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Precision Nutrition Model Predicts Glucose Control of Overweight Females Following the Consumption of Potatoes High in Resistant Starch

Individual glycemic responses following dietary intake result from complex physiological processes, and can be influenced by physical properties of foods, such as increased resistant starch (RS) from starch retrogradation. Predictive equations are needed to provide personalized dietary recommendatio...

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Autores principales: Nolte Fong, Joy V., Miketinas, Derek, Moore, Linda W., Nguyen, Duc T., Graviss, Edward A., Ajami, Nadim, Patterson, Mindy A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8779142/
https://www.ncbi.nlm.nih.gov/pubmed/35057449
http://dx.doi.org/10.3390/nu14020268
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author Nolte Fong, Joy V.
Miketinas, Derek
Moore, Linda W.
Nguyen, Duc T.
Graviss, Edward A.
Ajami, Nadim
Patterson, Mindy A.
author_facet Nolte Fong, Joy V.
Miketinas, Derek
Moore, Linda W.
Nguyen, Duc T.
Graviss, Edward A.
Ajami, Nadim
Patterson, Mindy A.
author_sort Nolte Fong, Joy V.
collection PubMed
description Individual glycemic responses following dietary intake result from complex physiological processes, and can be influenced by physical properties of foods, such as increased resistant starch (RS) from starch retrogradation. Predictive equations are needed to provide personalized dietary recommendations to reduce chronic disease development. Therefore, a precision nutrition model predicting the postprandial glucose response (PPGR) in overweight women following the consumption of potatoes was formulated. Thirty overweight women participated in this randomized crossover trial. Participants consumed 250 g of hot (9.2 g RS) or cold (13.7 g RS) potatoes on two separate occasions. Baseline characteristics included demographics, 10-day dietary records, body composition, and the relative abundance (RA) and α-diversity of gut microbiota. Elastic net regression using 5-fold cross-validation predicted PPGR after potato intake. Most participants (70%) had a favorable PPGR to the cold potato. The model explained 32.2% of the variance in PPGR with the equation: 547.65 × (0 [if cold, high-RS potato], ×1, if hot, low-RS potato]) + (BMI [kg/m(2)] × 40.66)—(insoluble fiber [g] × 49.35) + (Bacteroides [RA] × 8.69)—(Faecalibacterium [RA] × 73.49)—(Parabacteroides [RA] × 42.08) + (α-diversity × 110.87) + 292.52. This model improves the understanding of baseline characteristics that explain interpersonal variation in PPGR following potato intake and offers a tool to optimize dietary recommendations for a commonly consumed food.
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spelling pubmed-87791422022-01-22 Precision Nutrition Model Predicts Glucose Control of Overweight Females Following the Consumption of Potatoes High in Resistant Starch Nolte Fong, Joy V. Miketinas, Derek Moore, Linda W. Nguyen, Duc T. Graviss, Edward A. Ajami, Nadim Patterson, Mindy A. Nutrients Article Individual glycemic responses following dietary intake result from complex physiological processes, and can be influenced by physical properties of foods, such as increased resistant starch (RS) from starch retrogradation. Predictive equations are needed to provide personalized dietary recommendations to reduce chronic disease development. Therefore, a precision nutrition model predicting the postprandial glucose response (PPGR) in overweight women following the consumption of potatoes was formulated. Thirty overweight women participated in this randomized crossover trial. Participants consumed 250 g of hot (9.2 g RS) or cold (13.7 g RS) potatoes on two separate occasions. Baseline characteristics included demographics, 10-day dietary records, body composition, and the relative abundance (RA) and α-diversity of gut microbiota. Elastic net regression using 5-fold cross-validation predicted PPGR after potato intake. Most participants (70%) had a favorable PPGR to the cold potato. The model explained 32.2% of the variance in PPGR with the equation: 547.65 × (0 [if cold, high-RS potato], ×1, if hot, low-RS potato]) + (BMI [kg/m(2)] × 40.66)—(insoluble fiber [g] × 49.35) + (Bacteroides [RA] × 8.69)—(Faecalibacterium [RA] × 73.49)—(Parabacteroides [RA] × 42.08) + (α-diversity × 110.87) + 292.52. This model improves the understanding of baseline characteristics that explain interpersonal variation in PPGR following potato intake and offers a tool to optimize dietary recommendations for a commonly consumed food. MDPI 2022-01-09 /pmc/articles/PMC8779142/ /pubmed/35057449 http://dx.doi.org/10.3390/nu14020268 Text en © 2022 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
Nolte Fong, Joy V.
Miketinas, Derek
Moore, Linda W.
Nguyen, Duc T.
Graviss, Edward A.
Ajami, Nadim
Patterson, Mindy A.
Precision Nutrition Model Predicts Glucose Control of Overweight Females Following the Consumption of Potatoes High in Resistant Starch
title Precision Nutrition Model Predicts Glucose Control of Overweight Females Following the Consumption of Potatoes High in Resistant Starch
title_full Precision Nutrition Model Predicts Glucose Control of Overweight Females Following the Consumption of Potatoes High in Resistant Starch
title_fullStr Precision Nutrition Model Predicts Glucose Control of Overweight Females Following the Consumption of Potatoes High in Resistant Starch
title_full_unstemmed Precision Nutrition Model Predicts Glucose Control of Overweight Females Following the Consumption of Potatoes High in Resistant Starch
title_short Precision Nutrition Model Predicts Glucose Control of Overweight Females Following the Consumption of Potatoes High in Resistant Starch
title_sort precision nutrition model predicts glucose control of overweight females following the consumption of potatoes high in resistant starch
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8779142/
https://www.ncbi.nlm.nih.gov/pubmed/35057449
http://dx.doi.org/10.3390/nu14020268
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