Reliability and validity of body composition measures in female athletes.

The purpose of this investigation was to determine the reliability and validity of bioelectrical impedance (BIA) and near-infrared interactance (NIR) for estimating body composition in female athletes. Dual-energy X-ray absorptiometry was used as the criterion measure for fat-free mass (FFM). Studies were performed in 132 athletes [age = 20.4 +/- 1.5 (SD) yr]. Intraclass reliabilities (repeat and single trial) were 0.987-0.997 for BIA (resistance and reactance) and 0.957-0.980 for NIR (optical densities). Validity of BIA and NIR was assessed by double cross-validation. Because correlations were high (r = 0.969-0.983) and prediction errors low, a single equation was developed by using all 132 subjects for both BIA and NIR. Also, an equation was developed for all subjects by using height and weight only. Results from dual-energy X-ray absorptiometry analysis showed FFM = 49.5 +/- 6.0 kg, which corresponded to %body fat (%BF) of 20.4 +/- 3.1%. BIA predicted FFM at 49.4 +/- 5.9 kg (r = 0.981, SEE = 1.1), and NIR prediction was 49. 5 +/- 5.8 kg (r = 0.975, SEE = 1.2). Height and weight alone predicted FFM at 49.4 +/- 5.7 kg (r = 0.961, SEE = 1.6). When converted to %BF, prediction errors were approximately 1.8% for BIA and NIR and 2.9% for height and weight. Results showed BIA and NIR to be extremely reliable and valid techniques for estimating body composition in college-age female athletes.     

Validation of BIA in Obese Children and Adolescents and Re‐evaluation in a Longitudinal Study

Decrease in fat mass (FM) is a one of the aims of pediatric obesity treatment; however, measurement techniques suitable for routine clinical assessment are lacking. The objective of this study was to validate whole‐body bioelectrical impedance analysis (BIA; TANITA BC‐418MA) against the three‐component (3C) model of body composition in obese children and adolescents, and to test the accuracy of our new equations in an independent sample studied longitudinally. A total of 77 white obese subjects (30 males) aged 5–22 years, BMI‐standard deviation score (SDS) 1.6–3.9, had measurements of weight, height (HT), body volume, total body water (TBW), and impedance (Z). FM and fat‐free mass (FFM) were calculated using the 3C model or predicted from TANITA. FFM was predicted from HT²/Z. This equation was then evaluated in 17 other obese children (5 males) aged 9–13 years. Compared to the 3C model, TANITA manufacturer's equations overestimated FFM by 2.7 kg (P < 0.001). We derived a new equation: FFM = ?2.211 + 1.115 (HT²/Z), with r² of 0.96, standard error of the estimate 2.3 kg. Use of this equation in the independent sample showed no significant bias in FM or FFM (mean bias 0.5 ± 2.4 kg; P = 0.4), and no significant bias in change in FM or FFM (mean bias 0.2 ± 1.8 kg; P = 0.7), accounting for 58% (P < 0.001) and 55% (P = 0.001) of the change in FM and FFM, respectively. Our derived BIA equation, shown to be reliable for longitudinal assessment in white obese children, will aid routine clinical monitoring of body composition in this population.     

Assessing Body Composition among 3‐ to 8‐Year‐Old Children: Anthropometry,BIA, and DXA

