Submitted on May 30, 2003
Accepted on August 31, 2005
Reduced Sampling Schedule for the Glucose Minimal Model: Importance of Bayesian Estimation
P Magni1, G Sparacino2, R Bellazzi1, and C Cobelli2*
1 Informatica e Sistemistica, Universita' degli Studi di Pavia, Pavia, Pavia, Italy
2 Ingegneria dell'Informazione, Universita' degli Studi di Padova, Padova, Padova, Italy
* To whom correspondence should be addressed. E-mail: cobelli{at}dei.unipd.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 (SG) and insulin sensitivity (SI). The standard (frequent) IVGTT sampling schedule (FSS) for MM identification consists of 30 points over 4 hours. In order 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 SG and SI estimates deteriorates and, in certain cases, becomes unacceptably poor. To overcome this difficulty, population approaches such as the Iterative Two Stage (ITS) have been recently proposed, but, besides leaving some theoretical issues open, they appear 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 parameters estimates, together with a credible measure of their precision. Results on 16 subjects show that, in passing from FSS to RSS, there are no significant changes of point estimates in nearly all the subjects and that an only limited deterioration of parameter precision occurs. In addition, differently from the previously proposed IST 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.
