Do attention-deficit/hyperactivity symptoms influence treatment outcome in gambling disorder?

BACKGROUND AND AIMS: Numerous studies point to the comorbidity between gambling disorder (GD) and attention deficit hyperactivity disorder (ADHD). However, there is a lack of research exploring how ADHD symptoms might influence psychological treatment outcomes for GD. Therefore, we aimed to explore differences between patients with GD with and without self-reported ADHD symptoms regarding psychopathology, personality, sociodemographic and treatment outcome measures. METHOD: This longitudinal study included 170 patients with GD receiving cognitive behavioral therapy. Multiple self-reported instruments were used to assess clinical variables and sociodemographic measures prior to treatment. RESULTS: A clinical profile characterized by greater GD severity, higher psychopathology and impulsivity, and less adaptive personality features was observed in patients with self-reported ADHD symptoms compared to those without. No significant differences in treatment response (measured by dropout and relapse rates) were observed between the two groups. However, patients with self-reported ADHD symptoms experienced more severe relapses (i.e., gambled more money) and GD patients who relapsed scored higher on measures of ADHD, particularly inattention. CONCLUSION: Individuals with GD and self-reported symptoms of ADHD may experience more severe relapses following treatment, suggesting a need for more vigilant follow-up and interventions for patients with this comorbidity.

Cluster analysis in gambling disorder based on sociodemographic, neuropsychological, and neuroendocrine features regulating energy homeostasis.

BACKGROUND: The heterogeneity of gambling disorder (GD) has led to the identification of different subtypes, mostly including phenotypic features, with distinctive implications on the GD severity and treatment outcome. However, clustering analyses based on potential endophenotypic features, such as neuropsychological and neuroendocrine factors, are scarce so far. AIMS: This study firstly aimed to identify empirical clusters in individuals with GD based on sociodemographic (i.e., age and sex), neuropsychological (i.e., cognitive flexibility, inhibitory control, decision making, working memory, attention, and set-shifting), and neuroendocrine factors regulating energy homeostasis (i.e., leptin, ghrelin, adiponectin, and liver-expressed antimicrobial peptide 2, LEAP-2). The second objective was to compare the profiles between clusters, considering the variables used for the clustering procedure and other different sociodemographic, clinical, and psychological features. METHODS: 297 seeking-treatment adult outpatients with GD (93.6% males, mean age of 39.58 years old) were evaluated through a semi-structured clinical interview, self-reported psychometric assessments, and a protocolized neuropsychological battery. Plasma concentrations of neuroendocrine factors were assessed in peripheral blood after an overnight fast. Agglomerative hierarchical clustering was applied using sociodemographic, neuropsychological, and neuroendocrine variables as indicators for the grouping procedure. Comparisons between the empirical groups were performed using Chi-square tests (χ2) for categorical variables, and analysis of variance (ANOVA) for quantitative measures. RESULTS: Three-mutually-exclusive groups were obtained, being neuropsychological features those with the greatest weight in differentiating groups. The largest cluster (Cluster 1, 65.3%) was composed by younger males with strategic and online gambling preferences, scoring higher on self-reported impulsivity traits, but with a lower cognitive impairment. Cluster 2 (18.2%) and 3 (16.5%) were characterized by a significantly higher proportion of females and older patients with non-strategic gambling preferences and a worse neuropsychological performance. Particularly, Cluster 3 had the poorest neuropsychological performance, especially in cognitive flexibility, while Cluster 2 reported the poorest inhibitory control. This latter cluster was also distinguished by a poorer self-reported emotion regulation, the highest prevalence of food addiction, as well as a metabolic profile characterized by the highest mean concentrations of leptin, adiponectin, and LEAP-2. CONCLUSIONS: To the best of our knowledge, this is the first study to identify well-differentiated GD clusters using neuropsychological and neuroendocrine features. Our findings reinforce the heterogeneous nature of the disorder and emphasize a role of potential endophenotypic features in GD subtyping. This more comprehensive characterization of GD profiles could contribute to optimize therapeutic interventions based on a medicine of precision.

Association between endocrine and neuropsychological endophenotypes and gambling disorder severity.

BACKGROUND: Neurobiological characteristics have been identified regarding the severity of gambling disorder (GD). The aims of this study were: (1) to examine, through a path analysis, whether there was a relationship between neuroendocrine features, potentially mediational GD variables, and GD severity, and (2) to associate neuroendocrine variables, with GD severity-related variables according to gambling preferences. METHODS: The sample included 297 outpatients with GD. We analyzed endocrine concentrations of different appetite-related hormones (ghrelin, liver antimicrobial peptide 2 [LEAP-2], leptin, adiponectin), and neuropsychological performance (working memory, cognitive flexibility, inhibition, decision making, premorbid intelligence). Path analysis assessed mechanisms between neuroendocrine features and GD severity, including mediational GD variables (impulsivity traits and gambling-related cognitive distortions). Partial correlations evaluated the associations between neuroendocrine variables, including impulsivity traits, and variables related to GD severity (DSM-5, South Oaks Gambling Screen, illness duration, and gambling-related cognitive distortions). RESULTS: Lower adiponectin concentrations predicted greater GD severity, while higher LEAP-2 concentrations predicted more gambling-related cognitive distortions. Likewise, better neuropsychological performance directly predicted GD severity, but worse neuropsychological performance was associated with GD severity through the mediational variables of impulsivity traits and gambling-related cognitive distortions. Also, in non-strategic individuals with GD, poor working memory was associated with gambling expectancies and predictive control. In strategic individuals with GD, poor cognitive flexibility was associated with illusion of control, predictive control, and inability to stop gambling. CONCLUSIONS: These results provide updated information about the comprehension of the interaction between neuroendocrine features, clinical variables, and severity of GD. Thus, neurobiological functions seem to be strongly related to GD severity.

Plasma concentration of leptin is related to food addiction in gambling disorder: Clinical and neuropsychological implications.

Background: Data implicate overlaps in neurobiological pathways involved in appetite regulation and addictive disorders. Despite different neuroendocrine measures having been associated with both gambling disorder (GD) and food addiction (FA), how appetite-regulating hormones may relate to the co-occurrence of both entities remain incompletely understood. Aims: To compare plasma concentrations of ghrelin, leptin, adiponectin, and liver-expressed antimicrobial peptide 2 (LEAP-2) between patients with GD, with and without FA, and to explore the association between circulating hormonal concentrations and neuropsychological and clinical features in individuals with GD and FA. Methods: The sample included 297 patients diagnosed with GD (93.6% males). None of the patients with GD had lifetime diagnosis of an eating disorder. FA was evaluated with the Yale Food Addiction Scale 2.0. All patients were assessed through a semi-structured clinical interview and a psychometric battery including neuropsychological tasks. Blood samples to measure hormonal variables and anthropometric variables were also collected. Results: From the total sample, FA was observed in 23 participants (FA+) (7.7% of the sample, 87% males). When compared participants with and without FA, those with FA+ presented both higher body mass index (BMI) (p < 0.001) and leptin concentrations, after adjusting for BMI (p = 0.013). In patients with FA, leptin concentrations positively correlated with impulsivity, poorer cognitive flexibility, and poorer inhibitory control. Other endocrine measures did not differ between groups. Discussion and conclusions: The present study implicates leptin in co-occurring GD and FA. Among these patients, leptin concentration has been associated with clinical and neuropsychological features, such as impulsivity and cognitive performance in certain domains.