The ability to accurately assess the level of fatness in persons has major health consequences because of the association between obesity and conditions such as hypertension, insulin resistance, dyslipidemia, and hyperinsulinemia (26). A multicompartment approach would be ideal in determining body composition because the individual constituents of the fat-free mass (FFM) are measured by independent techniques (1,19). However, the use of a multicompartment approach is impractical for most research and clinical settings because of cost constraints. As a result, most laboratories have used hydrostatic weighing (HW). Unfortunately, HW is impractical in certain populations (children, obese, elderly), and for many subjects it is considered to be time consuming (typically six to eight trials of ∼30 min) and intimidating (water submersion), thus limiting its usefulness. In 1995, Dempster and Aitkens (7) demonstrated an alternative technique (BOD POD) derived from plethysmographic principles developed by others (9,12,14,15). Plethysmography determines body volume on the basis of the pressure/volume relationship. Boyle’s law explains this relationship in an isothermal testing chamber as: PV = k, where k is the proportionality constant (36). If the testing chamber temperature is not constant (adiabatic), Poisson’s law describes the pressure/volume relationship: PVγ = k, where γ is the ratio of the specific heat of the gas at constant pressure to that at constant volume (32,33). The literature comparing BOD POD and HW data is equivocal, as four studies show no significant difference between the line of identity and the regression between HW and the BOD POD (3,11,24,25), whereas two studies report a significant difference between the line of identity and the regression between the BOD POD and HW (5,22). To our knowledge, no study has attempted to validate the BOD POD with the four-compartment model (4C model) in adults. Therefore, the purpose of this study was threefold: first, to compare body density (Db) from the BOD POD against Db from HW; second, to compare percent body fat (%fat) by the BOD POD against %fat by the 4C model; and third, to investigate how variations in the aqueous and mineral factions of the FFM affect %fat estimates of the BOD POD.
An overview of the study design is presented in Table 1. All subjects came to the Division of Physiology and Metabolism at the University of Alabama at Birmingham (UAB) at 6:00 a.m. in a fasted state. After a baseline urine sample was obtained, the subjects were asked to ingest a container that had ∼10 g of deuterium. The %fat was then determined by either dual-energy x-ray absorptiometry (DXA) or the BOD POD (the order was randomized). After the first test (either DXA or BOD POD, depending on the order), a 45-min urine sample was obtained. HW was always performed after the DXA and the BOD POD because preliminary data have suggested that an increase in body temperature and moisture can affect %fat estimates by the BOD POD. After the completion of HW, the subjects were asked to wait quietly so a 3- and 4-h urine sample could be obtained.
The study sample included 42 adult females, of whom 39 were Caucasian and 3 were African American from the Birmingham area. All subjects gave their informed consent before participation in the study. Subjects who indicated apprehension about head submersion in the hydrostatic weighing tank before testing were excluded from the study. Approval for the use of human subjects was obtained from the Institutional Human Subject Review Board from both Auburn University and UAB.
Assessment of bone mineral content.
DXA was used to measure bone mineral content (BMC) (Lunar DPX-L densitometer, Lunar Radiation Corp., Madison, WI). The total dose of radiation to the subject was less than a typical chest radiograph. Whole-body scans were analyzed using the adult medium mode for all subjects (DPX-L version 3.6z). The repeat measures between days for BMC derived from DXA in eight healthy females had an intraclass correlation of R = 0.99 and a standard deviation of 48 g.
Assessment of total body density by HW.
Db was estimated by HW with simultaneous measurement of residual lung volume by using the closed-circuit oxygen dilution technique (35). Underwater weight was measured to the nearest 50 g in a stainless steel tank in which the subject was suspended from a LCL 20 Shear Beam Load Cell (Omega, Stanford, CT) calibrated from 0 to 10,000 g. After one practice trial, underwater weight and residual lung volume were measured simultaneously in five consecutive trials. The average of multiple trial densities within 0.001 g·cm−3 were used. The repeat measures between days for Db derived from HW in eight healthy females had an intraclass correlation of R = 0.99 and a standard deviation of 0.002 g·cm−3.