Objective: To examine the inter‐relationships of body composition variables derived from simple anthropometry [BMI and skinfolds (SFs)], bioelectrical impedance analysis (BIA), and dual energy x‐ray (DXA) in young children. Research Methods and Procedures: Seventy‐five children (41 girls, 34 boys) 3 to 8 years of age were assessed for body composition by the following methods: BMI, SF thickness, BIA, and DXA. DXA served as the criterion measure. Predicted percentage body fat (%BF), fat‐free mass (FFM; kilograms), and fat mass (FM; kilograms) were derived from SF equations [Slaughter (SL)1 and SL2, Deurenberg (D) and Dezenberg] and BIA. Indices of truncal fatness were also determined from anthropometry. Results: Repeated measures ANOVA showed significant differences among the methods for %BF, FFM, and FM. All methods, except the D equation (p = 0.08), significantly underestimated measured %BF (p < 0.05). In general, correlations between the BMI and estimated %BF were moderate (r = 0.61 to 0.75). Estimated %BF from the SL2 also showed a high correlation with DXA %BF (r = 0.82). In contrast, estimated %BF derived from SFs showed a low correlation with estimated %BF derived from BIA (r = 0.38); likewise, the correlation between DXA %BF and BIA %BF was low (r = 0.30). Correlations among indicators of truncal fatness ranged from 0.43 to 0.98. Discussion: The results suggest that BIA has limited utility in estimating body composition, whereas BMI and SFs seem to be more useful in estimating body composition during the adiposity rebound. However, all methods significantly underestimated body fatness as determined by DXA, and, overall, the various methods and prediction equations are not interchangeable.     

Relationship Between Basal Metabolic Rate,Gender, Age,and Body Composition in 8,780 White Obese Subjects

The objective of the present study was to explore the relationship between basal metabolic rate (BMR), gender, age, anthropometric characteristics, and body composition in severely obese white subjects. In total, 1,412 obese white children and adolescents (BMI > 97° percentile for gender and age) and 7,368 obese adults (BMI > 30 kg/m²) from 7 to 74 years were enrolled in this study. BMR was measured using an indirect calorimeter equipped with a canopy and fat free mass (FFM) were obtained using tetrapolar bioelectrical impedance analysis (BIA). Using analysis of covariance, we tested the effect of gender on the relationship between BMR, age, anthropometry, and body composition. In children and adolescents, the predictor × gender interaction was significant in all cases except for FFM × gender. In adults, all predictor × gender interactions were significant. A prediction equation based on body weight (BW), age, and gender had virtually the same accuracy of the one based on FFM, age, and gender to predict BMR in both children and adults (R²_adj = 0.59 and 0.60, respectively). In conclusion, gender was a significant determinant of BMR in children and adolescents but not in adults. Our results support the hypothesis that the age‐related decline in BMR is due to a reduction in FFM. Finally, anthropometric predictors of BMR are as accurate as body composition estimated by BIA.     

Serial Measurements of Body Composition in Obese Subjects During a Very-Low-Energy Diet (VLED) Comparing Bioelectrical Impedance with Hydrodensitometry

Bioelectrical impedance analysis (BIA) is a convenient, inexpensive, and noninvasive technique for measuring body composition. BIA has been strongly correlated with total body water (TBW) and also has been validated against hydrodensitometry (HD). The accuracy and clinical utility of BIA and HD during periods of substantial weight loss remain controversial. We measured body composition in moderately and severely obese patients serially using both methods during a very-low-energy diet (VLED). Mean initial weight in these patients was 116 (± 30) kg (range, 74–196 kg). Mean weight loss was 24 (± 13) kg with a decrease in fat mass (FM) by HD of 20 kg (p<0.001) and a decrease in fat-free mass (FFM) of 3.6 kg (p<0.05). Loss of FFM is best predicted by the rate (kg/wk) of weight loss (r² = 0.86, p<0.0001). FFM, as predicted from BIA equations, was highly correlated with FFM as estimated by HD during all testing sessions (r=0.92-0.98). Although highly correlated, BIA overestimated FFM relative to HD and this difference appeared to be more pronounced for taller patients with greater truncal obesity. Although the discrepancy was no greater during weight-loss treatment, the level of disagreement was considerable. Therefore, the two methods cannot be used interchangeably to monitor relative changes in body composition in patients with obesity during treatment with VLED. The discrepancy between BIA and HD may be caused by body mass distribution considerations and by perturbations in TBW which affect the hydration quotient for FFM (BIA) and/or which affect the density constants for FFM and FM (HD).     

Use of bioelectrical impedance to assess body composition changes at high altitude.

