Uising WinBUGS to fit nonlinear mixed models with an application to pharmacokinetic modelling of insulin response to glucose challenge in sheep exposed antenatally to glucocorticoids

Lyle C Gurrin, Timothy Moss, Deborah M Sloboda, Martin Hazelton, John Challis, John Newnham

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Many chronic diseases of adulthood, such as hypertension and diabetes, are now believed to have at least some of their origins before birth. Extensive studies in animal models have identified antenatal exposure to excess glucocorticoids as a leading candidate for the physiological cause of fetal compromise. The resulting adverse intra-uterine environment appears to program the individual for higher risk of subsequent disease. We present an analysis of blood glucose and insulin concentrations collected during glucose tolerance tests at 6 and 12 months postnatal age in a cohort of sheep that were treated antenatally with injections of betamethasone (a synthetic glucocorticoid) which, when injected into the mother, cross the placenta to the fetus. A simple pharmacokinetic model, essentially a modification of the single compartment model with first-order absorption and elimination, is developed to describe the time course of glucose concentration and the associated insulin response. The resulting nonlinear mixed model is implemented in a Bayesian framework using the Markov chain Monte Carlo technique Gibbs Sampling via the software package BUGS. This sampling process allows inferences to be made directly about derived quantities with an immediate physical interpretation, such as the maximum insulin concentration in response to glucose challenge.
Original languageEnglish
Pages (from-to)117 - 139
Number of pages23
JournalJournal of Biopharmaceutical Statistics
Issue number1
Publication statusPublished - 2003
Externally publishedYes

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