A novel dynamic modeling of Insulin Sensitivity in the Blood Glucose Minimal Model

Type 1 Diabetes Mellitus (T1DM) is an autoimmune condition characterized by the destruction of pancreatic beta cells, leading to insulin deficiency and requiring lifelong exogenous insulin administration. Effective management of T1DM depends on accurate insulin dosing, a challenge due to the dynamic nature of glucose-insulin interactions, which varies both between and within individuals over time due to variation in insulin sensitivity (SI). This paper presents an extension of the physiological long-term glucose-insulin model initially proposed by Ruan et al. [1], incorporating a novel nonlinearity in it. The new model reflects SI variability as a function of both physiological variables and circadian rhythms. By capturing the temporal fluctuations in SI, the model aims to enhance the predictive capability of glucose-insulin models without increasing complexity, facilitating future integration into real-time control systems like model predictive control (MPC) in artificial pancreas (AP) systems. Simulation scenarios with the UVA/Padova T1DM simulator validate the model, demonstrating improvements in blood glucose modeling compared to existing methods.