Cluster analysis of disorders characterized by impulsivity in patients with methamphetamine use disorder

Background Individuals with methamphetamine use disorder (MUD) frequently present with psychiatric comorbidities with impulsive features. Little research has been conducted on comorbidity with impulsive features in MUD. Therefore, this cross-sectional study aimed to delineate comorbid disorders with impulsivity in adult patients with a primary diagnosis of MUD. Methods Participants with lifetime MUD were included. Well established measures screened for comorbid psychiatric disorders with impulsive features. Illness severity was measured by the Yale Brown Obsessive-Compulsive Scale – adapted for drug use. The UPPS-P Impulsive Behavior Scale was used to assess impulsivity levels. A cluster analysis (CA) of lifetime comorbid disorders with impulsive features was performed. Demographic and clinical correlates of each identified cluster were identified. Results Sixty five (n = 65) adults with a primary diagnosis of MUD took part in the study. They were predominantly female (44 females; 21 males), with ages ranging between 18 and 44 years (mean = 30 years; SD = 6.53). The CA rendered 4 groups. Cases (n=12) in the “alcohol cluster” presented with AUD as their only impulsive disorder other than MUD. Cases (n=19) in the “healthy cluster” had no comorbidity. Cases (n=15) in the “antisocial cluster” all had comorbid antisocial personality disorder as well as polysubstance use disorders. Cases (n=19) in the “borderline cluster” had borderline personality disorder and polysubstance use disorders. Illness severity (Y-BOCS-du: p=0.03) and impulsivity levels (UPPS-P: p=0.01) differed significantly between the clusters. The “alcohol cluster” had the highest illness severity and the “antisocial cluster reported the highest levels of impulsivity. Conclusion The findings of this contribute to the paucity data on impulsivity in MUD and may have implications for treatment. Understanding how these conditions cluster in MUD, and remaining cognizant of the demographic and clinical correlates of each cluster in MUD, could potentially enable clinicians to identify patients who are at higher risk for engaging in risky behaviors rendering them more vulnerable to treatment non-adherence or relaps