Quantifying postprandial glucose responses using a hybrid modeling approach : Combining mechanistic and data-driven models in The Maastricht Study
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.
Main Authors: | , , , , , , , , , , , |
---|---|
Format: | Article/Letter to editor biblioteca |
Language: | English |
Subjects: | Life Science, |
Online Access: | https://research.wur.nl/en/publications/quantifying-postprandial-glucose-responses-using-a-hybrid-modelin |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|