequency were compared by two-tailed chi-square test. TT and CT were also binned for the purpose of a third statistical analysis. Additional variables were then incorporated in the genetic logistic regression analyses for MIP. Clinical risk factors for MIP identified by the logistic regression analysis described above were then tested for their interactions with the gene on MIP. Specifically, interaction between the binary genotypic variable (CC vs TT/CT) and a clinical risk factor was entered first into the binary logistic regression analysis of MIP, controlling subsequently by the remaining previously identified environmental risk factors. Finally, genetic associations of DBH with identified clinical risk factors for MIP and available related variables with the risk were explored by chi-square tests and logistic regression analyses after controlling for MIP status, demographic, diagnostic and MA-use variables.

Results

Inter-rater reliability and concurrent validity of MA dependence and MIP using Thai SSADDA

Diagnostic assessments of MD ascertained via the SSADDA and MINI were in substantial agreement (κ=0.69; MD prevalence = 78%; n = 79). Inter-rater reliability for number of DSM-IV MD criteria met on the SSADDA was high (ICC = 0.81; means = 5.2 ±2.0 vs 5.0 ± 2.0, n = 79). The Thai version of the SSADDA also showed moderate inter-rater reliability for the diagnosis of MIP (κ = 0.46; MIP prevalence = 16%; n = 79).

Clinical features of MIP using the Thai MEQ

Of 990 MA users, 727 (73.4%) met DSM-IV criteria for MD of whom 289 (39.8%) had MIP. In contrast, MIP occurred in 19 (7.2%) of 263 non-dependent MA users. Age of paranoia onset, latency of symptom onset (time between first MA use and first paranoid symptoms), clinical features of MIP and MIP behaviors, and co-occurring psychotic symptoms are shown in Table 1. MIP was endorsed as an aversive feeling and rated as significantly distressing. In general, paranoia occurred when using MA while alone by oneself, followed by using with others; when using MA in a familiar place, followed by using in new place and no difference in person or place respectively, at the time when MIP typically occurred. The majority smoked MA and used MA daily or almost daily.

Table 1.

Clinical features, behaviors and co-occurring psychotic symptoms associated with MIP in MA dependent individuals.

MIP (N = 289) %
Clinical features of MIP
Age of MIP onset (median; years) (mean ± SD = 21.6 ± 5.7; min, max = 12, 49) 20 -
Latency of MIP onset (median; years) (mean ± SD = 3.9 ± 3.8; min, max = 0, 18) 3 -
Paranoia at the time of first MA use 22 7.6
MIP occurred once in lifetime 31 10.7
Subsequent paranoia without MA use 24 8.3
Persistence MIP beyond intoxication 50 17.3
MIP frequently associated with continued MA use (median) b, c (mean ± SD = 3.2 ± 1.3) 3.0 -
Environmental variables when experiencing MIP
MA use with or without others d
 Alone by oneself 154 53.3
 With others 52 18.0
 No difference 48 16.6
MA use in familiar or new place d
 Familiar place 120 41.5
 New place 93 32.2
 No difference 43 14.9
Route of MA use
 Smoke 284 98.3
 Oral 5 1.7
MA use daily or almost daily 240 83.1
MIP intensified when using higher doses of MA c 164 63.6
MIP behaviors and co-occurring symptoms
Feeling distressed from MIP (median) a (mean ± SD = 3.2 ± 1.3) 3.0 -
Response when having MIP
 Hiding 217 75.1
 Obtain weapon 78 27.0
 Call for help 24 8.3
 Attack others 23 8.0
Other psychotic symptoms
 Auditory hallucination 188 65.1
 Visual hallucination 81 28.0
 Tactile hallucination 34 11.8
 Olfactory hallucination 13 4.5
Progression of MIP
Vividness of last MIP compared to first experience c, d
 More vivid 97 37.6
 Less vivid 75 29.1
 Equal 74 28.7
Latency to MIP onset within a binge c (median; hours)
 At MIP onset 11 p=0.02*
 At most recent MIP 6
Duration of MIP c (median; hours)
 At MIP onset 4 p=0.66
 At later episodes of MIP 3
Amount of MA use (median; pills per day) c
 At MIP onset 5 p=0.89
 At later episodes of MIP 5
Latency of staying awake (median; hours) c
 At MIP onset 48 p=0.16
 At later episodes of MIP 36

