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Evolutionary algorithm for the optimization of meal intake and insulin administration in patients with type 2 diabetes

The optimal management of type 2 diabetes (T2DM) is complex and involves an appropriate combination of diet, exercise, and different pharmacological treatments. Artificial intelligence-based tools have been shown to be very useful for the diagnosis and treatment of diverse pathologies, including dia...

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Autores principales: Gonzalez-Flo, Eva, Kheirabadi, Elaheh, Rodriguez-Caso, Carlos, Macía, Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10115945/
https://www.ncbi.nlm.nih.gov/pubmed/37089422
http://dx.doi.org/10.3389/fphys.2023.1149698
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author Gonzalez-Flo, Eva
Kheirabadi, Elaheh
Rodriguez-Caso, Carlos
Macía, Javier
author_facet Gonzalez-Flo, Eva
Kheirabadi, Elaheh
Rodriguez-Caso, Carlos
Macía, Javier
author_sort Gonzalez-Flo, Eva
collection PubMed
description The optimal management of type 2 diabetes (T2DM) is complex and involves an appropriate combination of diet, exercise, and different pharmacological treatments. Artificial intelligence-based tools have been shown to be very useful for the diagnosis and treatment of diverse pathologies, including diabetes. In the present study, we present a proof of concept of the potential of an evolutionary algorithm to optimize the meal size, timing and insulin dose for the control of glycemia. We found that an appropriate distribution of food intake throughout the day permits a reduction in the insulin dose required to maintain glycemia within the range recommended by the American Diabetes Association for patients with T2DM of a range of severities. Furthermore, the effects of restrictions to both the timing and amount of food ingested were assessed, and we found that an increase in the amount of insulin was required to control glycemia as dietary intake became more restricted. In the near future, the use of these computational tools should permit patients with T2DM to optimize their personal meal schedule and insulin dose, according to the severity of their diabetes.
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spelling pubmed-101159452023-04-21 Evolutionary algorithm for the optimization of meal intake and insulin administration in patients with type 2 diabetes Gonzalez-Flo, Eva Kheirabadi, Elaheh Rodriguez-Caso, Carlos Macía, Javier Front Physiol Physiology The optimal management of type 2 diabetes (T2DM) is complex and involves an appropriate combination of diet, exercise, and different pharmacological treatments. Artificial intelligence-based tools have been shown to be very useful for the diagnosis and treatment of diverse pathologies, including diabetes. In the present study, we present a proof of concept of the potential of an evolutionary algorithm to optimize the meal size, timing and insulin dose for the control of glycemia. We found that an appropriate distribution of food intake throughout the day permits a reduction in the insulin dose required to maintain glycemia within the range recommended by the American Diabetes Association for patients with T2DM of a range of severities. Furthermore, the effects of restrictions to both the timing and amount of food ingested were assessed, and we found that an increase in the amount of insulin was required to control glycemia as dietary intake became more restricted. In the near future, the use of these computational tools should permit patients with T2DM to optimize their personal meal schedule and insulin dose, according to the severity of their diabetes. Frontiers Media S.A. 2023-04-06 /pmc/articles/PMC10115945/ /pubmed/37089422 http://dx.doi.org/10.3389/fphys.2023.1149698 Text en Copyright © 2023 Gonzalez-Flo, Kheirabadi, Rodriguez-Caso and Macía. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Gonzalez-Flo, Eva
Kheirabadi, Elaheh
Rodriguez-Caso, Carlos
Macía, Javier
Evolutionary algorithm for the optimization of meal intake and insulin administration in patients with type 2 diabetes
title Evolutionary algorithm for the optimization of meal intake and insulin administration in patients with type 2 diabetes
title_full Evolutionary algorithm for the optimization of meal intake and insulin administration in patients with type 2 diabetes
title_fullStr Evolutionary algorithm for the optimization of meal intake and insulin administration in patients with type 2 diabetes
title_full_unstemmed Evolutionary algorithm for the optimization of meal intake and insulin administration in patients with type 2 diabetes
title_short Evolutionary algorithm for the optimization of meal intake and insulin administration in patients with type 2 diabetes
title_sort evolutionary algorithm for the optimization of meal intake and insulin administration in patients with type 2 diabetes
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10115945/
https://www.ncbi.nlm.nih.gov/pubmed/37089422
http://dx.doi.org/10.3389/fphys.2023.1149698
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