Modeling Oral Glucose Tolerance Test (OGTT) data and its Bayesian Inverse Problem

TL;DR

This paper introduces a compartmental dynamic model with Bayesian inference for analyzing OGTT data, providing a more efficient and informative approach to diabetes testing than traditional methods.

Contribution

It proposes a novel ODE-based model for OGTT data and applies Bayesian inverse problem techniques for parameter estimation using real data.

Findings

Model accurately describes blood glucose dynamics during OGTT

Bayesian inference effectively estimates model parameters

Provides a more informative analysis compared to traditional methods

Abstract

One common way to test for diabetes is the Oral Glucose Tolerance Test or OGTT. Most common methods for the analysis of the data on this test are wasteful of much of the information contained therein. We propose to model blood glucose during an OGTT using a compartmental dynamic model with a system of ODEs. Our model works well in describing most scenarios that occur during an OGTT considering only 4 parameters. Fitting the model to data is an inverse problem, which is suitable for Bayesian inference. Priors are specified and posterior inference results are shown using real data.