MIP = methamphetamine- induced paranoia, MA = methamphetamine

a

0 = not distressing at all, 5 = intolerable

b

0 = never, 5 = always

c

n = 258

d

the rest of the group did not know the difference

*

p < 0.05, McNemar test

After an initial paranoid episode, MIP was frequently associated with continued use. A majority of individuals who experienced MIP more than once reported that their paranoia intensified when using higher doses of MA and stated that their most recent MIP experience was more vivid than or equal to their first experience. Latency to MIP onset within a binge (the interval between the first dose of MA and the beginning of paranoid feelings) was significantly shorter in their most recent, as compared to their initial, MIP episode. However, the amount of MA (pills per day), duration of MIP occurrence, and latency of staying awake at the time of later episodes of MIP did not differ compared to those at initial occurrence (Table 1).

Demographic, diagnostic, and drug-use variables and MIP

Individuals with MIP were younger at first use of MA and reported more severe MA use than those without (Table 2). In addition, individuals with MIP were more likely to have a comorbid psychiatric diagnosis, including ASPD, social phobia, suicide attempt, pathological gambling, nicotine dependence, and alcohol dependence, than those without, by univariate analyses. With respect to the logistic regression analysis, severity of MA dependence as measured by DSM-IV symptom count was the most significant associated (likely risk) factor for the trait, followed by ASPD, alcohol dependence and nicotine dependence. Fewer episodes of MA use in the past year had a trend association with MIP (Table 2).

Table 2.

Methamphetamine (MA) use variables, demographics and diagnoses in individuals with (MIP) and without (non-MIP) paranoia.

