Eichenlaub, Manuel, Hattersley, John G., Gannon, Mary C., Nuttall, Frank Q. and Khovanova, N. A. (2021) Data for Bayesian parameter estimation in the oral minimal model of glucose dynamics from non-fasting conditions using a new function of glucose appearance. [Dataset]

	Microsoft Excel (Raw data) High CHO Diet.Glucose. JACN 1985 pub.xls - Published Version Available under License Creative Commons Attribution 4.0. Download (62Kb)
	Microsoft Excel (Raw data) High CHO Diet.Insulin. JACN 1985 pub.xls - Published Version Available under License Creative Commons Attribution 4.0. Download (62Kb)
	Microsoft Excel (Raw data) High Fat Diet.Glucose. JACN 1985 pub.xls - Published Version Available under License Creative Commons Attribution 4.0. Download (57Kb)
	Microsoft Excel (Raw data) High Fat Diet.Insulin. JACN 1985 pub.xls - Published Version Available under License Creative Commons Attribution 4.0. Download (57Kb)
	Microsoft Excel (Raw data) High Pro Diet.Glucose. JACN 1985 pub.xls - Published Version Available under License Creative Commons Attribution 4.0. Download (67Kb)
	Microsoft Excel (Raw data) High Pro Diet.Insulin. JACN 1985 pub.xls - Published Version Available under License Creative Commons Attribution 4.0. Download (63Kb)
	Plain Text (Readme file) README_updated.txt - Published Version Available under License Creative Commons Attribution 4.0. Download (910b)
	Microsoft Excel (Raw data) Standard Diet.Glucose. JACN 1985 pub.xls - Published Version Available under License Creative Commons Attribution 4.0. Download (62Kb)
	Microsoft Excel (Raw data) Standard Diet.Insulin. JACN 1985 pub.xls - Published Version Available under License Creative Commons Attribution 4.0. Download (63Kb)

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Abstract

Background and objective
The oral minimal model of glucose dynamics is one of the most prominent methods for assessing postprandial glucose metabolism. The model yields estimates of insulin sensitivity and the meal-related appearance of glucose from insulin and glucose data after an oral glucose challenge. Despite its success, the oral minimal modelling approach has several weaknesses that this paper addresses.
Methods
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.

Item Type:	Dataset
Subjects:	R Medicine > RA Public aspects of medicine
Divisions:	Faculty of Science, Engineering and Medicine > Engineering > Engineering
Type of Data:	Experimental data
Library of Congress Subject Headings (LCSH):	Glucose tolerance tests, Glucose tolerance tests -- Statistical methods
Publisher:	University of Warwick, School of Engineering
Official Date:	8 March 2021
Dates:	Date Event 8 March 2021 Available 5 January 2021 Created
Status:	Not Peer Reviewed
Publication Status:	Published
Media of Output (format):	.xls
Access rights to Published version:	Open Access (Creative Commons)
Copyright Holders:	University of Warwick
Description:	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.
Date of first compliant deposit:	8 March 2021
Date of first compliant Open Access:	8 March 2021
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID EP/T013648/1 [EPSRC] Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266
Related URLs:	Related item in WRAP Other
Contributors:	Contribution Name Contributor ID Depositor Khovanova, N. A. 33551

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