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1 Exercise Physiology Laboratory, ABSTRACT Siconolfi, Steven F., Randal J. Gretebeck, William W. Wong,
Robert A. Pietrzyk, and Sheril S. Suire. Assessing total body and
extracellular water from bioelectrical response spectroscopy. J. Appl. Physiol. 82(2): 704-710, 1997.
bioimpedance; frequency; body fluids
INTRODUCTION BIORESISTANCE is a technique that estimates body fluids
by using a variety of models. All of these models use the relationship that states the volume of a conductor is the product of the specific resistivity and the ratio of the squared length of the conductor to the
measured resistance (12). The specific resistivity of the conductor is
a characteristic of the conductor (12). The following equation shows
this relationship
This investigation sought to evaluate a new CM. The new electric CM
consists of two series circuits (resistor and capacitor, resistor and
inductor) placed in parallel to each other (Fig. 1). Similar to other circuits (19, 20), the
intracellular water was the resistor (R1 in Fig. 1) in series with the
capacitor. ECW was the value of the other resistor (R2 in Fig. 1). The
sum (for parallel circuits) of R1 (ECW) and R2 (intracellular water) is
the resistance of the total circuit and represents the TBW. The
capacitor is on the intracellular water side of the circuit and
represents the capacitance of cell membranes (1, 7). The inductor is on
the extracellular side of the circuit and represents blood and plasma
volumes. The inclusion of an inductor in this model is different from
the circuits used by other investigators (22). An inductor produces an
electric field (resistance that is orthogonal to electron flow) when
electrons flow through a wire coil (12). Siconolfi et al. (20)
theorized that the inductor in the new circuit represented the electric
field produced when the subjects' blood and plasma carry the electric
charge through the net of the vascular system. The Siconolfi et al.
(20) bioelectrical response spectroscopy (BRS) model for blood and
plasma volumes used the inductor and estimated vascular volumes to
within 5% of clinical
(125I-labeled human serum albumin)
values. The capacitance and inductance values from this model were not
used to estimate TBW or ECW. As in other models (8-10, 15, 17,
22), regression analyses determined the conversion of RT to TBW and R2
to ECW.
We hypothesized that estimates of TBW and ECW could be made by using
components of the new CM and would not be affected by fluid shifts
(occurring within the ECW) associated with supine rest. We evaluated
the new estimates by comparing values from chemical dilution and
estimates from previously published single- frequency (SF) models. The
comparison of these assessments with the chemical dilution value helped
determine the degree of accuracy of the proposed and previously
published models.
METHODS Table 1.
Subject characteristics
We developed and validated assessments for total body water
(TBW) and extracellular water (ECW) by using two resistance values of a
new electric circuit model (CM) (two resistors: a capacitor and an
inductor) with or without body mass. Fluid shifts occurring after 40 min of supine rest did not decrease the validity of either estimate. CM
estimates were valid; r = 0.941 to
0.969, low SE of estimates of 1.15-2.28 kg, nonsignificant mean
differences (CM 
dilution; %
= 0.4 to 1.3%) that
were close to the expected measurement errors for TBW (±1%) and
ECW (±5%), and Bland-Altman pairwise comparisons that showed
equivalence between methods. The CM estimates of TBW and ECW had
marginally better validity than the previously published bioimpedance
models. The advantage of the CM model is its assessments of multiple
fluid spaces and that it does not require gender-specific equations. We
conclude that CM estimate of TBW is acceptable, whereas further
validation is needed before the ECW estimate should be used in a
clinical or research setting.
where
is the specific resistivity (
*cm) of the conductor and
R () is the measured resistance.
The specific resistivity is a different constant for each conductive
medium. In human applications, the length of the conductor is generally
the subject's height (Ht). Previous researchers used a variety of
input frequencies to determine different fluid spaces (4, 7-10,
15-17). The capacitive nature of cell membranes led researchers to
use currents at higher frequency (50-100 kHz) to assess total body
water (TBW) (8-10, 15, 17, 22) and lower frequencies (1-5
kHz) to estimate extracellular water (ECW) (4, 9, 17, 22). These
researchers empirically chose the frequencies and evaluated their
effectiveness as predictors of specific fluid volumes. Other
investigators used an electric circuit model (CM), with multifrequency
inputs, to determine the size (magnitude in ohms, farradays, and
henrys) of the resistors, capacitors (22), and inductors (19, 20) of
different CM. These investigators used regression models, with the size
of the electric components as the dependent variable when estimating
fluid volumes.
