Reward, Relief and Habit Drinking: Initial Validation of a Brief Assessment Tool

Erica N. Grodin; Spencer Bujarski; Alexandra Venegas; Wave-Ananda Baskerville; Steven J. Nieto; J. David Jentsch; Lara A. Ray

Disclosures

Alcohol Alcohol. 2019;54(6):574-583. 

In This Article

Abstract and Introduction

Abstract

Aims: Alcohol use disorder is highly heterogeneous. One approach to understanding this heterogeneity is the identification of drinker subtypes. A candidate classification consists of reward and relief subtypes. The current study examines a novel self-report measure of reward, relief, and habit drinking for its clinical correlates and subjective response (SR) to alcohol administration.

Methods: Non-treatment-seeking heavy drinkers (n = 140) completed the brief reward, relief, habit drinking scale (RRHDS). A subset of this sample (n = 67) completed an intravenous alcohol administration. Individuals were classified into drinker subtypes. A crowdsourced sample of heavy drinkers (n = 187) completed the RRHDS and a validated reward relief drinking scale to compare drinking classification results.

Results: The majority of the sample was classified as reward drinkers (n = 100), with fewer classified as relief (n = 19) and habit (n = 21) drinkers. Relief and habit drinkers reported greater tonic alcohol craving compared to reward drinkers. Reward drinkers endorsed drinking for enhancement, while relief drinkers endorsed drinking for coping. Regarding the alcohol administration, the groups differed in negative mood, such that relief/habit drinkers reported a decrease in negative mood during alcohol administration, compared to reward drinkers. The follow-up crowdsourcing study found a 62% agreement in reward drinker classification between measures and replicated the tonic craving findings.

Conclusions: Our findings suggest that reward drinkers are dissociable from relief/habit drinkers using the brief measure. However, relief and habit drinkers were not successfully differentiated, which suggests that these constructs may overlap phenotypically. Notably, measures of dysphoric mood were better at detecting group differences than measures capturing alcohol's rewarding effects.

Introduction

Alcohol use disorder (AUD) is a heterogeneous disorder, and for many years, the field has attempted to identify subgroups within AUD. Early efforts used data primarily derived from treatment-seeking populations to cluster individuals with AUD into subgroups based on several criteria. Jellinek (1960) characterized five subtypes of problematic alcohol drinking based on etiological considerations, progression of alcohol use and resulting consequences of alcohol consumption. Cloninger et al. (1981) recommended the Type 1 versus Type 2 dichotomy based on age of onset of alcohol problems and personality traits. Babor et al. (1992) also proposed two types of drinkers, Type A and Type B, who can primarily be distinguished based on age onset of alcohol problems, number of childhood risk factors, sex, socioeconomic status, psychological dysfunction, polysubstance use, chronic treatment history, familial history of alcoholism and life stress. Lesch and Walter (1996) characterized four subtypes based on biological, psychological and sociological dysfunction. More recently, Moss et al. (2007) used national substance abuse survey data from the 2001 to 2002 National Epidemiological Survey on Alcohol and Related Conditions (NESARC-II) to distinguish between five clusters of alcohol dependency based on age of onset of alcohol dependency, familial alcoholism, antisocial personality traits, endorsement of DSM-IV alcohol abuse criteria and comorbid psychiatric and substance use disorders. Although these proposed typologies enhanced our understanding of the various facets of AUD, there is still a lack of consensus about which approach can best advance the field (Leggio et al., 2009).

Broadly speaking, the goal of classifying subgroups of patients with AUD is in the interest of providing more effective treatments that are tailored to common clinical features and putative pathophysiology. As such, attempts have been made to match behavioral treatments with specific alcohol drinking profiles. For example, using Babor's classification method, patients classified as Type A were shown to have better outcomes after group psychotherapy but do worse with coping skills training. Conversely, patients classified as Type B had better outcomes with coping skills training yet worse ones with interactional group therapy (Litt et al., 1992). However, the lack of knowledge regarding other contributing factors to AUD may have led to the failure of large randomized clinical trials (i.e. Project MATCH) to target interventions to specific subgroups of alcohol-dependent patients (Project MATCH Research Group, 1997).

