Prevalence and Predictors of ADHD in Adolescent Males with FXS and ASD

Thursday, May 12, 2016: 5:30 PM-7:00 PM
Hall A (Baltimore Convention Center)
S. L. O'Connor1, S. McGrath2, J. Ezell2, C. Smith2, L. Abbeduto3 and J. Roberts1, (1)Psychology, University of South Carolina, Columbia, SC, (2)University of South Carolina, Columbia, SC, (3)MIND Institute, UC Davis, Sacramento, CA
Background: Fragile X Syndrome (FXS) is the leading known genetic cause of Autism Spectrum Disorder (ASD). The behavioral phenotype of FXS is marked by challenging behaviors and symptoms such as inattention, hyperactivity, and hyper arousal related to Attention Deficit/Hyperactivity Disorder (ADHD). Approximately 60% of males with FXS, and 28-53% of children with ASD display features of ADHD. However, no study has determined diagnostic rates of ADHD in FXS or ASD with most studies relying on broad band rating scales reflecting the presence of specific behaviors. Also, no work has contrasted ADHD in FXS to idiopathic ASD (non-FXS). 

Objectives: In the present study, we characterize the prevalence and predictors of ADHD using a DSM-based diagnostic interview with group contrasts.  Our focus is on adolescent males with FXS and those with ASD given the high prevalence of ADHD in these populations.

Methods: Participants included 30 males with FXS and 7 males with ASD, 16-23 years-of-age. The Children’s Interview for Psychiatric Symptoms-Parent Version (PChIPS), a DSM based semi-structured parental interview was used as a diagnostic measure of ADHD with Inattentive, Hyperactive and Combined types specified. Chronological age, Leiter-Revised nonverbal IQ growth scores, and severity scores from the ADOS-2 were used as predictors of ADHD diagnostic estimates in logistic regression models. 

Results: Our results show that 40% of the FXS group met diagnostic criteria for a DSM diagnosis of ADHD, whereas 71% of the ASD group met (t (9.1) = 1.52, p > .05). Preliminary analyses indicate across the FXS and ASD groups respectively, 27% versus 57% met for Inattentive, 7% versus 14% met for Hyperactive, and 13% versus 0% met for Combined Type. Results from logistic regression models indicate that chronological age, nonverbal IQ and autism severity did not predict any type of ADHD in either the FXS or ASD group (p > .05). In logistic models reflecting predictors for individual subtypes in FXS, autism severity approached significance for Inattentive type (B=-0.35, p=0.08) with a moderate effect size (r = -0.33) and also for Hyperactive type (B=0.978, p= 0.11), with a large effect size (r=.67). In contrast these predictors did not approach significance for the ASD group.   

Conclusions: These findings suggest that diagnostic rates of ADHD in adolescents with FXS are lower than symptom prevalence estimates reported in childhood, and higher in ASD although not to a significant degree. Our finding that the rate meeting diagnostic criteria in FXS is lower than symptom prevalence is consistent with general patterns of higher symptom presentation on screening, broad-band measures and lower prevalence on more discreet diagnostic criteria. Our rate of 71% meeting diagnostic criteria for the ASD group could reflect the small sample. A lack of relationship between autism features and ADHD suggests that these disorders are distinct indicating that both assessment and treatment efforts should potentially be tailored to surveil and treat both of these disorders. We aim to add an additional 10 participants with ASD by May 2016.