This study determined the feasibility of using bioelectrical impedance analysis (BIA) to assess body composition alterations associated with body weight (BW) loss at high altitude. The BIA method was also evaluated relative to anthropometric assessments. Height, BW, BIA, skinfold (SF, 6 sites), and circumference (CIR, 5 sites) measurements were obtained from 16 males (23-35 yr) before, during, and after 16 days of residence at 3,700-4,300 m. Hydrostatic weighings (HW) were performed pre- and postaltitude. Results of 13 previously derived prediction equations using various combinations of height, BW, age, BIA, SF, or CIR measurements as independent variables to predict fat-free mass (FFM), fat mass (FM), and percent body fat (%Fat) were compared with HW. Mean BW decreased from 84.74 to 78.84 kg (P less than 0.01). As determined by HW, FFM decreased by 2.44 kg (P less than 0.01), FM by 3.46 kg (P less than 0.01), and %Fat by 3.02% (P less than 0.01). The BIA and SF methods overestimated the loss in FFM and underestimated the losses in FM and %Fat (P less than 0.01). Only the equations utilizing the CIR measurements did not differ from HW values for changes in FFM, FM, and %Fat. It was concluded that the BIA and SF methods were not acceptable for assessing body composition changes at altitude.     

Are Ethnic and Gender Specific Equations Needed to Derive Fat Free Mass from Bioelectrical Impedance in Children of South Asian,Black African-Caribbean and White European Origin? Results of the Assessment of Body Composition in Children Study

Background

Bioelectrical impedance analysis (BIA) is a potentially valuable method for assessing lean mass and body fat levels in children from different ethnic groups. We examined the need for ethnic- and gender-specific equations for estimating fat free mass (FFM) from BIA in children from different ethnic groups and examined their effects on the assessment of ethnic differences in body fat.

Methods

Cross-sectional study of children aged 8–10 years in London Primary schools including 325 South Asians, 250 black African-Caribbeans and 289 white Europeans with measurements of height, weight and arm-leg impedance (Z; Bodystat 1500). Total body water was estimated from deuterium dilution and converted to FFM. Multilevel models were used to derive three types of equation {A: FFM = linear combination(height+weight+Z); B: FFM = linear combination(height²/Z); C: FFM = linear combination(height²/Z+weight)}.

Results

Ethnicity and gender were important predictors of FFM and improved model fit in all equations. The models of best fit were ethnicity and gender specific versions of equation A, followed by equation C; these provided accurate assessments of ethnic differences in FFM and FM. In contrast, the use of generic equations led to underestimation of both the negative South Asian-white European FFM difference and the positive black African-Caribbean-white European FFM difference (by 0.53 kg and by 0.73 kg respectively for equation A). The use of generic equations underestimated the positive South Asian-white European difference in fat mass (FM) and overestimated the positive black African-Caribbean-white European difference in FM (by 4.7% and 10.1% respectively for equation A). Consistent results were observed when the equations were applied to a large external data set.

Conclusions

Ethnic- and gender-specific equations for predicting FFM from BIA provide better estimates of ethnic differences in FFM and FM in children, while generic equations can misrepresent these ethnic differences.     

Reliability and detecting change following short-term creatine supplementation: comparison of two-component body composition methods

The purpose of the present study was twofold: firstly, to assess the reliability of various body composition methods, and secondly, to determine the ability of the methods to estimate changes in fat-free mass (FFM) following creatine (Cr) supplementation. Fifty-five healthy male athletes (weight 78.3 +/- 10.3 kg, age 21 +/- 1 years) gave informed consent to participate in this study. Subjects' FFM was estimated by hydrostatic weighing (HW), air-displacement plethysmography (ADP), bioelectrical impedance analysis (BIA), near-infrared spectroscopy (NIR), and anthropometric measurements (ANTHRO). Measurements were taken on 2 occasions separated by 7 days to assess the reliability of the methods. Following this, 30 subjects returned to the laboratory for an additional test day following 7 days of Cr supplementation (20 g.d(-1) Cr + 140 g.d(-1) dextrose) to assess each method's ability to detect acute changes in FFM. In terms of reliability, we found excellent test-retest correlations for all 5 methods, ranging from 0.983 to 0.998 (p < 0.001). The mean biases for the 5 methods were close to 0 (range -0.1 to 0.3 kg) and their 95% limits of agreement (LOAs) were within acceptable limits (HW = -1.1 to 1.7 kg; ADP = -1.1 to 1.2 kg; BIA = -1.0 to 1.0 kg; NIR = -1.4 to 1.4 kg); however, the 95% LOAs were slightly wider for ANTHRO (-2.4 to 2.6 kg). Following Cr supplementation there was a significant increase in body mass (from 77.9 +/- 10.1 kg to 78.9 +/- 10.3 kg, p = 0.000). In addition, all 5 body composition techniques detected the change in FFM to a similar degree (mean change: HW = 0.9 +/- 0.6 kg; ADP = 0.9 +/- 0.6 kg; BIA = 0.9 +/- 0.6 kg; NIR = 0.8 +/- 0.5 kg; ANTHRO = 1.0 +/- 0.7 kg; intraclass correlation coefficient = 0.962). We conclude that between-day differences in FFM estimation were within acceptable limits, with the possible exception of ANTHRO. In addition, all 5 methods provided similar measures of FFM change during acute Cr supplementation.     

