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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...

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Autores principales: Herrgårdh, Tilda, Simonsson, Christian, Ekstedt, Mattias, Lundberg, Peter, Stenkula, Karin G., Nyman, Elin, Gennemark, Peter, Cedersund, Gunnar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
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.
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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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