Effects of mu opioid receptor antagonism on cognition ... - Springer Link

Psychopharmacology (2012) 224:501–509 DOI 10.1007/s00213-012-2778-x

ORIGINAL INVESTIGATION

Effects of mu opioid receptor antagonism on cognition in obese binge-eating individuals Samuel R. Chamberlain & Karin Mogg & Brendan P. Bradley & Annelize Koch & Chris M. Dodds & Wenli X. Tao & Kay Maltby & Bhopinder Sarai & Antonella Napolitano & Duncan B. Richards & Edward T. Bullmore & Pradeep J. Nathan

Received: 16 March 2012 / Accepted: 11 June 2012 / Published online: 3 July 2012 # Springer-Verlag 2012

Abstract Rationale Translational research implicates the mu opioid neurochemical system in hedonic processing, but its role in dissociable high-level cognitive functions is not well understood. Binge-eating represents a useful model of ‘behavioural addiction’ for exploring this issue. Objective The aim of this study was to objectively assess the cognitive effects of a mu opioid receptor antagonist in obese individuals with binge-eating symptoms. Methods Adults with moderate to severe binge-eating and body mass index ≥30 kg/m2 received 4 weeks of treatment with a mu opioid receptor antagonist (GSK1521498) 2 or 5 mg per day, or placebo, in a double-blind randomised parallel design. Neuropsychological assessment was undertaken at baseline and endpoint to quantify processing bias for food stimuli (visual dot probe with 500- and 2,000-ms stimulus presentations and food Stroop tasks) and other

distinct cognitive functions (N-back working memory, sustained attention, and power of attention tasks). Results GSK1521498 5 mg/day significantly reduced attentional bias for food cues on the visual dot probe task versus placebo (p00.042), with no effects detected on other cognitive tasks (all p>0.10). The effect on attentional bias was limited to the longer stimulus duration condition in the higher dose cohort alone. Conclusions These findings support a central role for mu opioid receptors in aspects of attentional processing of food cues but militate against the notion of major modulatory influences of mu opioid receptors in working memory and sustained attention. The findings have implications for novel therapeutic directions and suggest that the role of different opioid receptors in cognition merits further research.

Keywords Impulsivity . Binge-eating . Cognition . Opiate . Opioid . Mu . Mu-opioid S. R. Chamberlain : A. Koch : C. M. Dodds : W. X. Tao : K. Maltby : B. Sarai : A. Napolitano : D. B. Richards : E. T. Bullmore : P. J. Nathan (*) Clinical Unit Cambridge, GlaxoSmithKline, Addenbrooke’s Hospital, Cambridge CB0 0QQ, UK e-mail: [email protected] S. R. Chamberlain (*) : E. T. Bullmore : P. J. Nathan Department of Psychiatry, Level E4, Addenbrooke’s Hospital, University of Cambridge, Cambridge CB0 0QQ, UK e-mail: [email protected] S. R. Chamberlain : E. T. Bullmore Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK K. Mogg : B. P. Bradley Department of Psychology, University of Southampton, Southampton, UK

Introduction Although over 100 neurotransmitters have been identified in the human brain, relatively few of these have been established to exert important neuromodulatory influences over distinct cognitive functions. Most research to date has focused on the cognitive effects of manipulations of the serotonin, dopamine, and noradrenaline systems (Robbins and Arnsten 2009). Pharmacological challenge studies using objective cognitive tests have proven valuable in understanding the human brain in health, and the mechanisms by which treatments exert their beneficial effects on psychiatric symptoms (e.g. Chamberlain and Sahakian 2007; Chamberlain et al. 2010). The opioid system is strongly implicated both in substance (Le Merrer et al. 2009;