MIP (n = 289) Non-MIP (n = 438) Univariate analyses Multivariate analysis


n % n % χ2, df P values Wald Adjusted ORs 95% CI
P values
Lower Upper
MA use variables
MA use duration
 ≥ 6 years 182 63.0 272 62.2 0, 1 0.84 1.0 0.8 0.5 1.3 0.30
 0 – 5 years 107 37.0 165 37.8
Daily MA pills during period of heaviest use
 ≥ 5 172 59.5 218 49.8 7, 1 0.01** 0.2 1.1 0.7 1.7 0.68
 1 – 4 117 40.5 220 50.2
Daily spent for MA during period of heaviest use
 ≥ 1000 baht 147 51.0 185 42.7 5, 1 0.03* 0.04 1.04 0.7 1.6 0.85
 < 1000 baht 141 49.0 248 57.3
MA days per month during period of heaviest use
 21 – 30 197 68.2 262 59.8 5, 1 0.02* 0.3 1.1 0.8 1.6 0.57
 1 –20 92 31.8 176 40.2
Episodes of MA use in lifetime
 ≥ 1001 232 80.6 322 74.2 4, 1 0.05* 0.5 1.2 0.7 1.9 0.49
 11 – 1000 56 19.4 112 25.8
Episodes of MA use in last year
 ≥ 151 201 70.3 297 70.4 0, 1 0.98 3.8 0.7 0.4 1.0 0.05
 0 – 150 85 29.7 125 29.6
MA dependence severity (DSM-IV symptom count)
 5 – 7 boxes 265 91.7 316 72.1 42, 1 <0.001*** 19 3.2 1.9 5.5 <0.001 ***
 3 – 4 boxes 24 8.3 122 27.9
MA cessation 236 81.9 360 82.2 0, 1 0.93 1.5 0.7 0.5 1.2 0.22
Age of MA onset (mean (SD); years) 17.7 (4.9) 18.5 (5.7) t725 = − 2.2 0.03* (log) 1.1 3.1 0.4 25.8 0.30 (log)
Demographics
Age (years)
 ≥ 28 116 40.1 184 42.0 1, 2 0.79 0.4 1.1 0.8 1.5 0.54
 23–27 73 25.3 113 25.8
 18–22 100 34.6 141 32.2
Male 142 49.1 199 45.4 1, 1 0.33 0.02 1.0 0.7 1.5 0.90
Race (Thai) 280 96.9 419 95.7 1, 1 0.40 1.2 1.6 0.7 3.9 0.27
Employment 61 21.1 102 23.3 1, 1 0.49 0.3 0.9 0.6 1.4 0.60
HHI (baht / month) a
 0–15000 145 50.2 215 49.2 0, 1 0.80 1.9 1.3 0.9 1.8 0.17
 ≥ 15001 144 49.8 222 50.8
Marital status
 Never married 215 74.4 321 73.5 0, 2 0.90 0.1 1.0 0.7 1.2 0.71
 Not married b 41 14.2 61 14.0
 Married 33 11.4 55 12.6
Diagnoses
MDE c 2 0.7 4 0.9 0, 1 0.75 0.8 0.4 0.1 2.8 0.37
Suicidal attempt 72 25.0 78 17.8 6, 1 0.02* 2.5 1.4 0.9 2.1 0.11
Conduct disorder 28 9.7 28 6.4 3, 1 0.10 1.4 1.4 0.8 2.6 0.24
ASPD d 79 27.3 59 13.5 22, 1 <0.001*** 6.0 1.8 1.1 2.7 0.01*
ADHD e 3 1.0 2 0.5 1, 1 0.35 0.1 1.5 0.1 27 0.79
PTSD f 1 0.3 2 0.5 0, 1 0.82 0.7 0.3 0.03 4.5 0.41
Social phobia 12 4.2 5 1.1 7, 1 0.009** 1.2 1.8 0.6 5.6 0.28
Agoraphobia 1 0.3 1 0.2 0, 1 0.77 0.1 0.7 0.03 16 0.82
Pathological gambling 117 40.5 126 28.8 11, 1 0.001** 0.9 1.2 0.8 1.7 0.35
Other substances
Nicotine dependence 218 75.4 261 59.6 19, 1 <0.001*** 4.7 1.5 1.04 2.2 0.03*
Alcohol dependence 108 37.4 95 21.7 21, 1 <0.001*** 6 1.6 1.1 2.4 0.01*
Illicit drug use (≥100 times)
 Opioid 18 6.2 21 4.8 1, 1 0.40 0.1 1.1 0.5 2.5 0.74
 Cannabis 45 15.6 51 11.6 2, 1 0.13 0.2 1.1 0.7 1.9 0.63
 Solvent 25 8.7 37 8.4 0, 1 0.92 2.3 0.6 0.3 1.1 0.13
 Ice 43 14.9 59 13.5 0, 1 0.60 0.01 1.0 0.6 1.6 0.91

MIP = MA-induced paranoia

a

Household gross income,

b

Widowed, separated, divorced,

c

Major depressive episode,

d

Antisocial personality disorder,

e

Attention deficit hyperactivity disorder,

f

Posttraumatic stress disorder

***

p < 0.001

**

p < 0.01

*

p < 0.05

Other demographic (age, sex, race, marital status, employment status, and household gross income), diagnostic (PTSD, ADHD, conduct disorder), and drug use (duration, or cessation of MA use) variables did not differ between those with and without MIP (Table 2).

DBH gene variant and MIP

Only Thai subjects were entered into the analysis of genetic association (N = 646), to minimize population stratification. We observed no evidence of deviation from Hardy Weinberg Equilibrium expectations in the entire MA dependent sample (X22= 1.1, p = 0.58) and each subgroup (MIP, n = 261, X22= 0.8, p = 0.67; non-MIP, n = 385, X22= 0.4, p = 0.83).

Among MD individuals, lower T-allele frequency was observed in the MIP as compared to non-MIP group (Table 3). C-homozygotes did not significantly differ in MIP frequency compared to either the hetero- or T-homozygotes, but when the latter two groups were combined for a post-hoc exploratory analysis, the result was nominally significant. None of clinical features of MIP were associated with genotype or allele frequency of the gene (Table 3). Genetic associations with MIP were confirmed by logistic regression analysis (Tables 3 and 4). A significant interaction of gene by severity of MD was observed (Table 4). However, interaction between gene and ASPD or gene and nicotine/alcohol dependence did not predict MIP.

