Background To verify (1) the association between classic and specific bioelectrical impedance vector analysis (BIVA) with body composition, hydration, and physical performance in older adults with and without sarcopenia; (2) which BIVA most accurately distinguishes sarcopenia. Methods A sample of 94 older adults with and without sarcopenia (29 men and 65 women, 60–85 years) was evaluated. The classic and specific BIVA procedures, Dual energy X-ray absorptiometry (DXA), and deuterium dilution were performed. Sarcopenia was defined by muscle weakness and low skeletal muscle index, while severity was indicated by low physical performance. Results The BIVA's potential to monitor hydration and muscle mass loss in older adults seems feasible. Classic and specific BIVA were able to distinguish sarcopenia in women (p < 0.001), but not in men. When the sarcopenia criteria were individually analyzed, both classic and specific BIVA were able to distinguish low skeletal muscle index in women, while only classic BIVA did for men. For the criterion of slow physical performance, only the classic BIVA showed severity differences for women. The vectors of adults without sarcopenia of both sexes tended to be positioned in the left region of the ellipses, revealing a predominance of soft tissues. Conclusions Classic BIVA has a distinct sarcopenic association with body composition, hydration, and physical performance in older adults, while specific BIVA was similar between groups. Both BIVAs are sensible to detect female morphological changes (skeletal muscle index) but not for functional (handgrip, 6-min walk test) sarcopenia criteria. These procedures are promising tools for monitoring sarcopenia risks during aging.

Background During aging, changes occur in the proportions of muscle, fat, and bone. Body composition (BC) alterations have a great impact on health, quality of life, and functional capacity. Several equations to predict BC using anthropometric measurements have been developed from a bi-compartmental (2-C) approach that determines only fat mass (FM) and fat-free mass (FFM). However, these models have several limitations, when considering constant density, progressive bone demineralization, and changes in the hydration of the FFM, as typical changes during senescence. Thus, the main purpose of this study was to propose and validate a new multi-compartmental anthropometric model to predict fat, bone, and musculature components in older adults of both sexes. Methods This cross-sectional study included 100 older adults of both sexes. To determine the dependent variables (fat mass [FM], bone mineral content [BMC], and appendicular lean soft tissue [ALST]) whole total and regional dual-energy X-ray absorptiometry (DXA) body scans were performed. Twenty-nine anthropometric measures and sex were appointed as independent variables. Models were developed through multivariate linear regression. Finally, the predicted residual error sum of squares (PRESS) statistic was used to measure the effectiveness of the predicted value for each dependent variable. Results An equation was developed to simultaneously predict FM, BMC, and ALST from only four variables: weight, half-arm span (HAS), triceps skinfold (TriSK), and sex. This model showed high coefficients of determination and low estimation errors (FM: R2adj: 0.83 and SEE: 3.16; BMC: R2adj: 0.61 and SEE: 0.30; ALST: R2adj: 0.85 and SEE: 1.65). Conclusion The equations provide a reliable, practical, and low-cost instrument to monitor changes in body components during the aging process. The internal cross-validation method PRESS presented sufficient reliability in the model as an inexpensive alternative for clinical field use.

Background: During aging, changes occur in the proportions of muscle, fat, and bone. The body composition (BC) alterations have a great impact on health, quality of life, and functional capacity. Several equations to predict BC using anthropometric measurements have been developed from a bi-compartmental (2-C) approach that determines only fat mass (FM) and fat-free mass (FFM). However, these models have several limitations, when considering constant density, progressive bone demineralization, and changes in the hydration of the FFM, as typical changes during senescence. Thus, the main purpose of this study was to propose and validate a new multi-compartmental anthropometric model to predict fat, bone, and musculature components in older adults of both sexes.Methods: This cross-sectional study included 100 older adults of both sexes. To determine the dependent variables (fat mass [FM], bone mineral content [BMC], and appendicular lean soft tissue [ALST]) whole total and regional DXA body scans were performed. Twenty-nine anthropometric measures and sex were appointed as independent variables. Models were developed through multivariate linear regression. Finally, the predicted residual error sum of squares (PRESS) statistic was used to measure the effectiveness of the predicted value for each dependent variable.Results: An equation was developed to simultaneously predict FM, BMC, and ALST from only four variables: weight, half arm span (HAS), triceps skinfold (TriSK), and sex. This model showed high coefficients of determination and low estimation errors (FM: R2adj: 0.83 and SEE: 3.16; BMC: R2adj: 0.61 and SEE: 0.30; ALST: R2adj: 0.85 and SEE: 1.65). Conclusion: The equations provide a reliable, practical, and low-cost instrument to monitor changes in body components during the aging process. The internal cross-validation method PRESS presented sufficient reliability in the model as an inexpensive alternative for clinical field use.