A mathematical model of aging-related and cortisol induced hippocampal dysfunction

Abstract

BackgroundThe 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.ResultsThe 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.ConclusionBoth 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.

Investigating cholesterol metabolism and ageing using a systems biology approach

“…The systems biology paradigm contrasts with the more traditional reductionist approach, commonly used in nutrition research, and offers a more integrated way to study this multifaceted system (14,113,114) . A fundamental element of this paradigm shift is the close coupling of computational modelling with experimental work (115)(116)(117)(118) . Our group has used computational systems biology to investigate cholesterol metabolism and ageing (119) .…”

Integration of ‘omics’ data in aging research: from biomarkers to systems biology

“…Dozens of clinical phenotypes, such as Parkinson's (Reeve et al ., 2014), AD (McAuley et al ., 2009), body mass index, blood pressure (Mungreiphy et al ., 2011) and bone mineral density (Warming et al ., 2002), as well as lifestyle parameters, such as nutrition (Wieser et al ., 2011), smoking and physical activity, are strongly related to age (Harman, 1988; Wang et al ., 2009). Composite measures such as the Rockwood frailty index (Rockwood & Mitnitski, 2007) combine several of those clinical traits to form a more homogenous phenotype – frailty – from its diverse appearance.…”

“…Such frailty measures can be considered as measures for biological age (Mitnitski et al ., 2013). Many of these (and other) clinical phenotypes correlate or even depend on each other (McAuley et al ., 2009; Baylis et al ., 2014). Only extensive collection of data and their joint analysis will help to unveil these dependencies and find causal relationships.…”

“…One study investigated the influence of increased cortisol levels on hippocampus activity (McAuley et al ., 2009). A quantitative model was created to simulate the decline in hippocampal output with age and the acceleration of this process due to acute and chronic increases in cortisol levels.…”

Cardiovascular disease and healthy ageing

“…Recently, we used mathematical modelling to show that both acute and chronic levels of cortisol have a profound impact on brain ageing. It would be worthwhile adopting a similar framework to explore the long term impact of elevated cortisol on CVD risk [26].…”