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

Area of Science:

• Psychiatry
• Clinical Psychology
• Behavioral Science

Background:

• Gambling disorder (GD) is a complex condition with varied individual presentations.
• Tailored therapeutic interventions require precise clinical classifications based on distinct patient phenotypes.
• Identifying specific gambling profiles is crucial for effective GD treatment.

Purpose of the Study:

• To identify distinct gambling profiles within a large clinical sample of patients diagnosed with gambling disorder.
• To differentiate patient phenotypes for improved therapeutic targeting in GD treatment.

Main Methods:

• Utilized agglomerative hierarchical clustering on a dataset of 2,570 patients seeking GD treatment.
• Employed Schwarz Bayesian Information Criterion and log-likelihood for cluster definition.
• Included sociodemographic, gambling behavior, psychopathological, and personality variables as indicators.

Main Results:

• Identified three mutually exclusive clusters representing distinct GD profiles.
• Cluster 1 (35.5%): 'High emotional distress' - older patients, longest illness, highest GD severity, severe psychopathology.
• Cluster 2 (60.5%): 'Mild emotional distress' - lowest GD severity and psychopathology.
• Cluster 3 (4.2%): 'Moderate emotional distress' - youngest patients, shortest duration, highest education, moderate psychopathology.

Conclusions:

• General psychopathological state emerged as the most significant factor in patient clustering.
• The identified profiles highlight the heterogeneity of gambling disorder.
• These findings support the development of phenotype-specific treatment strategies for gambling disorder.