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Berrendero et al. 2010) and behavioural addictions (Grant et al. 2006). While chronic abuse of opioids has been associated with a variety of cognitive impairments (e.g. in domains of sustained attention, psychomotor speed and working memory), it has proven difficult to disentangle the extent to which these deficits reflect primary effects of illicit drugs on the opioid receptors as opposed to other confounding factors, such as presumed neurotoxic effects of chronic drug use, or the presence of comorbid disorders including depression (Gruber et al. 2007; Fernandez-Serrano et al. 2011). Binge-eating represents a useful behavioural model for studying the effects of opioid manipulations on cognitive processes germane to addiction without these potential confounds. Binge-eating shares considerable phenomenological and neurobiological overlap with substance addiction (Stunkard 1990; Goodman 2008; Davis and Carter 2009; Avena et al. 2011) and represents an important clinical phenomenon that cuts across several disorders. This clinical overlap is particularly pronounced in the context of obesity, with around one-third of people who regularly binge-eat being overweight, and obesity occurring in around twothirds of binge-eaters (Stunkard and Wadden 1992; Striegel-Moore et al. 2001; Jacobs-Pilipski et al. 2007; Swanson et al. 2011). The risk of weight regain following treatment appears to be higher in obese people who bingeeat (Corwin et al. 2011). The opioid system in fact comprises several distinct receptors. Mu opioid receptors are particularly involved in appetite control and hedonic processing associated with food-related stimuli (e.g. Sanger and McCarthy 1981; Leventhal et al. 1995; Yeomans and Gray 2002; Berridge 2009; Nathan and Bullmore 2009; Fulton 2010). Mu receptor agonists preferentially increase the consumption of energyrich and high sugar-content foodstuffs in animals (Sanger and McCarthy 1981; Evans and Ludbrook 1990; Will et al. 2003), while antagonists reduce such intake (Apfelbaum and Mandenoff 1981; Marks-Kaufman and Kanarek 1981; Leventhal et al. 1995; Riba et al. 2005). Furthermore, in animals, excessive consumption of foodstuffs leads to changes in mu opioid receptor expression/activity in neural regions involved in addiction (Bello et al. 2011; Colantuoni et al. 2011). In humans, mu opioid antagonists have been found to reduce intake of palatable/highly calorific foodstuffs in the short term (Yeomans and Gray 2002), including in people with binge-eating habits and/or obesity (Mitchell et al. 1986, 1987; Spiegel et al. 1987) and the consumption of alcohol clinically (Anton et al. 2004, 2006; Rosner et al. 2010). However, short-term effects on food intake have not generally been found to translate into weight benefits following chronic opioid antagonist treatment (Nathan and Bullmore 2009), highlighting the need to characterise effects of novel more receptor-selective agents. Other

Psychopharmacology (2012) 224:501–509

receptors besides mu opioid are likely to play a role in hedonics and appetite such as kappa receptors (Asad et al. 2010), although mu opioid receptors are the focus of the current paper. Some of the reported effects of opioid manipulations on food-related behaviours may be mediated by important interactions between the opioid and dopamine neurochemical systems. For example, mu opioid receptors are localised on GABAergic interneurons in the ventral tegmental area (Milner et al. 2011). The inhibition of opioid receptors is believed to precipitate decreased dopamine release via disinhibition of such ventral tegmental GABAergic interneurons (Nathan and Bullmore 2009). Food-induced dopamine release in the nucleus accumbens is reduced by administration of the opioid antagonist naltrexone in animals (Milner et al. 2012). The role of mu opioids in aspects of cognition besides those relating to appetite control and food-related hedonic processing are less well studied (Quednow et al. 2008). Administration of the selective mu opioid agonist DAMGO and endogenous mu opioid receptor agonists endomorphin1 and -2 impaired working memory in mice (Itoh et al. 1994; Ukai et al. 2000). Furthermore, in vitro application of the selective mu opioid receptor agonist PL017 dosedependently reduced the amplitude of postsynaptic currents in hippocampal slices, and this effect was reversed by the opioid antagonist naloxone and selective mu antagonist beta-funaltrexamine hydrochloride (Xie et al. 1992). Mice with knockout of mu opioid receptors manifested spatial memory impairment (Jang et al. 2003). In humans, studies that have explored cognitive effects of mu opioid agonists are inconsistent, with little evidence overall for impairing effects in healthy volunteers over the short term (Quednow et al. 2008). There are few studies indeed that have examined effects of antagonists. In healthy volunteers, acute intravenous administration of the mu opioid antagonist naloxone impaired verbal learning and sustained attention in one study (Cohen et al. 1983); while another reported slowing of reaction times and accuracy impairment when subjects attempted to recall order of letters and numbers (arguably reflecting working memory function) (Martin del Campo et al. 1992). In a study conducted in obese men, 8-week treatment with the mu opioid antagonist naltrexone did not significantly impact mood status or cognition (trail making, digit span, digit symbol, selective reminding, and visual retention) (Hatsukami et al. 1986). More recently, 10-day treatment with the opioid receptor antagonist GSK1521498 in obese but otherwise healthy volunteers was associated with no effect on sustained attention, but significant psychomotor slowing quantified using power of attention (POA) scores—a composite measure of reaction times derived from digit vigilance, simple reaction time, and choice reaction time tests (Nathan et al. 2011a).