Comparability of Resting Energy Expenditure in Nigerians and U.S. Blacks

Objective: To determine the influence of environmental factors on resting energy expenditure (REE) and its relationship to adiposity in two populations of West African origin, Nigerians and U.S. blacks. Research Methods and Procedures: REE and body composition were measured in a cross‐sectional sample of 89 Nigerian adults (39 women and 50 men), and 181 U.S. black adults (117 women and 65 men). Both groups represent randomly selected population samples. REE was measured by indirect calorimetry after an overnight fast in both sites using the same instrument. Body composition was estimated using bioelectrical impedance analysis (BIA) in 72 Nigerians and 156 U.S. participants. Multivariate regression analysis was used to determine the significant predictors of REE. The analyses were repeated in a set of 17 Nigerians and 28 U.S. blacks in whom body composition was measured using deuterium dilution. Results: U.S. black adults were significantly heavier and had both more fat‐free mass (FFM) and body fat than Nigerians. FFM was the only significant determinant of REE in both population groups, whether body composition was measured using BIA or deuterium dilution. The relationship between REE and body composition did not differ by site. There was no relationship between REE and adiposity. Discussion: Differences in current environmental settings did not impact REE. The differences observed in mean levels of body fat between Nigerians and U.S. blacks were not the result of differences in REE adjusted for body composition.     

Free carnitine and acetyl carnitine plasma levels and their relationship with body muscular mass in athletes

Summary The purpose of the present study was to investigate the relationship between plasma carnitine concentration and body composition variation in relation to muscular and fat masses since there is no experimentally proved correlation between plasma carnitine and body masses. We used bioelectric impedance analysis (BIA), to determine body composition and to have a complete physical fitness evaluation. The post-absorptive plasma free carnitine and acetyl carnitine plasma levels, body composition as Fat-Free Mass (FFM) and Fat Mass (FM) in kg, as well as in percent of body mass, were analysed in 33 healthy subjects. A significant negative correlation was found between plasma acetyl carnitine and FFM in weight (kg) as well as in percent of body mass (respectively p < 0.0001; p < 0.01); a significant positive correlation was found only between FM in percent and plasma acetyl carnitine (p < 0.01). The observed negative correlation between plasma acetyl carnitine and muscular mass variation might reflect an oxidative metabolic muscle improvement in relation to muscular fat free mass increment and might be evidence that muscle metabolism change is in relation to plasma acetyl carnitine concentration.     

