Supplementation with whey protein, essential amino acids, and vitamin D, in conjunction with age-appropriate exercise, not only boosts fat-free mass and strength but also enhances other aspects that contribute to well-being in sarcopenic elderly. This trial was registered at clinicaltrials.gov as NCT02402608.
Body composition studies, when based on two-compartment volumetric estimates, can hardly assess nutritional states. Phase-sensitive impedance analysis can be used to reflect directly the proportions between intra- and extracellular spaces (ECM/BCM), which is one of the most sensitive indexes of malnutrition. Resistance and reactance values actually measured with BIA are referred to as the series RC model; however, due to the morphology of the FFM, which is composed of cells surrounded by interstitial fluids, in reality this should be modeled as a parallel RC circuit. A nomogram developed with series-to-parallel transformations of resistance and reactance measured with commercial BIA on controls and patients shows interesting gender and disease sensitivity and specificity.
Background: Bioimpedance vector analysis (BIVA) is a widely used method based on the interpretation of raw bioimpedance parameters to evaluate body composition and cellular health in athletes. However, several variables contribute to influencing BIVA patterns by militating against an optimal interpretation of the data. This study aims to explore the association of morphological characteristics with bioelectrical properties in volleyball, soccer, and rugby players. Methods: 164 athletes belonging to professional teams (age 26.2 ± 4.4 yrs; body mass index (BMI) 25.4 ± 2.4 kg/m2) underwent bioimpedance and anthropometric measurements. Bioelectric resistance (R) and reactance (Xc) were standardized for the athlete’s height and used to plot the vector in the R-Xc graph according to the BIVA approach. Total body water (TBW), phase angle (PhA), and somatotype were determined from bioelectrical and anthropometric data. Results: No significant difference (p > 0.05) for age and for age at the start of competition among the athletes was found. Athletes divided into groups of TBW limited by quartiles showed significant differences in the mean vector position in the R-Xc graph (p < 0.001), where a higher content of body fluids resulted in a shorter vector and lower positioning in the graph. Furthermore, six categories of somatotypes were identified, and the results of bivariate and partial correlation analysis highlighted a direct association between PhA and mesomorphy (r = 0.401, p < 0.001) while showing an inverse correlation with ectomorphy (r = −0.416, p < 0.001), even adjusted for age. On the contrary, no association was observed between PhA and endomorphy (r = 0.100, p = 0.471). Conclusions: Body fluid content affects the vector length in the R-Xc graph. In addition, the lateral displacement of the vector, which determines the PhA, can be modified by the morphological characteristics of the athlete. In particular, higher PhA values are observed in subjects with a high mesomorphic component, whereas lower values are found when ectomorphy is dominant.
Background: Bioelectrical impedance vector analysis (BIVA) is a body composition assessment method based on the interpretation of the raw bioimpedance parameters. While it was initially proposed in clinical settings, its use in the sports field has grown considerably. The aim of this study was: (i) to explore the role of somatotype on BIVA patterns and (ii) to propose a new target zone to improve BIVA analysis in ball games athletes. Methods: One hundred and sixty-four male volleyball, soccer, and rugby players (age 26.2 ± 4.4 yrs; body mass index (BMI) 25.4 ± 2.4 kg/m2) were included in this study. Somatotype and BIVA were measured from anthropometric and bioelectrical data, respectively. Results: Forty-six athletes were classified with an endomorphic mesomorphic somatotype, 26 showed a balanced mesomorphy, 55 were ectomorphic mesomorph, 10 resulted as mesomorph ectomorphs, 13 with a mesomorphic ectomorph somatotype, and in 14 athletes a balanced ectomorphy was assessed. The results of the Hotelling’s T2 test showed significant differences in BIVA patterns for the endomorphic mesomorph group (p < 0.001) in comparison with all the other groups, while mesomorphic balanced athletes presented a more inclined vector compared to the athletes with a balanced ectomorphy (p < 0.003). In addition, the endomorphic mesomorph group showed a greater BMI (p < 0.001) with respect to the athletes grouped in the other somatotype categories. Discriminant analysis revealed two significant functions (p < 0.001). The first discriminant function primarily represented differences based on the bioelectrical standardized resistance parameter (R/H) measure, while the second function reflected differences based on the bioelectrical standardized reactance parameter (Xc/H). Conclusions: Athletes presenting a higher endomorphic component have a lower vector, whereas those with a larger mesomorphic component display higher vector inclinations on the R-Xc graph. We propose a new target zone to improve the interpretation of BIVA analysis in athletes engaged in team sports.
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