Thus, some of the reported detrimental effects of mu opioid antagonists on cognition may be a consequence of potential sedative effects. The aim of this study was to expand upon these existing findings concerning the role of mu opioid receptors in cognition. We used binge-eating as a model of behavioural addiction and characterised the effects of 4-week mu opioid receptor antagonism on cognition. Based on the extant literature, we hypothesised that mu opioid antagonism would reduce attentional bias for food stimuli, quantified using objective indices of cognitive prioritisation of food (visual dot probe bias; modified Stroop interference), and potentially disrupt working memory and psychomotor speed.

Methods Subjects Sixty-three volunteers (n 028 [44 %] males), aged 18– 60 years (mean ± SD, 41.5±10.0 years), endorsing moderate to severe binge-eating (Binge eating scale, BES scores≥19; mean 26.4±6.7) (Gormally et al. 1982; Gladis et al. 1998), and with Body Mass Index ≥30 kg/m2 (mean 37.3±4.76 kg/ m2), were entered into the study after meeting criteria. The study was approved by Berkshire Research Ethics Committee (identification number EudraCT 2009-016663-11), UK, and individuals provided informed written consent. Participants had no history of axis-I psychiatric disorders (apart from endorsing binge-eating symptoms) and were excluded if they reported alcohol intake greater than 14 units/week, screened positive for illicit drugs on urine screen (amphetamines, barbiturates, cocaine, opiates, cannabinoids or benzodiazepines), had used nicotinecontaining products in the last 3 months or had taken any centrally active medications in the past 2 weeks. Also, subjects with Beck depression inventory II scale total score greater than 13 and/or suicide question score greater than zero at screening were excluded (Beck et al. 1961; Beck and Beamesderfer 1974). Study design This was a multi-centre, randomised, double-blind, placebocontrolled design. The current manuscript concerns cognitive findings, which constitute part of a broader range of endpoints, which will be presented in separate manuscripts. The sample size was based on the endpoint of body weight change from baseline, for which prior data were available from a placebo-controlled study examining the effects of sibutramine 10 mg/day for 28 days in obese individuals (Napolitano et al. 2012). Weight loss after 28-day treatment

