Search Results

Low-carbohydrate and very-low-carbohydrate diets are often used as weight-loss strategies by exercising individuals and athletes. Very-low-carbohydrate diets can lead to a state of ketosis, in which the concentration of blood ketones (acetoacetate, 3-β-hydroxybutyrate, and acetone) increases as a result of increased fatty acid breakdown and activity of ketogenic enzymes. A potential concern of these ketogenic diets, as with other weight-loss diets, is the potential loss of fat-free mass (e.g., skeletal muscle). On examination of the literature, the majority of studies report decreases in fat-free mass in individuals following a ketogenic diet. However, some confounding factors exist, such as the use of aggressive weight-loss diets and potential concerns with fat-free mass measurement. A limited number of studies have examined combining resistance training with ketogenic diets, and further research is needed to determine whether resistance training can effectively slow or stop the loss of fat-free mass typically seen in individuals following a ketogenic diet. Mechanisms underlying the effects of a ketogenic diet on fat-free mass and the results of implementing exercise interventions in combination with this diet should also be examined.

The purpose of this investigation was to evaluate three bioelectrical impedance analysis (BIA) prediction models for fat-free mass (FFM) using the U.S. National Women’s Gymnastics team (N = 48; age = 15.8 ± 1.8 years). One model had been developed recently using dual-energy x-ray absorptiometry (DEXA) as the criterion measure, whereas the other two used hydrodensitometry. In this investigation, FFM predictions were compared with measures obtained via DEXA. FFM measured by DEXA averaged 40.5 ± 7.4 kg (± SD), whereas values generated using the three BIA models were within 0.8 kg of this actual measure. Validity coefficients for all models were high (Rxy = .95-98). FFM prediction error was lowest with the model using DEXA as the criterion measure (1.3 kg) compared with the other two (1.9 and 2.4 kg). All BIA models underpredicted FFM in the heaviest girls, and the Lohman and Van Loan et al. models overpredicted FFM in the lightest girls. Whereas prediction error was significantly correlated to the girls’ bone mineral density in all BIA models, this relationship was strongest in the two that were developed using hydrodensitometry.

The purpose of this study was to determine whether overfeeding and high-intensity physical training increase organ mass. We examined this question using cross-sectional and longitudinal studies in which we measured collegiate male American football players. Freshman (n = 10) and senior players in their second and third years of college (n = 17) participated in the cross-sectional study. The same measurements of the same freshman players (n = 10) were assessed after the one-year weight gain period in the longitudinal study. Fat-free mass (FFM), skeletal muscle, and adipose tissue mass were obtained using dual-energy X-ray absorptiometry. Liver, kidney, brain, and heart volumes were calculated using magnetic resonance imaging or echocardiography. Compared with the freshman players, the senior players had 10.8 kg more FFM, and 0.29 kg, 0.08 kg, and 0.09 kg greater liver, heart, and kidney mass, respectively. In the longitudinal study, FFM, liver, heart, and kidney mass of the freshman players increased by 5.2 kg, 0.2 kg, 0.04 kg, and 0.04 kg, respectively, after one year of overfeeding and physical training. On the other hand, the organ-tissue mass to FFM ratio did not change, except for the brain, in either the cross-sectional or longitudinal studies. Our results indicated that the organtissue masses increased with overfeeding and physical training in male collegiate American football players.

Body composition assessment techniques provide estimates of percent body fat (%BF), fat mass (FM), and fat-free mass (FFM) based on indirect assessment models and methods. Prediction equations for %BF developed using a two-component model based on adult body composition constants will overestimate %BF in youths, especially prepubescent youths. Body composition prediction equations that have been validated and cross-validated using multiple-component criterion models which include measurements of body density and the water and mineral components of FFM provide the most accurate means for assessment of body composition in youths. Use of appropriate prediction equations and proper measurement techniques, for either bioelectrical impedance or skinfolds, results in body composition estimates with standard errors of estimate (prediction errors) of 3 to 4% BF and 2.0 to 2.5 kg of FFM. Poor measurement technique and inappropriate prediction equations will result in much larger prediction errors.