Abstract

The role of the HPA axis response to an acute social stressor in predicting resilience to anxiety and depression in college freshmen

Khalil H, Turner CA, Murphy-Weinberg V, Gates L, Tang Y, Weinberg L, Floran-Garduno L, Arakawa K, Lopez JF, Watson SJ, Akil H
52nd Annual Meeting of Society for Neuroscience. 2023.

The hypothalamic-pituitary-adrenal (HPA) axis plays an important role in emotion regulation and stress coping. Here, we report the results of the HPA axis response to an acute social stressor, the Trier Social Stress Test (TSST), in a relatively healthy group of young adults with a view towards predicting psychological resilience. The TSST was administered to 246 University of Michigan freshmen (130 females, 116 males), and their heart rate, blood cortisol and ACTH levels were captured before, during and after the TSST. The subjects were genotyped and characterized at baseline using multiple psychological instruments and were subsequently sampled at various timepoints during their freshman year on clinical rating scales, namely the GAD-7 for anxiety and the PHQ-9 for depression. Initial results show that the shape of the cortisol and ACTH curves were different between those who did and did not have depression symptoms at the time of the TSST, with the depressed group showing a blunted cortisol and ACTH response to the stressor. Moreover, the subjects who developed depression and/or anxiety symptoms during their freshman year tended to have higher pre-TSST cortisol levels. In addition, the change in cortisol levels during the TSST significantly correlated with both baseline and follow-up PHQ-9 scores. Finally, we applied machine learning approaches to understand how the shape of the stress response curve differs. The Euclidean distance between the cortisol response of each subject with each other was calculated, and clustering was performed between subjects. Using a K-medoid clustering algorithm, we asked whether the clusters correspond to the group of subjects who did or did not develop depression symptoms during their freshman year. This simple clustering algorithm worked better than chance would predict. These results show the interplay between psychological and physiological measures in predicting vulnerability or resilience to mood disorders.