ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

Endocrine Abstracts (2013) 31 APW1.2 | DOI: 10.1530/endoabs.31.APW1.2

Distinguishing normal, depressive and PTSD cortisol dynamics in humans through mathematical modelling

Maria Rodriguez-Fernandez1, K. Sriram2 & Francis J. Doyle1

1Institute of Collaborative Biotechnologies, University of California Santa Barbara, Santa Barbara, California, USA; 2Indraprastha Institute of Information Technology (IIIT), Delhi, India.

PTSD is an anxiety disorder that occurs among persons exposed to a traumatic event involving life threat and injury. This is a co-morbid psychiatric disorder that occurs along with depression. Cortisol, secreted in the adrenal cortex in response to stress, is an informative biomarker that distinguishes anxiety disorders such as major depression and post-traumatic stress disorder (PTSD) from normal subjects. In comparison to normal subjects, hypocortisolemia was observed during the night in PTSD, while hypercortisolemia was observed in depressed subjects. Yehuda et al. proposed a hypothesis that, in humans, the hypersensitive hypothalamus–pituitary–adrenal (HPA) axis is responsible for the occurrence of differing levels of cortisol in anxiety disorders. Specifically, PTSD subjects have lower cortisol levels during the late subjective night in comparison to normal subjects, and this was assumed to occur due to strong negative feedback loops in the HPA axis. We complemented this hypothesis by constructing a mathematical model for cortisol dynamics in HPA axis using nonlinear ordinary differential equations and estimated the kinetic parameters that fitted the cortisol time series obtained from the clinical data of normal, depressed and PTSD patients. The parameters obtained from the simulated phenotypes strongly support the hypothesis that, due to disruptive negative feedback loops, cortisol levels are different in normal, PTSD and depressed subjects during the night. Bifurcation analysis carried out with the optimized parameters exhibited two supercritical Hopf points and, for the choice of parameters, the oscillations were found to be circadian in nature. Importantly, the model predicted the transitions from normal to various diseased states, and these transitions were shown to occur due to changes in the strength of the negative feedback loop and the stress intensity in the neuro-endocrine axis.

Declaration of funding

This work was financially supported by the Institute for Collaborative Biotechnologies through the U.S. Army Research Office (Grants W911NF-10-2-0111 and W911NF-09-D-0001). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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