Theoretical evaluation of the parameters of glucose metabolism on the basis of continuous glycemia monitoring data using mathematical modeling
- 30 Downloads
The aim of this paper is to construct a mathematical model that takes the main physiological parameters of blood-glucose regulation into account, in order to identify these parameters for an individual patient according to continuous glucose-monitoring data. The constructed mathematical model consists of six ordinary differential equations that describe the dynamics of changes in glucose concentrations, as well as insulin and anti-insulin factors in the blood. Estimation of the parameters of the equations was performed using an evolutionary programming method. The model predictions were fitted to the continuous glucosemonitoring data. As a result of the identification of the model parameters for two patients with type 1 diabetes mellitus, the estimated insulin secretion was close to zero and the estimated glucose utilization and insulin clearance were increased in comparison with the data for healthy donors. Here, we present a personalized model of the regulation of blood glucose, which can be used to predict the results of continuous glucose monitoring depending on modification of the prescribed glucose-lowering therapy. This approach can significantly reduce the number of iterations of the selection of medical hypoglycemic therapy and therefore increase the effectiveness of treatment according to glucose-monitoring data.
Keywordsmathematical modeling pharmacological modeling type 1 diabetes evolutionary programming continuous glucose monitoring
Unable to display preview. Download preview PDF.
- 3.A. Dreval’, Diabetes Mellitus: Diagnosis and Treatment Standards. A Pharmacologocal Guidebook (Litres, 2014) [in Russian].Google Scholar
- 7.J. T. Sorensen, A Physiologic Model of Glucose Metabolism in Man and Its Use to Design and Assess Improved Insulin Therapies for Diabetes (1985). http://dspace.mit.edu/handle/1721.1/15234 (accessed April 28, 2016).Google Scholar
- 10.T. P. Shestakova, A. V. Dreval’, and O. A. Dreval’, in Abstr. VII All-Russia Diabetological Congress (2015) [in Russian].Google Scholar
- 11.M. G. Markakis, G. D. Mitsis, and V. Z. Marmarelis, in Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (2008), pp. 5445–5448. doi 10.1109/IEMBS.2008. 4650446Google Scholar
- 12.A. G. Zalevskaya, N. I. Verbovaya, T. I. Rodionova, et al., Sakharnyi Diabet No. 2, 106 (2010).Google Scholar