Phenotypes in Gambling Disorder Using Sociodemographic and Clinical Clustering Analysis: An Unidentified New Subtype?
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Frontiers in Media
Gambling DisorderPersonality traitsClusteringPsychopathologySeverity
Jiménez-Murcia S, Granero R, Fernández-Aranda F, Stinchfield R, Tremblay J, Steward T, Mestre-Bach G, Lozano-Madrid M, Mena-Moreno T, Mallorquí-Bagué N, Perales JC, Navas JF, Soriano-Mas C, Aymamí N, Gómez-Peña M, Agüera Z, del Pino-Gutiérrez A, Martín-Romera V and Menchón JM (2019) Phenotypes in Gambling Disorder Using Sociodemographic and Clinical Clustering Analysis: An Unidentified New Subtype? Front. Psychiatry 10:173.
SponsorshipFinancial support was received through the Ministerio de Economía y Competitividad (grant PSI2015-68701-R) and the Investigación subvencionada por la Delegación del Gobierno para el Plan Nacional sobre Drogas (2017I067). FIS PI14/00290, FIS PI17/01167, and 18MSP001 - 2017I067 received aid from the Ministerio de Sanidad, Servicios Sociales e Igualdad. CIBER Fisiología Obesidad y Nutrición (CIBERobn) and CIBER Salud Mental (CIBERSAM), both of which are initiatives of ISCIII. GM-B is supported by a predoctoral AGAUR grant (2018 FI_B200174), grant co-funded by the European Social Fund (ESF) ESF, investing in your future. With the support of the Secretariat for Universities and Research of the Ministry of Business and Knowledge of the Government of Catalonia.
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.