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

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 effective...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011733/
https://www.ncbi.nlm.nih.gov/pubmed/33788828
http://dx.doi.org/10.1371/journal.pcbi.1008852
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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.
author_facet 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.
author_sort Erdős, Balázs
collection PubMed
description 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.
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spelling pubmed-80117332021-04-07 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. PLoS Comput Biol Research Article 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. Public Library of Science 2021-03-31 /pmc/articles/PMC8011733/ /pubmed/33788828 http://dx.doi.org/10.1371/journal.pcbi.1008852 Text en © 2021 Erdős et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
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
title Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge
title_full Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge
title_fullStr Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge
title_full_unstemmed Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge
title_short Personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge
title_sort personalized computational model quantifies heterogeneity in postprandial responses to oral glucose challenge
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8011733/
https://www.ncbi.nlm.nih.gov/pubmed/33788828
http://dx.doi.org/10.1371/journal.pcbi.1008852
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