Bioelectrical impedance analysis for the prediction of fat-free mass in buffalo calf

The objective of this study has been to develop a prediction equation of fat-free mass (FFM) from buffalo calves. Twenty buffaloes were fed ad libitum access at unifeed, with vitamin-mineral integration, for 14 months. Seven days before slaughtering, the animals were weighed and bioelectrical impedance measurements were collected. The data were analyzed by multiple linear regressions to evaluate the relationship between FFM and various predictor variables. Stepwise regression was used to eliminate variables that did not influence variation in the model. The value of resistance collected showed a decrease when the electrical frequency increases, while the values of reactance (Xc) increase. When using live weight (LW) and reactance at 500 and at 1000 kHz as independent variables, we obtained the best R2 Adj (0.967) and Durbin Watson statistic (2.596) that explain the prediction model (FFM = - 30.59 + 0.993LW + 0.150Xc500 - 0.123Xc1000 + 9.11). These results indicate that the use of bioelectrical impedance analysis has excellent potential as a rapid method, with minimal perturbation for the animal, to predict FFM in buffalo.     

Magnitude and variation of fat-free mass density: a cellular-level body composition modeling study

The mean density of fat-free mass (FFM) is remarkably stable at 1.10 g/cm(3) in healthy adult humans, and this stability is a cornerstone of the widely applied densitometry-based two-compartment model for estimating total body fat. At present, the usual means of exploring FFM density is by in vitro or in vivo experimental studies. The purpose of the present investigation was to develop a cellular-level body composition model that includes seven factors that determine FFM density. The model, when applied with available empirical coefficients, predicted an FFM density similar to that observed in vivo. An analysis of the seven model components indicates that the ratio of extracellular solids to total body water is a major determinant of individual variation in FFM density. The difference in FFM density across sex, race, and age groups was examined with the developed model. The present study thus provides a conceptual framework for the systematic study of FFM density in humans.     

Magnitude and variation of ratio of total body potassium to fat-free mass: a cellular level modeling study

Potassium is an essential element of living organisms that is found almost exclusively in the intracellular fluid compartment. The assumed constant ratio of total body potassium (TBK) to fat-free mass (FFM) is a cornerstone of the TBK method of estimating total body fat. Although the TBK-to-FFM (TBK/FFM) ratio has been assumed constant, a large range of individual and group values is recognized. The purpose of the present study was to undertake a comprehensive analysis of biological factors that cause variation in the TBK/FFM ratio. A theoretical TBK/FFM model was developed on the cellular body composition level. This physiological model includes six factors that combine to produce the observed TBK/FFM ratio. The ratio magnitude and range, as well as the differences in the TBK/FFM ratio between men and women and variation with growth, were examined with the proposed model. The ratio of extracellular water to intracellular water (E/I) is the major factor leading to between-individual variation in the TBK/FFM ratio. The present study provides a conceptual framework for examining the separate TBK/FFM determinants and suggests important limitations of the TBK/FFM method used in estimating total body fat in humans and other mammals.     

Bioelectrical impedance vs. four-compartment model to assess body fat change in overweight adults

Objective: The Tanita TBF‐305 body fat analyzer is marketed for home and clinical use and is based on the principles of leg‐to‐leg bioelectrical impedance analysis (BIA). Few studies have investigated the ability of leg‐to‐leg BIA to detect change in percentage fat mass (%FM) over time. Our objective was to determine the ability of leg‐to‐leg BIA vs. the four‐compartment (4C) model to detect small changes in %FM in overweight adults. Research Methods and Procedures: Thirty‐eight overweight adults (BMI, 25.0 to 29.9 kg/m²; age, 18 to 44 years; 31 women) participated in a 6‐month, randomized, double‐blind, placebo‐controlled study of a nutritional supplement. Body composition was measured at 0 and 6 months using the Tanita TBF‐305 body fat analyzer [using equations derived by the manufacturer (%FM_T‐Man) and by Jebb et al. (%FM_T‐Jebb)] and the 4C model (%FM_4C). Results: Subjects in the experimental group lost 0.9%FM_4C (p = 0.03), a loss that did not reach significance using leg‐to‐leg BIA (0.6%FM_T‐Man, p = 0.151; 0.6%FM_T‐Jebb, p = 0.144). We observed large standard deviations (SDs) in the mean difference in %FM between the 4C model and the Tanita_Manufacturer (2.5%) and Tanita_Jebb (2.2%). Ten subjects fell outside ±1 SD of the mean differences at 0 and 6 months; those individuals were younger and shorter than those within ±1 SD. Discussion: Leg‐to‐leg BIA performed reasonably well in predicting decreases in %FM in this group of overweight adults but resulted in wide SDs vs. %FM_4C in individuals. Cross‐sectional determinations of %FM of overweight individuals using leg‐to‐leg BIA should be interpreted with caution.     

