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首页> 外文期刊>The British Journal of Nutrition >Body fat and fat-free mass inter-relationships: Forbes's theory revisited.
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Body fat and fat-free mass inter-relationships: Forbes's theory revisited.

机译:体内脂肪与无脂肪物质之间的相互关系:重新审视《福布斯》的理论。

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A theoretical equation was developed by Forbes that quantifies the fat-free proportion of a weight change as a function of the initial body fat. However, Forbes's equation was strictly valid only for infinitesimal weight changes. Here, I extended Forbes's equation to account for the magnitude and direction of macroscopic body weight changes. The new equation was also re-expressed in terms of an alternative representation of body composition change defined by an energy partitioning parameter called the P-ratio. The predictions of the resulting equations compared favourably with data from human underfeeding and overfeeding experiments and accounted for previously unexplained trends in the data. The magnitude of the body weight change had a relatively weak effect on the predicted body composition changes and the results were very similar to Forbes's original equation for modest weight changes. However, for large weight changes, such as the massive weight losses found in patients following bariatric surgery, Forbes's original equation consistently underestimated the fat-free mass loss, as expected. The new equation that accounts for the magnitude of the weight loss provides better predictions of body composition changes in such patients.
机译:福布斯(Forbes)开发了一个理论方程,该方程量化了体重变化的无脂肪比例作为初始体内脂肪的函数。但是,福布斯方程仅对无限微小的重量变化严格有效。在这里,我扩展了福布斯方程,以说明宏观体重变化的幅度和方向。新的方程式也根据能量分配参数P比率定义的身体成分变化的替代表示重新表达。结果方程的预测与人类食物不足和食物过量实验的数据相比具有优势,并解释了数据中先前无法解释的趋势。体重变化的幅度对预测的身体成分变化影响相对较小,其结果与福布斯关于适度体重变化的原始方程式非常相似。但是,对于较大的体重变化,例如减肥手术后患者的大量体重减轻,福布斯的原始方程式一直低估了无脂肪的体重减轻,这与预期的一样。解释体重减轻幅度的新方程式可以更好地预测此类患者的身体成分变化。

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