Percentage of total body fat as estimated by three automatic bioelectrical impedance analyzers

The present study aimed to compare the accuracy of estimating the percentage of total body fat (%TBF) among three bioelectrical impedance analysis (BIA) devices: a single-frequency BIA with four tactile electrodes (SF-BIA4), a single-frequency BIA with eight tactile electrodes (SF-BIA8) and a multi-frequency BIA with eight tactile electrodes (MF-BIA8). Dual-energy x-ray absorptiometry (DXA) and hydrostatic weighing (HW) were used as references for the measured values. Forty-five healthy college student volunteers (21 males: 172.9 +/- 5.5 cm and 65.8 +/- 9.1 kg and 24 females: 160.7 +/- 6.6 cm, 52.6 +/- 6.2 kg) were the subjects. Correlation coefficients between the BIA measurements and the references were calculated. The standard error of estimation (SEE) was calculated by regression analysis when estimating the reference measures (DXA and HW) from the predictor (SF-BIA4, SF-BIA8 and MF-BIA8). The differences in %TBF between the reference and the predictor, calculated by the reference minus the predictor, were plotted against the %TBF measured by the references. The MF-BIA 8 here showed the highest correspondence to the reference and the least estimation error compared with the other BIA methods. It is considered that there is a limit to directly estimate FFM from a regression equation using impedance, weight, height and age as independent variables, and that %TBF can be more accurately estimated by measuring segmental impedances using eight electrodes and multi-frequency electric currents and then estimating total body water from these impedances.     

USE OF BIOELECTRICAL IMPEDANCE ANALYSIS TO ASSESS BODY COMPOSITION OF SEALS

Abstract: Bioelectrical impedance analysis (BIA) measures resistance and reactance of a current as it passes through an organism. The validity of using BIA as a tool to measure body water content, and hence body composition and condition, was tested on harp and ringed seals. The resistance and reactance readings from BIA were compared to estimates of total body water (TBW) determined via tritiated water dilution. The relationship between resistance and TBW (% of body mass) was linear after logarithmic transformation and the two variables were highly correlated. We describe the electrode configuration and placements which provide reliable results in these seals. Our findings indicate that BIA has considerable potential as an inexpensive, rapid, and reliable technique for estimating body composition of phocid seals.     

Metabolically active components of fat free mass and resting energy expenditure in nonobese adults

Resting energy expenditure (REE) and components of fat-free mass (FFM) were assessed in 26 healthy nonobese adults (13 males, 13 females). Detailed body composition analyses were performed by the combined use of dual-energy X-ray absorptiometry (DEXA), magnetic resonance imaging (MRI), bioelectrical impedance analysis (BIA), and anthropometrics. We found close correlations between REE and FFM(BIA) (r = 0.92), muscle mass(DEXA) (r = 0.89), and sum of internal organs(MRI) (r = 0.90). In a multiple stepwise regression analysis, FFM(BIA) alone explained 85% of the variance in REE (standard error of the estimate 423 kJ/day). Including the sum of internal organs(MRI) into the model increased the r(2) to 0.89 with a standard error of 381 kJ/day. With respect to individual organs, only skeletal muscle(DEXA) and liver mass(MRI) significantly contributed to REE. Prediction of REE based on 1) individual organ masses and 2) a constant metabolic rate per kilogram organ mass was very close to the measured REE, with a mean prediction error of 96 kJ/day. The very close agreement between measured and predicted REE argues against significant variations in specific REEs of individual organs. In conclusion, the mass of internal organs contributes significantly to the variance in REE.     

Segmental body composition assessment for obese Japanese adults by single-frequency bioelectrical impedance analysis with 8-point contact electrodes

This study aimed to determine the accuracy of segmental body composition variables estimated by single-frequency BIA with 8-point contact electrodes (SF-BIA8), compared with dual-energy X-ray absorptiometry (DXA). Subjects were 72 obese Japanese adults (43 males and 29 females) aged 30 to 66 years. Segmental body composition variables (fat free mass: FFM, fat mass: FM, and percent fat mass: %FAT) were measured by these techniques. The correlations between impedance values and FFM measured by DXA were calculated. To examine the consistency in predicted values (SF-BIA8) with the reference (DXA), significant mean differences were tested by t-test and the degree of the difference was assessed by effect size. Correlations between the reference and predicted values were calculated. Additionally, the standard error of estimation (SEE) when estimating the reference from the predictor and the relative value of the SEE to the mean value of the DXA measurement (%SEE) were calculated. Systematic error was examined by Bland-Altman plots. High correlations were found between impedance and FFM measured by SF-BIA8. FFM in the extremities showed high correlations with the reference values, but systematic error was found. SF-BIA8 tended to overestimate FFM in the trunk. The consistencies in %FAT and FM with the reference value are inferior to those for FFM, and SEE values in %FAT and FM were greater than those for FFM. The accuracy of the estimated values in the trunk (FFM, %FAT, and FM) are inferior to those of the total body and extremities.     

