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This Article Full Text Full Text (PDF) All Versions of this Article: 290/1/E177    most recent 00241.2003v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Magni, P. Articles by Cobelli, C. Search for Related Content PubMed PubMed Citation Articles by Magni, P. Articles by Cobelli, C.

Reduced sampling schedule for the glucose minimal model: importance of Bayesian estimation

¹Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, Pavia; and ²Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Padova, Padua, Italy

Submitted 30 May 2003 ; accepted in final form 31 August 2005

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.

identification of glucose minimal model; intravenous glucose tolerance test; normal subjects; insulin sensitivity; glucose effectiveness.