A parsimonious model of blood glucose homeostasis

• Published in: PLOS digital health Vol. 1; no. 7; p. e0000072
• Main Authors: Ng, Eric, Kaufman, Jaycee M., van Veen, Lennaert, Fossat, Yan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.07.2022; Public Library of Science (PLoS)
• Subjects: Biology and Life Sciences; Computer and Information Sciences; Control systems; Diabetes; Differential equations; Digital computers; Engineers; Glucose; Homeostasis; Hormones; Insulin; Mathematical models; Medicine and Health Sciences; Ordinary differential equations; Physical Sciences; Physiology; Regulation; United States > US
• ISSN: 2767-3170; 2767-3170
• DOI: 10.1371/journal.pdig.0000072

The mathematical modelling of biological systems has historically followed one of two approaches: comprehensive and minimal. In comprehensive models, the involved biological pathways are modelled independently, then brought together as an ensemble of equations that represents the system being studied, most often in the form of a large system of coupled differential equations. This approach often contains a very large number of tuneable parameters (> 100) where each describes some physical or biochemical subproperty. As a result, such models scale very poorly when assimilation of real world data is needed. Furthermore, condensing model results into simple indicators is challenging, an important difficulty in scenarios where medical diagnosis is required. In this paper, we develop a minimal model of glucose homeostasis with the potential to yield diagnostics for pre-diabetes. We model glucose homeostasis as a closed control system containing a self-feedback mechanism that describes the collective effects of the physiological components involved. The model is analyzed as a planar dynamical system, then tested and verified using data collected with continuous glucose monitors (CGMs) from healthy individuals in four separate studies. We show that, although the model has only a small number (3) of tunable parameters, their distributions are consistent across subjects and studies both for hyperglycemic and for hypoglycemic episodes.