Phenotypes in gambling disorder using sociodemographic and clinical clustering analysis: An unidentified new subtype?

Susana Jiménez-Murcia, Roser Granero, Fernando Fernández-Aranda, Randy Stinchfield, Joel Tremblay, Trevor Steward, Gemma Mestre-Bach, María Lozano-Madrid, Teresa Mena-Moreno, Núria Mallorquí-Bagué, José C. Perales, Juan F. Navas, Carles Soriano-Mas, Neus Aymamí, Mónica Gómez-Peña, Zaida Agüera, Amparo Del Pino-Gutiérrez, Virginia Martín-Romera, José M. Menchón

Research output: Contribution to journalArticleResearch

5 Citations (Scopus)

Abstract

Copyright © 2019 Jiménez-Murcia, Granero, Fernández-Aranda, Stinchfield, Tremblay, Steward, Mestre-Bach, Lozano-Madrid, Mena-Moreno, Mallorquí-Bagué, Perales, Navas, Soriano-Mas, Aymamí, Gómez-Peña, Agüera, del Pino-Gutiérrez, Martín-Romera and Menchón. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. 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.
Original languageEnglish
Article number173
JournalFrontiers in Psychiatry
Volume10
DOIs
Publication statusPublished - 1 Jan 2019

Keywords

  • Clustering
  • Gambling disorder
  • Personality traits
  • Psychopathology
  • Severity

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