Early initiation of amphetamine-type stimulants (ATS) use associated with lowered cognitive performance among individuals with co-occurring opioid and ATS use disorders in Malaysia.

Abstract

Amphetamine-type stimulants (ATS) use is increasingly prevalent in Malaysia, including among individuals who also use opioids. We evaluated cognitive functioning profiles among individuals with co-occurring opioid and ATS dependence and their lifetime patterns of drug use. Participants (N=50) enrolling in a clinical trial of buprenorphine/naloxone treatment with or without atomoxetine completed the Raven’s Standard Progressive Matrices, Rey-Osterrieth Complex Figure test, Digit Span, Trail Making and Symbol Digit Substitution tasks. Multidimensional scaling and a K-means cluster analyses were conducted to classify participants into lower versus higher cognitive performance groups. Subsequently, analyses of variance procedures were conducted to evaluate between group differences on drug use history and demographics. Two clusters of individuals with distinct profiles of cognitive performance were identified. The age of ATS use initiation, controlling for the overall duration of drug use, was significantly earlier in the lower than in the higher cognitive performance cluster: 20.9 (95% CI: 18.0 – 23.8) versus 25.2 (95% CI: 22.4 – 28.0, p = 0.038). While adverse effects of ATS use on cognitive functioning can be particularly pronounced with younger age, potentially related to greater vulnerability of the developing brain to stimulant and/or neurotoxic effects of these drugs, the current study findings cannot preclude lowered cognitive performance before initiation of ATS use.

Introduction

Amphetamine-type stimulants (ATS) use is rising globally and its use has exceeded the use of heroin and cocaine (UNODC 2014). In Asia, individuals with ATS use disorders increasingly seek treatment for ATS use disorders and associated problems (UNODC 2015). In Malaysia, increasing rates of ATS use particularly among those under age 20 (NADA 2015) and among individuals who are also opioid dependent are observed (Chawarski 2012).

Chronic use of ATS has been consistently associated with learning and memory impairments (Ersche et al. 2006, Hoffman et al. 2006, Moon et al. 2007, Ornstein et al. 2000), and with response inhibition impairments (Ersche and Sahakian 2007, Salo et al. 2009, Clark et al. 2006). It is uncertain if cognitive deficits were present prior to the onset of drug use or are the result of drug use, but recent research has indicated potential recovery of some cognitive impairments following sustained abstinence from drug use (Schulte et al. 2014).

Thus far there are no studies of potential cognitive impairments among individuals with co-occurring (dual) opioid and stimulant dependence. Recent studies of drug using individuals in Malaysia found that concurrent use of ATS (primarily crystal methamphetamine and amphetamine/methamphetamine tablets) and opioids (primarily heroin) is prevalent and may be associated with increased behavioral risks and higher prevalence of HIV infection (Chawarski et al. 2012; Desrosiers et al., 2016). The reported behavioral risks may be related to cognitive impairments associated with chronic use of both ATS and opioids. In particular, cognitive functioning among individuals dependent on both ATS and opioids in Malaysia, where co-occurring ATS and opioid dependence has become increasingly prevalent over the past two decades (Singh et al. 2013), has not been evaluated extensively. Consequently, we evaluated cognitive performance profiles among individuals enrolling in a randomized, placebo-controlled, double-blind, pilot study (RCT) investigating medication assisted treatment for co-occurring ATS and opioid dependence in Kota Bharu, Malaysia. The cognitive assessments component of the RCT was conducted as an exploratory evaluation to characterize the sample baseline cognitive functioning levels prior to treatment initiation and to explore potential relationships between cognitive functioning and lifetime history and patterns of drug use. No hypotheses were stated in advance and the study findings were planned to guide future studies of cognitive impairments among dually dependent individuals in Malaysia and in other regions.

Methods and procedures

Participants

The RCT inclusion criteria were: age 18 years and above; meeting DSM-IV criteria for both opioid and ATS dependence; ATS use on two or more days per week in the month prior to study admission; and opioid- and ATS-positive urine toxicology test results at study enrollment. The exclusion criteria included current psychotic and/or mood disorder and current participation in treatment for a substance use disorder. Potential participants were recruited by outreach efforts in communities with a high prevalence of drug use in Kota Bharu, the capital city of Kelantan state, and the surrounding rural areas. The study was reviewed and approved by the Institutional Review Boards of Yale University and Universiti Sains Malaysia (USM). All study participants provided written informed consent.

