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DDMODEL00000112: Magni_2006_diabetes_MinimalModel

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Minimal model describing glucose kinetics during an intravenous glucose tolerance test to estimate glucose effectiveness and insulin sensitivity in reduced sampling schedules via Bayesian approach.
PharmML (0.6.1)
  • Reduced sampling schedule for the glucose minimal model: importance of Bayesian estimation.
  • Sparacino G, Bellazzi R, Cobelli C, Magni Paolo
  • American journal of physiology. Endocrinology and metabolism, 1/2006, Volume 290, Issue 1, pages: E177-E184
  • Dipartimento di Informatica e Sistemica, Università degli Studi di Pavia,Pavia, Italy. Università degli Studi di Padova, Italy. paolo.magni@unipv.it
  • The minimal model (MM) of glucose kinetics during an intravenous glucose tolerance test (IVGTT) is widely used in clinical studies to measure metabolic indexes such as glucose effectiveness (S(G)) and insulin sensitivity (S(I)). The standard (frequent) IVGTT sampling schedule (FSS) for MM identification consists of 30 points over 4 h. To facilitate clinical application of the MM, reduced sampling schedules (RSS) of 13-14 samples have also been derived for normal subjects. These RSS are especially appealing in large-scale studies. However, with RSS, the precision of S(G) and S(I) estimates deteriorates and, in certain cases, becomes unacceptably poor. To overcome this difficulty, population approaches such as the iterative two-stage (ITS) approach have been recently proposed, but, besides leaving some theoretical issues open, they appear to be oversized for the problem at hand. Here, we show that a Bayesian methodology operating at the single individual level allows an accurate determination of MM parameter estimates together with a credible measure of their precision. Results of 16 subjects show that, in passing from FSS to RSS, there are no significant changes of point estimates in nearly all of the subjects and that only a limited deterioration of parameter precision occurs. In addition, in contrast with the previously proposed ITS method, credible confidence intervals (e.g., excluding negative values) are obtained. They can be crucial for a subsequent use of the estimated MM parameters, such as in classification, clustering, regression, or risk analysis.
Paolo Magni
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  • Model owner: Paolo Magni
  • Submitted: Dec 11, 2015 11:52:30 PM
  • Last Modified: Oct 13, 2016 6:56:17 PM
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  • Version: 17 public model Download this version
    • Submitted on: Oct 13, 2016 6:56:17 PM
    • Submitted by: Paolo Magni
    • With comment: Edited model metadata online.
  • Version: 15 public model Download this version
    • Submitted on: Oct 11, 2016 4:29:11 PM
    • Submitted by: Paolo Magni
    • With comment: Edited model metadata online.
  • Version: 8 public model Download this version
    • Submitted on: Jun 2, 2016 8:06:03 PM
    • Submitted by: Paolo Magni
    • With comment: Model revised without commit message
  • Version: 4 public model Download this version
    • Submitted on: Dec 11, 2015 11:52:30 PM
    • Submitted by: Paolo Magni
    • With comment: Edited model metadata online.
 
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