We continue to develop and test our mathematical models of human metabolism and body weight dynamics. We recently compared mathematical models underlying 2 popular web-based weight-loss prediction tools, our 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. The models were initialized using baseline CALERIE data, and changes in body weight (BW), fat mass (FM), and energy expenditure (EE) were simulated in response to time-varying changes in energy intake (EI) objectively measured using the intake-balance method. No model parameters were adjusted from their previously published values. 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 BWP simulations were much closer to the data, with an overall mean BW 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 EI measurement uncertainties were a major contributor to the individual variability in NIH BWP model simulations.Our NIH BWP outperformed the PBRC WLP and accurately simulated average weight-loss and energy balance dynamics in response to long-term calorie restriction.
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