Date of Award
Master of Science (MS)
Diabetes affects nearly 26 million Americans, according to the American Diabetes Association, with as many as three million Americans who have Type 1 Diabetes (ADA, 2015). Type 1 Diabetes (T1D) is autoimmune and characterized by little to no insulin production whereas Type 2 Diabetes (T2D) concerns insulin resistance and inability to use produced insulin. Factors contributing to current diabetes management and regulation include exercise type, daily movement activities, and distinct tissue compartment metabolism, each challenging to model in a robust and comprehensive manner. Past models are highly limited in regard to exercise and varying glucose fluctuations dependent on type, intensity, and duration. Modeling could greatly enhance factors that contribute to diabetes management—currently, T1D is managed with a pump and/or injections, informed by constant blood glucose monitoring. This thesis addresses knowledge gaps in the management and etiology of diabetes through development of a novel dynamic mathematical model informing controller design and implementation (artificial pancreas, continuous glucose monitors, and pumps). Diet and meal content on the basis of varying glycemic index and on the effects of activity and exercise, with lifestyle habit implications are a main focus. Emphasis is placed on model personalization with a T1D athlete example. The following model and case study implement specific aims: • 10th order model designed in Matlab with 4 interrelated sub-models to integrate meal diversity, exercise activities, and personalized body composition. o 3-State Glucose Compartmental Model o 2-State Control Mechanisms: Insulin and Glucagon o 2-State Digestion Model o 2-State Exogenous Insulin Control o Skeletal Muscle Model with Mitochondrial State o Nonlinear relations including Hill Functions • A 2 Phase Case Study, IRB approved for a Type 1 athletic 23-year-old female to evaluate and develop the model. Results illustrate effects of meal type (slow vs. fast glycemic index) and exercise/activity based glucose-glycogen consumption on blood plasma glucose predictions and hormonal control action for both non-diabetic and diabetic model versions. Current challenges are addressed with model personalization, providing input flexibility for body mass, muscle ratio, stress, and types of diabetes (T1D, T2D) informing artificial pancreas design and possible sports performance applications.