Table 3.

Genotype and allele frequency of DBH −1021 CT and logistic regression analyses of genetic influences on MIP and MIP clinical features.

DBH genotypes (N = 646)
P value DBH group
P value DBH alleles
P value Multivariate analysis b
TT CT CC TT/CT CC T-allele C-allele Adjusted ORs 95% CI P Values




n % n % n % n % n % n % n % Lower Upper
MIP vs Non-MIP
 MIP 9 32.1 64 34.6 188 43.4 0.08 73 34.3 188 43.4 0.03* 82 34.0 440 41.9 0.03* 0.6 0.4 0.9 0.02**
 Non-MIP 19 67.9 121 65.4 245 56.6 140 65.7 245 56.6 159 66.0 611 58.1
Prolonged (≥ 4 hours) MIP (N = 645)
 Prolonged 3 11.1 33 17.8 93 21.5 0.29 36 17.0 93 21.5 0.18 39 16.3 219 20.8 0.11 0.8 0.5 1.3 0.33
 Others a 24 88.9 152 82.2 340 78.5 176 83.0 340 78.5 200 83.7 832 79.2
Early (≤ 18 years) MIP onset
 Early 5 17.9 25 13.5 65 15.0 0.79 30 14.1 65 15.0 0.75 35 14.5 155 14.7 0.93 0.9 0.5 1.6 0.70
 Others a 23 82.1 160 86.5 368 85.0 183 85.9 368 85.0 206 85.5 896 85.3
Early (< 3 years) latency to MIP onset
 Early 7 25.0 26 14.1 88 20.3 0.13 33 15.5 88 20.3 0.14 40 19.2 202 16.6 0.35 0.6 0.4 1.0 0.06
 Others a 21 75.0 159 85.9 345 79.7 180 84.5 345 79.7 201 80.8 849 83.4
Early (<11 hours) latency to MIP onset within a MA binge
 Early 5 17.9 60 32.4 125 28.9 0.26 65 30.5 125 28.9 0.67 70 29.5 310 29.0 0.89 1.1 0.7 1.6 0.64
 Others a 23 82.1 125 67.6 308 71.1 148 69.5 308 71.1 171 70.5 741 71.0
Frequent MIP experience (score > 3 out of 5) with MA use
 Frequent 5 17.9 21 11.4 67 15.5 0.36 26 12.2 67 15.5 0.27 31 12.9 155 14.7 0.45 0.8 0.4 1.3 0.36
 Others a 23 82.1 164 88.6 366 84.5 187 87.8 366 84.5 210 87.1 896 85.3
Accompanying hallucinations
 Yes 6 21.4 46 24.9 132 30.5 0.26 52 24.4 132 30.5 0.11 58 24.1 310 29.5 0.09 0.7 0.5 1.1 0.13
 No 22 78.6 139 75.1 301 69.5 161 75.6 301 69.5 183 75.9 741 70.5

MA = methamphetamine, DBH = dopamine β-hydroxylase, OR = Odds ratio, 95% CI = 95% confidence interval

a

Included non-MIP and MIP without the feature.

b

Influence of DBH gene (TT/CT compared to CC) on MIP and MIP clinical features after demographic, diagnostic and MA-use variables were controlled.

*

X21 = 5, p < 0.05, two tailed.

**

p < 0.05, logistic regression analysis

Table 4.

Logistic regression analysis of genetic influence and gene x environment interaction on MIP compared to non-MIP

Univariate analyses
Wald df P values Adjusted ORs 95% CI
G x E interactionb (P values)
χ2, df P values Lower Upper
DBH gene a 4.9, 1 0.03 4 1 0.04 * 0.7 0.5 0.98 -
Severity of MA dependence 42, 1 <0.001 22 1 <0.001** 3.4 2.1 5.7 0.02 *
Antisocial personality 22, 1 <0.001 14 1 <0.001** 2.2 1.5 3.4 0.45
Alcohol dependence 21, 1 <0.001 4 1 0.05 1.4 1.0 2.1 0.99
Nicotine dependence 19, 1 <0.001 4 1 0.06 1.4 1.0 2.1 0.51

MIP = methamphetamine-induced paranoia, MA = methamphetamine, DBH = dopamine β-hydroxylase 95% CI = 95% confidence interval, df = degree of freedom, G x E = gene by environment

a

TT/CT compared to CC genotype.

b

After controlling for other clinical variables in the Table.