Metabolically active portion of fat-free mass: a cellular body composition level modeling analysis

The proportion of fat-free mass (FFM) as body cell mass (BCM) is highly related to whole body resting energy expenditure. However, the magnitude of BCM/FFM may have been underestimated in previous studies. This is because Moore's equation [BCM (kg) = 0.00833 x total body potassium (in mmol)], which was used to predict BCM, underestimates BCM by approximately 11%. The aims of the present study were to develop a theoretical BCM/FFM model at the cellular level and to explore the influences of sex, age, and adiposity on the BCM/FFM. Subjects were 112 adults who had the following measurements: total body water by (2)H(2)O or (3)H(2)O dilution; extracellular water by NaBr dilution; total body nitrogen by in vivo neutron activation analysis; and bone mineral by dual-energy X-ray absorptiometry. FFM was calculated using a multicomponent model and BCM as the difference between FFM and the sum of extracellular fluid and solids. The developed theoretical model revealed that the proportion of BCM to FFM is mainly determined by water distribution (i.e., E/I, the ratio of extracellular to intracellular water). A significant correlation (r = 0.90, P < 0.001) was present between measured and model-predicted BCM/FFM for all subjects pooled. Measured BCM/FFM [mean (SD)] was 0.584 +/- 0.041 and 0.529 +/- 0.041 for adult men and women (P < 0.001), respectively. A multiple linear regression model showed that there are independent significant associations of sex, age, and fat mass with BCM/FFM.     

Bioelectrical parameters of the whole human body obtained through bioelectrical impedance analysis

Knowledge of electrical properties of body tissues across the frequency spectrum is useful for tissue characterization. The bioelectric impedance analysis method, operating from 1 to 250 kHz (multi-frequency), was used in 23 normal male human subjects between the ages of 21 and 52 years, for estimation of their bioelectrical parameters. Amplitude of the output current was set to 800 microA(RMS). The experimental data showed that bioelectric parameters were highly dependent on frequency and the presence of a threshold frequency around 4 kHz. In order to explain the unusual features observed in our experimental data, the human body was simulated through the Cole-Fricke-Cole model (RC circuit) and the Extended Cole-Fricke-Cole model (RLC circuit). The simulated data showed that the Extended Cole-Fricke-Cole model had a higher accuracy than the traditional Cole-Fricke-Cole model. These results suggest that the unusual features could be due to the possible existence of inductive effects in biological cells and body tissues and that the inductive parameter and the threshold frequency could be used for characterizing the healthy tissues as well as the traditional bioelectric parameters.     

Bioimpedance spectroscopy for the estimation of fat-free mass in end-stage renal disease

BACKGROUND #ENTITYSTARTX00026; AIMS: Bioimpedance spectroscopy may provide reliable estimates of fat-free mass in end-stage renal disease patients. We aimed to evaluate the ability of bioimpedance spectroscopy to estimate fat-free mass in end-stage renal disease patients using dual-energy X-ray absorptiometry as a reference. METHODS: Fat-free mass measured by bioimpedance spectroscopy was compared to fat-free mass measured by dual-energy X-ray absorptiometry in 16 end-stage renal disease patients on hemodialysis, 12 undialysed end-stage renal disease patients and 23 control subjects. RESULTS: Methods were highly correlated for fat-free mass in all subject groups (r = 0.87, P < 0.001). Mean bioimpedance spectroscopy measures of fat-free mass were not different from the dual-energy X-ray absorptiometry measures in any subject group. Individual comparisons revealed wide limits of agreement between methods in hemodialysis (11.6 to -9.72 kg) and undialysed patients (10.95 to -14.73 kg). CONCLUSIONS: Although bioimpedance spectroscopy estimates of fat-free mass in the end-stage renal disease patient groups were not different from dual-energy X-ray absorptiometry and the methods were highly correlated, there was great individual variability. From these data it is clear that future studies are warranted before bioimpedance spectroscopy can be recommended as a valid clinical tool for assessing fat-free mass in end-stage renal disease patients.     