Assessment of total body density and %fat by plethysmography (BOD POD).
Whole-body air displacement was evaluated with the BOD POD version 1.69 (Body Composition System, Life Measurement Instruments, Concord, CA). The BOD POD is a single, “egg-shaped” unit consisting of two chambers: a testing chamber where the subject sits, and a reference chamber where the breathing circuit, pressure transducers, and electronics are stored (7). The testing procedure involved several steps. First, calibration was conducted before the subject’s entry into the BOD POD. Calibration involved the computation of the ratio of the pressure amplitudes (reference chamber and testing chamber) for an empty chamber and a known volume (49.860 L). The software calculated a regression equation between the testing chamber volume and the ratio of the pressure amplitudes (7). Essentially, the relationship is linear for any testing chamber volume and the ratio of the pressure amplitudes (7,33). After the calibration was completed and the procedures fully explained, the subject was given a nose clip and swim cap (worn to minimize isothermal air trapped within the hair). The subject entered the BOD POD for two trials of approximately 45 s each. During this stage, the subject’s uncorrected body volume (Vbraw) was determined with the testing chamber door being opened between trials. If both volumes were within 150 mL, then the two trials were averaged. However, if the volumes were not within 150 mL, a third trial was performed and the two volumes that were the closest were averaged. The last step involved the measurement of the thoracic gas volume (VTG). This stage required the subject to sit quietly in the BOD POD and breathe through a disposable tube and filter that was connected to the reference chamber in the rear of the BOD POD. After four or five normal breaths, the airway was occluded during midexhalation and the subject was instructed to make two quick light pants. VTG was considered successful when the four criteria were met. First, the merit had to be < 1. The merit is a theoretical value that demonstrates subject compliance during the measurement of the VTG. A merit of < 1 indicates perfect agreement between chamber pressure and the airway pressure in the tube, with a merit > 1 demonstrating poor compliance (usually because of leaking of air around the mouth) (7). Second, airway pressure had to be below 35 cm H2O. Third, tidal volume had to be between 0.40 and 0.70 L. Finally, the measured VTG had to be within 0.70 L of the predicted VTG(27). Db from the BOD POD was calculated as follows: Db = M/(Vbraw + 0.40VTG − SAA), where SAA and 0.40VTG are used to correct for the isothermic conditions within the chamber and M is the mass of the subject. The %fat was then calculated using the Siri equation (31). The repeat measures between consecutive days for Db derived from the BOD POD in eight healthy females (all wearing a one-piece swimsuit) had an intraclass correlation of R = 0.98 and a standard deviation of 0.006 g·cm−3.
Assessment of total body water by isotope dilution.
Total body water (TBW) was determined using 2H20 (deuterated water). The dosing procedure followed the technique developed by Schoeller (29) and Schoeller et al. (30). After an initial urine sample was collected (baseline), the subject was weighed in minimal clothing and a 10-g dose of 2H20 was given. Approximately 45 min later the subject voided, and 3- and 4-h postdosing urine samples were then obtained. Deuterium was prepared by the zinc reduction method described by Kendall and Coplen (21). All samples were analyzed on an OPTIMA (Micromass, Inc., Beverly, MA) mass spectrometer in triplicate. The calculation of 2H2O dilution space was calculated from the enrichment of 2H2O in the body at zero time by extrapolation of the log enrichment versus time plots back to zero time (6) using the following equation (29) : Dilution space (L) = d/20.02 · 18.02 · 1/R · E, where d is grams of 2H2O given, R is the standard ratio of 2H/1H (0.00015576), and E is enrichment of 2H2O at the extrapolated zero time (the percent above background). Total body water was determined by taking the mean of the zero-time isotope dilution space for 2H2O and dividing by 1.04 (to correct for the exchange with nonaqueous tissues) (30).
Calculation of %fat using the 4C model.