Fig. 1.
This new electric circuit model (CM) was modified from the model of Van
Loan et al. (22) by inclusion of an inductor on the extracellular side.
R1, the intracellular resistor; C, membrane capacitance; R2,
extracellular resistor; and L, inductor.
[View Larger Version of this Image (5K GIF file)]
Development Groups
Validity Groups
TBW
ECW
TBW
ECW
Age, yr
32.0 ± 6.6
33.8 ± 5.8
35 ± 6.4
34.0 ± 7.0
Height, cm
168 ± 6
170 ± 11
170 ± 10
170 ± 9
Body mass, kg
67.8 ± 13.0
64.7 ± 11.4
69.2 ± 14.0
72.9 ± 17.1
TBW, kg
38.8 ± 7.7
34.6 ± 6.3
36.8 ± 7.8
38.6 ± 9.6
ECW, kg
NA
15.4 ± 2.9
16.0 ± 3.4
16.2 ± 3.8
Gender
10 M/13 F
6 M/11 F
16 M/15 F
6 M/3 F
Values are means ± SD. TBW, total body water; ECW, extracellular
water; M, male; F, female.
20°C
until analysis. We prepared samples according to the procedures of Wong
et al. (25) for
18O/16O
or
2H/1H
isotope-ratio measurements by gas-isotope-ratio mass spectrometry.
Dilution space was calculated from baseline, 4-h, and 5-h sample
collections by using the equation (10)
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is enrichment of dose A, tap water (t),
peak postdose sample (s), and predose baseline sample (p). To account
for incorporation of tracer into nonaqueous tissue, a correction factor
of 1.04 (deuterium) or 1.01 (18O-labeled water) was used for
the relationship between the isotope dilution space and TBW (15). The
error rate in our laboratory for this measurement is <1% (based on
difference between 4- and 5-h samples).
ECW.
We assumed that ECW was the bromide dilution space. Baseline bromide
levels were determined from an initial blood sample. Subjects then
ingested an oral dose of bromide (1.2 g of NaBr). Additional blood
samples were collected 3 and 4 h after administration of the dose. All
samples were centrifuged, and the plasma was stored at 20°C.
Plasma proteins were removed from the sample before ion chromatography
by adding 0.3 ml of the sample to Ultra-free-PF Filter units (10,000 nominal mol mass limit; Millipore, Bedford, MA). Pressurizing the
filter assembly with 10 ml of air from a plastic syringe activated the
units (23). The protein-free filtrate (60 µl) was diluted 1:100 with
ion chromatography eluant (1.8 mM
Na2CO3/1.7
mM NaHCO3). Recovery of
bromide-spiked plasma samples was >90%.
Bromide concentration in the samples was determined by using ion
chromatography (Dionex model 2000i suppression-based system; Dionex,
Sunnyvale, CA). Samples (500 µl) were automatically injected onto the
AS4A column (Dionex) by using the Dionex autosampler module with a flow
rate set at 1 ml/min. Bromide was determined by suppression-based
conductivity detection and quantitated by using a calibration curve
(least squares linear regression).
ECW volumes were determined from the difference in plasma bromide
concentrations between the baseline and 3-h samples. ECW were
calculated as
![ECW = Br<SUB>dose</SUB>/[Br] ∗ 0.90 ∗ 0.95 ∗ 0.94](/content/vol82/issue2/fulltext/704/img003.gif)
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after 40 min of supine rest in gym shorts and
T-shirts. This increase was minimized (~10-15 ) when subjects
were kept warm with a blanket. The difference between the BRS TBW at 0 and 40 min evaluated the effects of shifting fluids between
interstitial and vascular fluid spaces (5, 14, 18). This comparison is
important, because Roos et al. (14) and Sherrifs and Maughan (18)
showed an association between the increases in total body resistance
(measured at a set frequency) and decreases in hematocrit that
accompany a change in posture.
