Dynamic of Glucose Homeostasis in Virtual Patients: A Comparison between Different Behaviors

Abstract

This work presents a mathematical model of homeostasis dynamics in healthy individuals, focusing on the generation of conductive data on glucose homeostasis throughout the day under dietary and physical activity factors. Two case studies on glucose dynamics for populations under conditions of physical activity and sedentary lifestyle were developed. For this purpose, two types of virtual populations were generated, the first population was developed according to the data of a total of 89 physical persons between 20 and 75 years old and the second was developed using the Monte Carlo approach, obtaining a total of 200 virtual patients. In both populations, each participant was classified as an active or sedentary person depending on the physical activity performed. The results obtained demonstrate the capacity of virtual populations in the generation of in-silico approximations similar to those obtained from in-vivo studies. Obtaining information that is only achievable through specific in-vivo experiments. Being a tool that generates information for the approach of alternatives in the prevention of the development of type 2 Diabetes.

Observer‐based control for plasma glucose regulation in type 1 diabetes mellitus patients with unknown input delay

“…33 This is a common approach adopted in the literature in the context of automatic control. 7,11,32,[34][35][36] In this sense, the control signal u is the additional insulin bolus to control glucose increases associated with food intake or to correct elevated levels.…”

“…However, since the objective here is to address the glucose increments caused by unplanned meals, it is assumed that the patient is a stable diabetic individual controlled by subcutaneous infusion, and the effect of the basal insulin administration () is considered in the model by setting to an appropriate value 33 . This is a common approach adopted in the literature in the context of automatic control 7,11,32,34–36 . In this sense, the control signal is the additional insulin bolus to control glucose increases associated with food intake or to correct elevated levels.…”

“…10 The advantage of AP over manual insulin introduction is that the patient will have his glucose levels constantly controlled without being severely affected by his activities, such as unplanned meals, exercise, or stress. 11 The development of APs has generated interest in the scientific community, where different models have been formulated to avoid hypoglycemia and hyperglycemia 10 ; personalized model-based algorithms for patients with different insulin responses 12 ; algorithms for detection and classification for different types of diabetes [13][14][15] ; optimal state feedback controllers with a Kalman filter designed to minimize the elevated glucose levels, 16 among others. The Bergman minimal model (BMM) has been popular since it is a simple nonlinear glucose-insulin model that considers physiological parameters such as glucose efficiency, insulin sensitivity, and insulin degradation.…”

“…An AP is an insulin pump driven by a control algorithm that regulates glucose levels, emulating the natural pancreas functions 10 . The advantage of AP over manual insulin introduction is that the patient will have his glucose levels constantly controlled without being severely affected by his activities, such as unplanned meals, exercise, or stress 11 …”