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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...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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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. |
format | Online Article Text |
id | pubmed-8011733 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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