Determination of circuit components.
The determination of circuit components did not use the Cole-Cole
method but used a new approach (6) that has been shown to produce
resistances similar to graphic techniques based on the Cole-Cole theory
[Cole and Curtis (3)]. We regressed (3rd-order least square
regression) each subject's impedance and resistance values on
frequency. The R2 (ECW) resistor (Fig. 2)
was the intercept (when frequency was zero) of the resistance on
frequency regression. At a zero frequency, the current flow
preferentially goes through the extracellular side of the circuit,
because the capacitor acts as a gap on the intracellular side of the
circuit. This figure represents data for a typical subject who had a
correlation of 0.99 for impedance on frequency. The mean ± SD of
the correlations for the individualized regressions was 0.99 ± 0.02. The resistance of the total circuit (RT) was the resistance at
the frequency where impedance changed by only 1% with a frequency
increase of 25 kHz (Fig. 2). We used the 1% limit because it is an
industry standard for high-precision resistors (12). This analytical approach uses the theory that, at very low frequencies, the electrical current does not enter cells, whereas at high frequencies, the current
enters both the intracellular and extracellular fluid spaces (1, 3, 4,
9, 17). The R1 (intracellular water) resistor was the difference
between one divided by the RT and one divided by R2 (ECW).
of the 3rd-order
regression intercept of Z on frequency), and RT is resistance at the
frequency (open arrow) where Z changes = 1%.
This new method of circuit analysis represents a teleological approach. This is different from the traditional Cole-Cole analysis that solves for resistances when reactance is zero (3, 7). The Xitron BIS 4000B analyzer (Xitron Technologies, San Diego, CA) uses a modified Cole-Cole approach with iterative curve fitting (26). Unlike the Cole-Cole approach, this statistical approach allows for the removal of 25% of the data to increase the fit of the resistance and reactance values. Our approach uses all the data. Our laboratory (6) reported a high correlation (r = 0.987-0.994) between the analysis techniques for the R2 and RT resistors. However, the main predictor of TBW from BRS (Ht2/RT) had a significantly weaker correlation and larger SE of the estimate (SEE; r = 0.693 ± 5.6 kg) for the Cole-Cole analysis than that observed from our model (r = 0.945 ± 2.6 kg). Therefore, we concluded that the new circuit analysis was preferable. SF estimates of TBW and ECW. The gender-specific equations by Kushner and Schoeller (8) estimated TBW from Ht2/R (resistance at 50 kHz) and body mass (M) by using the following sex-specific equations
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of accepted)
between methods and the average of these methods. Bland and Altman (2)
suggest that if all the values are within the ±2 SD of the averaged
values (±2 coefficient of variations for %) and there is no
correlation between the differences vs. the averaged values, then the
methods are clinically equivalent. Validity of a new method decreases
if 1) the mean difference is greater
than the measurement error, 2) the
Bland-Altman plot shows data points outside confidence intervals, and
3) there is a significant relationship indicating that one method overestimates or underestimates the other as a function of size.
RESULTS
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) of the circuit and M is the mass (in kg) of
the body. The mean ± SD of RT for the development group was 465 ± 83 . The stepwise multiple regression for ECW yielded the
following prediction equation for ECW with a multiple R of
0.858 and SEE of 1.72 kg
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) of the circuit at a frequency of 0 Hz. The
mean ± SD of R2 for the development group was 683 ± 88 . No
other independent variables (age or gender) significantly improved the
strength of the ECW estimation.
Validity of TBW assessment.
