EVANS, ELLEN M.; ARNGRIMSSON, SIGURBJORN A.; CURETON, KIRK J.
Estimates of body fatness (%BF) from body density based on a two-component model assume a constant density of fat-free mass (FFM; 1.1 g·mL-1) which, in theory, is based on the assumption that the proportions and densities of the constituents of FFM (water, mineral, and protein) are uniform across individuals (23). Estimates of %BF from three- and four-component models in which body water, or body water and mineral, are measured in addition to body density are more accurate than estimates from body density alone based on a two-component model, because variation in the water, or water and mineral, content of the body is measured and not assumed to be constant (16).
However, measurement of total body water (TBW) using a dilution method is time consuming, technically demanding, and relatively expensive. If body water could be estimated with sufficient accuracy from bioelectrical impedance analysis (BIA), this would facilitate the use of multicomponent models to estimate body composition. Alternatively, it is possible that the theoretical advantage of measuring TBW for use in multicomponent models to estimate %BF could be negated by the additional error introduced by estimating TBW rather than measuring it using a dilution method.
The purpose of this study was to compare %BF estimates from three- and four-component models with TBW determined by single-frequency BIA (TBWBIA) to %BF estimates from densitometry (%BF2C-D) and from three- and four-component models with TBW determined by deuterium dilution (TBWD2O), the criterion methods. The primary questions were: Can estimates of TBWBIA be substituted for estimates of TBWD2O in estimating body fatness from multicomponent models without significant loss in accuracy? And, are estimates of %BF from three- and four-component models with TBW estimated from BIA more accurate than estimates of %BF from densitometry alone, using estimates of %BF from a four-component model with TBW determined by deuterium dilution as the criterion? It was hypothesized that: 1) estimates of %BF from three- and four-component models with TBW estimated from BIA are less accurate than estimates of %BF from three- and four-component models with TBW determined by deuterium dilution; and 2) estimates of %BF from three- and four-component models with TBW estimated from BIA are more accurate than estimates of %BF from densitometry alone, using estimates of %BF from a four-component model with TBW determined by deuterium dilution as the criterion.
Because it was desirable to include subjects with a broad range of body fatness and TBW to investigate the question, the data from a diverse group of men and women were utilized. The sample (N = 133) included: 40 men, 93 women; and 3 Blacks, 14 Hispanics, and 116 Whites. Physical characteristics of the subjects are presented in Table 1. The subjects were free from any known medical or nutritional conditions or medication use known to affect body composition measures. The study was approved by the University’s Institutional Review Board, and written consent was obtained before data collection.
Reliability of the body composition methods was evaluated in an independent group of nine subjects (4 female, 5 male, 24 ± 3.8 yr, 16.6 ± 11.6%BF4C-D2O) who were tested on two occasions 1 wk apart (see Table 2).
Data Collection Protocol
Physical characteristics, body density, total body water, total body bone mineral, and body composition from DXA, body resistance from BIA, and anthropometric measures were measured during a single test session (∼3.5 h). Subjects were asked to arrive at the laboratory well hydrated after a 12-h fast. Water consumption was encouraged based on personal preference and habit. A baseline urine sample, from the second void of the morning indicated that, on average, the subjects were adequately hydrated with a urine specific gravity of 1.020 ± 0.01 g·mL-1(2). No food or beverages, including water, were consumed during the testing session.
Body Composition Measures
Body mass (BM) in air was determined using an electronic scale to the nearest 0.01 kg with subjects wearing the same attire as when weighed under water. Barefoot standing height was measured to the nearest 0.1 cm using a stadiometer.
Body density was measured using underwater weighing and Archimedes’ principle to determine body volume. Body mass under water was determined using a Chatillon (Greensboro, NC) autopsy scale to the nearest 0.25 kg, with residual lung volume measured simultaneously using a closed-circuit oxygen-rebreathing, nitrogen-dilution technique modified from Goldman and Buskirk (11). Volume of gas in the gastrointestinal tract was assumed to be 0.1 L.
