Elizabeth F. Sutton

Developmental programming : State-of-the-science and future directions: Summary from a Pennington Biomedical symposium

On December 8–9, 2014, the Pennington Biomedical Research Center convened a scientific symposium to review the state‐of‐the‐science and future directions for the study of developmental programming of obesity and chronic disease. The objectives of the symposium were to discuss: (i) past and current scientific advances in animal models, population‐based cohort studies, and human clinical trials, (ii) the state‐of‐the‐science of epigenetic‐based research, and (iii) considerations for future studies.

Smartphone applications to aid weight loss and management: current perspectives

The development and dissemination of smart devices has cultivated a global environment of hyperconnectivity and increased our access to information. The paralleled launch and success of the Mobile Health industry has created a market of commercially available applications or “apps” along with tools or sensors, which allow the user to receive and collect personal health information. Apps and accompanying tools now allow an individual to “self-digitize” and, pertaining to weight management, monitor their body weight, caloric intake, physical activity, and more. These products possess the ability to improve the scalability of traditional in-person weight management services considering their near ubiquity, affordability, and capability to deliver information directly and personally to the user. However, similar to the dietary supplement market, the anecdotal value of these products has driven their popularity and acceptance by the general public without requirement of scientific validation or, in the area of weight management or diet/exercise, validation of the safety and efficacy by the Food and Drug Administration prior to market launch. By conducting a literature and clinical trial search, we found remarkably few active, completed, or published studies testing the efficacy of smart device applications using randomized controlled trials. Research efforts must be focused on illuminating the efficacy of behavioral interventions and remote self-monitoring for weight loss/maintenance treatment with true, randomized controlled trials.

No evidence for metabolic adaptation in thermic effect of food by dietary protein

Determine whether prolonged consumption of high‐ or low‐protein diets modifies the thermogenic response to a standard meal.

Deep Brain Stimulation of the Hypothalamus Leads to Increased Metabolic Rate in Refractory Obesity

OBJECTIVE Obesity has become a worldwide epidemic, with very few long-term successful treatment options for refractory disease. Deep brain stimulation (DBS) of the bilateral lateral hypothalamus (LH) in refractory obesity has been performed safely. However, questions remain regarding the optimal settings and its effects on metabolic rate. The goals of our experiment were to determine the optimal DBS settings and the actual effect of optimal stimulation on energy expenditure. METHODS After bilateral LH DBS implantation, 2 subjects with treatment refractory obesity underwent 4 days of metabolic testing. The subjects slept overnight in a respiratory chamber to measure their baseline sleep energy expenditure, followed by 4 consecutive days of resting metabolic rate (RMR) testing at different stimulation settings. On day 4, the optimized DBS settings were used, and sleep energy expenditure was measured again overnight in the room calorimeter. RESULTS During daily testing, the RMR fluctuated acutely with changes in stimulation settings and returned to baseline immediately after turning off the stimulation. Optimal stimulation settings selected for participants showed a 20% and 16% increase in RMR for the 2 participants. Overnight sleep energy expenditure measurements at these optimized settings on day 4 yielded a 10.4% and 4.8% increase over the baseline measurements for the 2 participants. CONCLUSIONS These findings have demonstrated the efficacy of optimized DBS of the LH on increasing the RMR acutely and maintaining this increase during overnight sleep. These promising preliminary findings have laid the groundwork for the possible treatment of refractory obesity with DBS.

Plasma Amino Acids During 8 Weeks of Overfeeding: Relation to Diet Body Composition and Fat Cell Size in the PROOF Study: Amino Acids During Overfeeding

Different amounts of dietary protein during overfeeding produced similar fat gain but different amounts of gain in fat‐free body mass. Protein and energy intake may have differential effects on amino acids during overfeeding.

Assessing Energy Requirements in Women With Polycystic Ovary Syndrome: A Comparison Against Doubly Labeled Water

Context Weight loss is prescribed to offset the deleterious consequences of polycystic ovary syndrome (PCOS), but a successful intervention requires an accurate assessment of energy requirements. Objective Describe energy requirements in women with PCOS and evaluate common prediction equations compared with doubly labeled water (DLW). Design Cross-sectional study. Setting Academic research center. Participants Twenty-eight weight-stable women with PCOS completed a 14-day DLW study along with measures of body composition and resting metabolic rate and assessment of physical activity by accelerometry. Main Outcome Total daily energy expenditure (TDEE) determined by DLW. Results TDEE was 2661 ± 373 kcal/d. TDEE estimated from four commonly used equations was within 4% to 6% of the TDEE measured by DLW. Hyperinsulinemia (fasting insulin and homeostatic model assessment of insulin resistance) was associated with TDEE estimates from all prediction equations (both r = 0.45; P = 0.02) but was not a significant covariate in a model that predicts TDEE. Similarly, hyperandrogenemia (total testosterone, free androgen index, and dehydroepiandrosterone sulfate) was not associated with TDEE. In weight-stable women with PCOS, the following equation derived from DLW can be used to determine energy requirements: TDEE (kcal/d) = 438 - [1.6 * Fat Mass (kg)] + [35.1 * Fat-Free Mass (kg)] + [16.2 * Age (y)]; R2 = 0.41; P = 0.005. Conclusions Established equations using weight, height, and age performed well for predicting energy requirements in weight-stable women with PCOS, but more precise estimates require an accurate assessment of physical activity. Our equation derived from DLW data, which incorporates habitual physical activity, can also be used in women with PCOS; however, additional studies are needed for model validation.