Literature DB >> 8944683

NONMEM improves group parameter estimation for the minimal model of glucose kinetics.

A De Gaetano1, G Mingrone, M Castageneto.   

Abstract

The minimal model of glucose kinetics interprets blood glucose and insulin concentrations after an interprets blood glucose and insulin concentrations after an intravenous glucose tolerance test (IVGTT) and provides parameters describing tissue insulin sensitivity and glucose-dependent tissue glucose disposal. In the standard application, the model is fitted to each experimental subject's points by nonlinear least squares, with suitable weighing. The variability of parameter estimates may, however, represent a problem, making the model in practice unidentifiable on a group of experimental subjects undergoing some treatment of interest. To obviate this problem, a specific modification to the original protocol has been introduced: administering tolbutamide 20 min after the glucose bolus has been shown to improve parameter stability. With this modification, however, the converse model of pancreatic secretion can no more be fitted on the collected series of concentrations. The application of the nonlinear mixed effects model (NONMEM) loss function allows estimation of parameter population means, variances, and covariances to be made on all sampled subjects simultaneously. Although this procedure does not allow an individual subject's parameters to be estimated, the variability of the group parameter estimates is greatly reduced compared with the standard method. In the present work, 20 healthy volunteers have been studied with an IVGTT, and group parameters have been computed in both standard and NONMEM ways: asymptotic parameter coefficients of variation with NONMEM were at least twenty times smaller than the corresponding sample parameter coefficients of variation obtained with the classical method.

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Year:  1996        PMID: 8944683     DOI: 10.1152/ajpendo.1996.271.5.E932

Source DB:  PubMed          Journal:  Am J Physiol        ISSN: 0002-9513


  4 in total

1.  Analysis of intravenous glucose tolerance test data using parametric and nonparametric modeling: application to a population at risk for diabetes.

Authors:  Vasilis Z Marmarelis; Dae C Shin; Yaping Zhang; Alexandra Kautzky-Willer; Giovanni Pacini; David Z D'Argenio
Journal:  J Diabetes Sci Technol       Date:  2013-07-01

2.  A novel global search algorithm for nonlinear mixed-effects models using particle swarm optimization.

Authors:  Seongho Kim; Lang Li
Journal:  J Pharmacokinet Pharmacodyn       Date:  2011-06-30       Impact factor: 2.745

3.  Predictive performance for population models using stochastic differential equations applied on data from an oral glucose tolerance test.

Authors:  Jonas B Møller; Rune V Overgaard; Henrik Madsen; Torben Hansen; Oluf Pedersen; Steen H Ingwersen
Journal:  J Pharmacokinet Pharmacodyn       Date:  2009-12-16       Impact factor: 2.745

4.  IVGTT glucose minimal model covariate selection by nonlinear mixed-effects approach.

Authors:  Paolo Denti; Alessandra Bertoldo; Paolo Vicini; Claudio Cobelli
Journal:  Am J Physiol Endocrinol Metab       Date:  2010-01-26       Impact factor: 4.310

  4 in total

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