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A multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects
BACKGROUND: The increased prevalence of insulin resistance is one of the major health risks in society today. Insulin resistance involves both short-term dynamics, such as altered meal responses, and long-term dynamics, such as the development of type 2 diabetes. Insulin resistance also occurs on di...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10694923/ https://www.ncbi.nlm.nih.gov/pubmed/38044443 http://dx.doi.org/10.1186/s13098-023-01223-6 |
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author | Herrgårdh, Tilda Simonsson, Christian Ekstedt, Mattias Lundberg, Peter Stenkula, Karin G. Nyman, Elin Gennemark, Peter Cedersund, Gunnar |
author_facet | Herrgårdh, Tilda Simonsson, Christian Ekstedt, Mattias Lundberg, Peter Stenkula, Karin G. Nyman, Elin Gennemark, Peter Cedersund, Gunnar |
author_sort | Herrgårdh, Tilda |
collection | PubMed |
description | BACKGROUND: The increased prevalence of insulin resistance is one of the major health risks in society today. Insulin resistance involves both short-term dynamics, such as altered meal responses, and long-term dynamics, such as the development of type 2 diabetes. Insulin resistance also occurs on different physiological levels, ranging from disease phenotypes to organ-organ communication and intracellular signaling. To better understand the progression of insulin resistance, an analysis method is needed that can combine different timescales and physiological levels. One such method is digital twins, consisting of combined mechanistic mathematical models. We have previously developed a model for short-term glucose homeostasis and intracellular insulin signaling, and there exist long-term weight regulation models. Herein, we combine these models into a first interconnected digital twin for the progression of insulin resistance in humans. METHODS: The model is based on ordinary differential equations representing biochemical and physiological processes, in which unknown parameters were fitted to data using a MATLAB toolbox. RESULTS: The interconnected twin correctly predicts independent data from a weight increase study, both for weight-changes, fasting plasma insulin and glucose levels, and intracellular insulin signaling. Similarly, the model can predict independent weight-change data in a weight loss study with the weight loss drug topiramate. The model can also predict non-measured variables. CONCLUSIONS: The model presented herein constitutes the basis for a new digital twin technology, which in the future could be used to aid medical pedagogy and increase motivation and compliance and thus aid in the prevention and treatment of insulin resistance. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13098-023-01223-6. |
format | Online Article Text |
id | pubmed-10694923 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106949232023-12-05 A multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects Herrgårdh, Tilda Simonsson, Christian Ekstedt, Mattias Lundberg, Peter Stenkula, Karin G. Nyman, Elin Gennemark, Peter Cedersund, Gunnar Diabetol Metab Syndr Research BACKGROUND: The increased prevalence of insulin resistance is one of the major health risks in society today. Insulin resistance involves both short-term dynamics, such as altered meal responses, and long-term dynamics, such as the development of type 2 diabetes. Insulin resistance also occurs on different physiological levels, ranging from disease phenotypes to organ-organ communication and intracellular signaling. To better understand the progression of insulin resistance, an analysis method is needed that can combine different timescales and physiological levels. One such method is digital twins, consisting of combined mechanistic mathematical models. We have previously developed a model for short-term glucose homeostasis and intracellular insulin signaling, and there exist long-term weight regulation models. Herein, we combine these models into a first interconnected digital twin for the progression of insulin resistance in humans. METHODS: The model is based on ordinary differential equations representing biochemical and physiological processes, in which unknown parameters were fitted to data using a MATLAB toolbox. RESULTS: The interconnected twin correctly predicts independent data from a weight increase study, both for weight-changes, fasting plasma insulin and glucose levels, and intracellular insulin signaling. Similarly, the model can predict independent weight-change data in a weight loss study with the weight loss drug topiramate. The model can also predict non-measured variables. CONCLUSIONS: The model presented herein constitutes the basis for a new digital twin technology, which in the future could be used to aid medical pedagogy and increase motivation and compliance and thus aid in the prevention and treatment of insulin resistance. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13098-023-01223-6. BioMed Central 2023-12-04 /pmc/articles/PMC10694923/ /pubmed/38044443 http://dx.doi.org/10.1186/s13098-023-01223-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Herrgårdh, Tilda Simonsson, Christian Ekstedt, Mattias Lundberg, Peter Stenkula, Karin G. Nyman, Elin Gennemark, Peter Cedersund, Gunnar A multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects |
title | A multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects |
title_full | A multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects |
title_fullStr | A multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects |
title_full_unstemmed | A multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects |
title_short | A multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects |
title_sort | multi-scale digital twin for adiposity-driven insulin resistance in humans: diet and drug effects |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10694923/ https://www.ncbi.nlm.nih.gov/pubmed/38044443 http://dx.doi.org/10.1186/s13098-023-01223-6 |
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