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Optimization of nutritional strategies using a mechanistic computational model in prediabetes: Application to the J-DOIT1 study data

Lifestyle interventions have been shown to prevent or delay the onset of diabetes; however, inter-individual variability in responses to such interventions makes lifestyle recommendations challenging. We analyzed the Japan Diabetes Outcome Intervention Trial-1 (J-DOIT1) study data using a previously...

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Autores principales: Chen, Julia H., Fukasawa, Momoko, Sakane, Naoki, Suganuma, Akiko, Kuzuya, Hideshi, Pandey, Shikhar, D’Alessandro, Paul, Venkatapurapu, Sai Phanindra, Dwivedi, Gaurav
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10688723/
https://www.ncbi.nlm.nih.gov/pubmed/38033033
http://dx.doi.org/10.1371/journal.pone.0287069
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author Chen, Julia H.
Fukasawa, Momoko
Sakane, Naoki
Suganuma, Akiko
Kuzuya, Hideshi
Pandey, Shikhar
D’Alessandro, Paul
Venkatapurapu, Sai Phanindra
Dwivedi, Gaurav
author_facet Chen, Julia H.
Fukasawa, Momoko
Sakane, Naoki
Suganuma, Akiko
Kuzuya, Hideshi
Pandey, Shikhar
D’Alessandro, Paul
Venkatapurapu, Sai Phanindra
Dwivedi, Gaurav
author_sort Chen, Julia H.
collection PubMed
description Lifestyle interventions have been shown to prevent or delay the onset of diabetes; however, inter-individual variability in responses to such interventions makes lifestyle recommendations challenging. We analyzed the Japan Diabetes Outcome Intervention Trial-1 (J-DOIT1) study data using a previously published mechanistic simulation model of type 2 diabetes onset and progression to understand the causes of inter-individual variability and to optimize dietary intervention strategies at an individual level. J-DOIT1, a large-scale lifestyle intervention study, involved 2607 subjects with a 4.2-year median follow-up period. We selected 112 individuals from the J-DOIT1 study and calibrated the mechanistic model to each participant’s body weight and HbA1c time courses. We evaluated the relationship of physiological (e.g., insulin sensitivity) and lifestyle (e.g., dietary intake) parameters with variability in outcome. Finally, we used simulation analyses to predict individually optimized diets for weight reduction. The model predicted individual body weight and HbA1c time courses with a mean (±SD) prediction error of 1.0 kg (±1.2) and 0.14% (±0.18), respectively. Individuals with the most and least improved biomarkers showed no significant differences in model-estimated energy balance. A wide range of weight changes was observed for similar model-estimated caloric changes, indicating that caloric balance alone may not be a good predictor of body weight. The model suggests that a set of optimal diets exists to achieve a defined weight reduction, and this set of diets is unique to each individual. Our diabetes model can simulate changes in body weight and glycemic control as a result of lifestyle interventions. Moreover, this model could help dieticians and physicians to optimize personalized nutritional strategies according to their patients’ goals.
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spelling pubmed-106887232023-12-01 Optimization of nutritional strategies using a mechanistic computational model in prediabetes: Application to the J-DOIT1 study data Chen, Julia H. Fukasawa, Momoko Sakane, Naoki Suganuma, Akiko Kuzuya, Hideshi Pandey, Shikhar D’Alessandro, Paul Venkatapurapu, Sai Phanindra Dwivedi, Gaurav PLoS One Research Article Lifestyle interventions have been shown to prevent or delay the onset of diabetes; however, inter-individual variability in responses to such interventions makes lifestyle recommendations challenging. We analyzed the Japan Diabetes Outcome Intervention Trial-1 (J-DOIT1) study data using a previously published mechanistic simulation model of type 2 diabetes onset and progression to understand the causes of inter-individual variability and to optimize dietary intervention strategies at an individual level. J-DOIT1, a large-scale lifestyle intervention study, involved 2607 subjects with a 4.2-year median follow-up period. We selected 112 individuals from the J-DOIT1 study and calibrated the mechanistic model to each participant’s body weight and HbA1c time courses. We evaluated the relationship of physiological (e.g., insulin sensitivity) and lifestyle (e.g., dietary intake) parameters with variability in outcome. Finally, we used simulation analyses to predict individually optimized diets for weight reduction. The model predicted individual body weight and HbA1c time courses with a mean (±SD) prediction error of 1.0 kg (±1.2) and 0.14% (±0.18), respectively. Individuals with the most and least improved biomarkers showed no significant differences in model-estimated energy balance. A wide range of weight changes was observed for similar model-estimated caloric changes, indicating that caloric balance alone may not be a good predictor of body weight. The model suggests that a set of optimal diets exists to achieve a defined weight reduction, and this set of diets is unique to each individual. Our diabetes model can simulate changes in body weight and glycemic control as a result of lifestyle interventions. Moreover, this model could help dieticians and physicians to optimize personalized nutritional strategies according to their patients’ goals. Public Library of Science 2023-11-30 /pmc/articles/PMC10688723/ /pubmed/38033033 http://dx.doi.org/10.1371/journal.pone.0287069 Text en © 2023 Chen et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Chen, Julia H.
Fukasawa, Momoko
Sakane, Naoki
Suganuma, Akiko
Kuzuya, Hideshi
Pandey, Shikhar
D’Alessandro, Paul
Venkatapurapu, Sai Phanindra
Dwivedi, Gaurav
Optimization of nutritional strategies using a mechanistic computational model in prediabetes: Application to the J-DOIT1 study data
title Optimization of nutritional strategies using a mechanistic computational model in prediabetes: Application to the J-DOIT1 study data
title_full Optimization of nutritional strategies using a mechanistic computational model in prediabetes: Application to the J-DOIT1 study data
title_fullStr Optimization of nutritional strategies using a mechanistic computational model in prediabetes: Application to the J-DOIT1 study data
title_full_unstemmed Optimization of nutritional strategies using a mechanistic computational model in prediabetes: Application to the J-DOIT1 study data
title_short Optimization of nutritional strategies using a mechanistic computational model in prediabetes: Application to the J-DOIT1 study data
title_sort optimization of nutritional strategies using a mechanistic computational model in prediabetes: application to the j-doit1 study data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10688723/
https://www.ncbi.nlm.nih.gov/pubmed/38033033
http://dx.doi.org/10.1371/journal.pone.0287069
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