Department of Biostatistics and Bioinformatics, Duke University Medical Center, Center on Aging, DUMC, Durham, NC, USA.
Clin Trials. 2011 Apr;8(2):155-64. doi: 10.1177/1740774511398369. Epub 2011 Mar 8.
objective measures are needed to quantify dietary adherence during caloric restriction (CR) while participants are freeliving. One method to monitor adherence is to compare observed weight loss to the expected weight loss during a prescribed level of CR. Normograms (graphs) of expected weight loss can be created from mathematical modeling of weight change to a given level of CR, conditional on the individual's set of baseline characteristics. These normograms can then be used by counselors to help the participant adhere to their caloric target.
(1) To develop models of weight loss over a year of caloric restriction-given demographics, and well-defined measurements of body mass index, total daily energy expenditure (TDEE) and %CR. (2) To utilize these models to develop normograms, given the level of caloric restriction prescribed, and measures of these variables.
Seventy-seven individuals completing a 6-12-month caloric restriction intervention (CALERIE) at three sites (Pennington Biomedical Research Center, Tufts University, and Washington University) and had body weight and body composition measured frequently. Energy intake (and %CR) was estimated from TDEE (by doubly labeled water) and body composition (by DXA) at baseline and months 1, 3, 6, and 12. Bodyweight was modeled to determine the predictors and distribution of the expected trajectory of percent weight change over 12 months of CR.
As expected, CR was related to change in body weight. Controlling for time-varying measures, initially simple models of the functional form indicated that the trajectory of percent weight change was predicted by a nonlinear function of age, TDEE, %CR, and sex. Using these estimates, normograms for the weight change were developed. Our model estimates that the mean weight loss (% change from baseline weight) for an individual adherent to a 25% CR regimen is -10.9 ± 6.3% for females and -13.9 + 6.4% for men after 12 months.
There are several limitations. Sample sizes are small (n = 77), and, by design, the protocols, including prescribed CR, for the interventions differed by site, and not all subjects completed a year of follow-up. In addition, the inclusion of subjects by age and initial BMI was constricted, so that these results may not generalize to other populations including older and obese subjects.
The trajectory of percent weight change during CR interventions in the presence of well-measured covariates can be modeled using simple nonlinear functions, and is related level of CR, the percent change in TDEE, gender, and age. Displayed on a normogram, individually tailored trajectories can be used by counselors and participants to monitor weight loss and adherence to a CR regimen.
在热量限制(CR)期间,需要客观的措施来量化饮食依从性,而参与者是自由生活的。一种监测依从性的方法是将观察到的体重减轻与规定的 CR 水平下预期的体重减轻进行比较。可以通过对体重变化到给定的 CR 水平进行数学建模来创建预期体重减轻的图表(图表),这取决于个体的一组基线特征。这些图表可以由顾问用来帮助参与者遵守他们的卡路里目标。
(1)建立一年热量限制的体重减轻模型——考虑到人口统计学因素,以及对体重指数、总每日能量消耗(TDEE)和%CR 的明确测量。(2)利用这些模型,根据规定的热量限制水平和这些变量的测量值,制定图表。
77 名参与者在三个地点(彭宁顿生物医学研究中心、塔夫茨大学和华盛顿大学)完成了为期 6-12 个月的热量限制干预(CALERIE),并经常测量体重和身体成分。能量摄入(和%CR)是根据 TDEE(通过双标记水)和身体成分(通过 DXA)在基线和第 1、3、6 和 12 个月测量得出的。对体重进行建模,以确定 12 个月 CR 期间预期体重变化轨迹的预测因素和分布。
正如预期的那样,CR 与体重变化有关。控制随时间变化的措施,最初简单的函数形式模型表明,体重百分比变化的轨迹是由年龄、TDEE、%CR 和性别非线性函数预测的。使用这些估计值,开发了体重变化的图表。我们的模型估计,对于坚持 25%CR 方案的个体,在 12 个月后,女性的平均体重减轻(与基线体重相比的百分比变化)为-10.9±6.3%,男性为-13.9+6.4%。
有几个局限性。样本量较小(n=77),并且,根据设计,干预措施的方案,包括规定的 CR,因地点而异,并非所有受试者都完成了一年的随访。此外,包括的受试者按年龄和初始 BMI 进行限制,因此这些结果可能不适用于包括老年人和肥胖者在内的其他人群。
在有良好测量协变量的情况下,CR 干预期间的体重百分比变化轨迹可以使用简单的非线性函数进行建模,并且与 CR 水平、TDEE 变化的百分比、性别和年龄有关。显示在图表上,个性化的轨迹可以由顾问和参与者用来监测体重减轻和遵守 CR 方案。