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Simulating long-term human weight-loss dynamics in response to calorie restriction.(Report)(Author abstract)

Background: Mathematical models have been developed to predict body weight (BW) and composition changes in response to lifestyle interventions, but these models have not been adequately validated over the long term. Objective: We compared mathematical models of human BW dynamics underlying 2 popular... Full description

Journal Title: American Journal of Clinical Nutrition April, 2018, Vol.107(4), p.558(8)
Main Author: Guo, Juen
Other Authors: Brager, Danielle C. , Hall, Kevin D.
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: ISSN: 0002-9165 ; DOI: 10.1093/ajcn/nqx080
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recordid: gale_hrca537718688
title: Simulating long-term human weight-loss dynamics in response to calorie restriction.(Report)(Author abstract)
format: Article
creator:
  • Guo, Juen
  • Brager, Danielle C.
  • Hall, Kevin D.
subjects:
  • Low Calorie Diet -- Physiological Aspects
  • Low Calorie Diet -- Health Aspects
  • Weight Loss -- Models
  • Weight Loss -- Physiological Aspects
ispartof: American Journal of Clinical Nutrition, April, 2018, Vol.107(4), p.558(8)
description: Background: Mathematical models have been developed to predict body weight (BW) and composition changes in response to lifestyle interventions, but these models have not been adequately validated over the long term. Objective: We compared mathematical models of human BW dynamics underlying 2 popular web-based weight-loss prediction tools, the National Institutes of Health Body Weight Planner (NIH BWP) and the Pennington Biomedical Research Center Weight Loss Predictor (PBRC WLP), with data from the 2-year Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) study. Design: Mathematical models were initialized using baseline CALERIE data, and changes in body weight ([DELTA]BW), fat mass ([DELTA]FM), and energy expenditure ([DELTA]EE) were simulated in response to time-varying changes in energy intake ([DELTA]EI) objectively measured using the intake-balance method. No model parameters were adjusted from their previously published values. Results: The PBRC WLP model simulated an exaggerated early decrease in EE in response to calorie restriction, resulting in substantial underestimation of the observed mean (95% CI) BW losses by 3.8 (3.5, 4.2) kg. The NIH WLP simulations were much closer to the data, with an overall mean ABW bias of -0.47 (-0.92, -0.015) kg. Linearized model analysis revealed that the main reason for the PBRC WLP model bias was a parameter value defining how spontaneous physical activity expenditure decreased with caloric restriction. Both models exhibited substantial variability in their ability to simulate individual results in response to calorie restriction. Monte Carlo simulations demonstrated that AEI measurement uncertainties were a major contributor to the individual variability in NIH BWP model simulations. Conclusions: The NIH BWP outperformed the PBRC WLP and accurately simulated average weight-loss and energy balance dynamics in response to long-term calorie restriction. However, the substantial variability in the NIH BWP model predictions at the individual level suggests cautious interpretation of individual-level simulations. This trial was registered at clinicaltrials.gov as NCT00427193. Keywords: mathematical model, energy balance, weight loss, calorie restriction doi: https://doi.org/10.1093/ajcn/nqx080
language: English
source:
identifier: ISSN: 0002-9165 ; DOI: 10.1093/ajcn/nqx080
fulltext: fulltext
issn:
  • 0002-9165
  • 00029165
url: Link


