Quantifying postprandial glucose responses using a hybrid modeling approach : Combining mechanistic and data-driven models in The Maastricht Study
Copyright: © 2023 Erdős et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:18 |
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| Enthalten in: |
PloS one - 18(2023), 7 vom: 09., Seite e0285820 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Erdős, Balázs [VerfasserIn] |
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| Links: |
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| Themen: |
Blood Glucose |
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| Anmerkungen: |
Date Completed 31.07.2023 Date Revised 01.08.2023 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1371/journal.pone.0285820 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright: © 2023 Erdős et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | ||
| 520 | |a 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 | ||
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| 650 | 7 | |a Blood Glucose |2 NLM | |
| 650 | 7 | |a Insulin |2 NLM | |
| 700 | 1 | |a van Sloun, Bart |e verfasserin |4 aut | |
| 700 | 1 | |a Goossens, Gijs H |e verfasserin |4 aut | |
| 700 | 1 | |a O'Donovan, Shauna D |e verfasserin |4 aut | |
| 700 | 1 | |a de Galan, Bastiaan E |e verfasserin |4 aut | |
| 700 | 1 | |a van Greevenbroek, Marleen M J |e verfasserin |4 aut | |
| 700 | 1 | |a Stehouwer, Coen D A |e verfasserin |4 aut | |
| 700 | 1 | |a Schram, Miranda T |e verfasserin |4 aut | |
| 700 | 1 | |a Blaak, Ellen E |e verfasserin |4 aut | |
| 700 | 1 | |a Adriaens, Michiel E |e verfasserin |4 aut | |
| 700 | 1 | |a van Riel, Natal A W |e verfasserin |4 aut | |
| 700 | 1 | |a Arts, Ilja C W |e verfasserin |4 aut | |
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