Phenotypes in Gambling Disorder Using Sociodemographic and Clinical Clustering Analysis: An Unidentified New Subtype?

Abstract

Background: Gambling disorder (GD) is a heterogeneous disorder which has clinical manifestations that vary according to variables in each individual. Considering the importance of the application of specific therapeutic interventions, it is essential to obtain clinical classifications based on differentiated phenotypes for patients diagnosed with GD. Objectives: To identify gambling profiles in a large clinical sample of n = 2,570 patients seeking treatment for GD. Methods: An agglomerative hierarchical clustering method defining a combination of the Schwarz Bayesian Information Criterion and log-likelihood was used, considering a large set of variables including sociodemographic, gambling, psychopathological, and personality measures as indicators. Results: Three-mutually-exclusive groups were obtained. Cluster 1 ( n = 908 participants, 35.5%), labeled as “high emotional distress,” included the oldest patients with the longest illness duration, the highest GD severity, and the most severe levels of psychopathology. Cluster 2 ( n = 1,555, 60.5%), labeled as “mild emotional distress,” included patients with the lowest levels of GD severity and the lowest levels of psychopathology. Cluster 3 ( n = 107, 4.2%), labeled as “moderate emotional distress,” included the youngest patients with the shortest illness duration, the highest level of education and moderate levels of psychopathology. Conclusion: In this study, the general psychopathological state obtained the highest importance for clustering.

The paradoxical relationship between emotion regulation and gambling-related cognitive biases

“…More importantly, the fact that negative urgency was not independently associated with gambling cognitions (in contrast with positive urgency and sensation seeking) is congruent with recent reports that gambling-related cognitions are stronger, and cognitive biases more prevalent, in gamblers who are highly sensitive to appetitive stimuli and motives [83,84]. Although it was hypothesized that, as long as cognitive biases are affect-driven and motivated, they should be linked to affect-driven impulsivity dimensions, it has been consistently shown that negative urgency is specifically linked to complications in the form of generalized externalizing problems beyond gambling [85,86].…”

Delay Discounting in Gambling Disorder: Implications in Treatment Outcome

“…In addition, a steeper discount rate showed a relationship with high levels of harm avoidance and low levels of cooperativeness in middle-aged patients. Harm avoidance is characterized by a greater tendency to anxiety, worry and insecurity, isolation, and disconnection from the environment, poor decision-making skills, and planning skills [ 73 ]. Studies showed harm avoidance in more severe GD patients who tend to use gambling as a dysfunctional mechanism to avoid problems and regulate emotions [ 73 ].…”

“…Harm avoidance is characterized by a greater tendency to anxiety, worry and insecurity, isolation, and disconnection from the environment, poor decision-making skills, and planning skills [ 73 ]. Studies showed harm avoidance in more severe GD patients who tend to use gambling as a dysfunctional mechanism to avoid problems and regulate emotions [ 73 ]. Therefore, gambling could be a dysfunctional habit acquired and maintained over time as a mechanism to avoid problems and difficulties in emotion regulation.…”

“…In this group, higher discount rates correlated with higher severity of GD and higher general psychopathology. More severe clinical profiles have been frequently reported in strategic gamblers, often measured in terms of higher bets, higher levels of psychopathology [ 73 , 75 ], cognitive distortions, and more severe GD symptoms [ 75 , 76 , 77 ]. Moreover, higher discount rates in strategic gamblers were associated with higher levels of negative urgency and harm avoidance, which are personality traits related to emotional dysregulation.…”

Educational Tool and Active-Learning Class Activity for Teaching Agglomerative Hierarchical Clustering

“…It often serves as an exploratory data analysis tool to guide the selection of appropriate methods for statistical inference at the later stage of a research study. It has wide applications in many different disciplines, such as market segmentation (Wedel and Kamakura 1999), bioinformatics (Abu-Jamous, Fa, and Nandi 2015), clinical psychiatry (Jiménez-Murcia et al 2019), environmental policy (Hu et al 2019), among many others.…”