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titleSimulating long-term human weight-loss dynamics in response to calorie restriction.(Report)(Author abstract)
creatorGuo, Juen ; Brager, Danielle C. ; Hall, Kevin D.
ispartofAmerican Journal of Clinical Nutrition, April, 2018, Vol.107(4), p.558(8)
identifierISSN: 0002-9165 ; DOI: 10.1093/ajcn/nqx080
subjectLow Calorie Diet -- Physiological Aspects ; Low Calorie Diet -- Health Aspects ; Weight Loss -- Models ; Weight Loss -- Physiological Aspects
descriptionBackground: Mathematical models have been developed to predict body weight (BW) and composition changes in response to lifestyle interventions, but these models have not been adequately validated over the long term. Objective: We compared mathematical models of human BW dynamics underlying 2 popular web-based weight-loss prediction tools, the National Institutes of Health Body Weight Planner (NIH BWP) and the Pennington Biomedical Research Center Weight Loss Predictor (PBRC WLP), with data from the 2-year Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) study. Design: Mathematical models were initialized using baseline CALERIE data, and changes in body weight ([DELTA]BW), fat mass ([DELTA]FM), and energy expenditure ([DELTA]EE) were simulated in response to time-varying changes in energy intake ([DELTA]EI) objectively measured using the intake-balance method. No model parameters were adjusted from their previously published values. Results: The PBRC WLP model simulated an exaggerated early decrease in EE in response to calorie restriction, resulting in substantial underestimation of the observed mean (95% CI) BW losses by 3.8 (3.5, 4.2) kg. The NIH WLP simulations were much closer to the data, with an overall mean ABW bias of -0.47 (-0.92, -0.015) kg. Linearized model analysis revealed that the main reason for the PBRC WLP model bias was a parameter value defining how spontaneous physical activity expenditure decreased with caloric restriction. Both models exhibited substantial variability in their ability to simulate individual results in response to calorie restriction. Monte Carlo simulations demonstrated that AEI measurement uncertainties were a major contributor to the individual variability in NIH BWP model simulations. Conclusions: The NIH BWP outperformed the PBRC WLP and accurately simulated average weight-loss and energy balance dynamics in response to long-term calorie restriction. However, the substantial variability in the NIH BWP model predictions at the individual level suggests cautious interpretation of individual-level simulations. This trial was registered at clinicaltrials.gov as NCT00427193. Keywords: mathematical model, energy balance, weight loss, calorie restriction doi: https://doi.org/10.1093/ajcn/nqx080
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titleSimulating long-term human weight-loss dynamics in response to calorie restriction.(Report)(Author abstract)
descriptionBackground: Mathematical models have been developed to predict body weight (BW) and composition changes in response to lifestyle interventions, but these models have not been adequately validated over the long term. Objective: We compared mathematical models of human BW dynamics underlying 2 popular web-based weight-loss prediction tools, the National Institutes of Health Body Weight Planner (NIH BWP) and the Pennington Biomedical Research Center Weight Loss Predictor (PBRC WLP), with data from the 2-year Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) study. Design: Mathematical models were initialized using baseline CALERIE data, and changes in body weight ([DELTA]BW), fat mass ([DELTA]FM), and energy expenditure ([DELTA]EE) were simulated in response to time-varying changes in energy intake ([DELTA]EI) objectively measured using the intake-balance method. No model parameters were adjusted from their previously published values. Results: The PBRC WLP model simulated an exaggerated early decrease in EE in response to calorie restriction, resulting in substantial underestimation of the observed mean (95% CI) BW losses by 3.8 (3.5, 4.2) kg. The NIH WLP simulations were much closer to the data, with an overall mean ABW bias of -0.47 (-0.92, -0.015) kg. Linearized model analysis revealed that the main reason for the PBRC WLP model bias was a parameter value defining how spontaneous physical activity expenditure decreased with caloric restriction. Both models exhibited substantial variability in their ability to simulate individual results in response to calorie restriction. Monte Carlo simulations demonstrated that AEI measurement uncertainties were a major contributor to the individual variability in NIH BWP model simulations. Conclusions: The NIH BWP outperformed the PBRC WLP and accurately simulated average weight-loss and energy balance dynamics in response to long-term calorie restriction. However, the substantial variability in the NIH BWP model predictions at the individual level suggests cautious interpretation of individual-level simulations. This trial was registered at clinicaltrials.gov as NCT00427193. Keywords: mathematical model, energy balance, weight loss, calorie restriction doi: https://doi.org/10.1093/ajcn/nqx080
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abstractBackground: Mathematical models have been developed to predict body weight (BW) and composition changes in response to lifestyle interventions, but these models have not been adequately validated over the long term. Objective: We compared mathematical models of human BW dynamics underlying 2 popular web-based weight-loss prediction tools, the National Institutes of Health Body Weight Planner (NIH BWP) and the Pennington Biomedical Research Center Weight Loss Predictor (PBRC WLP), with data from the 2-year Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) study. Design: Mathematical models were initialized using baseline CALERIE data, and changes in body weight ([DELTA]BW), fat mass ([DELTA]FM), and energy expenditure ([DELTA]EE) were simulated in response to time-varying changes in energy intake ([DELTA]EI) objectively measured using the intake-balance method. No model parameters were adjusted from their previously published values. Results: The PBRC WLP model simulated an exaggerated early decrease in EE in response to calorie restriction, resulting in substantial underestimation of the observed mean (95% CI) BW losses by 3.8 (3.5, 4.2) kg. The NIH WLP simulations were much closer to the data, with an overall mean ABW bias of -0.47 (-0.92, -0.015) kg. Linearized model analysis revealed that the main reason for the PBRC WLP model bias was a parameter value defining how spontaneous physical activity expenditure decreased with caloric restriction. Both models exhibited substantial variability in their ability to simulate individual results in response to calorie restriction. Monte Carlo simulations demonstrated that AEI measurement uncertainties were a major contributor to the individual variability in NIH BWP model simulations. Conclusions: The NIH BWP outperformed the PBRC WLP and accurately simulated average weight-loss and energy balance dynamics in response to long-term calorie restriction. However, the substantial variability in the NIH BWP model predictions at the individual level suggests cautious interpretation of individual-level simulations. This trial was registered at clinicaltrials.gov as NCT00427193. Keywords: mathematical model, energy balance, weight loss, calorie restriction doi: https://doi.org/10.1093/ajcn/nqx080
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