Total body water was measured using deuterium oxide dilution (7). After a baseline blood sample was taken, subjects ingested a known quantity of D2O [0.3 g D2O·kg-1 BM] in 100 mL of distilled water. Another 100 mL of distilled water was used as a rinse and then consumed to insure complete ingestion of the tracer. After a 3-h equilibrium period during which all urine was collected, another blood sample was taken. Blood samples were centrifuged at 3000 rpm and plasma was stored at −70°C. Plasma samples were purified by diffusion by incubating equal volumes of plasma and deionized water at 37°C for 48 h in incubation dishes (Bel-Air Products, Pequannock, NJ). Isotope abundance in the purified plasma sample was determined in duplicate with an isotope-ratio mass spectrometer (N = 44; Finnigan MAT 251, Bremen, Germany) or in triplicate by single-beam infrared spectrophotometer (N = 90) as previously described (19).
TBWD2O was corrected for D2O loss in urine and decreased 4% to account for hydrogen exchange with protein and carbohydrate during the 3-h equilibrium period (22). Agreement between the two methods for analyzing deuterium was close as determined using 50 TBW samples [T̄×Diff = −0.06 ± 0.9 (SD) L].
Determination of body resistance was made using a standard four-surface electrode bioimpedance analyzer (RJL Systems, Inc., Clinton TWP, MI) on the right side of the body after 10–15 min of supine rest utilizing standardized procedures (14,18).
Bone mineral ash was determined from whole-body scans by DXA (Hologic QDR-1000W, enhanced whole body analysis software version 5.71, Waltham, MA). Bone mineral ash was multiplied by 1.27 to estimate total body mineral content. The constant 1.27 assumes that 4% of the bone mineral is lost during the ashing process and that nonosseous mineral mass is 23% of bone mineral ash (5).
Body Composition Calculations
%BF was estimated from body density (Db, %BF2C-D) using the Siri equation (23) based on a two-component (fat and FFM) model:
Equation 1 assumes the density of the fat is 0.9 g·mL-1 and the density of the FFM is 1.1 g·mL-1.
%BF was estimated from body density and body water using the three-component model (fat, water, and residual) modified from Siri (23) : with w being equal to total body water measured by D2O dilution or estimated by body resistance expressed in relation to body mass (i.e., w/BM). Equation 2 assumes that the mineral-to-protein ratio of the FFM is 0.35 (5).
%BF was estimated from body density, total body water, and total body mineral using the following equation from Lohman (16) based on a four-component model (fat, water, mineral, and protein):MATHwhere Db is body density, w is TBW and m is total body mineral estimated from bone mineral ash expressed relative to body mass (i.e., w/BM and m/BM).
Data were analyzed with SPSS for Windows version 7.0 (SPSS Inc., Chicago IL). Reliability and repeatability of the body composition measures were assessed using an intraclass correlation coefficient (ICC) from a one-way ANOVA, the SD of the difference between repeat measures (SDDiff) and the coefficient of variation (CV%;Table 2).
An equation predicting TBWD2O from BIA resistance and other variables was developed and double cross-validated on the present sample. Subjects were randomly assigned to either a model or a validation group. Stepwise multiple regression analysis was applied to each sample to identify the best predictors of TBWD2O utilizing the following independent variables: height squared/resistance, mass, gender (coded as male = 1 and female = 0) and age. In both samples, all variables except age contributed significantly to the prediction. Then, the equation predicting TBWD2O developed on the model group was used to predict TBW in the validation sample, and vice versa. Correlations between measured and predicted TBWD20, tests of the deviations of the slope and intercept from 1 and 0, respectively, and a dependent t-test of significance of the differences between measured and predicted means in the two samples were used to assess how well the equations cross-validated.
Repeated measures analysis of variance was used to determine the significance of differences among body fatness estimates with planned comparisons (contrasts) used in the event of a significant finding. An experiment-wise alpha level of 0.05 was used for all tests. A Bonferroni adjustment was used for the family of 4 contrasts (0.05/4 =P fw < 0.013). Relations between variables were described using simple linear regression analysis. Individual agreement between %BF estimates of interest was assessed using the Bland-Altman approach (1). The significance of differences in variance for differences between methods (error) when utilizing the Bland-Altman approach was assessed using a t-test for dependent samples.
