Interactions between the Hypothalamic-Pituitary-Adrenal Axis and Autonomic Nervous System As Predictors of Depressive Symptoms in Children with ASD

Poster Presentation
Saturday, May 4, 2019: 11:30 AM-1:30 PM
Room: 710 (Palais des congres de Montreal)
R. A. Muscatello1, E. McGinn2, S. Ioannou2, J. Andujar3 and B. A. Corbett2, (1)Neuroscience Graduate Program, Vanderbilt University, Nashville, TN, (2)Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN, (3)Vanderbilt University, Nashville, TN
Background: The hypothalamic-pituitary-adrenal (HPA) axis and autonomic nervous system (ANS) are physiological systems involved in arousal response and regulation, and independently, have been implicated in negative behavioral health outcomes, including internalizing disorders. Dysregulation of these systems has been reported in autism spectrum disorder (ASD), including elevated evening cortisol, as well as hyperarousal of the sympathetic (SNS) and under-responsivity of the parasympathetic (PNS) branches of the ANS. Previous research to identify associations with physiological dysregulation and internalizing symptoms in ASD has produced inconsistent findings, and little attention has been paid to the interactions of the distinct, yet interrelated, HPA axis and ANS.

Objectives: The current study examined differences in physiological regulation for 96 children 10-to-13 years of age with ASD (11.25 years) and typical development (TD; 11.24 years). Subsequently, interactions between the HPA axis, PNS, and SNS were examined within the ASD group. The extent to which the individual systems, as well as the interactions between them, predicted parent-reported internalizing symptoms in children with ASD was investigated.

Methods: In 96 children with ASD (N=64) and TD (N=32), diurnal rhythm of the HPA axis was measured via salivary cortisol, collected through passive drool at home over 3 days, collected in the morning (twice), afternoon, and evening. Baseline respiratory sinus arrhythmia (RSA) and pre-ejection period (PEP) were collected in the lab via electrocardiography and impedance cardiography to examine PNS and SNS regulation, respectively. Parents completed the Child Behavior Checklist (CBCL), and the Withdrawn/Depressed, Anxious, and Internalizing subscales were included in analyses. ANOVAs were used to compare group differences, and hierarchal multiple linear regression was used to examine the extent to which physiological variables and their interactions predict internalizing symptoms.

Results: Consistent with previous research, children with ASD showed elevated evening cortisol compared to TDs (F(1,93)=11.77, p=0.001). There were no significant group differences in baseline RSA or PEP (all p>0.05); however, the ASD group reported higher scores on CBCL subscales Withdrawn/Depressed (t(94)=-7.51, p<0.001), Internalizing (t(94)=-8.50, p<0.001), and Anxious (t(94)=-5.44, p<0.001). No significant main effects of physiological variables on CBCL subscales were seen using hierarchal linear regression while controlling for age, IQ and gender (all p>0.05). However, in the next step of the model, controlling for demographics and main effects, there was a significant interaction for cortisol and RSA, which accounted for 9% of the unique variance in CBCL Withdrawn/Depressed symptoms in ASD (∆F(1,42)=5.51, p=0.03). Posthoc analysis revealed that ASD participants with low evening cortisol showed a negative trend association between RSA and withdrawn symptoms (t(92)=-1.89, p=0.06).

Conclusions: The results extend previous findings on physiological dysregulation in ASD to reveal the presence of unique interactions, which predict parent-reported symptoms of depression. Children with high PNS regulation and low evening cortisol showed the fewest symptoms. In contrast, hyper-arousal in the HPA axis and/or PNS was associated with elevated depressive symptoms, even in the presence of more adaptive physiological regulation in the other system. The findings underscore the importance of examining arousal across multiple systems to more accurately identify response profiles associated with behavioral outcomes in ASD.