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Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals

BACKGROUND: Controlled glycemic concentrations are associated with a lower risk of conditions such as cardiovascular disease and diabetes. Models commonly used to guide interventions to control the glycemic response to food have low efficacy, with recent clinical guidelines arguing for the use of pe...

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Autores principales: Mendes-Soares, Helena, Raveh-Sadka, Tali, Azulay, Shahar, Ben-Shlomo, Yatir, Cohen, Yossi, Ofek, Tal, Stevens, Josh, Bachrach, Davidi, Kashyap, Purna, Segal, Lihi, Nelson, Heidi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6599737/
https://www.ncbi.nlm.nih.gov/pubmed/31095300
http://dx.doi.org/10.1093/ajcn/nqz028
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author Mendes-Soares, Helena
Raveh-Sadka, Tali
Azulay, Shahar
Ben-Shlomo, Yatir
Cohen, Yossi
Ofek, Tal
Stevens, Josh
Bachrach, Davidi
Kashyap, Purna
Segal, Lihi
Nelson, Heidi
author_facet Mendes-Soares, Helena
Raveh-Sadka, Tali
Azulay, Shahar
Ben-Shlomo, Yatir
Cohen, Yossi
Ofek, Tal
Stevens, Josh
Bachrach, Davidi
Kashyap, Purna
Segal, Lihi
Nelson, Heidi
author_sort Mendes-Soares, Helena
collection PubMed
description BACKGROUND: Controlled glycemic concentrations are associated with a lower risk of conditions such as cardiovascular disease and diabetes. Models commonly used to guide interventions to control the glycemic response to food have low efficacy, with recent clinical guidelines arguing for the use of personalized approaches. OBJECTIVE: We tested the efficacy of a predictive model of personalized postprandial glycemic response to foods that was developed with an Israeli cohort and that takes into consideration food components and specific features, including the microbiome, when applied to individuals from the Midwestern US. DESIGN: We recruited 327 individuals for this study. Participants provided information regarding lifestyle, dietary habits, and health, as well as a stool sample for characterization of their gut microbiome. Participants were connected to continuous glucose monitors for 6 d, and the glycemic response to meals logged during this time was computed. The ability of a model trained using meals logged by the Israeli cohort to correctly predict glycemic responses in the Midwestern cohort was assessed and compared with that of a model trained using meals logged by both cohorts. RESULTS: When trained on the Israeli cohort meals only, model performance for predicting responses of individuals in the Midwestern cohort was better (R = 0.596) than that observed for models taking into consideration the carbohydrate (R = 0.395) or calorie content of the meals alone (R = 0.336). Performance increased (R = 0.618) when the model was trained on meals from both cohorts, likely because of the observed differences in age distribution, diet, and microbiome. CONCLUSIONS: We show that the modeling framework described in Zeevi et al. for an Israeli cohort is applicable to a Midwestern population, and outperforms commonly used approaches for the control of blood glucose responses. The adaptation of the model to the Midwestern cohort further enhances performance and is a promising means for designing effective nutritional interventions to control glycemic responses to foods. This trial was registered at clinicaltrials.gov as NCT02945514.
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spelling pubmed-65997372019-07-03 Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals Mendes-Soares, Helena Raveh-Sadka, Tali Azulay, Shahar Ben-Shlomo, Yatir Cohen, Yossi Ofek, Tal Stevens, Josh Bachrach, Davidi Kashyap, Purna Segal, Lihi Nelson, Heidi Am J Clin Nutr Original Research Communications BACKGROUND: Controlled glycemic concentrations are associated with a lower risk of conditions such as cardiovascular disease and diabetes. Models commonly used to guide interventions to control the glycemic response to food have low efficacy, with recent clinical guidelines arguing for the use of personalized approaches. OBJECTIVE: We tested the efficacy of a predictive model of personalized postprandial glycemic response to foods that was developed with an Israeli cohort and that takes into consideration food components and specific features, including the microbiome, when applied to individuals from the Midwestern US. DESIGN: We recruited 327 individuals for this study. Participants provided information regarding lifestyle, dietary habits, and health, as well as a stool sample for characterization of their gut microbiome. Participants were connected to continuous glucose monitors for 6 d, and the glycemic response to meals logged during this time was computed. The ability of a model trained using meals logged by the Israeli cohort to correctly predict glycemic responses in the Midwestern cohort was assessed and compared with that of a model trained using meals logged by both cohorts. RESULTS: When trained on the Israeli cohort meals only, model performance for predicting responses of individuals in the Midwestern cohort was better (R = 0.596) than that observed for models taking into consideration the carbohydrate (R = 0.395) or calorie content of the meals alone (R = 0.336). Performance increased (R = 0.618) when the model was trained on meals from both cohorts, likely because of the observed differences in age distribution, diet, and microbiome. CONCLUSIONS: We show that the modeling framework described in Zeevi et al. for an Israeli cohort is applicable to a Midwestern population, and outperforms commonly used approaches for the control of blood glucose responses. The adaptation of the model to the Midwestern cohort further enhances performance and is a promising means for designing effective nutritional interventions to control glycemic responses to foods. This trial was registered at clinicaltrials.gov as NCT02945514. Oxford University Press 2019-07 2019-05-16 /pmc/articles/PMC6599737/ /pubmed/31095300 http://dx.doi.org/10.1093/ajcn/nqz028 Text en Copyright © American Society for Nutrition 2019. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Original Research Communications
Mendes-Soares, Helena
Raveh-Sadka, Tali
Azulay, Shahar
Ben-Shlomo, Yatir
Cohen, Yossi
Ofek, Tal
Stevens, Josh
Bachrach, Davidi
Kashyap, Purna
Segal, Lihi
Nelson, Heidi
Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals
title Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals
title_full Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals
title_fullStr Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals
title_full_unstemmed Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals
title_short Model of personalized postprandial glycemic response to food developed for an Israeli cohort predicts responses in Midwestern American individuals
title_sort model of personalized postprandial glycemic response to food developed for an israeli cohort predicts responses in midwestern american individuals
topic Original Research Communications
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6599737/
https://www.ncbi.nlm.nih.gov/pubmed/31095300
http://dx.doi.org/10.1093/ajcn/nqz028
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