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Competing models of obesity and its treatment often contrast the relative roles of dietary fat versus carbohydrate. Advocates of low-carbohydrate diets posit that intake of high glycemic carbohydrates leads to elevated postprandial insulin thereby promoting body fat accumulation while increasing hunger and energy intake according to the carbohydrate-insulin model of obesity. Alternatively, proponents of low-fat diets argue that high fat intake promotes body fat storage due to passive overconsumption of energy resulting from the high energy density of dietary fat. To test these competing models, 20 adults without diabetes aged (mean±SE) 29.9±1.4 y with BMI=27.8±1.3 kg/m2 were admitted as inpatients to the NIH Clinical Center and randomized to consume ad libitum either a plant-based, low-fat (PBLF) diet (75.2% carbohydrate, 10.3% fat, non-beverage energy density = 1.1 kcal/g) or an animal-based, ketogenic, low-carbohydrate (ABLC) diet (75.8% fat,10.0% carbohydrate, non-beverage energy density = 2.2 kcal/g) for two weeks followed immediately by the alternate diet for two weeks. Three daily meals plus snacks amounting to twice each subject’s estimated energy requirements were provided and subjects were instructed to eat as much or as little as desired. The PBLF diet resulted in substantially greater glucose and insulin levels whereas the ABLC diet led to increased blood ketones of ~3 mM which is thought to suppress appetite. However, ad libitum energy intake was 689±73 kcal/d lower during the PBLF diet as compared to the ABLC diet (p

OBJECTIVES: To describe the effects of ad libitum low-fat (LF) and low-carbohydrate (LC) diets on body weight and fat mass. METHODS: Sixteen adults without diabetes spent 29 continuous days residing at the Metabolic Clinical Research Unit of the NIH Clinical Center where they were fed ad libitum either an animal-based, LC diet (75% fat, 10% carbohydrates, 15% protein) or a plant-based, LF diet (75% carbohydrates, 10% fat, 15% protein). Participants were randomly assigned to one diet for the first phase of the study (14 days), after which they were switched to the other diet for the remainder of the study. Participants were given three meals daily and were provided with additional snacks amounting to 200% of their daily energy requirements as determined by their resting energy expenditure multiplied by 1.6. Subjects were told that this was not a weight loss study and were not informed about the primary study aim. They were instructed to eat as much or as little as they desired. Total body weight and fat mass were measured using a calibrated scale and dual-energy X-ray absorptiometry, respectively. Subjects were blinded to their data and wore loose-fitting scrubs to avoid any feedback regarding changes in the fit of their clothing. RESULTS: Subjects included 7 women and 9 men, with an age of (mean ± SE) 29 ± 1.7 years and BMI of 27.5 ± 1.5 at baseline. Participants lost weight on both diets, with the LC diet resulting in 1.34 ± 0.31 kg of weight loss (P = 0.0006) and the LF diet resulting in 1.09 ± 0.31 kg of weight loss (P = 0.003) which was not significantly different from the LC diet (P = 0.58). However, participants lost 0.6 ± 0.17 kg of body fat on the LF diet (P = 0.002) but the LC diet did not result in significant body fat loss (0.04 ± 0.17 kg; P = 0.8) and the difference in body fat loss between the diets was statistically significant (P = 0.03). CONCLUSIONS: While participants lost similar amounts of weight on both diets, only the LF diet led to significant body fat loss. Early weight loss with a LC diet does not necessarily reflect a similar state of negative energy balance as compared with a LF diet. FUNDING SOURCES: Intramural Research Program of the National Institutes of Diabetes and Digestive and Kidney Diseases.

In the face of stiff competition, many organizations turn to business intelligence tools to successfully compete in the marketplace. However, successfully implementing and growing a business intelligence solution to combat market pressure is an arduous task. The stages of growth models present one approach that could guide organizations in the implementation and growth of successful business intelligence (BI) efforts. At Monster.com, upper management chose to design and implement a business intelligence framework to compete in the online job search arena. While the progressive steps that led to Monster’s successful BI solution did not strictly follow established BI stage models, it helped the company effectively steer clear of competition and remain a major player in the online job search marketplace.