Uncertainty in allometric exponent estimation: a case study in scaling metabolic rate with body mass

Many factors could influence the allometric scaling exponent β estimation, but have not been explored systematically. We investigated the influences of three factors on the estimate of β based on a data set of 626 species of basal metabolic rate and mass in mammals. The influence of sampling error was tested by re-sampling with different sample sizes using a Monte Carlo method. Small random errors were introduced to measured data to examine their influence on parameter estimations. The influence of analysis method was also evaluated by applying nonlinear and linear regressions to the original data. Results showed that a relative large sample size was required to lower statistical inference errors. When sample size n was 10% of the base population size (n=63), 35% of the samples supported β=2/3, 39% supported β=3/4, and 15% rejected β=0.711, even though the base population had a β=0.711. The controversy surrounding the estimation of β in the literature could be partially attributable to such small sample sizes in many studies. Measurement errors in body mass and base metabolic rate, especially in body mass, could largely increase alpha and beta errors. Analysis methods also affected parameter estimations. Nonlinear regressions provided better estimates of the scaling exponent that were significantly higher than these commonly estimated by linear regressions. This study demonstrated the importance of the quantity and quality of data as well as analysis method in power law analysis, raising caution in interpreting power law results. Meta-data synthesis using data from independent studies seems to be a proper approach in the future, but caution should be taken to make sure that such measurements are made using similar protocols.     

Muscularity and the density of the fat-free mass in athletes.

The purpose of this study was to use estimates of body composition from a four-component model to determine whether the density of the fat-free mass (D(FFM)) is affected by muscularity or musculoskeletal development in a heterogenous group of athletes and nonathletes. Measures of body density by hydrostatic weighing, body water by deuterium dilution, bone mineral by whole body dual-energy X-ray absorptiometry (DXA), total body skeletal muscle estimated from DXA, and musculoskeletal development as measured by the mesomorphy rating from the Heath-Carter anthropometric somatotype were obtained in 111 collegiate athletes (67 men and 44 women) and 61 nonathletes (24 men and 37 women). In the entire group, D(FFM) varied from 1.075 to 1.127 g/cm3 and was strongly related to the water and protein fractions of the fat-free mass (FFM; r = -0.96 and 0.89) and moderately related to the mineral fraction of the FFM (r = 0.65). Skeletal muscle (%FFM) varied from 40 to 68%, and mesomorphy varied from 1.6 to 9.6, but neither was significantly related to D(FFM) (r = 0.11 and -0.14) or to the difference between percent fat estimated from the four-component model and from densitometry (r = 0.09 and -0.16). We conclude that, in a heterogeneous group of young adult athletes and nonathletes, D(FFM) and the accuracy of estimates of body composition from body density using the Siri equation are not related to muscularity or musculoskeletal development. Athletes in selected sports may have systematic deviations in D(FFM) from the value of 1.1 g/cm3 assumed in the Siri equation, resulting in group mean errors in estimation of percent fat from densitometry of 2-5% body mass, but the cause of these deviations is complex and not simply a reflection of differences in muscularity or musculoskeletal development.     

The estimation and evolution of hominin body mass

Body mass is a critical variable in many hominin evolutionary studies, with implications for reconstructing relative brain size, diet, locomotion, subsistence strategy, and social organization. We review methods that have been proposed for estimating body mass from true and trace fossils, consider their applicability in different contexts, and the appropriateness of different modern reference samples. Recently developed techniques based on a wider range of modern populations hold promise for providing more accurate estimates in earlier hominins, although uncertainties remain, particularly in non-Homo taxa. When these methods are applied to almost 300 Late Miocene through Late Pleistocene specimens, the resulting body mass estimates fall within a 25–60 kg range for early non-Homo taxa, increase in early Homo to about 50–90 kg, then remain constant until the Terminal Pleistocene, when they decline.     

