Details

Autor(en) / Beteiligte

• Erdős, Balázs
• van Sloun, Bart
• Goossens, Gijs H
• O'Donovan, Shauna D
• de Galan, Bastiaan E
• van Greevenbroek, Marleen M J
• Stehouwer, Coen D A
• Schram, Miranda T
• Blaak, Ellen E
• Adriaens, Michiel E
• van Riel, Natal A W
• Arts, Ilja C W
• Maric, Gorica

Titel

Quantifying postprandial glucose responses using a hybrid modeling approach: Combining mechanistic and data-driven models in The Maastricht Study

Ist Teil von

• PloS one, 2023-07, Vol.18 (7), p.e0285820-e0285820

Ort / Verlag

United States: Public Library of Science

Erscheinungsjahr

2023

Quelle

Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals

Beschreibungen/Notizen

• Computational models of human glucose homeostasis can provide insight into the physiological processes underlying the observed inter-individual variability in glucose regulation. Modelling approaches ranging from "bottom-up" mechanistic models to "top-down" data-driven techniques have been applied to untangle the complex interactions underlying progressive disturbances in glucose homeostasis. While both approaches offer distinct benefits, a combined approach taking the best of both worlds has yet to be explored. Here, we propose a sequential combination of a mechanistic and a data-driven modeling approach to quantify individuals' glucose and insulin responses to an oral glucose tolerance test, using cross sectional data from 2968 individuals from a large observational prospective population-based cohort, the Maastricht Study. The best predictive performance, measured by R2 and mean squared error of prediction, was achieved with personalized mechanistic models alone. The addition of a data-driven model did not improve predictive performance. The personalized mechanistic models consistently outperformed the data-driven and the combined model approaches, demonstrating the strength and suitability of bottom-up mechanistic models in describing the dynamic glucose and insulin response to oral glucose tolerance tests.