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. 2009 Mar 25;10:26.
doi: 10.1186/1471-2202-10-26.

A Mathematical Model of Aging-Related and Cortisol Induced Hippocampal Dysfunction

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Free PMC article

A Mathematical Model of Aging-Related and Cortisol Induced Hippocampal Dysfunction

Mark T McAuley et al. BMC Neurosci. .
Free PMC article

Abstract

Background: The hippocampus is essential for declarative memory synthesis and is a core pathological substrate for Alzheimer's disease (AD), the most common aging-related dementing disease. Acute increases in plasma cortisol are associated with transient hippocampal inhibition and retrograde amnesia, while chronic cortisol elevation is associated with hippocampal atrophy. Thus, cortisol levels could be monitored and managed in older people, to decrease their risk of AD type hippocampal dysfunction. We generated an in silicomodel of the chronic effects of elevated plasma cortisol on hippocampal activity and atrophy, using the systems biology mark-up language (SBML). We further challenged the model with biologically based interventions to ascertain if cortisol associated hippocampal dysfunction could be abrogated.

Results: The in silicoSBML model reflected the in vivoaging of the hippocampus and increased plasma cortisol and negative feedback to the hypothalamic pituitary axis. Aging induced a 12% decrease in hippocampus activity (HA), increased to 30% by acute and 40% by chronic elevations in cortisol. The biological intervention attenuated the cortisol associated decrease in HA by 2% in the acute cortisol simulation and by 8% in the chronic simulation.

Conclusion: Both acute and chronic elevations in cortisol secretion increased aging-associated hippocampal atrophy and a loss of HA in the model. We suggest that this first SMBL model, in tandem with in vitroand in vivostudies, may provide a backbone to further frame computational cortisol and brain aging models, which may help predict aging-related brain changes in vulnerable older people.

Figures

Figure 1
Figure 1
Physiological Basis of SBML Model. This figure describes the relationships between cortisol synthesis by the HPA axis and cortisol binding to hippocampal MR receptors with respect to synaptic firing at CA1 hippocampal neurons. Hippocampal atrophy is related to the numbers of neurons within the CA1 region, further defined by the branching of neurons therein. Ageing-related changes are shown chiefly as cell loss from hypothalamic hippocampal and pituitary tissues, along with a decline in the synthesis and availability of trophic factors. Stress associated changes are shown by the dashed arrow to GR receptors whereby elevated cortisol induces the expression of GR receptors, which are associated with CA1 neuronal synaptic inhibition. The combined effects of aging and stress may decrease hippocampal activity- defined as a combination of synaptic excitation and inhibition, and increase hippocampal atrophy.
Figure 2
Figure 2
Network diagram of SBML Model. This flow chart illustrates the relationship between the main components of the SMBL model and is an abstracted simplified version of the physiological systems (HPA axis, hippocampus etc) modelled.
Figure 3
Figure 3
Simulation of effects of age, acute, chronic and a biological intervention based change in cortisol levels on Hippocampal Activity and Volume. Graphs produced using MathSBML show the relationship between cortisol and hippocampal activity/volume with respect to a) age b) an acute (blue line) and chronic (dashed line) increase in cortisol and c) acute and chronic stress after an intervention, modelled using parameters described in the methods section using SBML.
Figure 4
Figure 4
Response of the model to negative feedback of cortisol regulation from a) the hypothalamus and b) pituitary c) increased CRH and d) decreased CRH production. Parameters were changed by a factor of 10–50% inducing changes in circulating cortisol levels as described in accompanying tables, modelled using SBML as detailed in the methods section.
Figure 5
Figure 5
Response of cortisol levels to changes in negative feedback from the hippocampus. Parameters were changed by a factor of 20–80%, introducing significant changes in circulating cortisol levels.
Figure 6
Figure 6
Simulation showing the effects of a) increased rate of cortisol production and b) decreased rate of cortisol production on plasma cortisol levels. Parameters were changed by a factor of 10–50% inducing changes in circulating cortisol levels as described in accompanying tables, modelled using SBML as detailed in the methods section.
Figure 7
Figure 7
Plasma Cortisol responses to central cortisol secretion. Simulations shows the effects of a) increased and b) decreased somatic tissue utilisation of cortisol on plasma cortisol levels. Simulation of the response of the model to parameters were changed by a factor of 10–50% inducing changes in circulating cortisol levels as described in accompanying tables, modelled using SBML as detailed in the methods section.
Figure 8
Figure 8
Diurnal rhythm of cortisol in response to changes in ODEs 1–3 Diurnal oscillations of cortisol over a 72 hour period. This graph was produced by making changes to equations 1–3. These changes are detailed in the appendix.

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