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was on average 2.1 kg (standard error 0.5 kg) in the sibutramine group. Assuming a similar magnitude of effect with GSK1521498, it was determined for the overall study protocol that the proposed sample size would yield ≥95 % power to detect this difference with an overall 5 % type-I error risk. It was assumed that cognitive effects would be comparable to efficacy effects in terms of expected magnitude. Subjects received 1-week placebo run-in, followed by randomization to 4 weeks of treatment with placebo (n0 21), 2 mg/day of GSK1521498 (n021), or 5 mg/day of GSK1521498 (n021). GSK1521498 is a centrally active mu opioid receptor antagonist (Nathan et al. 2011a, b; Rabiner et al. 2011) with 14- to 20-fold greater selectivity for mu compared to kappa/delta subtypes (Nathan et al. 2011a, b). The treatment doses were selected on the basis of previous positron emission tomography findings (Rabiner et al. 2011) and a healthy volunteer safety and tolerability study (Nathan et al. 2011a). These doses were expected to yield average steady-state 24 h mu opioid receptor occupancy of 60 and 80 %, respectively, and to be well-tolerated. Neurocognitive functions were assessed following placebo run-in (hereafter referred to as ‘baseline’) and at study end-point. Behavioural tasks were performed in a fixed order between 3 and 6 p.m. in the satiated state, and examined aspects of attentional processing for food stimuli, working memory, sustained attention and composite reaction times. The task domains were selected on the basis of the literature reviewed above, suggesting a possible role for mu opioid receptors in hedonic processing for rewarding stimuli and aspects of mnemonic function; and that mu opioid receptor antagonists may produce sedative effects. Visual dot probe task The visual dot probe task provided a well-established measure of attentional bias for pictorial stimuli relating to food versus non-food (Brignell et al. 2009; Hepworth et al. 2010; Nathan et al. 2011a, b, c). On each trial, a fixation cross was presented on-screen for 500 ms, followed by a pair of pictorial stimuli (e.g. a food picture paired with a non-food picture, shown side by side), presented for either 500 or 2,000 ms. Thereafter, the pair of pictorial stimuli was removed and a single black dot (‘probe’) was presented in the location of either the previously displayed left- or rightsided pictorial stimulus, until the subject made a response on a two-button box. Subjects were asked to press a left button for a left-sided probe, and vice versa. The inter-trial interval was varied randomly from 500 to 1,500 ms. There were 12 practise trials (food/non-food pairs), followed by 240 trials, which comprised 160 critical trials of food/non-food pairs and 80 filler trials of non-food/nonfood pairs, randomly interspersed. There was a short rest

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break half way through the task. Each block was preceded by two buffer trials (food/non-food pairs). The reader is referred to the previous study for a full description of the task (Nathan et al. 2011c). The primary outcome measures were the attentional bias score for the 500- and 2,000-ms task conditions respectively, calculated by subtracting the subject’s mean reaction time when the probe replaced a food stimulus (“congruent” location trials) from the subject’s mean reaction time when the probe replaced a non-food stimulus (“non-congruent” location trials). As such, a positive value of the bias score indicated a relative attentional bias towards food stimuli.


and sensitive to pharmacological manipulation (Callicott et al. 1998). The term ‘N-back’ refers to the number of previous stimuli that the subject has to recall. The stimuli consist of numbers (1–4) shown in random order. Each number had its own unique and fixed position on the four corners of a diamond-shaped box on-screen. During 0-back, the subject was required to respond to the number seen on the screen, and during the 1-back task, the subject was required to respond to the number in the previous presentation. In the 2-back, the subject was required to press the button corresponding to the number seen 2 stimuli previously. The primary outcome measure was the percentage correct responses on the hardest level of the task.

Food Stroop task Sustained attention The food Stroop task provided a measure of the interference effect of food cues on performance on a primary colournaming task; this methodology has been widely used to assess processing biases in both eating-related and addictive disorders, and the interference index is assumed to reflect the extent to which processing resources are captured by the personal motivational salience of the task-irrelevant stimuli (Cox et al. 2006; Nathan et al. 2011c). The food Stroop interference index has been found to be predictive of weight gain in non-obese individuals (Calitri et al. 2010). For each trial, a coloured word was presented on-screen and the subject was required to state aloud the colour the word was written in, ignoring the word’s meaning. Response times were recorded. The food Stroop task consisted of a block of 64 trials, which presented palatable foodrelated words (e.g., cookie), non-palatable food-related words (e.g. potato), and neutral control words (e.g. ferry) randomly interspersed (equal number of food-related words to neutral words overall). This was followed by a standard colour Stroop task, which comprised a block of 64 trials presenting colour-incongruent words, colour-congruent words, and colour-unrelated words (equal number of colour-related words to colour-unrelated words overall). Blocks were presented in a fixed order. The outcome measures on this task were: (a) interference scores for palatable food stimuli (mean reaction time for palatable food words minus mean reaction time for control words); (b) interference scores for non-palatable food stimuli (mean reaction time for non-palatable food words minus mean reaction time for neutral control words, calculated for each subject); and (c) interference scores for colour stimuli (mean reaction time for colour-incongruent words minus mean reaction time for neutral incongruent trials). Working memory The N-back test is a well-validated test of working memory function that is impaired in patients with cognitive disorders

