Virtual diabetic patient with physical activity dynamics. (January 2022) - Islam, Md. Jahirul; Hoque, Abu Sayed Md. Latiful
Highlights: Physiological model of blood glucose regulation consisting of exercise dynamics and internal insulin secretion is needed to describe diabetic behavior of type-2 diabetes in free living environment. Exercise dynamics model with minimal representation of glucose regulation cannot describe diabetic behavior properly. This research builds MATLAB/Simulink implementation of a virtual diabetic patient that can be leveraged to produce synthetic diabetic dataset consisting of glucose fluctuation due to external disturbances of carbohydrate, insulin, and exercise. The model can also be used in operation research for taking theraputic decision. Abstract: Background and objective: Diabetes is a disease of impaired blood glucose regulation due to the absence or insufficient secretion of insulin hormone or insulin resistance induced in the human body. In literature, the impact of exercise is considered in few models based on the minimal representation of glucose dynamics along with the assumption that no endogenous insulin is produced in the body. Hence these models are not capable of describing diabetic behavior which is independent of exogenous insulin. This type of diabetes, known as type-2, affects almost 90% of the total diabetes population. In this article, a constraint-based comprehensive physiological model of blood glucose dynamics is aimed to build for filling up the gap in the literature. Methods: For physiological comprehensiveness, the model is considered to consist of several compartments separately connected with a common compartment named 'plasma'. Plasma is the only accessible compartment and contains the state variables. Plasma variables are the integrated result of the net change in rates of metabolic processes and basal rates are influenced between two saturation constraints for an operating range of each plasma variable. The influence of a plasma variable on a metabolic rate is represented using a form of the hyperbolic tangent function. Validation is done by fitting the model with clinical experiments and continuous glucose monitoring data of a free-living environment. Results: The proposed model generates an average correlation coefficient of 0.85 ± 0.13 on all simulated responses with the target in the fitting experiments. Besides this, the model can produce a spectrum of metabolic effects of plasma variables for showing more insight into glucose metabolism. Conclusions: A constraint-based comprehensive glucose regulation with exercise dynamics for modeling diabetes is pursued. The model doesn't consider age, gender, physical, and mental condition of the human body but can be applied in operation research by mathematical programming.