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Oscillatory dynamics of an intravenous glucose tolerance test model with delay interval.
Shi, Xiangyun; Kuang, Yang; Makroglou, Athena; Mokshagundam, Sriprakash; Li, Jiaxu.
Afiliación
  • Shi X; School of Mathematics and Statistics, Xinyang Normal University, Xinyang 464000, Henan, People's Republic of China.
  • Kuang Y; School of Mathematical and Statistical Sciences, Arizona State University, Tempe, Arizona 85287-1804, USA.
  • Makroglou A; Department of Mathematics, University of Portsmouth, 1st Floor Lion Gate Bldg, Portsmouth PO1 3HE, United Kingdom.
  • Mokshagundam S; Department of Medicine, School of Medicine, University of Louisville, Louisville, Kentucky 40292, USA.
  • Li J; Department of Mathematics, University of Louisville, Louisville, Kentucky 40292, USA.
Chaos ; 27(11): 114324, 2017 Nov.
Article en En | MEDLINE | ID: mdl-29195308
Type 2 diabetes mellitus (T2DM) has become prevalent pandemic disease in view of the modern life style. Both diabetic population and health expenses grow rapidly according to American Diabetes Association. Detecting the potential onset of T2DM is an essential focal point in the research of diabetes mellitus. The intravenous glucose tolerance test (IVGTT) is an effective protocol to determine the insulin sensitivity, glucose effectiveness, and pancreatic ß-cell functionality, through the analysis and parameter estimation of a proper differential equation model. Delay differential equations have been used to study the complex physiological phenomena including the glucose and insulin regulations. In this paper, we propose a novel approach to model the time delay in IVGTT modeling. This novel approach uses two parameters to simulate not only both discrete time delay and distributed time delay in the past interval, but also the time delay distributed in a past sub-interval. Normally, larger time delay, either a discrete or a distributed delay, will destabilize the system. However, we find that time delay over a sub-interval might not. We present analytically some basic model properties, which are desirable biologically and mathematically. We show that this relatively simple model provides good fit to fluctuating patient data sets and reveals some intriguing dynamics. Moreover, our numerical simulation results indicate that our model may remove the defect in well known Minimal Model, which often overestimates the glucose effectiveness index.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Glucosa / Modelos Biológicos Tipo de estudio: Prognostic_studies Idioma: En Revista: Chaos Asunto de la revista: CIENCIA Año: 2017 Tipo del documento: Article Pais de publicación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Glucosa / Modelos Biológicos Tipo de estudio: Prognostic_studies Idioma: En Revista: Chaos Asunto de la revista: CIENCIA Año: 2017 Tipo del documento: Article Pais de publicación: Estados Unidos