Recent efforts to identify discrete subgroups of patients with AUD have been informed by neurobiological models. Specifically, these neurobiological models of addiction have identified unique neural substrates underlying reward, relief and habit pathways. The allostatic model of addiction posits a heuristic framework that involves three stages of addiction: binge/intoxication, withdrawal/negative affect and preoccupation/intoxication stages (Koob and Le, 1997; Koob and Volkow, 2010; Koob, 2013). Initial alcohol use is characterized by positive reinforcement and impulsivity, such that alcohol's rewarding properties increase the likelihood of continued alcohol seeking and consumption (Wise, 1987). These initial features are mediated by both the dopaminergic and opioidergic activity within the ventral striatum (Volkow et al., 2003; Gilman et al., 2008; Mitchell et al., 2012). In a subset of drinkers, repeated cycles of intoxication and withdrawal shift motivation to consume alcohol from positive reinforcement to negative reinforcement, wherein individuals drink alcohol to alleviate negative emotional states (Koob and Le Moal, 2005). The emergence of negative emotional states is mediated by neuroadaptations to stress systems in the extended amygdala (Koob and Kreek, 2007; Koob, 2008). Additionally, concurrent decreases in alcohol reward are associated with blunted dopaminergic activity in the ventral striatum (Koob and Bloom, 1988; Volkow et al., 2007). Although the rewarding effects of alcohol diminish, alcohol-associated stimuli develop incentive-salience via dopaminergic and glutamatergic signaling in the dorsal striatum, contributing to automaticity and habit learning (Robinson and Berridge, 1993; George and Koob, 2017). Over time, chronic alcohol consumption decreases executive functioning regulated by frontal lobe areas (Oscar-Berman and Marinković, 2007), leading to an overactive "Go" system that drives craving and habits, and an underactive "Stop" system that inhibits these behaviors (Bechara et al., 1999; Lobo and Nestler, 2011; Volkow and Morales, 2015; George and Koob, 2017). Taken together, compulsive alcohol drinking is the result of combined neuroadaptations in reward, stress, habit formation and executive function circuitry.

As with early efforts to classify AUD patients, neuroscience-informed clinical groupings are meant to inform treatment and personalize clinical care. Given that the initial stages of the addiction cycle are motivated by alcohol's ability to indirectly increase dopamine levels in brain reward pathways, it is reasonable to assume that a pharmacological treatment that diminishes alcohol reward would be a viable therapeutic option for AUD. Naltrexone, a mu opioid antagonist, blunts alcohol-induced dopamine release but is only modestly effective in treating AUD (O'Malley et al., 1992; Volpicelli et al., 1992; Bouza et al., 2004). However, clinical responses to naltrexone are highly variable, which may be due, at least in part, to factors such as family history of alcoholism (King et al., 1997; Rubio et al., 2005; Krishnan-Sarin et al., 2007) and variation in the OPRM1 gene (Oslin et al., 2003; Ray and Hutchison, 2007; Anton et al., 2008). Two recent studies have found that classifying individuals into reward and relief drinking subtypes impacts the effectiveness of pharmacological treatments for AUD (Roos et al., 2017; Mann et al., 2018). In the first study, individuals in the COMBINE study who were classified as high relief drinkers, defined as those who drink alcohol mainly to relieve negative affect, had better drinking outcomes when treated with the glutamatergic modulator acamprosate compared to placebo. There was no significant effect found for reward drinkers and naltrexone in improving drinking outcomes (Roos et al., 2017). In contrast, Mann and colleagues utilized the PREDICT study sample and found that reward drinkers, defined as those who drink alcohol for its pleasurable/euphoric effects, benefited more from naltrexone in reducing heavy drinking compared to those treated with acamprosate (Mann et al., 2018). The discrepancy between these two findings may be due to the differing protocols of the COMBINE and PREDICT studies, particularly in the duration of pre-randomization abstinence. In the COMBINE study, the majority of participants were abstinent for only a short period prior to study randomization, whereas in the PREDICT study, participants underwent a full medication detoxification protocol. These differences likely impacted glutamatergic neurotransmission, which has been hypothesized as acamprosate's mechanism of action (Holmes et al., 2013; Mann et al., 2018) and thereby limited the ability of Mann and colleagues to identify a relationship between relief drinking and acamprosate. Despite the contrasting medication findings, both studies reliably identified reward and relief AUD subtypes, which also replicates earlier work which classified reward and relief craving subtypes and identified specific clinical characteristics subserving them (Glockner-Rist et al., 2013). However, for these clinical groups to be reliably identified, clinical instruments must be developed and validated. In the current context of healthcare, brief measures that can be easily administered are most likely to be adopted and to impact practice. To that end, this study examines a novel self-report measure of reward, relief and habit drinking developed a priori by our group and administered in a human laboratory study of non-treatment seeking problem drinkers (Bujarski et al., 2018). In this report, we examine (a) the test-retest reliability of the new measure, (b) the clinical correlates of the three drinking subtypes and (c) patterns of subjective response (SR) to alcohol and self-administration of alcohol across the drinker subtypes. A follow-up study comprised of a crowdsourcing-based survey was undertaken to explore the agreement between our approach to identifying reward/relief drinkers compared to the approach undertaken by Mann et al. (2018).

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