*

p < 0.05

**

p < 0.001

DBH gene variant and identified clinical risk factors for MIP

Clinical risk factors for MIP (e.g., severity of MD, ASPD, nicotine dependence, alcohol dependence) and available clinical data related to the risk factors, including MA use variables (related to severity of MD), conduct disorder (related to ASPD), nicotine initiation (46) (ever vs never been a regular smoker, e.g., used ≥ 100 cigarettes lifetime; related to nicotine dependence), and alcohol flush syndrome (i.e., flushing after 1–2 drink of alcohol; related to protection from alcohol dependence) were explored for genetic association with DBH gene variant. TT/CT genotypes and T-allele were associated with nicotine initiation, non-alcohol dependence and shorter duration of MA use in lifetime (Table 5). However, only a genetic association of DBH with nicotine initiation was confirmed by logistic regression analysis (p=0.003). TT/CT genotypes were associated at trend level with protection for alcohol dependence (p=0.05) and prolonged duration of MA-use (p=0.06), from logistic regression analysis.

Table 5.

Genotype and allele frequency of DBH −1021 CT and logistic regression analyses of genetic influences on identified clinical risk factors for MIP and related variables.

DBH genotypes (N = 646)
P values DBH group
P values DBH alleles
P values Multivariate analysis a
TT CT CC TT/CT CC T-allele C-allele Adjusted 95% CI P values




n % n % n % n % n % n % n % ORs Lower Upper
MA use variables
MA use ≥ 6 years b 16 57.1 101 54.6 286 66.2 0.02* 117 54.9 286 66.2 0.005** 133 55.2 673 64.2 0.01** 0.6 0.3 1.0 0.06
Daily MA ≥ 5 pills b, c 14 50.0 102 55.1 232 53.6 0.86 116 54.5 232 53.6 0.83 130 53.9 566 53.9 0.98 1.0 0.6 1.7 0.86
Daily MA ≥ 1000 baht b, c 10 35.7 90 48.9 198 46.2 0.41 100 47.2 198 46.2 0.81 110 45.8 486 46.6 0.82 1.0 0.6 1.6 0.93
MA ≥ 21 days per month b, c 18 64.3 120 64.9 272 62.8 0.89 138 64.8 272 62.8 0.62 156 64.7 664 63.2 0.65 1.2 0.8 1.7 0.49
Lifetime ≥ 1001 MA episodes b 21 75.0 137 74.9 330 76.7 0.87 158 74.9 330 76.7 0.60 179 74.9 797 76.4 0.62 1.1 0.6 1.8 0.86
Last year ≥ 151 MA episodes b 17 65.4 128 72.3 295 69.6 0.69 145 71.4 295 69.6 0.64 162 70.7 718 70.0 0.84 0.9 0.5 1.4 0.54
Severe MA dependence 23 82.1 143 77.3 352 81.3 0.50 166 77.9 352 81.3 0.31 189 78.4 847 80.6 0.45 0.8 0.5 1.4 0.49
Diagnoses
Conduct disorder 2 7.1 14 7.6 37 8.5 0.90 16 7.5 37 8.5 0.65 18 7.5 88 8.4 0.64 1.3 0.6 2.5 0.53
ASPD 7 25.0 37 20.0 77 17.8 0.56 44 20.7 77 17.8 0.38 51 21.2 191 18.2 0.28 1.2 0.7 2.1 0.44
Other substances
Nicotine dependence 18 64.3 123 66.5 286 66.1 0.97 141 66.2 286 66.1 0.97 159 66.0 695 66.1 0.96 0.9 0.6 1.4 0.55
Nicotine initiation 28 100 174 94.1 384 88.7 0.02* 202 94.8 384 88.7 0.01* 230 95.4 942 89.6 0.005* 3.9 1.6 9.7 0.003**
Alcohol dependence 4 14.3 45 24.3 137 31.6 0.04* 49 23.0 137 31.6 0.02* 53 22.0 319 30.4 0.01** 0.6 0.4 1.0 0.05
Alcohol flush syndrome 6 21.4 63 34.1 170 39.3 0.10 69 32.4 170 39.3 0.09 75 31.1 403 38.3 0.04* 0.7 0.5 1.1 0.11

