A Physiology-Based Model Describing Heterogeneity in Glucose Metabolism

Abstract

Diabetes is a serious and life-threatening condition that reduces the quality of life of the patient and is also costly, both in medical costs and in lost work-hours. 1 The incidence and severity of the complications of diabetes can considerably be reduced if patients develop a lifestyle that leads to good glycemic control. 2,3 Research has shown that diabetes education can reduce HbA1c over a longer period, 4,5 resulting in a lower risk of complications. 6,7 Education is therefore a fundamental part of diabetes care. It is currently provided in several 1-on-1 or group sessions with a physician, diabetes nurse, dietician, or podiatrist. This is time-consuming and costly.A major part of diabetes education is learning how to adjust insulin injections based on carbohydrate intake, exercise and factors like stress or illness. However, possibilities for the patient to safely practice with this newly acquired knowledge are limited to trying different strategies on his own body. This gives a considerable risk of hypo-or hyperglycemia. Here, we describe the development and verification of the physiological model for healthy subjects that forms the basis of the Eindhoven Diabetes Education Simulator (E-DES). E-DES shall provide diabetes patients with an individualized virtual practice environment incorporating the main factors that influence glycemic control: food, exercise, and medication.

Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge

“…While the Bergman model can be applied to individual data, the Dalla-Man model has mostly been applied to population average data for in silico simulation and testing of insulin pump systems. The Eindhoven-Diabetes Education Simulator (E-DES) is a comparatively simple multi-compartmental model containing 12 parameters that has been used to describe the dynamics of the glucose homeostasis in healthy, type 1 and type 2 diabetic populations [ 23 , 24 ].…”

An organ-based multi-level model for glucose homeostasis: organ distributions, timing, and impact of blood flow

“…to aid insulin-pumps, and to develop a so-called artifical pancreas(Huang et al 2012; Fabris and Kovatchev 2020). Another application of glucose homeostasis models exist for meal response T2D simulator model, developed for pedagogical and motivational purposes (Maas et al 2015). None of these models have subdivided glucose uptake in the different organs, or included intracellular responses, in multi-level and multi-organ models.…”

“…One of the more comprehensive efforts is a series of nonlinear mixed effects models (Silber et al 2007; Silber et al 2010; Jauslin et al 2007) developed to describe plasma levels of glucose and insulin after different interventions for single patients with T2D. Another effort has developed a glucose homeostasis model, based partly on (Dalla Man et al 2007), to create a simulator to use in education and to simulate scenarios of disease (Maas et al 2015). A third effort is the multi-level model of human glucose homeostasis we created 10 years ago (Nyman et al 2011).…”

E-DES-PROT: A novel computational model to describe the effects of amino acids and protein on postprandial glucose and insulin dynamics in humans

“…Computational models of whole-body glucose homeostasis describe and incorporate the current mechanistic understanding of insulin-mediated regulation of circulating glucose concentrations. 5 , 6 , 7 These processes are represented by model parameters, which can be estimated from postprandial time-series data without requiring direct invasive measurements. One of the earliest computational glucose models, the Bergman minimal model, 5 was able to determine insulin sensitivity (i.e.…”

“…The Eindhoven-Diabetes Education Simulator (E-DES), a multi-compartmental ordinary differential equation model, has been used to describe glucose dynamics following a glucose challenge in healthy individuals as well as patients with type 1 and type 2 diabetes. 7 , 9 , 10 We have previously individualized the E-DES model to allow accurate description of individual postprandial responses compared to population-based models, demonstrating it is capable of providing mechanistic insight into glucose homeostasis of individuals. 11 While the E-DES model performs very well in response to an oral glucose challenge, modeling the response to more complex meals is still challenging because these contain fat and protein, which also influence glucose homeostasis.…”