Effect of physical training on body composition in Moscow adolescents

The influence of physical activity on body mass components has been studied using a sample of Moscow children. 195 girls and 259 boys of Russian ethnicity from 12 to 17 years old were investigated cross-sectionally in 2005 in different Moscow schools. According to the level of physical activity they were divided into three groups: 1-those who did not take part in regular physical exercise (44 boys and 50 girls); 2-those who took part in special sports programs in general education schools (82 boys and 82 girls); 3-students of special sports schools with a high sports ranking (133 boys and 63 girls). The program included anthropometric measurements, evaluation of sexual maturation indices, somatotypes, and "functional" traits (diastolic and systolic blood pressure, pulse rate, hand grip, etc). For the study of body composition, bioelectrical impedance analysis (BIA) was used. The estimates of body mass components were also calculated using the anthropometric measurements. For the fat component, the estimates obtained by BIA and the anthropometric methods were highly correlated: r=0.85-0.88. Age changes of BIA measurements and body components were analysed. With multiple regression analysis it was shown that BIA measurements are dependent on a great number of morphological and functional traits, with the most informative sets of traits being selected. The degree of physical activity has a strong effect on body components: the contents of fat-free mass (FFM) and total body water (TBW) significantly increase, and the fat mass (FM) in girls decreases.     

Noninvasive estimation of body composition in small mammals: a comparison of conductive and morphometric techniques

Body fat stores may serve as an index of condition in mammals. Thus, techniques that measure fat content accurately are important for assessing the ecological correlates of condition in mammal populations. We compared the ability of two conductive techniques, bioelectrical impedance analysis (BIA) and total body electrical conductivity (TOBEC), to predict body composition with that of morphometric methods in three small mammal species: red squirrels (n=13), snowshoe hares (n=30), and yellow-bellied marmots (n=4). Animals were livetrapped in northern Idaho; BIA (all subjects) and TOBEC (squirrels only) measurements were taken following chemical immobilization in the field, and morphometric measurements were taken postmortem. Information provided by BIA and TOBEC failed to improve upon the predictive power of morphometric equations for total body water (TBW) and lean body mass (LBM) in squirrels and hares, which do not store substantial amounts of fat (<5% body mass comprised of fat). Although the same pattern held with respect to LBM in marmots, which accumulate substantial amounts of body fat (>10% body mass), a BIA-based model proved best at estimating TBW, suggesting that the usefulness of conductive techniques may be a function of fat deposition. However, regardless of the technique used to predict body composition, estimates of body fat furnished by our equations failed to approximate actual fat levels accurately in all three test species, probably because these techniques only provide indirect estimates of fat content. These results highlight the limitations inherent in contemporary methods of animal fat estimation and underscore the need for the development of direct and accurate measures of body fat in mammals.     

Ethnic Variability in Body Size,Proportions and Composition in Children Aged 5 to 11 Years: Is Ethnic-Specific Calibration of Bioelectrical Impedance Required?

Background

Bioelectrical Impedance Analysis (BIA) has the potential to be used widely as a method of assessing body fatness and composition, both in clinical and community settings. BIA provides bioelectrical properties, such as whole-body impedance which ideally needs to be calibrated against a gold-standard method in order to provide accurate estimates of fat-free mass. UK studies in older children and adolescents have shown that, when used in multi-ethnic populations, calibration equations need to include ethnic-specific terms, but whether this holds true for younger children remains to be elucidated. The aims of this study were to examine ethnic differences in body size, proportions and composition in children aged 5 to 11 years, and to establish the extent to which such differences could influence BIA calibration.

