Metabolic impact of protein feeding prior to moderate-intensity treadmill exercise in a fasted state: a pilot study

Background Augmenting fat oxidation is a primary goal of fitness enthusiasts and individuals desiring to improve their body composition. Performing aerobic exercise while fasted continues to be a popular strategy to achieve this outcome, yet little research has examined how nutritional manipulations influence energy expenditure and/or fat oxidation during and after exercise. Initial research has indicated that pre-exercise protein feeding may facilitate fat oxidation while minimizing protein degradation during exercise, but more research is needed to determine if the source of protein further influences such outcomes. Methods Eleven healthy, college-aged males (23.5 ± 2.1 years, 86.0 ± 15.6 kg, 184 ± 10.3 cm, 19.7 ± 4.4%fat) completed four testing sessions in a randomized, counter-balanced, crossover fashion after observing an 8–10 h fast. During each visit, baseline substrate oxidation and resting energy expenditure (REE) were assessed via indirect calorimetry. Participants ingested isovolumetric, solutions containing 25 g of whey protein isolate (WPI), 25 g of casein protein (CAS), 25 g of maltodextrin (MAL), or non-caloric control (CON). After 30 min, participants performed 30 min of treadmill exercise at 55–60% heart rate reserve. Substrate oxidation and energy expenditure were re-assessed during exercise and 15 min after exercise. Results Delta scores comparing the change in REE were normalized to body mass and a significant group x time interaction (p = 0.002) was found. Post-hoc comparisons indicated the within-group changes in REE following consumption of WPI (3.41 ± 1.63 kcal/kg) and CAS (3.39 ± 0.82 kcal/kg) were significantly greater (p \u3c 0.05) than following consumption of MAL (1.57 ± 0.99 kcal/kg) and tended to be greater than the non-caloric control group (2.00 ± 1.91 kcal/kg, p = 0.055 vs. WPI and p = 0.061 vs. CAS). Respiratory exchange ratio following consumption of WPI and CAS significantly decreased during the post exercise period while no change was observed for the other groups. Fat oxidation during exercise was calculated and increased in all groups throughout exercise. CAS was found to oxidize significantly more fat (p \u3c 0.05) than WPI during minutes 10–15 (CAS: 2.28 ± 0.38 g; WPI: 1.7 ± 0.60 g) and 25–30 (CAS: 3.03 ± 0.55 g; WPI: 2.24 ± 0.50 g) of the exercise bout. Conclusions Protein consumption before fasted moderate-intensity treadmill exercise significantly increased post-exercise energy expenditure compared to maltodextrin ingestion and tended to be greater than control. Post-exercise fat oxidation was improved following protein ingestion. Throughout exercise, fasting (control) did not yield more fat oxidation versus carbohydrate or protein, while casein protein allowed for more fat oxidation than whey. These results indicate rates of energy expenditure and fat oxidation can be modulated after CAS protein consumption prior to moderate-intensity cardiovascular exercise and that fasting did not lead to more fat oxidation during or after exercise

Determining a Resting Metabolic Rate Prediction Equation for Collegiate Female Athletes

A lack of evidence exists regarding the accuracy of common resting metabolic rate (RMR) prediction equations in athletic female populations. The purpose of this research was to measure RMR in a large cohort of NCAA Division II female athletes and use regression techniques to develop new prediction equations. Sixty-six female athletes from 11 different sports completed this protocol, which included skinfold measurements followed by an RMR assessment using indirect calorimetry. The average RMR was 1,466 ± 150 kcal·d−1. Many between-sport differences in body composition were identified, with gymnastics athletes having the lowest body fat percentage (p \u3c 0.05) and basketball athletes having the greatest absolute fat-free mass (p \u3c 0.05). Resting metabolic rate was moderately correlated (p \u3c 0.05) with height (r = 0.52), total mass (r = 0.59), and fat-free mass (r = 0.54). Two equations were developed, both of which were more accurate for this population than other RMR prediction equations. One of the new equations, which used height and body mass as covariates (equation 1), was slightly more accurate than the equation using body composition parameters (equation 2). The new equations were cross-validated using a randomly selected subset (n = 22) of the original sample. The subset did not show statistically different results from the remainder of the sample (n = 44) between equation 1 (p = 0.083) and equation 2 (p = 0.22). Equation 1, which had more easily measurable parameters, exhibited heightened accuracy, which has important implications for implementation among athletes, coaches, and athletic support staff