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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Psychiatry | doi: 10.3389/fpsyt.2019.00173

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

  • 1Biomedical Research Institute of Bellvitge, Spain
  • 2CIBER of Pathophysiology of Obesity and Nutrition (CIBEROBN), Spain
  • 3Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Spain
  • 4Departament de Psicobiologia i Metodologia, Autonomous University of Barcelona, Spain
  • 5Department of Psychiatry, University of Minnesota Twin Cities, United States
  • 6Département de Psychoéducation, Université du Québec à Trois-Rivières, Canada
  • 7Departament of Experimental Psychology, University of Granada, Spain
  • 8Brain, Mind and Behavior Research Center (CIMCYC), University of Granada, Spain
  • 9Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Spain
  • 10Departament d'Infermeria de Salut Pública, Salut Mental i Matemoinfantil. Escola Universitària d'Infermeria, University of Barcelona, Spain
  • 11Department of Psychology, University of Alcalá, Spain

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.

Keywords: gambling disorder, clustering, distress, personality traits, Psychopathology

Received: 03 Sep 2018; Accepted: 08 Mar 2019.

Edited by:

Yasser Khazaal, Département de Psychiatrie, Centre Hospitalier Universitaire Vaudois, Switzerland

Reviewed by:

Xiaohui Xu, Texas A&M Health Science Center, United States
Mercedes Lovrecic, National Institute for Public Health, Slovenia  

Copyright: © 2019 Jiménez-Murcia, Granero, Fernandez-Aranda, Stinchfield, Tremblay, Steward, Mestre-Bach, Lozano-Madrid, Mena-Moreno, Mallorquí-Bagué, Perales, Navas, Soriano-Mas, Aymamí, Gómez-Peña, Aguera, Del Pino-Gutierrez, Martín-Romera and Menchon. 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.

* Correspondence:
PhD. Susana Jiménez-Murcia, Biomedical Research Institute of Bellvitge, Barcelona, 08908, Catalonia, Spain, sjimenez@bellvitgehospital.cat
PhD. Juan F. Navas, University of Granada, Departament of Experimental Psychology, Granada, 18071, Spain, jfnavas@ugr.es