Methods

In a multi-ethnic population of 2171 London primary school-children (47% boys; 34% White, 29% Black African/Caribbean, 25% South Asian, 12% Other) detailed anthropometric measurements were performed and ethnic differences in body size and proportion were assessed. Ethnic differences in fat-free mass, derived by deuterium dilution, were further evaluated in a subsample of the population (n = 698). Multiple linear regression models were used to calibrate BIA against deuterium dilution.

Results

In children <11 years of age, Black African/Caribbean children were significantly taller, heavier and had larger body size than children of other ethnicities. They also had larger waist and limb girths and relatively longer legs. Despite these differences, ethnic-specific terms did not contribute significantly to the BIA calibration equation (Fat-free mass = 1.12+0.71*(height²/impedance)+0.18*weight).

Conclusion

Although clear ethnic differences in body size, proportions and composition were evident in this population of young children aged 5 to 11 years, an ethnic-specific BIA calibration equation was not required.     

Dual-energy X-ray absorptiometry (DXA) can accurately and nondestructively measure the body composition of small, free-living rodents

Dual-energy x-ray absorptiometry (DXA) is a nondestructive technique that can potentially measure specific components of whole-body composition in free-living and lab-raised animals. Our aim was to test the ability of DXA to measure the composition of a common arvicoline rodent, the northern red-backed vole (Clethrionomys rutilus). We used a DXA apparatus to obtain measurements of fat mass (FM), lean mass (LM),bone mineral content, bone mineral density, and fat-free mass(FFM) in carcasses of free-living and lab-raised voles. We then used chemical carcass analysis to derive predictive algorithms for actual values of FM, total body water, total protein, total mineral, LM, and FFM. Unexplained error in the equations for all voles grouped collectively ranged from R(2) = 0.82 to R(2) = 0.98. The DXA FM measurement had the highest coefficient of variation, and it was higher for free-living voles than for lab-raised voles. However, FM can be determined by difference with excellent precision by using the FFM equation (R(2) = 0.98). We also derived corrective terms for passive integrated transponder-tagged animals. Thus, DXA is a nonlethal, nondestructive tool capable of precisely and accurately measuring many specific parameters of whole-body composition in small free-living and lab-raised rodents.     

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.     

Comparison of three bioelectrical impedance methods with DXA in overweight and obese men

Objective: To compare bioelectrical impedance analysis (BIA) of body composition using three different methods against DXA in overweight and obese men. Research Methods and Procedures: Forty‐three healthy overweight or obese men (ages 25 to 60 years; BMI, 28 to 43 kg/m²) underwent BIA assessment of body composition using the ImpediMed SFB7 (version 6; ImpediMed, Ltd., Eight Mile Plains, Queensland, Australia) in multifrequency mode (Imp‐MF) and DF50 single‐frequency mode (Imp‐SF) and the Tanita UltimateScale (Tanita Corp., Tokyo, Japan). Validity was assessed by comparison against DXA using linear regression and limits of agreement analysis. Results: All three BIA methods showed good relative agreement with DXA [Imp‐MF: fat mass (FM), r² = 0.81; fat‐free mass (FFM), r² = 0.81; percentage body fat (BF%), r² = 0.69; Imp‐SF: FM, r² = 0.65; FFM, r² = 0.76; BF%, r² = 0.40; Tanita: BF%, r² = 0.44; all p < 0.001]. Absolute agreement between DXA and Imp‐MF was poor, as indicated by a large bias and wide limits of agreement (bias, ±1.96 standard deviation; FM, ?6.6 ± 7.7 kg; FFM, 8.0 ± 7.1 kg; BF%, ?7.0 ± 6.6%). Imp‐SF and Tanita exhibited a smaller bias but wide limits of agreement (Imp‐SF: FM, ?1.1 ± 8.5 kg; FFM, 2.5 ± 7.9 kg; BF%, ?1.7 ± 7.3% Tanita: BF%, 1.2 ± 9.5%). Discussion: Compared with DXA, Imp‐MF produced large bias and wide limits of agreement, and its accuracy estimating body composition in overweight or obese men was poor. Imp‐SF and Tanita demonstrated little bias and may be useful for group comparisons, but their utility for assessment of body composition in individuals is limited.