Reduced sampling schedule for the glucose minimal model

Importance of Bayesian estimation

Paolo Magni, Giovanni Sparacino, Riccardo Bellazzi, Claudio Cobelli

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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 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 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) 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.

Original languageEnglish
JournalAmerican Journal of Physiology - Endocrinology and Metabolism
Volume290
Issue number1
DOIs
Publication statusPublished - Jan 2006

Fingerprint

Appointments and Schedules
Sampling
Glucose
Glucose Tolerance Test
Risk analysis
Regression analysis
Cluster Analysis
Deterioration
Insulin Resistance
Identification (control systems)
Hand
Confidence Intervals
Insulin
Kinetics
Population

Keywords

  • Glucose effectiveness
  • Identification of glucose minimal model
  • Insulin sensitivity
  • Intravenous glucose tolerance test
  • Normal subjects

ASJC Scopus subject areas

  • Physiology
  • Endocrinology
  • Biochemistry

Cite this

Reduced sampling schedule for the glucose minimal model : Importance of Bayesian estimation. / Magni, Paolo; Sparacino, Giovanni; Bellazzi, Riccardo; Cobelli, Claudio.

In: American Journal of Physiology - Endocrinology and Metabolism, Vol. 290, No. 1, 01.2006.

Research output: Contribution to journalArticle

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