Sustained attention was measured using the Rapid Visual Information Processing (RVIP) task from the Cognitive Drug Research (CDR) cognitive assessment battery (Kennedy et al. 2000). A series of single-digit numbers were presented one at a time in quick succession on-screen. Subjects monitored the stream of single digits for occurrence of three consecutive even or odd digits (‘target sequences’). When target sequences occurred, subjects made a response using a yes button. The primary outcome measure on the RVIP was the percentage of targets accurately detected (percentage accuracy). Power of attention The POA provided a validated composite measure designed to quantify drug-related sedation (Shah et al. 2006). The POA combined three related sub-tests from the CDR battery (Kennedy et al. 2000), namely: digit vigilance (observing a series of single numerical digits for presentation of a specific target digit and pressing ‘yes’ when this occurred), simple reaction time (pressing ‘yes’ when the word ‘yes’ appeared on-screen) and choice reaction time (pressing ‘yes’ when ‘yes’ appeared and ‘no’ when ‘no’ appeared on-screen). The primary outcome measure was the POA score, which is a composite measure of speed of responding derived from all thee subtasks (Shah et al. 2006); higher scores indicate worse performance. Data analysis Potential differences in the baseline characteristics of the three treatment groups were evaluated using analysis of variance. Potential drug effects on cognition were explored using analysis of covariance (ANCOVA) for each measure of interest, with baseline score as a covariate and fitting gender, treatment, day and study day-by-treatment interaction as fixed effects. To minimise statistical comparisons, effects


of the higher dose (5 mg/day) versus placebo were first considered. Where there was a significant effect of this higher dose, follow-up ANCOVA was used to determine any dose-dependent effects by considering the lower dose (2 mg/day) group versus placebo. Where significant effects of drug treatment were identified on a given measure, gender effects were also reported (main effect of gender; treatment-by-gender interaction). Prior to ANCOVAs, data were inspected for normality assumptions and outliers; transforms were undertaken as appropriate or nonparametric tests as indicated. This being a hypothesisdriven study, statistical significance was defined as p 0.10). Cognitive data were unavailable for three participants in the 2-mg/day treatment arm due to dropout. ANCOVA findings are described below (see also Fig. 1 and Table 2). Visual dot probe task There was a significant effect of 5 mg/day GSK1521498 versus placebo on attentional bias scores for the 2,000-ms task condition (LS mean difference −12.3 ms, 95 % CI −24.2 to −0.48, p00.042) but not for the 500-ms task condition (LS mean difference −2.7 ms, 95 % CI −14.2 to +8.7, p00.633). As can be seen in Fig. 1, 5-mg/day GSK1521498 treatment was associated with relative reduction in bias scores for 2,000-ms food cues, suggesting a shifting of attentional bias away from foodstuffs under conditions of longer stimulus presentation. There was no significant effect of gender or gender-bytreatment interaction for this parameter (p>0.10). Follow-up ANCOVA in the 2-mg/day GSK1521498 group showed no significant effect on attentional bias scores for the 2,000-ms condition versus placebo (LS mean difference −4.5, 95 % CI −16.8 to +7.9, p 0 0.473).

Table 1 Demographic characteristics of the randomised set Age (years), mean (SD) Gender (F/M), N Weight (kg) BMI (kg/m2) Total BES

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Food Stroop task On the interference scores for palatable food, no significant effect of 5-mg/day GSK1521498 versus placebo was detected (LS mean difference 12.1 ms, 95 % CI −43.8 to +68.0, p0 0.666), nor were significant effects found in terms of interference scores for non-palatable food (LS mean difference 44.9, 95 % CI −15.8 to 105.5, p00.144) or colour interference (LS mean difference −52.3, 95 % CI −136.7 to +32.0, p00.219). N-back working memory task No significant effect of 5 mg/day GSK1521498 versus placebo was detected for N-back accuracy on the hardest level of the task (2-back) (LS mean difference 2.2, 95 % CI −4.3 to +8.8, p00.502). Power of attention No significant effect of 5 mg/day GSK1521498 versus placebo was detected for POA scores (LS mean difference 2.86 ms, 95 % CI −48.8 to +54.5, p00.912). Rapid visual information processing task No significant effects of 5 mg/day GSK1521498 versus placebo were detected for RVIP accuracy (LS mean difference 1.7, 95 % CI −5.2 to +8.6, p00.628).