Potassium per kilogram fat-free mass and total body potassium: predictions from sex,age, and anthropometry

Total body potassium (TBK) is located mainly intracellularly and constitutes an index of fat-free mass (FFM). The aim was to examine whether TBK and the TBK-to-FFM ratio (TBK/FFM) can be estimated from sex, age, weight, and height. A primary study group (164 males, 205 females) and a validation group (161 and 206), aged 37-61 yr, were randomly selected from the general population. TBK was determined by whole body counting, and FFM was obtained by dual-energy X-ray absorptiometry (DEXA; FFM(DEXA)). The primary study group was used to construct sex-specific equations predicting TBK and TBK/FFM from age, weight, and height. The equations were used to estimate TBK and TBK/FFM in the validation group. The estimates were compared with measured values. TBK in different age ranges was predicted, with errors ranging from 5.0 to 6.8%; errors for TBK/FFM ranged from 2.7 to 4.8%, respectively. By adding FFM(DEXA) as a fourth predictor, the error of the TBK prediction decreased by approximately two percentage units. In conclusion, TBK and TBK/FFM can be meaningfully estimated from sex, age, weight, and height.     

Bioelectrical impedance underestimates total and truncal fatness in abdominally obese women

Objective: To compare estimates of total and truncal fatness from eight‐electrode bioelectrical impedance analysis equipment (BIA₈) with those from DXA in centrally obese women. The secondary aim was to examine BMI and waist circumference (WC) as proxy measures for percentage total body fat (%TBF) and truncal body fat percentage (tr%BF). Research Methods and Procedures: This was a cross‐sectional study of 136 women (age, 48.1 ± 7.7 years; BMI, 30.4 ± 2.9 kg/m²; %TBF_DXA, 46.0 ± 3.7%; WC, 104 ± 8 cm). Fatness was measured by DXA and Tanita BC‐418 equipment (Tanita Corp., Tokyo, Japan). Agreement among methods was assessed by Bland‐Altman plots, and regression analysis was used to evaluate anthropometric measures as proxies for total and abdominal fatness. Results: The percentage of overweight subjects was 41.9%, whereas 55.9% of the subjects were obese, as defined by BMI, and all subjects had a WC exceeding the World Health Organization cut‐off point for abdominal obesity. Compared with DXA, the BIA₈ equipment significantly underestimated total %BF (?5.0; ?3.6 to ?8.5 [mean; 95% confidence interval]), fat mass (?3.6; ?3.9 to ?3.2), and tr%BF (?8.5; ?9.1 to ?7.9). The discrepancies between the methods increased with increasing adiposity for both %TBF and tr%BF (both p < 0.001). Variation in BMI explained 28% of the variation in %TBF_DXA and 51% of %TBF_BIA8. Using WC as a proxy for truncal adiposity, it explained only 18% of tr%BF_DXA variance and 27% of tr%BF_BIA8 variance. The corresponding figures for truncal fat mass were 49% and 35%, respectively. No significant age effects were observed in any of the regressions. Discussion: BIA₈ underestimated both total and truncal fatness, compared with DXA, with higher dispersion for tr%BF than %TBF. The discrepancies increased with degree of adiposity, suggesting that the accuracy of BIA is negatively affected by obesity.     

Validity of bioelectric impedance for body composition assessment in children

Whole-body bioelectrical impedance analysis (BIA) was evaluated for its reliability and accuracy in estimating body composition in children. The hypothesis that the index, body height2 divided by resistance (RI), can accurately predict fat-free body mass (FFB) and percent fat (%FAT) in children was tested on 94 caucasian children 10-14 yr old. Criterion variables were FFB and %FAT estimated using multicomponent equations developed for children. BIA measurements (resistance and reactance) were found to be reliable. Prediction accuracy (standard error of the estimate, SEE) for FFB from RI alone was 2.6 kg and for %FAT from RI and body weight was 4.2%. For RI, anthropometric variables and reactance, the SEE improved to 1.9 kg FFB. For RI and anthropometric variables, the SEE was 3.3% FAT. For anthropometric variables alone, the SEE's were 2.1 kg FFB and 3.2% FAT. Adult FFB and %FAT prediction equations cross-validated with this sample resulted in SEE's similar to those for adult samples. We conclude that RI together with anthropometry is a reliable and an acceptably accurate method of estimating FFB mass and %FAT in children.