A total of 90 participants were enrolled in the RCT; 63 of them were invited to take part in cognitive assessments: among 27 not invited to participate, 13 were training cases in the RCT, 10 withdrew early from the RCT, and 4 were enrolled before implementation of the cognitive assessment component. Twelve of the 63 invited declined to participate in the cognitive assessment component. One participant did not complete Raven’s SPM and therefore is not included in all subsequent analyses. Data from 50 patients were analyzed and reported in the current study.

Setting and Procedures

The research was conducted at the Department of Psychiatry, Hospital USM (HUSM) in Kota Bharu, Malaysia between March 2013 and August 2014. In the RCT, all participants were admitted for 10 days to an inpatient unit for withdrawal from ATS, induction and stabilization on buprenorphine/naloxone, initiation of cognitive behavioral drug and HIV risk reduction counseling, and randomization to atomoxetine or placebo. Participants started buprenorphine/naloxone treatment on day one of their inpatient stay.

Buprenorphine/naloxone is a medication combining an opioid agonist (buprenorphine) and an opioid antagonist (naloxone) in a ratio 4:1 and it is approved and used for a maintenance treatment of opioid use disorder. On day seven of the inpatient stay, participants were also randomized in a 1:1 ratio to a double-blind atomoxetine or placebo. Atomoxetine is a non-stimulant medication approved for treatment of attention deficit/hyperactivity disorder (ADHD) and hypothesized to improve response inhibition among adults with ADHD who frequently display deficits in response inhibition. In the RCT, atomoxetine was evaluated for safety and potential efficacy to treat ATS use disorder. None of the study participants was diagnosed with ADHD. Cognitive assessments were administered between days 3 and 6 of the inpatient stay after all participants were stabilized on buprenorphine/naloxone and were receiving therapeutically appropriate doses (8 to 16 mg daily) and atomoxetine (or placebo) was initiated after participants completed cognitive assessments. Access to drugs and opportunities to use drugs were restricted in the locked, closely supervised inpatient unit, and all participants tested negative on urine tests conducted during their inpatient stay. The research assistant who conducted the cognitive assessments checked for physical signs of withdrawals, intoxication, or sedation and also asked participants how they felt and if they wanted to postpone the assessment session for any reason. None of the participants had any signs of withdrawal, intoxication, or sedation at the time of cognitive assessments.

Cognitive Assessments & Outcome Measures

The cognitive tasks consisted of non-verbal paper-and-pencil and computerized tests and included Raven’s Standard Progressive Matrices (SPM) (Raven 1981), Rey-Osterrieth Complex Figure (ROCF) test (Shin et al. 2006), computerized Digit Span Test (DST) (Wechsler 1997), Trail Making Test (TMT) version A and B (Bowie and Harvey 2006), and computerized Symbol Digit Substitutions Task (SDST) (Mattila et al. 1994).

The Raven’s SPM is a nonverbal test of general cognitive ability that involves visual-perceptual abilities and logical reasoning (Raven and Raven 2003). The ROCF is a visual memory test that engages primarily visual-perceptual and visuo-constructional skills (Stern et al. 1994, Shin et al. 2006). The computerized DST (forward and backward) is a modification of the digit span test (Wechsler 1997) that measures the span of one’s short-term or working memory (Woods et al. 2011). The TMT is a test of attention, perceptual motor speed and cognitive flexibility or task-set switching ability (Arbuthnott and Frank 2000). Part A measures primarily perceptual motor speed, attention and visual scanning. Part B involves selective attention, sequencing and shifting between two sets of stimuli simultaneously (Salthouse 2011). The computerized SDST, an adaptation of Symbol-Digit Modalities Test (Smith 1968), is an information-processing test for attention and visual scanning.

Additionally, a computerized version of The Stroop Test (Killikelly and Szücs 2010), the Continuous Performance Test (Riccio et al. 2002), the Test of Variables of Attention (TOVA) (Leark et al. 2007), and the Balloon Analogue Risk Test (BART) (Lejuez et al. 2002) were administered. Eighteen participants did not complete the CPT, 11 did not complete the Stroop, and 31 did not complete the TOVA. Participants who did not complete the TOVA complained of attentional fatigue from staring at the computer screen with blinking stimuli. Those who did not complete the Stroop reported inability to see clearly, color blindness, or difficulties navigating the keyboard. The main outcome measure of the BART, the adjusted average number of pumps for each balloon of 64 and higher that indicates potentially risk-disadvantageous choices (Lauriola et al. 2014), was reached by only two participants. Due to low completion rates these tests are not included in the data analyses.