The %fat by the 4C model was determined by the Baumgartner et al. (1) equation: % body fat = 205 (1.34/Db − 0.35 A + 0.56 M − 1), where Db is total body density from HW, and A and M are the fractions of body mass that are aqueous and mineral, respectively. Although the derivation of this model came from subjects 65–94 yr of age, 4C model equations are (almost) free of assumptions and are theoretically derived. It would be almost impossible to derive new 4C model equations for every population group, and we cannot think of any reason why the equation by Baumgartner et al. (1) would not be applicable to our study population.
To determine the accuracy of Db and %fat estimates by the BOD POD, linear regression analysis was used. The accuracy of Db by the BOD POD was determined by Db measured by the BOD POD as the independent variable and Db by HW as the dependent variable. The accuracy of %fat by the BOD POD was evaluated with %fat by the BOD POD as the independent variable and %fat measured by the 4C model as the dependent variable. If the slope was not significantly different from 1 and the intercept not significantly different from 0, the estimates of Db and %fat derived from the BOD POD were not considered significantly different from the dependent variable. Potential bias in Db and %fat estimates by the BOD POD were examined using residual plots. This analysis examines the discrepancy between techniques across the range of fatness. Also, the R2 and the standard error of the estimate (SEE) for Db and %fat by the BOD POD were calculated. Paired t-tests were used to compare group means. Additionally, the difference between estimates of %fat by the BOD POD and estimates of %fat by the 4C model were calculated and regressed on the aqueous (AFFM) and mineral (MFFM) fractions of the DXA FFM. The pure or total error between the BOD POD and HW with the 4C model was also calculated:
where Y1 is %fat assessed by the 4C model and Y2 is %fat assessed by either the BOD POD or HW (20). Statistical significance was set at P < 0.05.
The physical characteristics and group mean estimates of %fat by the different techniques for the 42 females are presented in Table 2. The AFFM is similar to what was reported by Hewitt et al. (16) (71%) and Bergsma-Kadijk et al. (2) (72%), but not work by Visser et al. (34) (74%). The MFFM in this study (5.6%) is lower than the 6.8% and 7.5% reported in the same population (2,34).
The total error for both the BOD POD and HW was 2.3% body fat and 2.4% body fat, respectively. Paired t-test results revealed no significant difference (P = 0.35) between HW Db (1.0352 g·cm−3) and BOD POD Db (1.0349 g·cm−3); this represents a difference of 0.14% body fat. The regression for Db by HW versus Db by the BOD POD significantly deviated from the line of identity because of an intercept significantly different from 0 and a slope significantly different from 1, where Db by HW = 0.099 + 0.90 × (Db by BOD POD) (Fig. 1). Db by the BOD POD explained 94% of the variance in Db by HW, whereas the SEE was 0.005 g·cm−3. A residual plot was performed to determine if bias existed between the Db by the BOD POD and the Db by HW (Fig. 2). The Db by the BOD POD showed no bias across the range of fatness as indicated by a nonsignificant correlation (r = 0.26;P = 0.09). The accuracy of the %fat from the BOD POD was examined by regression analysis in each individual. The regression of %fat assessed by the 4C model versus %fat assessed by the BOD POD significantly deviated from the line of identity because of an intercept that was significantly different from 0 and a slope significantly different from 1, where %fat by the 4C model = 5.41 + 0.88 × (%fat from the BOD POD) (Fig. 3). The %fat by the BOD POD explained 95% of the variance in %fat by the 4C model, whereas the SEE was 2.7% body fat. A residual plot was performed to determine if bias existed between estimates in %fat between the BOD and the 4C model (Fig. 4). BOD POD %fat did not exhibit any bias across the range of body fatness as indicated by a nonsignificant correlation (r = 0.27;P = 0.09).
The magnitudes of the difference between the BOD POD and 4C model estimates of %fat had a significantly positive correlation (r = 0.51;P < 0.01) with the fraction of FFM that was water (AFFM), as shown in Figure 5. However, the fraction of the FFM that was mineral (MFFM) had a negative association with the difference between the BOD POD and 4C model estimates of %fat (r = −0.17;P = 0.27) (Fig. 6). Additionally, HW showed the same trends (not presented).
