Mathematical Modelling of Insulin Resistance Development Caused by Chronic Inflammation

Detta är en Master-uppsats från Linköpings universitet/Institutionen för medicinsk teknik

Sammanfattning: Obesity has in recent times become a more serious health issue and was estimated to affect over 650 million people world-wide in 2016. Furthermore, the list of obesity-associated diseases is countless, many of which have severe consequences. Type 2 diabetes (T2D) is such a disease, and it was estimated to be over 1.5 million new cases in America alone in 2015. It is thought that insulin resistance development which causes T2D is associated with a low-level chronic inflammation in the adipose tissue. The inflammatory state is caused by the pro-inflammatory cytokine tumor necrosis factor-alpha (TNF-α) which is secreted by macrophages. To further understand the complexity of the underlying mechanisms of both the adipocytes as well as the macrophages, mathematical models are being developed in the fields of systems biology. However, as of now, no mathematical model has been developed which can explain the association between chronic inflammation and the development of insulin resistance. Because of this, a first model will be presented which is able to describe the mechanisms of insulin resistance development caused by chronic inflammation. The model was fitted to data from intraperitoneal glucose tolerance test in mice and yielded a cost below the threshold of chi-square test, which suggests that the model cannot be rejected. Furthermore, the model was expanded, introducing more complexity in the intracellular cascade reaction of an activated macrophage. Once again, the model was fitted to the same data and yielded a cost below the threshold of chi-square test. Uncertainty tests were made to further validate the models and showed a low uncertainty for both models. These results increase the understanding regarding the association between adipocytes and macrophages, in the role of insulin resistance caused by chronic inflammation. This increased knowledge can help, for instance, in the development of new drugs which are able to prevent the development of insulin resistance and T2D.

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