The Raven’s SPM test was administered without a time constraint, and the total number of correct responses (maximum of 60) was the measured outcome. Performance on ROCF task was evaluated based on the 20-minute delay recall. During the delay, BART, Stroop, DST and SDST were administered. The drawings of the recalled ROCF figures were coded using the Boston Qualitative Scoring System, BQSS (Stern et al. 1994). Performance on the TMT was measured by the ratio of time taken (seconds) to complete part B over time to complete part A. This performance measure is an indicator of executive functioning (Arbuthnott and Frank 2000) and has been found to be significantly related to information processing speed and fluid cognitive abilities (Salthouse 2011). Performance on the DST was measured by the number of digits recalled in each condition (forward and backward) and the total of number of digits recalled in both conditions. Performance on the SDST was measured by the total number of errors during all 5 trials.

Baseline Assessments

Demographic information, self-reported lifetime history and patterns of drug use (frequency, amounts, and routes), urine toxicology tests (morphine and amphetamine/methamphetamine metabolites using rapid immunoassays) and self-reported drug use data using a timeline follow back method were collected at baseline during the RCT admission.

Statistical analyses

Data analysis consisted of three sequential steps. In the first step, a multidimensional scaling technique in SPSS (ALSCAL; (Moore 1990)) using outcomes of five cognitive tests with complete data was employed to identify cognitive performance tests that are most sensitive to individual differences within the study cohort. In the second step, a K-means cluster analysis (Hartigan and Wong 1979) was used to group participants into distinct separate clusters based on the results of assessments identified by the multidimensional scaling technique as best differentiating among the study sample. In the third step, a General Linear Model analysis of variance was conducted to evaluate differences in drug use indices (age of drug use initiation, years of use, frequency and routes of use) between the clusters identified by the K-means analysis. We also used chi-square and t-tests to evaluate differences in demographics, other patterns of drug use, and cognitive tests results between the identified clusters of participants.

Results

All participants were males (N=50) of the Malay ethnicity. Mean (SD) age was 34.3 (6.4) years (range: 23 to 48 years). The age of ATS use initiation ranged from 13 to 42, with 44% reporting the first ATS use at 20 or younger. Participants reported initiating heroin use between the ages of 12 and 28, with 50% reporting initiation of heroin use at the age of 20 or younger. At study admission, 36% reported using ATS every day and 98% of participants reported using heroin every day during the previous month. Demographic information of all participants are presented in Table 1. History and patterns of drug use are presented in Table 2. There were no significant differences on any of the evaluated variables between those who participated and did not participate in the cognitive assessments.

Table 1:

Demographic characteristics of study participants (N = 50).

Sample characteristics	%
Education level (years of schooling)
Completed high school (11)	48
Completed junior high (9)	44
Completed primary school (6)	8
Marital status
Never married	58
Married	26
Divorced	10
Widowed	6
Employment status
Full-time	36
Part-time	44
Unemployed	20
Duration of alcohol use
0 – 5 years	26
6 – 25 years	18
No reported history of alcohol use	56

Table 2:

Drug use pattern among study participants (N = 50).

Drug Use Patterns	Mean [95% CI]
Duration of heroin use	14.0[12.0, 16.0] years
Days of heroin use in the month prior to study admission	29.9 [29.7, 30.0] days
Daily frequency of heroin use	4.8 [4.2, 5.4] times/day
Method of heroin use:	%
Chasing (inhalation of drug smoke or vapor)	22
Injecting	78)
Duration of ATS use	11.0 [9.6, 12.2] years
Days of ATS use in the month prior to study admission	17.5 [14.6, 20.4] days
Weekly frequency of ATS use	6.1 [4.3, 8.0] times/week
Method of ATS use:	%
Chasing	60
Injecting	32
Combined chasing and injecting	8

The multidimensional scaling technique revealed that Raven’s SPM and ROCF were most sensitive to individual differences among the study sample and performance scores on these two tests differentiated study participants better than the performance on the other three tests (i.e., TMT, DST, and SDST). Consequently, we conducted a K-means cluster analysis using the Raven’s SPM and ROCF scores to identify unique groups of participants. Two distinct clusters were identified by the K-means cluster analysis. Participants in the lower cognitive performance cluster (Lower CP, n₁=21) were characterized by significantly lower performance on the Raven’s SPM (t(48) = 6.22, p < 0.01) and ROCF (t(48) = 7.50, p < 0.01) compared to those in the higher cognitive performance cluster (Higher CP, n₂=29). The two clusters also differed on the scores of the other three tests (see Table 3).