The multiple regression equations for the estimation of TBW in the model and validation groups were similar [Equation 1: TBW = 0.283 Ht2/R + 0.180 Mass + 6.137 Gender + 7.029 (R = 0.95, SEE = 2.3 L) and Equation 2: TBW = 0.331 Ht2/R + 0.137 Mass + 5.709 Gender + 7.855 (R = 0.94, SEE = 2.7 L)]. The cross-validation R between the measured TBW in the validation group and TBW predicted in the validation group using the equation developed on the model group was high (R = 0.91). The slope and intercept of the regression prediction equation (TBWD2O = 1.00 TBWBIA + 0.06) were not significantly different from 1 (t = 0.01, P = 0.99) and zero (t = 0.04, P = 0.97), respectively. There was no significant difference between measured (38.0 ± 7.4 L) and predicted (38.0 ± 7.0 L) TBW means. Likewise, the cross validation R between the measured TBW in the model group and TBW predicted in the model group using the equation developed on the validation group was high (R = 0.88). The slope and intercept of the regression prediction equation (TBWD2O = 0.99 TBWBIA + 0.42) were not significantly different from 1 (t = 0.10 P = 0.95) and 0 (t = 0.23, P = 0.82), respectively. There was no significant difference between measured (38.1 ± 7.4 L) and predicted (38.2 ± 7.0 L) TBW means.
Because the equations strongly cross-validated in each group of subjects, a prediction equation predicting TBWD2O from BIA resistance and other variables based on the total sample was used for the remainder of the data analysis:MATH MATHwhere R = resistance measured in Ohms, Ht is in cm, mass is in kilograms and gender is 1 = male and 0 = female.
The density of the FFM (DFFM; 1.101 ± 0.001) did not differ from that assumed by densitometry based on the cadaver data summarized by Brozek et al. (5), [DFFM = 1.100 g·mL-1, water fraction of the FFM (W/FFM) = 73.8%, mineral fraction of the FFM (M/FFM) = 6.8%, and protein fraction of the FFM (P/FFM) = 19.4%]; however, the W/FFM and M/FFM were significantly lower (72.3 ± 2.2 and 6.2 ± 0.6%) and the P/FFM was significantly higher (21.5 ± 2.3) than assumed (P < 0.05). Because DFFM did not significantly differ from 1.100 g·mL-1, mean %BF2C-D was not significantly different from mean %BF4C-D2O (Table 3). However, individual differences between %BF4C-D2O and %BF2C-D ranged from −5.3 to 7.8% BF [T̄×Diff = 0.4 ± 2.3% BF (SD);Table 4, Fig. 1]. As expected, there was a strong relation of the difference in estimates of body fatness from a four-component model and densitometry (%BF4C-D2O − %BF2C-D) with DFFM (r = 0.98, P < 0.05) and with W/FFM (r = 0.91, P < 0.05; data not shown).
Taking into account individual differences in body water greatly increased the accuracy of predicting the criterion measure of body fatness from body density, indicating that most of the difference between %BF4C-D2O and %BF2C-D was due to variability in W/FFM. Despite the statistically significant difference between means (Table 3), the agreement between %BF4C-D2O and %BF3C-D2O was very close (Table 4, Fig. 2). Individual differences ranged from −1.8 to 1.1% BF [T̄×Diff = −0.6 ± 0.5%BF (SD)].
A strong relation existed between the D2O and BIA estimates of TBW (r = 0.94, SEE = 2.4 L;Fig. 3). Individual differences ranged from −5.5 to 6.5 L [T̄×Diff = 0.0 ± 2.4 L (SD)]. Substituting TBWBIA for TBWD2O in estimating body fatness using the three- and four-component models resulted in similar mean values for %BF3C-BIA and %BF4C-BIA compared with %BF4C-D2O (Tables 3). However, individual differences were substantial, with differences between %BF4C-D2O and %BF3C-BIA ranging from −7.1 to 5.0%BF and between %BF4C-D2O and %BF4C-BIA ranging from −5.6 to 5.5%BF (Table 4, Figs. 4 and 5).