MA = metha mphetamine, DBH = dopamine β-hydroxylase, ASPD = Antisocial personality disorder, OR = Odds ratio, 95% CI = 95% confidence interval

a

Influence of DBH gene (TT/CT compared to CC) on identified risk and related factors on MIP after demographic, other diagnostic and MA-use variables and MIP were controlled.

b

Compared to less severe MA use.

c

During period of heaviest MA use.

*

p < 0.05

**

p < 0.01

Discussion

Results of our study point strongly to the importance of severity of MD, comorbid alcohol or nicotine dependence, and ASPD in predicting the onset of MIP. The low-activity T-allele at DBH −1021C→T possibly protects from the occurrence of MIP and predicts nicotine initiation. In addition, and consistent with mechanisms of sensitization, MIP occurred more rapidly over the course of MA use in the face of unchanging MA intake. Ours is the largest cohort studied to date to examine such clinical and genetic risk factors for MIP.

DBH is a gene located on chromosome 9q34 spanning 22,982 base pairs. The −1021C→T marker of the DBH is located at the 5′ promoter region and previously shown to associate with markers of DβH enzyme activity (and is thus functionally relevant). While a low-activity allele or haplotype of DBH was previously found to be associated with paranoia while under the influence of cocaine (26, 28), the high-activity C-allele was nominally associated with MIP in the current study. Although there is a very large preclinical and clinical literature demonstrating similarities across stimulants (e.g., including cocaine and methamphetamine) (47, 48), we cannot rule out the possibility of differences that derive from pharmacological mechanisms (e.g., pure reuptake inhibitor vs. a releaser/reuptake inhibitor, respectively) and/or pharmacokinetics (e.g., relatively shorter vs. longer half-lives) of the drugs (49, 50). For example, the latency to MIP within a binge is much longer than that of CIP (hours for MIP (Table 1) vs. minutes for CIP (6, 51)), suggesting different mechanism of the trait (perhaps oxidative stress and neurotoxicity in MA use (3, 52)) rather than immediate synaptic DA hyperactivity. Although this seems an unlikely explanation, alternatively, methodological limitations of the current work may account for such differences. Further, when corrected for multiple statistical tests, our results are not considered statistically significant, and thus replication in a larger sample is necessary.

The effects of dependence on other substances (e.g., alcohol, nicotine) on MIP are consistent with previous findings (7, 12, 13). In addition, the low-activity T-allele and TT/CT genotypes were associated with cigarette-smoking initiation, while appearing statistically protective towards alcohol dependence and long-term MA use. While such findings are consistent with previous reports on DBH and smoking behaviors (37, 41, 46, 53, 54) and alcohol dependence (36, 38), future replication is warranted to confirm these seemingly opposite effects on substance dependence vulnerability.

The effect of severity of MA dependence (DSM symptom count) on MIP is consistent with previous findings (7, 12, 13). While other measures of MA use were also associated with MIP in initial analyses, they did not survive the logistic regression analysis (except for a trend association between MIP and “fewer” episodes of MA use in last year, consistent with the aversive effect of drug-induced paranoia in previous studies) (6, 55). Findings of an interaction between DBH and MD severity with respect to the occurrence of MIP are also intriguing, suggesting the importance of gene by environment interactions, as previously suggested.