Table 3:

Comparisons of baseline cognitive performance for each cluster by independent t-tests.

Cognitive measures	Cluster 1 Lower CP (n=21)	Cluster 2 Higher CP (n=29)
	Mean [95% CI]	Mean [95% CI]	p-value
Raven’s SPM total score	25.8 [22.2, 29.4]	38.8 [36.2, 41.4]	p < 0.001
ROCF total recall score	16.5 [13.4, 19.6]	32.7 [29.6, 35.7]	p < 0.001
Digit Span Forward	5.1 [4.4, 5.9]	6.0 [5.6, 6.6]	p = 0.036
Digit Span Backward	3.7 [3.2, 4.2]	4.4 [3.9, 4.9]	p = 0.059
Total DST (F+B)	8.9 [8.0, 9.7]	10.5 [9.6, 11.3]	p = 0.011
TMT ratio (B/A)	2.2 [1.9, 2.6]	1.7 [1.5, 1.9]	p = 0.008
SDST total errors	2.1 [1.2, 3.0]	2.0 [1.2, 2.7]	p = 0.808

A multivariate analysis of variance evaluating between cluster differences on lifetime history of drug use, patterns of drug use, and other available characteristics showed that the age of ATS use initiation and the current age were the only two significantly different variables between the two clusters. In the Lower CP cluster, the mean (95% CI) age of ATS use initiation was 20.9 (18.0, 23.8), whereas in the Higher CP cluster it was 25.2 (22.4, 28.0). This difference was statistically significant with and without controlling for the overall duration of ATS use (F(1, 48) = 4.57, p = 0.038; F(2, 48) = 7.40, p = 0.002). Participants in the two clusters also differed significantly on current age – mean (95% CI) age was 32 (29.2, 34.7) in the Lower CP cluster and 36 (33.6, 38.4) in the Higher CP cluster (t(48) = 2.28; p = 0.027). The two clusters did not differ on other evaluated characteristics (see Table 4).

Table 4:

Comparisons of demographic and drug use characteristics for each cluster (N = 50).

	Lower CP cluster (n=21) Mean [Range]	Higher CP cluster (n=29) Mean [Range]
Age	32.0 [22, 43]	36.0 [24, 48]
Age of ATS use initiation	20.9 [13, 36]	25.2 [13, 42]
Total years of ATS use	11.2 [6, 18]	10.9 [3, 19]
Number of days ATS use in the month prior to study	15.6 [1, 30]	18.9 [2, 30]
ATS weekly frequency of use (times/week)	7.1 [2, 28]	5.4 [2, 21]
Age of first heroin use	19.4 [13, 26]	20.6 [12, 28]
Total years of heroin use	12.6 [3, 25]	15.0 [2, 26]
Number of days heroin use in the month prior to study	29.8 [25, 30]	30 [30, 30]
Heroin daily frequency of use (times/day)	5.2 [1, 30]	4.6 [2, 10]
Education (years of schooling)
Completed high school (11)	57.1%	44.8%
Completed junior high (9)	23.8%	51.7%
Completed primary school (6)	19.0%	3.4%
Marital status
Never married	52.4%	62.1%
Married	28.6%	24.1%
Divorced	19.0%	3.4%
Widowed	0%	10.3%
Employment status
Full-time	38.1%	34.5%
Part-time	38.1%	48.3%
Unemployed	23.8%	17.2%
Duration of alcohol use
0 – 5 years	14.3%	34.5%
6 – 25 years	23.8%	13.8%
No reported history of alcohol use	61.9%	51.7%

Note: The between cluster differences on current age and the age of ATS use initiation are statistically different (boldface). All other comparisons in Table 4 are not statistically different.

Discussion

Participants dually dependent on opioids and ATS who had a lower cognitive performance in tasks evaluating reasoning skills and general cognitive ability (Raven’s SPM) and visual memory (ROCF) reported a significantly earlier age of ATS use initiation compared to those with a higher cognitive performance on these tasks. They also showed a lower level of cognitive performance on other tasks evaluating short term or working memory (DST), and attention and executive functioning (TMT B/A ratio). Participants in both groups in the current study shared similar patterns of opioid use: they reported consuming heroin multiple times a day and mostly every day; and ATS use: they reported consuming crystal methamphetamine or amphetamine/methamphetamine tablets on approximately half days in each month (see Table 4). Notably, age of heroin initiation and overall duration of ATS or heroin use among participants in the current study were not significantly different between the groups with lower versus higher cognitive performance.

