A Minimal Model Approach for the Description of Postprandial Glucose Responses from Glucose Sensor Data in Diabetes Mellitus

Modelling of the gluco-regulatory system in response to an oral glucose tolerance test (OGTT) has been the subject of research for decades. This paper presents an adaptation to the well-established oral minimal model that is identifiable from glucose data only and is able to capture the dynamics of glucose following both OGTT and mixed meal consumption. The model is in the form of low-dimensional differential equations with a recently introduced input function consisting of Gaussian shaped components. It was identified from glucose data recorded from six subjects without diabetes, prediabetes and type 2 diabetes under controlled conditions. The inferred parameters of the model are shown to have physiological meaning and produce realistic steady state behavior. This model may be useful in the development of clinical advisory tools for the treatment and prevention of non-insulin dependent type 2 diabetes mellitus.

[1]  Johan Karlsson,et al.  An Efficient Method for Structural Identifiability Analysis of Large Dynamic Systems , 2012 .

[2]  Yan Zhang,et al.  Generalised stochastic model for characterisation of subcutaneous glucose time series , 2014, IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI).

[3]  Carola van Pul,et al.  Model-based analysis of postprandial glycemic response dynamics for different types of food , 2018, Clinical Nutrition Experimental.

[4]  Pranay Goel,et al.  A Minimal Model Approach for Analyzing Continuous Glucose Monitoring in Type 2 Diabetes , 2018, Front. Physiol..

[5]  David Rodbard,et al.  Continuous Glucose Monitoring: A Review of Successes, Challenges, and Opportunities. , 2016, Diabetes technology & therapeutics.

[6]  Lionel Rigoux,et al.  VBA: A Probabilistic Treatment of Nonlinear Models for Neurobiological and Behavioural Data , 2014, PLoS Comput. Biol..

[7]  J. Thyfault,et al.  Exercise and Postprandial Glycemic Control in Type 2 Diabetes. , 2016, Current diabetes reviews.

[8]  I. A. Khovanov,et al.  Characterisation of linear predictability and non-stationarity of subcutaneous glucose profiles , 2013, Comput. Methods Programs Biomed..

[9]  Ronald Brazg,et al.  Accuracy and acceptability of the 6-day Enlite continuous subcutaneous glucose sensor. , 2014, Diabetes technology & therapeutics.

[10]  E. Segal,et al.  Personalized Nutrition by Prediction of Glycemic Responses , 2015, Cell.

[11]  Reawika Chaikomin,et al.  Effects of fat on gastric emptying of and the glycemic, insulin, and incretin responses to a carbohydrate meal in type 2 diabetes. , 2006, The Journal of clinical endocrinology and metabolism.

[12]  Claudio Cobelli,et al.  The oral glucose minimal model: Estimation of insulin sensitivity from a meal test , 2002, IEEE Transactions on Biomedical Engineering.

[13]  Yan Zhang,et al.  A data driven nonlinear stochastic model for blood glucose dynamics , 2015, Comput. Methods Programs Biomed..

[14]  M. Weickert,et al.  Impact of Diet Composition on Blood Glucose Regulation , 2016, Critical reviews in food science and nutrition.

[15]  J. Hattersley,et al.  A Model Describing the Multiphasic Dynamics of Mixed Meal Glucose Responses in Healthy Subjects , 2019 .