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Bayesian parameter estimation in the oral minimal model of glucose dynamics from non-fasting conditions using a new function of glucose appearance
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Eichenlaub, Manuel, Hattersley, John G., Gannon, Mary C., Nuttall, Frank Q. and Khovanova, Natasha (2021) Bayesian parameter estimation in the oral minimal model of glucose dynamics from non-fasting conditions using a new function of glucose appearance. Computer Methods and Programs in Biomedicine, 200 . 105911. doi:10.1016/j.cmpb.2020.105911 ISSN 0169-2607.
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Official URL: http://dx.doi.org/10.1016/j.cmpb.2020.105911
Abstract
Background and objective
The oral minimal model (OMM) of glucose dynamics is a prominent method 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 OMM approach has several weaknesses that this paper addresses.
Methods
A novel procedure introducing three methodological adaptations to the OMM 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
It is shown that the glucose effectiveness parameter of the OMM 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. The identification procedure is implemented in freely accesible MATLAB and Python software packages.
Conclusions
We propose an improved and freely available method for the identification of the OMM which could become the future standardard for the oral minimal modelling method of glucose dynamics.
Item Type: | Journal Article | ||||||||
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Subjects: | R Medicine > RA Public aspects of medicine | ||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Glucose tolerance tests , Glucose tolerance tests -- Statistical methods | ||||||||
Journal or Publication Title: | Computer Methods and Programs in Biomedicine | ||||||||
Publisher: | Elsevier Ireland Ltd. | ||||||||
ISSN: | 0169-2607 | ||||||||
Official Date: | March 2021 | ||||||||
Dates: |
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Volume: | 200 | ||||||||
Article Number: | 105911 | ||||||||
DOI: | 10.1016/j.cmpb.2020.105911 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 25 January 2021 | ||||||||
Date of first compliant Open Access: | 25 January 2021 | ||||||||
RIOXX Funder/Project Grant: |
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