Compared with the criterion method, %BF4C-D2O, the accuracy of estimating %BF from three- and four-component models that included TBWBIA was comparable to the accuracy of estimating %BF from body density alone and much poorer than the accuracy of estimates from a three-component model including TBWD2O. The SDDiff between %BF4C-D2O and %BF3C-BIA (2.4% BF) was similar to the SDDiff between %BF4C-D2O and %BF2C-D (2.3% BF, P > 0.05, Table 4). In addition, the SDDiff between %BF4C-D2O and %BF4C-BIA (2.3% BF) was comparable to the SDDiff between %BF4C-D2O and %BF2C-D (2.3% BF, P > 0.05, Table 4). The SDDiff between %BF4C-D2O and %BF3C-BIA (2.4% BF) and between %BF4C-D2O and %BF4C-BIA (2.3% BF) were much greater than the SDDiff between %BF4C-D2O and %BF3C-D2O (0.5% BF, P < 0.05, Table 4).
The primary objective of this study was to determine whether estimates of body water from bioelectrical impedance analysis could be substituted for estimates of total body water from deuterium dilution in estimating body fatness from multicomponent models without significant loss in accuracy. A related objective was to determine whether estimates of %BF from body density and TBWBIA were more accurate than those from body density alone, using %BF4C-D2O as the criterion. The primary findings of the study are that, despite a strong relation between TBWBIA and TBWD2O, estimates of %BF from three- and four-component models using TBWBIA were considerably less accurate than estimates from the same models using TBWD2O and were not more accurate than estimates from body density alone using a two-component model.
The accuracy of estimating TBWD2O from single-frequency BIA at 50 Hz in our study (r = 0.94, SEE = 2.4 L) was similar to that reported by others. Kushner and Schoeller (15) reported a multiple correlation of 0.99 and a SEE of 1.37 L for TBWD2O predicted from height2/BIA resistance at 50 Hz, weight and gender in a heterogeneous sample of 40 men and women. Van Loan and Mayclin (24) obtained a multiple correlation of 0.87 and a SEE of 2.9 L for TBWD2O predicted from BIA resistance at 50 Hz, age, height, weight, and gender in 188 men and women. Lukaski and Bolonchuk (17) found a multiple correlation of 0.97 and a SEE of 1.5 L for TBWD2O predicted using multiple regression from height2/BIA resistance at 50 Hz, gender, weight, and age in 110 men and women. Heitmann (12) reported a multiple R of 0.92 and a SEE of 3.48 L in predicting TBW measured by deuterium dilution from height 2/BIA resistance at 50 Hz, weight, gender, and age in 139 men and women. The strength of the relation of TBWD2O and TBWBIA in our data and in the studies discussed above indicate that TBWD2O can be predicted quite accurately from BIA.
There was a strong relation of %BF2C-D to the criterion %BF4C-D2O (r = 0.99) with no systematic difference between the two estimates. However, the SDDiff between the two estimates (2.3% BF) indicated substantial individual variation, as reported by others (9,10,20,25,26). Taking into account differences in TBWD2O by predicting %BF from body density and TBWD2O using a three-component model substantially decreased this individual variation, reducing the SDDiff to 0.5% BF. This reduction in error indicated that most of the differences between %BF2C-D and %BF4C-D2O were due to individual variation in W/FFM. Others have also reported very high relations between %BF4C-D2O and %BF3C-D20, and, due to the large percentage of FFM that water comprises compared with mineral, variation in W/FFM is more important than variation in M/FFM in contributing to differences between estimates of %BF4CD2O and %BF2C-D(3,4,6,19,25,26).
Substitution of TBWBIA for TBWD2O in estimating %BF using a three-component model significantly increased the error relative to the criterion (%BF4C-D2O) and produced error comparable to that between the criterion and %BF2C-D. The SDDiff between %BF4C-D2O and %BF3C-BIA (2.4% BF) was comparable to the SDDiff between %BF4C-D2O and %BF2C-D (2.3% BF), and much larger than the SDDIFF between %BF4C-D2O and %BF3C-D2O (0.5% BF).When TBWBIA was substituted for TBWD2O in estimating %BF using a four-component model, the error was comparable to that between the criterion and %BF2C-D. The SDDIFF between %BF4C-D2O and %BF4C-BIA (2.3% BF) was the same as the SDDIFF between %BF4C-D2O and %BF2C-D (2.3% BF), and it was much larger than the SDDIFF between %BF4C-D2O and %BF3C-D2O (0.5% BF).