This study examined the accuracy of and bias in measurements of %fat as assessed by the BOD POD relative to the 4C model in 42 females. Additionally, the relative fractions of the FFM that are water and mineral and its effect on %fat estimates using the BOD POD was investigated. This is significant because this is the first study to compare the BOD POD with the 4C model in adults. However, Fields and Goran (10) demonstrated that the BOD POD could accurately, precisely, and without bias estimate %fat in children 9–14 yr old using the 4C model as the criterion method.
Four studies have demonstrated excellent agreement between the BOD POD and HW, with the mean difference in %fat ranging from 0.05–1.0%(3,11,24,25). However, three studies have shown that the BOD POD significantly underpredicted %fat by ∼2% as compared with HW (5,8,22). Caution should be used in interpreting these results (3,5,8,11,22,24,25) because the BOD POD and HW are derived from two-compartment model (2C model) assumptions; thus, the BOD POD was not validated against a multicompartment approach.
The major finding of this study is that the BOD POD underestimated %fat in females of varying age and fatness when compared with the 4C model. Our data comparing the estimates of %fat by the BOD POD with the 4C model and data making the same comparisons with HW and the 4C model are somewhat similar (1,4,13,34). This would be reasonable because both methods measure total body density; essentially the only difference is that HW measures body volume by water displacement and the BOD POD measures body volume by air displacement. Group mean estimates in %fat by the BOD POD were 1.8% lower than the 4C model, whereas regression analysis indicated a moderate degree of agreement (2.7%) on an individual basis (SEE). Others have found group mean estimates in fatness between HW and the 4C model to be relatively small (∼1.5%) and %fat estimates on an individual basis to be somewhat higher (∼4%) (4,13,34). Additionally, studies in children (16,28) and older females have shown these group mean differences to be somewhat larger (6–10%). This large discrepancy in %fat in these two populations most likely is attributable to deviations from the basic assumptions in the 2C model (1,16,17,23). To date, this is the first study to compare BOD POD estimates of %fat with the 4C model in an adult population.
The difference in estimates of %fat between the BOD POD and the 4C model were significantly related to the aqueous fraction of the FFM. Visser et al. (34) and Baumgartner et al. (1) found the measurement of the aqueous and mineral fractions of the FFM increased the accuracy of the 4C model while taking into account biological variation resulting from aging, disease, ethnicity, and training status. However, the mineral fraction in those two studies contributed minimally to the model. Our data showed a statistically significant positive relationship between the difference in %fat by the BOD POD and the 4C model and the aqueous fraction of the FFM. This is in agreement with other studies (2,16), but not work by Baumgartner et al. (1) and Visser et al. (34). Our data demonstrated a negative relationship between the mineral fraction of the FFM and the difference between %fat by the BOD POD and the 4C model; this has been observed by others making the comparison with HW and the 4C model (2). However, not all studies have reported a negative relationship (1,34). The role of the aqueous and mineral fractions of the FFM and their relationships in explaining differences between %fat by a 2C model and the 4C model are debatable; however, it would appear the 4C model does increase accuracy in estimating body composition in a wide range of populations (1,4,13,18,26,33).
The BOD POD demonstrated a moderate degree of individual variation in %fat estimates as compared with the 4C model in adult females. Additionally, %fat estimates by the BOD POD were affected by the aqueous fraction of the FFM, thus highlighting the importance of a multicompartment approach in the evaluation of body composition analysis.
We would like to thank the Division of Physiology and Metabolism, Department of Nutrition Sciences at the University of Alabama at Birmingham for making available lab equipment and lab resources.
Address for correspondence: David A. Fields, Ph.D., Department of Internal Medicine, Center for Human Nutrition, Washington University, Campus Box 8031, St. Louis, MO 63110-1010; E-mail: [email protected]
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