Bayesian Analysis of Glucose Dynamics during the Oral Glucose Tolerance Test (OGTT)

TL;DR

This paper introduces a Bayesian model to analyze blood glucose dynamics during OGTT, incorporating insulin and glucagon actions, and proposes a classification method for insulin sensitivity impairment.

Contribution

It presents a novel Bayesian framework for modeling glucose regulation during OGTT, including new indicators for patient classification.

Findings

Effective parameter inference for insulin and glucagon secretion.

A new classification scheme for impaired insulin sensitivity.

Demonstrated model's ability to distinguish patient groups.

Abstract

In this paper, we propose a model of the dynamics of the blood glucose level during an Oral Glucose Tolerance Test (OGTT). This dynamic includes the action of insulin and glucagon in the glucose homeostasis process as the reaction of an oral stimulus. We propose a Bayesian approach in the inference of five parameters related to insulin secretion, glucagon secretion, gastrointestinal emptying, and basal glucose level. Two insulin indicators related to the glucose level in blood and in the gastrointestinal tract allow us to suggest a classification for patients with impaired insulin sensitivity.