Data for Bayesian parameter estimation in the oral minimal model of glucose dynamics from non-fasting conditions using a new function of glucose appearance

A novel procedure introducing three methodological adaptations to the oral minimal modelling approach is proposed. These are: (1) the use of a fully Bayesian and efficient method for parameter estimation, (2) the model identification from non-fasting conditions using a generalised model formulation and (3) the introduction of a novel function to represent the meal-related glucose appearance based on two superimposed components utilising a modified structure of the log-normal distribution.

The proposed modelling procedure is applied to glucose and insulin data from subjects with normal glucose tolerance consuming three consecutive meals in intervals of four hours. Results Firstly, it is shown that the glucose effectiveness parameter is, contrary to previous results, structurally globally identifiable. In comparison to results from existing studies that use the conventional identification procedure, the proposed approach yields an equivalent level of model fit and a similar precision of insulin sensitivity estimates.

Furthermore, the new procedure shows no deterioration of model fit when data from non-fasting conditions are used. In comparison to the conventional, piecewise linear function of glucose appearance, the novel log-normally based function provides an improved model fit in the first 30 min of the response and thus a more realistic estimation of glucose appearance during this period. Conclusions Unlike the conventional approach, the model identification procedure proposed in this paper is implemented as an openly accessible library of MATLAB functions which facilitates its application by other research groups and could thus set the future standard for the identification of the oral minimal model of glucose dynamics.<br><br>Data record consists of eight excel sheets containing the raw data and an accompanying readme file.

Further information on the data sheets can be found in the readme file.