A data driven nonlinear stochastic model for blood glucose dynamics. Issue 125 (March 2016) - Zhang, Yan; Holt, Tim A.; Khovanova, Natalia
Abstract : Highlights: Data-driven model is presented for the response of glucose to food intake. Model describes blood glucose dynamics in people with and without diabetes. First study introducing a continuous data-driven nonlinear stochastic model. Model's parameters belong to different ranges for diabetes and controls. Variational Bayesian learning approach was employed for model identification. Abstract: The development of adequate mathematical models for blood glucose dynamics may improve early diagnosis and control of diabetes mellitus (DM). We have developed a stochastic nonlinear second order differential equation to describe the response of blood glucose concentration to food intake using continuous glucose monitoring (CGM) data. A variational Bayesian learning scheme was applied to define the number and values of the system's parameters by iterative optimisation of free energy. The model has the minimal order and number of parameters to successfully describe blood glucose dynamics in people with and without DM. The model accounts for the nonlinearity and stochasticity of the underlying glucose–insulin dynamic process. Being data-driven, it takes full advantage of available CGM data and, at the same time, reflects the intrinsic characteristics of the glucose–insulin system without detailed knowledge of the physiological mechanisms. We have shown that the dynamics of some postprandial blood glucose excursions can be described by a reduced (linear) model, previously seen in the literature. A comprehensive analysis demonstrates that deterministic system parameters belong to different ranges for diabetes and controls. Implications for clinical practice are discussed. This is the first study introducing a continuous data-driven nonlinear stochastic model capable of describing both DM and non-DM profiles.