We examined the validity of the TBW and ECW estimations at
time 0 (BRS taken immediately after
subjects became supine) and after 40 min of supine rest. The CM
estimate of TBW had high correlations (r = 0.956 to 0.964) and low SEE
(2.08-2.28 kg) compared with measures using isotopic dilution in
the validation group. The CM estimate of TBW was not significantly
different from the isotopic values for both time points (1.3 ± 6.1 and 0.4 ± 5.5% for 0 and 40 min, respectively). These
results were similar to those observed for the SF-BRS estimates
(r = 0.951-0.956, SEE = 2.39-2.42 kg). The Kushner and Schoeller (8) estimate of TBW was
significantly (P < 0.05) greater
than isotopic dilution at 0 min (4.2 ± 6.2%) but not at 40 min
(2.7 ± 6.2%). The Bland-Altman correlations for %
(BRS-2H2O)
vs. averaged TBW (Fig. 3 and Table
2) were not significantly different from zero. This
indicated there was no trend for either [CM or Kushner and
Schoeller (8)] BRS assessment of TBW. Values for both BRS
assessments were within two coefficients of variation of the averaged
values. Similar values were observed for the BRS- estimated TBW after
40 min of supine rest (Table 2).
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10.5 ± 15.7 and
6.1 ± 16.4%, respectively). The CM and the Lukaski and
Bolonchuk (10) BRS estimates of ECW were not significantly different
from the dilution values for both time points (CM, 1.2 ± 7.7 and
1.7 ± 7.6%; Lukaski and Bolonchuk (10), 5.7 ± 7.3 and 8.8 ± 7.0% for time at 0 and 40 min, respectively).
The Bland-Altman correlations for % (BRS-ECW) vs. averaged ECW
(Fig. 4) were not significantly different
from zero for CM or Lukaski and Bolonchuk (10) at 0 and 40 min. This
indicated there was no trend for these BRS assessments of ECW. However, the Segal et al. (17) estimate of ECW had significant correlations for
both time points (Table 2). Segal et al. (17) BRS assessment of ECW was
within two coefficients of variation of the averaged values at 0 and 40 min, whereas CM estimates and the results of Lukaski and Bolonchuk (10)
were within one coefficient of variation (Table 2).
6.1 ± 16.4%). Plain arrow, mean difference for Lukaski and
Bolonchuk (6; 5.7 ± 7.3%). Plain arrow, mean difference for
CM (1.2 ± 7.7%). *Significant correlation between difference and
averaged ECW, P < 0.05.
DISCUSSION
We developed and validated assessments for TBW and ECW from the
resistor values of a new electric CM and body mass (TBW only). These
BRS estimates of TBW and ECW were not affected by fluid shifts that
occur after 40 min of supine rest. These estimates have good validity,
based on the strength of the correlations (r = 0.941-0.969), low SEE
(1.15-2.28 kg), nonsignificant mean differences (CM dilution; % = 0.4 to 1.3%) that were close to the expected
measurement errors for TBW (±1%) and ECW (±5%), and the
Bland-Altman pairwise comparisons that showed no significant trend.
These findings are not surprising because other researchers have shown
that BRS estimates of fluid volumes have been successful (2, 5-7,
13, 14, 18).
Kushner and Schoeller (8) reported similar correlations (r = 0.93-0.96) and SEE (2.03-2.25) when assessing TBW in their validity group. In a review by the same authors (15), estimates of TBW from SF-BRS had correlations with dilution methods ranging from 0.87 to 0.98 and SEE of 1.37-3.03 kg. All the prediction equations minimally used Ht2/R and body mass. The resistance was measured at 50 kHz for these studies. The present study only used Ht2/RT and body mass, not needing gender-specific equations or gender and age as factors.
We evaluated the validity of the Kushner and Schoeller (8) model with data from the present study. This model yielded similar correlations, SEE, and Bland-Altman pairwise comparisons (Table 2) as those derived from CM. The only difference between the methods was the significantly higher (+4.2% greater than 2H2O) mean values at 0 min. This difference may have been due to the amount of time between placing electrode on the subject and recording of BRS. In the present study, a technician placed the electrodes on the subjects before they became supine; in the Kushner and Schoeller study (8), the amount of time in the supine position before the resistance was recorded may have varied. This is supported by the lack of a difference after 40 min of rest for this model (Table 2). The CM estimate of TBW may be an improvement over the Kushner and Schoeller (8) model because of the following findings: 1) no significant mean difference between dilution and CM, 2) mean differences closer to measurement error for CM, and 3) CM does not require sex-specific equations for the estimation of TBW.
