Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge

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Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge. / Erdős, Balázs; van Sloun, Bart; Adriaens, Michiel E; O'Donovan, Shauna D; Langin, Dominique; Astrup, Arne; Blaak, Ellen E; Arts, Ilja C W; van Riel, Natal A W.

In: P L o S Computational Biology (Online), Vol. 17, No. 3, e1008852, 2021.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Erdős, B, van Sloun, B, Adriaens, ME, O'Donovan, SD, Langin, D, Astrup, A, Blaak, EE, Arts, ICW & van Riel, NAW 2021, 'Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge', P L o S Computational Biology (Online), vol. 17, no. 3, e1008852. https://doi.org/10.1371/journal.pcbi.1008852

APA

Erdős, B., van Sloun, B., Adriaens, M. E., O'Donovan, S. D., Langin, D., Astrup, A., Blaak, E. E., Arts, I. C. W., & van Riel, N. A. W. (2021). Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge. P L o S Computational Biology (Online), 17(3), [e1008852]. https://doi.org/10.1371/journal.pcbi.1008852

Vancouver

Erdős B, van Sloun B, Adriaens ME, O'Donovan SD, Langin D, Astrup A et al. Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge. P L o S Computational Biology (Online). 2021;17(3). e1008852. https://doi.org/10.1371/journal.pcbi.1008852

Author

Erdős, Balázs ; van Sloun, Bart ; Adriaens, Michiel E ; O'Donovan, Shauna D ; Langin, Dominique ; Astrup, Arne ; Blaak, Ellen E ; Arts, Ilja C W ; van Riel, Natal A W. / Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge. In: P L o S Computational Biology (Online). 2021 ; Vol. 17, No. 3.

Bibtex

@article{ed003c2bf22d410382b42c30f108b6ef,
title = "Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge",
abstract = "Plasma glucose and insulin responses following an oral glucose challenge are representative of glucose tolerance and insulin resistance, key indicators of type 2 diabetes mellitus pathophysiology. A large heterogeneity in individuals' challenge test responses has been shown to underlie the effectiveness of lifestyle intervention. Currently, this heterogeneity is overlooked due to a lack of methods to quantify the interconnected dynamics in the glucose and insulin time-courses. Here, a physiology-based mathematical model of the human glucose-insulin system is personalized to elucidate the heterogeneity in individuals' responses using a large population of overweight/obese individuals (n = 738) from the DIOGenes study. The personalized models are derived from population level models through a systematic parameter selection pipeline that may be generalized to other biological systems. The resulting personalized models showed a 4-5 fold decrease in discrepancy between measurements and model simulation compared to population level. The estimated model parameters capture relevant features of individuals' metabolic health such as gastric emptying, endogenous insulin secretion and insulin dependent glucose disposal into tissues, with the latter also showing a significant association with the Insulinogenic index and the Matsuda insulin sensitivity index, respectively.",
author = "Bal{\'a}zs Erd{\H o}s and {van Sloun}, Bart and Adriaens, {Michiel E} and O'Donovan, {Shauna D} and Dominique Langin and Arne Astrup and Blaak, {Ellen E} and Arts, {Ilja C W} and {van Riel}, {Natal A W}",
note = "CURIS 2021 NEXS 117",
year = "2021",
doi = "10.1371/journal.pcbi.1008852",
language = "English",
volume = "17",
journal = "P L o S Computational Biology (Online)",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "3",

}

RIS

TY - JOUR

T1 - Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge

AU - Erdős, Balázs

AU - van Sloun, Bart

AU - Adriaens, Michiel E

AU - O'Donovan, Shauna D

AU - Langin, Dominique

AU - Astrup, Arne

AU - Blaak, Ellen E

AU - Arts, Ilja C W

AU - van Riel, Natal A W

N1 - CURIS 2021 NEXS 117

PY - 2021

Y1 - 2021

N2 - Plasma glucose and insulin responses following an oral glucose challenge are representative of glucose tolerance and insulin resistance, key indicators of type 2 diabetes mellitus pathophysiology. A large heterogeneity in individuals' challenge test responses has been shown to underlie the effectiveness of lifestyle intervention. Currently, this heterogeneity is overlooked due to a lack of methods to quantify the interconnected dynamics in the glucose and insulin time-courses. Here, a physiology-based mathematical model of the human glucose-insulin system is personalized to elucidate the heterogeneity in individuals' responses using a large population of overweight/obese individuals (n = 738) from the DIOGenes study. The personalized models are derived from population level models through a systematic parameter selection pipeline that may be generalized to other biological systems. The resulting personalized models showed a 4-5 fold decrease in discrepancy between measurements and model simulation compared to population level. The estimated model parameters capture relevant features of individuals' metabolic health such as gastric emptying, endogenous insulin secretion and insulin dependent glucose disposal into tissues, with the latter also showing a significant association with the Insulinogenic index and the Matsuda insulin sensitivity index, respectively.

AB - Plasma glucose and insulin responses following an oral glucose challenge are representative of glucose tolerance and insulin resistance, key indicators of type 2 diabetes mellitus pathophysiology. A large heterogeneity in individuals' challenge test responses has been shown to underlie the effectiveness of lifestyle intervention. Currently, this heterogeneity is overlooked due to a lack of methods to quantify the interconnected dynamics in the glucose and insulin time-courses. Here, a physiology-based mathematical model of the human glucose-insulin system is personalized to elucidate the heterogeneity in individuals' responses using a large population of overweight/obese individuals (n = 738) from the DIOGenes study. The personalized models are derived from population level models through a systematic parameter selection pipeline that may be generalized to other biological systems. The resulting personalized models showed a 4-5 fold decrease in discrepancy between measurements and model simulation compared to population level. The estimated model parameters capture relevant features of individuals' metabolic health such as gastric emptying, endogenous insulin secretion and insulin dependent glucose disposal into tissues, with the latter also showing a significant association with the Insulinogenic index and the Matsuda insulin sensitivity index, respectively.

U2 - 10.1371/journal.pcbi.1008852

DO - 10.1371/journal.pcbi.1008852

M3 - Journal article

C2 - 33788828

VL - 17

JO - P L o S Computational Biology (Online)

JF - P L o S Computational Biology (Online)

SN - 1553-734X

IS - 3

M1 - e1008852

ER -

ID: 259506651