A majority of MIP individuals reported an increase in the intensity of their paranoia over the course of their use. The more rapid onset of paranoia during use in the face of other unchanging clinical features (latency to sleep, lack of change in MA use), are consistent with mechanisms of sensitization (6, 12, 56). Although MIP duration was usually about 3–4 hours and did not change significantly over the course of MIP, nearly one in five individuals endorsing MIP reported persistence of symptoms even after discontinuing MA and even after other intoxicating effects of the drug (e.g., ‘high’ or euphoria) had waned, raising questions about potential continuities between the trait and more severe versions of MA-psychosis (at least in such subgroups). Interestingly, a majority (68%, 34 out of 50) of the individuals who endorsed a ‘sensitizing’ pattern to their MIP experienced prolonged paranoia (e.g., MIP lasting longer than the average duration of MIP or ≥ 4 hours).

Several limitations deserve mention. First, the study was performed retrospectively, and clinical features of MIP and its associated variables might be subject of recall bias. In addition, despite our cohort being the largest sample to report on MIP to date, it is still modest in size, and we cannot exclude the possibility that a larger cohort would have substantially changed our findings. The power of the study is 50% based on a MAF for DBH C->1021->T of 18% (57). In addition, the current approach, namely a candidate gene study, carries a number of limitations. Although we based our rationale for examining DBH on two prior findings of stimulant- (cocaine-) induced paranoia, this is the first study, to our knowledge, to examine MIP. It remains our long-term interest to employ genome-wide methods to study genetic risk factors for MIP. However, our current sample remains modest in comparison to samples typically required of complex trait genomewide association studies (where case-control cohorts of several thousand or more are typical). In addition, the current study did not employ genetic methods (e.g., structured association or genomic control) to exclude the possibility of Type I error resulting from population stratification artifact. For example, though a relatively homogenous population in comparison to many western populations, and although we took steps to specifically exclude individuals of non-Thai heritage from our genetic association analyses, Thais are known to have two primary ancestral origins, including both Tai and Chinese roots; and there are also individuals of minority Hill Tribe ancestry. In addition, recent analysis of data from genome-wide association studies conducted in the Thai population have indicated that at least four genetically distinct subpopulation clusters may exist, requiring up to 5000 ancestry-informative SNP markers to identify with 99% accuracy (unpublished data, Tongsima 2012). Thus, we view this as a major limitation of the current study, and we believe our finding requires future replication efforts that specifically control for population stratification before the current genetic association can be viewed as anything other than preliminary. Finally, we did not apply Bonferroni-corrected p-values for the multiple comparisons conducted, and for these reasons as well, they must be regarded as preliminary.

In conclusion, our results show variation in the clinical features of MIP and support its sensitizing nature over the course of MA dependence. Our preliminary findings also raise the possibility of a genetic risk factor for the trait, but require verification. Future studies of much larger case:control cohorts employing more rigorous genetic methods will ultimately be required to more definitively identify genetic risk factors for the trait.

Acknowledgments

Funding Source: This study was sponsored by Chulalongkorn University (Ratchadapiseksompotch Fund, Budget Year 2010), the Thailand Research Fund (TRF; co-funded by the Office of the Higher Education Commission of Thailand and Chulalongkorn University) (RMU5380025, MRG5080249), and supported by D43 TW006166 US-Thai training grant (JG & RTM; co-funded by Fogarty International Center, National Institute on Drug Abuse or NIDA, and National Human Genome Research Institute) and a NIDA career award (K24 017899; RTM) and Faculty of Medicine, Chulalongkorn University (Ratchadapiseksompotch Fund; RA056/50, RA005/51, RA/54).

This study was sponsored by Chulalongkorn University (Ratchadapiseksompotch Fund, Budget Year 2010), the Thailand Research Fund (TRF; co-funded by the Office of the Higher Education Commission of Thailand and Chulalongkorn University) (RMU5380025, MRG5080249), the Faculty of Medicine, Chulalongkorn University (Ratchadapiseksompotch Fund; RA056/50, RA005/51, RA/54) and supported by US-Thai training grant (D43 TW006166; JG & RTM) co-funded by the Fogarty International Center (FIC), National Institute on Drug Abuse (NIDA), and National Human Genome Research Institute (NHGRI), as well as a NIDA career award (K24 017899; RTM). We would like to thank the staff at Thanyarak Institute for facilitating data collection and Mr. Prakasit Ratanatanyong for laboratory assistance.

Footnotes

Conflict of interest: None.

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