Lifestyle intervention can successfully induce weight loss in obese persons, at least temporarily. However, there currently is no way to quantitatively estimate the changes of diet or physical activity required to prevent weight regain. Such a tool would be helpful for goal-setting, because obese patients and their physicians could assess at the outset of an intervention whether long-term adherence to the calculated lifestyle change is realistic.
We aimed to calculate the expected change of steady-state body weight arising from a given change in dietary energy intake and, conversely, to calculate the modification of energy intake required to maintain a particular body-weight change.
We developed a mathematical model using data from 8 longitudinal weight-loss studies representing 157 subjects with initial body weights ranging from 68 to 160 kg and stable weight losses between 7 and 54 kg.
Model calculations closely matched the change data (R(2) = 0.83, chi(2) = 2.1, P < 0.01 for weight changes; R(2) = 0.91, chi(2) = 0.87, P < 0.0004 for energy intake changes). Our model performed significantly better than the previous models for which chi(2) values were 10-fold those of our model. The model also accurately predicted the proportion of weight change resulting from the loss of body fat (R(2) = 0.90).
Our model provides realistic calculations of body-weight change and of the dietary modifications required for weight-loss maintenance. Because the model was implemented by using standard spreadsheet software, it can be widely used by physicians and weight-management professionals.
"The model provided the first realistic calculations of body weight and composition change as well as the dietary modifications required for weight loss maintenance. Importantly, the model was implemented using standard spreadsheet software and can therefore be widely used by physicians and weight management professionals . "
[Show abstract][Hide abstract] ABSTRACT: The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a "middle-out" strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from "-omics" signatures are identified as key elements of a successful systems biology modeling approach in nutrition research--one that integrates physiological mechanisms and data at multiple space and time scales.
[Show abstract][Hide abstract] ABSTRACT: Taxing sugar-sweetened beverages has been proposed as a means to reduce calorie intake, improve diet and health, and generate revenue that governments can use to address the obesity-caused health and economic burden. Two beverage demand systems were estimated using beverage purchase data for high-income and low-income households. Using the estimated demand elasticities we examined the impacts of a hypothetical 20-percent effective tax rate (or about 0.5 cent per ounce) on beverage consumption, calorie intake, tax revenue and burden. Our results suggest that such a tax would induce an average reduction of 35 and 41 calories a day among adults and children, respectively. The tax burden is found to be regressive, although representing less than one percent of household spending on food and beverages. Tax revenue is estimated to be $5.8 billion using 2007 population estimates.
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