A glucose-only model to extract physiological information from postprandial glucose profiles in subjects with normal glucose tolerance

Background: Current mathematical models of postprandial glucose metabolism in people with normal and impaired glucose tolerance rely on insulin measurements and are therefore not applicable in clinical practice. This research aims to develop a model that only requires glucose data for parameter estimation while also providing useful information on insulin sensitivity, insulin dynamics and the meal-related glucose appearance (GA). Methods: The proposed glucose-only model (GOM) is based on the oral minimal model (OMM) of glucose dynamics and substitutes the insulin dynamics with a novel function dependant on glucose levels and GA. A Bayesian method and glucose data from 22 subjects with normal glucose tolerance are utilised for parameter estimation. To validate the results of the GOM, a comparison to the results of the OMM, obtained by using glucose and insulin data from the same subjects is carried out. Results: The proposed GOM describes the glucose dynamics with comparable precision to the OMM with an RMSE of 5.1 ± 2.3 mg/dL and 5.3 ± 2.4 mg/dL, respectively and contains a parameter that is significantly correlated to the insulin sensitivity estimated by the OMM (r = 0.7) Furthermore, the dynamic properties of the time profiles of GA and insulin dynamics inferred by the GOM show high similarity to the corresponding results of the OMM. Conclusions: The proposed GOM can be used to extract useful physiological information on glucose metabolism in subjects with normal glucose tolerance. The model can be further developed for clinical applications to patients with impaired glucose tolerance under the use of continuous glucose monitoring data

Generalised stochastic model for characterisation of subcutaneous glucose time series

A generalised stochastic model with second order differential equations is proposed to describe the response of blood glucose concentration to meals in groups of nondiabetic people and two types of diabetic patients. A variational Bayesian approach is applied in order to infer parameters of the models, and the best model was selected based on the computed log-evidence for each prandial event. The model with a linear structure represents most of the events, while the nonlinear terms need to be included more frequently for Type II diabetic patients. This indicates different physiological mechanisms of glucose absorption for different groups. The deterministic parameters and intensities of stochastic components are compared by groups using the ANOVA test, and the results show significant differences between the groups. This model can potentially be used for long term prediction of the glucose concentration response to external stimuli