Laboratory of Biological Modeling, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD.
School of Mathematical and Statistical Sciences, Arizona State University, Tempe, AZ.
Am J Clin Nutr. 2018 Apr 1;107(4):558-565. doi: 10.1093/ajcn/nqx080.
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.
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.
Mathematical models were initialized using baseline CALERIE data, and changes in body weight (ΔBW), fat mass (ΔFM), and energy expenditure (ΔEE) were simulated in response to time-varying changes in energy intake (ΔEI) 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 WLP simulations were much closer to the data, with an overall mean ΔBW 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 ΔEI measurement uncertainties were a major contributor to the individual variability in NIH BWP model simulations.
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.
已经开发出数学模型来预测体重（BW）和组成变化，以响应生活方式干预，但这些模型在长期内没有得到充分验证。
我们将比较两种流行的基于网络的减肥预测工具，即美国国立卫生研究院体重计划（NIH BWP）和彭宁顿生物医学研究中心减肥预测器（PBRC WLP）背后的人体 BW 动力学数学模型，与为期 2 年的减少能量摄入的长期综合评估（CALERIE）研究的数据。
使用基线 CALERIE 数据初始化数学模型，并使用摄入量平衡法客观测量的能量摄入（ΔEI）的时变变化来模拟体重（ΔBW）、脂肪量（ΔFM）和能量消耗（ΔEE）的变化。没有调整模型参数，使其与之前发表的值不同。
PBRC WLP 模型模拟了对热量限制的 EE 的早期夸张下降，导致对观察到的平均（95%CI）BW 损失的大量低估，为 3.8（3.5，4.2）kg。NIH WLP 模拟更接近数据，总体平均ΔBW 偏差为-0.47（-0.92，-0.015）kg。线性化模型分析表明，PBRC WLP 模型偏差的主要原因是一个参数值，该参数定义了自发性体力活动支出随热量限制的下降情况。这两个模型在模拟热量限制个体结果的能力方面都存在很大的变异性。蒙特卡罗模拟表明，ΔEI 测量不确定性是 NIH BWP 模型模拟个体差异的主要原因。
NIH BWP 优于 PBRC WLP，并准确模拟了长期热量限制对平均体重减轻和能量平衡动态的影响。然而，NIH BWP 模型预测的个体水平的巨大变异性表明对个体水平的模拟应谨慎解释。该试验在 clinicaltrials.gov 上注册为 NCT00427193。