The design of the current study does not preclude the possibility that pre-existing cognitive deficits (e.g., resulting in impaired judgment, diminished cognitive control) were responsible for an earlier ATS use initiation. Further research is needed to disentangle the direction of the potential relationship between cognitive impairments and initiation of psychoactive and stimulant substance use.

The mental processes engaged in the battery of the study cognitive assessments are subsets of higher order cognitive processes that govern goal-directed behaviors and involve decision making, planning and selecting goals, and monitoring of activities to facilitate attainment of the selected goals. Such abilities play important roles in decisions and behaviors related to drug use, drug discontinuation, treatment participation, and selection and attainment of recovery goals; hence lowered performance in these cognitive functions may pose a barrier to recovery from drugs. The findings that participants with lower cognitive performance who initiated ATS use at an earlier age were also significantly younger (mean age 32 years) than participants with higher cognitive functioning (mean age 36 years) at the time of testing further emphasize potentially negative consequences of the early initiation of ATS use since performance in memory and reasoning skills tests have been shown to decline with age (Schroeder and Salthouse 2004), therefore, strengthening the main finding.

Findings are consistent with other published studies on the relationships between the age of onset of substance use and cognitive functioning. In a recent study on poly-drug users, early onset of substance use (before age 16) was associated with greater cognitive impairments, including lower IQ, deficits in processing speed, visual perception and executive functioning (Capella, Benaiges, and Adan 2015). Poorer verbal memory, visuo-spatial functioning, executive functioning, deficits in attention and processing speed were observed among individuals with early onset alcohol use (before age 25) (Hanson et al. 2011, Thoma et al. 2011). There is growing evidence that adverse effects of cannabis use are more profound among chronic users who initiated cannabis use during adolescence (Volkow et al. 2016) – those who initiated use before age 18 had reduced verbal IQ (Pope Jr et al. 2003); those who initiated use before age 16 performed worse on tasks engaging executive functions (Meier et al. 2012); those who initiated use before age 15 exhibited poorer sustained attention, cognitive inhibition, and abstract reasoning (Fontes et al. 2011). A recent epidemiological survey also found that nonmedical stimulant use during adolescence may increase the likelihood of substance use and related problems (McCabe, Veliz, and Boyd 2016). These findings together with the proposed mechanism of how drugs disrupt critical brain development during adolescence (Lubman, Cheetham, and Yucel 2015) may suggest that initiating drug use at younger age affects cognitive performance more adversely than later drug use initiation.

Findings from our study of dually dependent individuals add to the accumulating observations that initiation of chronic substance use at early age may be associated with potentially long-term cognitive deficits and support health care policies and messages forewarning about harmful effects of substance use on the developing brain.

Findings should be interpreted in context of study limitation. Participants were not evaluated for other factors, including history of head injury, meningitis, encephalitis or septicemia, episodes of loss of consciousness and visual acuity which could have affected their cognitive performance. Acute effects of recent drug use may have affected cognitive performance but such effects were reduced because participants were in a closed inpatient unit, and all urine samples collected during the inpatient stay were negative for opioids and ATS. Participants were at an early stage of induction onto buprenorphine, but they participated in cognitive assessments after achieving a stable response to this medication, and before receiving atomoxetine or placebo. The study did not evaluate cognitive processes related to speed of information processing, response inhibition, or impulsivity because of low discriminability of obtained data. Finally, the study sample size was relatively small.

Conclusions

In the current study of people with co-occurring ATS and opioid dependence, individuals with lower cognitive performance on measures related to reasoning, short term or working memory, attention, and visual processing reported a younger age of ATS use initiation. These findings add to the growing evidence that the developing brain may be more vulnerable to chronic drug exposure, including ATS, and combined effects of ATS and opioids. However, the current study findings cannot preclude lowered cognitive performance before the ATS use onset. ATS substances are often initially taken as performance enhancing drugs and while immediate, short term effects of these drugs may be perceived as beneficial, preventive interventions targeting adolescents and young adults should forewarn that early initiation and prolonged use of ATS has a potential to result in harmful effects including longer-term cognitive impairments. Individuals with early onset of ATS use may also have greater cognitive difficulties during the time of treatment initiation at the later age. Treatment providers should evaluate cognitive performance of patients initiating treatment and consider providing cognitive remediation strategies to patients with significant cognitive performance impediments.