Beheshti Rahmatollah, Treesukosol Yada, Igusa Takeru, Moran Timothy H
Johns Hopkins Global Obesity Prevention Center , Baltimore, Maryland.
Johns Hopkins Bloomberg School of Public Health , Baltimore, Maryland.
Am J Physiol Regul Integr Comp Physiol. 2018 Aug 1;315(2):R256-R266. doi: 10.1152/ajpregu.00337.2017. Epub 2018 Jan 17.
Easy access to high-energy food has been linked to high rates of obesity in the world. Understanding the way that access to palatable (high fat or high calorie) food can lead to overconsumption is essential for both preventing and treating obesity. Although the body of studies focused on the effects of high-energy diets is growing, our understanding of how different factors contribute to food choices is not complete. In this study, we present a mathematical model that can predict rat calorie intake to a high-energy diet based on their ingestive behavior to a standard chow diet. Specifically, we propose an equation that describes the relation between the body weight ( W), energy density ( E), time elapsed from the start of diet ( T), and daily calorie intake ( C). We tested our model on two independent data sets. Our results show that the suggested model can predict the calorie intake patterns with high accuracy. Additionally, the only free parameter of our proposed equation (ρ), which is unique to each animal, has a strong correlation with their calorie intake.
在全球范围内，容易获取高能量食物与肥胖率居高不下有关。了解获取美味（高脂肪或高热量）食物会导致过度消费的方式，对于预防和治疗肥胖症至关重要。尽管专注于高能量饮食影响的研究数量在不断增加，但我们对不同因素如何影响食物选择的理解仍不完整。在本研究中，我们提出了一个数学模型，该模型可以根据大鼠对标准饲料的摄食行为来预测其对高能量饮食的卡路里摄入量。具体而言，我们提出了一个方程，该方程描述了体重（W）、能量密度（E）、饮食开始后经过的时间（T）和每日卡路里摄入量（C）之间的关系。我们在两个独立的数据集上测试了我们的模型。结果表明，所提出的模型能够高精度地预测卡路里摄入模式。此外，我们提出的方程中唯一的自由参数（ρ），它因动物个体而异，与它们的卡路里摄入量有很强的相关性。