Discussion This is one of few studies seeking to clarify the role of mu opioid receptors in human cognition. The key finding was that treatment with GSK1521498 5 mg/day, versus placebo, was associated with a significant reduction in attentional bias for food-related stimuli on the visual dot probe task in obese people with binge-eating symptoms. There was an absence of significant effects of opioid antagonism on working memory, sustained attention or psychomotor speed. These results suggest that mu opioid receptor antagonism impacted the processing of food-related stimuli in a selective fashion.

Placebo (N021)

498 2 mg (N021)

498 5 mg (N021)

41.3 (9.22) 11/10 109.2 (18.17) 37.8 (4.87) 27.1 (6.30)

44.9 (10.08) 12/9 110.9 (23.63) 37.1 (5.37) 25.9 (6.59)

37.8 (10.06) 12/9 104.0 (9.82) 37.1 (4.38) 26.4 (7.30)

Fixation

Picture pair presentation

Dot probe (R position, R button response)

Picture pair presentation Dot probe (L position, L button response)

Time

10

5

0

-5

Placebo 2mg/day GSK498

*

5mg/day GSK498

-10

Day 28

Fixation

15

Day -1

Fig. 1 On each trial of the visual dot probe task (see left panel), a pair of pictorial stimuli was presented (each stimulus was a foodstuff or control image) and subjects made a manual response indicating the location of the subsequent ‘dot probe’ (left or right). There was a significant effect of GSK1521498 5 mg/day versus placebo on attentional bias for food stimuli in the 2,000-ms presentation condition of the task (right panel, the asterisk indicates p80 % central receptor occupancy (Rabiner et al. 2011). Nonetheless, it remains an unanswered question as to whether higher doses (such as with 10 mg/day, which would have resulted in >90 % receptor occupancy) would have manifested broader cognitive effects than seen here. It may also be the case that more profound cognitive effects of opioid pharmacological intervention would occur if administered to populations with profound baseline impairment. The lack of a significant main effect of gender or treatment-by-gender interaction on the dot probe 2,000-ms measure could represent a false negative (type II) error since the study may not have been statistically powered to characterise such effects. This issue is of interest since effects of other opioid antagonists appear to be influenced by gender (e.g. Nathan and Bullmore 2009; McClure et al. 2011), and more pronounced effects of GSK1521498 on food-taking were observed in male versus female rats (Giuliano et al. 2012). In summary, this study found evidence to support a role for mu opioid receptors in the control of attentional bias for food-related stimuli in the external environment, but not working memory, sustained attention or psychomotor speed. GSK1521498 thus appears to reduce reward-related cognitive bias without impairing normal cognitive processes relevant for day to day functioning. The findings have implications for novel therapeutic directions for bingeeating and other behavioural—and substance—addictions. Exploration of the effects of opioid manipulations (including of other receptor types) in the context of this and other behavioural addictions would be informative. It will also be important to clarify whether cognitive effects map onto meaningful therapeutic, i.e. clinical, benefits in different contexts.

Acknowledgments The authors wish to thank all participants and support staff at GSK. This study was funded and conducted by GSK. SRC is an NIHR Academic Clinical Fellow in Psychiatry; he received no financial compensation from GSK for work on this project. SRC consults for Cambridge Cognition, Shire, Lilly, and P1Vital. KM and BB’s primary employer is the University of Southampton, and these authors received compensation from GSK for work on this project. The following authors are employees of GSK and have shares in the company: CD, WT, KM, BS, AN, DR, EB and PN.

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