Using WinBUGS to Fit Nonlinear Mixed Models with an Application to Pharmacokinetic Modelling of Insulin Response to Glucose Challenge in Sheep Exposed Antenatally to Glucocorticoids
Bibliographic record
Abstract
Lyle C. Gurrina*, Timothy J. Mossb, Deborah M. Slobodab, Martin L. Hazeltonc, John R. G. Challisd & John P. Newnhamb a Women and Infants Research Foundation , King Edward Memorial Hospital , Subiaco, Western Australia, Australia b Department of Obstetrics and Gynecology , The University of Western Australia , Western Australia, Australia c Department of Mathematics and Statistics , The University of Western Australia , Western Australia, Australia d Department of Physiology , University of Toronto , Toronto, Canada * lyle.gurrin@bhpbilliton.com 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. At 6 months postnatal age, sheep treated with antenatal injections of synthetic glucocorticoids had raised insulin concentration in comparison to controls after bolus administration of glucose. This effect persisted to 12 months postnatal age only in the sheep that received multiple doses of glucocorticoids. Moreover, the raised insulin concentration in sheep that received direct injections of synthetic glucocorticoid as fetuses is accompanied by better glucose clearance than in those sheep that received only saline injections, a phenomenon that is not observed in the animals that received maternal injections. It is argued that the fitting of an appropriate statistical model to complex physiological data does not necessarily proclude a result that has a clear interpretation for clinical scientists.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".