The finding that errors in estimating body fat from multicomponent models that included TBWBIA were larger than those from body density alone was particularly surprising and indicates that using TBWBIA apparently introduces new error that offsets any theoretical advantage of taking into account variation in body water when predicting %BF from body density. These data unequivocally indicate that despite the high relation between TBWD2O and TBWBIA, the magnitude of individual differences between TBWBIA and TBWD2O are such that it is not advantageous to substitute TBWBIA for TBWD2O in estimating body composition from multicomponent models. Using TBWBIA in conjunction with body density, or body density and body mineral, in estimating %BF using a multicomponent model is not better than predicting %BF from body density alone.
The interpretation of results of this study are based on the assumption that estimates of TBW from deuterium dilution and estimates of %BF4C-D2O are error-free criterion measures. Although these are among the best estimates of body composition available, this is obviously not the case. Day-to-day biological variation and technical errors contribute to variation in these measures. The coefficient of variation for repeated measures of TBWD2O in our hands and by others is 1–2%, and differences from values determined by desiccation have ranged from 2 to 6%(22). Of particular concern in validation studies in which the four-component model has been used as the criterion is the possibility that propagation of errors associated with the three measures used in the calculation of %BF4C-D2O may negate the advantage of measuring more of the sources of variability. We (19) and others (9), however, have reported that errors associated with these measures are quite small, with the within-subjects SD for replicate determinations of %BF4C-D2O being approximately 1% BF. Thus, errors in the criterion measures should not comprise our basic conclusions.
We chose to evaluate the accuracy of substituting TBW estimated from BIA for TBWD2O using a regression equation for predicting total body water developed on our sample, instead of using one of the many prediction equations available in the literature (15,17,24). This approach provides the most accurate prediction of TBWD2O from BIA, eliminating error due to sample differences, differences between studies in the methodology in measuring deuterium or BIA resistance. Most clinicians or researchers who would like to use TBWBIA to estimate body composition using a multicomponent model will not have a local validation of TBWBIA. If TBWD2O is predicted from BIA resistance with an equation developed at another site with a different BIA analyzer and technician and on a sample with different characteristics, and used to estimate %BF with a multicomponent model, poorer accuracy than the results reported in this study would be expected. For example, we predicted TBWD2O using the equation of Kushner and Scholler (15) and found that the agreement between our measured TBWD2O and that predicted from their equation [r = 0.93, T̄×Diff = 0.9 ± 2.6 L (SD) vs r = 0.94, T̄×Diff = 0.0 ± 2.4 L (SD)], between %BF3C-BIA and %BF4C-D2O [r = 0.98, T̄×Diff = −1.5 ± 2.7% BF (SD) vs r = 0.99, T̄×Diff = −0.4 ± 2.4% BF] and between %BF4C-BIA and %BF4C-D2O [r = 0.98, T̄×Diff = −0.8 ± 2.5% BF (SD) vs r = 0.99, T̄×Diff = 0.2 ± 2.3% BF] were poorer compared with using TBWD2O predicted from our equation.
The findings of this study have important implications for clinicians and researchers who need accurate estimates of body composition, particularly in individuals who may have alterations in W/FFM from that assumed by densitometry, such as children (21), pregnant women (13), the obese (8) or individuals who have edema or who may be on medication or nutritional supplements that alter the water content of the FFM, or certain groups of athletes (19). Although estimation of TBW using BIA is attractive because of its ease, speed, and low cost, its accuracy in the general population is insufficient to be of value in providing estimates of body composition using multicomponent models that are more accurate than those from body density alone.
We conclude that, because estimates of %BF using multicomponent models with TBW estimated from BIA are not more accurate than from body density alone using a two-component model, estimates of %BF from three- and four-component models using TBWBIA are not acceptable substitutes for estimates from the same models using TBWD2O.
Address for correspondence: Ellen M. Evans, Ph.D., Washington University, School of Medicine, 660 South Euclid Avenue, Campus Box 8113, St. Louis MO 63110; E-mail: firstname.lastname@example.org.
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