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An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from Pisum sativum, Decreases HbA1c in a Prediabetic Population

The prevalence of prediabetes is rapidly increasing, and this can lead to an increased risk for individuals to develop type 2 diabetes and associated diseases. Therefore, it is necessary to develop nutritional strategies to maintain healthy glucose levels and prevent glucose metabolism dysregulation...

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Autores principales: Chauhan, Sweeny, Kerr, Alish, Keogh, Brian, Nolan, Stephanie, Casey, Rory, Adelfio, Alessandro, Murphy, Niall, Doherty, Aoife, Davis, Heidi, Wall, Audrey M., Khaldi, Nora
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8152294/
https://www.ncbi.nlm.nih.gov/pubmed/34068000
http://dx.doi.org/10.3390/nu13051635
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author Chauhan, Sweeny
Kerr, Alish
Keogh, Brian
Nolan, Stephanie
Casey, Rory
Adelfio, Alessandro
Murphy, Niall
Doherty, Aoife
Davis, Heidi
Wall, Audrey M.
Khaldi, Nora
author_facet Chauhan, Sweeny
Kerr, Alish
Keogh, Brian
Nolan, Stephanie
Casey, Rory
Adelfio, Alessandro
Murphy, Niall
Doherty, Aoife
Davis, Heidi
Wall, Audrey M.
Khaldi, Nora
author_sort Chauhan, Sweeny
collection PubMed
description The prevalence of prediabetes is rapidly increasing, and this can lead to an increased risk for individuals to develop type 2 diabetes and associated diseases. Therefore, it is necessary to develop nutritional strategies to maintain healthy glucose levels and prevent glucose metabolism dysregulation in the general population. Functional ingredients offer great potential for the prevention of various health conditions, including blood glucose regulation, in a cost-effective manner. Using an artificial intelligence (AI) approach, a functional ingredient, NRT_N0G5IJ, was predicted and produced from Pisum sativum (pea) protein by hydrolysis and then validated. Treatment of human skeletal muscle cells with NRT_N0G5IJ significantly increased glucose uptake, indicating efficacy of this ingredient in vitro. When db/db diabetic mice were treated with NRT_N0G5IJ, we observed a significant reduction in glycated haemoglobin (HbA1c) levels and a concomitant benefit on fasting glucose. A pilot double-blinded, placebo controlled human trial in a population of healthy individuals with elevated HbA1c (5.6% to 6.4%) showed that HbA1c percentage was significantly reduced when NRT_N0G5IJ was supplemented in the diet over a 12-week period. Here, we provide evidence of an AI approach to discovery and demonstrate that a functional ingredient identified using this technology could be used as a supplement to maintain healthy glucose regulation.
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spelling pubmed-81522942021-05-27 An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from Pisum sativum, Decreases HbA1c in a Prediabetic Population Chauhan, Sweeny Kerr, Alish Keogh, Brian Nolan, Stephanie Casey, Rory Adelfio, Alessandro Murphy, Niall Doherty, Aoife Davis, Heidi Wall, Audrey M. Khaldi, Nora Nutrients Article The prevalence of prediabetes is rapidly increasing, and this can lead to an increased risk for individuals to develop type 2 diabetes and associated diseases. Therefore, it is necessary to develop nutritional strategies to maintain healthy glucose levels and prevent glucose metabolism dysregulation in the general population. Functional ingredients offer great potential for the prevention of various health conditions, including blood glucose regulation, in a cost-effective manner. Using an artificial intelligence (AI) approach, a functional ingredient, NRT_N0G5IJ, was predicted and produced from Pisum sativum (pea) protein by hydrolysis and then validated. Treatment of human skeletal muscle cells with NRT_N0G5IJ significantly increased glucose uptake, indicating efficacy of this ingredient in vitro. When db/db diabetic mice were treated with NRT_N0G5IJ, we observed a significant reduction in glycated haemoglobin (HbA1c) levels and a concomitant benefit on fasting glucose. A pilot double-blinded, placebo controlled human trial in a population of healthy individuals with elevated HbA1c (5.6% to 6.4%) showed that HbA1c percentage was significantly reduced when NRT_N0G5IJ was supplemented in the diet over a 12-week period. Here, we provide evidence of an AI approach to discovery and demonstrate that a functional ingredient identified using this technology could be used as a supplement to maintain healthy glucose regulation. MDPI 2021-05-13 /pmc/articles/PMC8152294/ /pubmed/34068000 http://dx.doi.org/10.3390/nu13051635 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chauhan, Sweeny
Kerr, Alish
Keogh, Brian
Nolan, Stephanie
Casey, Rory
Adelfio, Alessandro
Murphy, Niall
Doherty, Aoife
Davis, Heidi
Wall, Audrey M.
Khaldi, Nora
An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from Pisum sativum, Decreases HbA1c in a Prediabetic Population
title An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from Pisum sativum, Decreases HbA1c in a Prediabetic Population
title_full An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from Pisum sativum, Decreases HbA1c in a Prediabetic Population
title_fullStr An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from Pisum sativum, Decreases HbA1c in a Prediabetic Population
title_full_unstemmed An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from Pisum sativum, Decreases HbA1c in a Prediabetic Population
title_short An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived from Pisum sativum, Decreases HbA1c in a Prediabetic Population
title_sort artificial-intelligence-discovered functional ingredient, nrt_n0g5ij, derived from pisum sativum, decreases hba1c in a prediabetic population
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8152294/
https://www.ncbi.nlm.nih.gov/pubmed/34068000
http://dx.doi.org/10.3390/nu13051635
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