The stability of RT during the 40 min of supine rest provides a stable estimate of TBW when internal fluid shifts are occurring. This may be due to our definition of RT as the resistance where impedance is changing at 1% over increments of 25 kHz. The use of 1% of impedance (not resistance) kept RT-sensitive changes in all of the electrical components representing fluid volumes. Therefore, shifts of fluids may have altered one or more of the components [as shown with inductance by Siconolfi and Gretebeck (19)], but these did not affect the 1% impedance value and kept RT stable.
BRS models that estimate ECW by using a resistance response from a single frequency have reported results similar to those found in the present study with the use of the CM model. Lukaski and Bolonchuk (10) reported a correlation of 0.907 with a SEE of 1.40 kg when they applied their model to a validation group. Segal et al. (17) did not report a validation (n = 18) group's correlation or SEE, but they did report nonsignificant mean differences between their model's estimate and their validity group's dilution value. They also reported that the residuals were not related to either the predicted or measured values. Van Loan et al. (22) developed an estimate of ECW with the use of a similar electrical circuit. Their circuit does not contain an inductor. They determined the value of their resistors through iterative statistical regression (repeating curvilinear regression while removing up to 25% of the data points). They report a correlation of 0.893 (SEE of 0.947) and no significant difference between their BRS estimate and their reference method (22).
The Van Loan et al. (22) regression estimates of ECW and TBW are based on the resistances when reactance is zero. The Cole-Cole model (3) assumes that these resistances represent the points where fluids (TBW and ECW, for our purposes) become pure conductors. Van Loan et al. (22) computed (by statistical semicircle regression of reactance on resistance) the Cole-Cole values from resistance and reactances over a wide frequency range obtained from the Xitron 4000B bioimpedance analyzer (Xitron Technologies, San Diego, CA). However, the Xitron computation of the TBW and ECW resistances allows for removal of data (up to 25%). This is not part of the Cole-Cole method for determining these resistances. We compared resistance, capacitance, and inductance values for the Xitron 4000B to the Hewlett-Packard LCR, by using standard electrical components. We found differences between the instruments (6). Therefore, we could not compare the Van Loan et al. (22) regression estimates of body fluids to those in the present study.
The present study compared the three BRS estimates for ECW with the dilution-determined value. All models had good correlations (r >0.9) with low SEE (<1.6 kg). The Segal et al. (14) model yielded significantly lower ECW after 40 min of supine rest. The CM model had low mean differences (<2%) that were less than the expected measurement error (5%). The means for the Lukaski and Bolonchuk (10) model were not significantly different from the dilution values, but they did exceed the expected measurement error. Significant Bland-Altman correlations at 0 and 40 min were observed only for the Segal et al. (17) model. These correlations suggest this model overestimated the high ECW values and underestimated the low values (Fig. 4). This was not found for the other two models. Lohman (9) recommends that the regression weight for body mass should be as small as possible in an assessment of body fluids. The model of Lukaski and Bolonchuk (10) met this recommendation, but it was not evident for the Segal et al. (17) model. The Segal et al. (17) model uses a regression weight for body mass that is twice that used by Lukaski and Bolonchuk (10). In the SF models, body mass accounts for 50 and 23% of the predicted ECW, using the Segal et al. (17) and Lukaski and Bolonchuk (10) equations, respectively. The high contribution of body mass in the Segal et al. (17) estimate of ECW may have produced the significant Bland-Altman trends since ECW and body mass were correlated (r = 0.783) for these subjects. The CM estimate of ECW did not require body mass as a factor in its model and therefore has very small Bland-Altman correlations. A limitation of the present study's analysis of these three models is the small number of subjects in the validation group. Therefore, these validation results for ECW are preliminary and should be verified with an appropriate number (n = 30) of subjects.
The CM estimates of TBW and ECW had marginally better validity than the previously published models that used SF-BRS inputs plus body mass and gender. The advantage of the CM model is that it provides assessments of TBW, ECW, and blood volume (20) using only body mass as an additional independent factor. The second advantage was the stability of the estimates during fluid shifts. We conclude that CM estimate of TBW is acceptable, although further validation is needed for the ECW estimate before it could be used in a clinical or research setting.
ACKNOWLEDGEMENTS
We thank Alice Rogers for helping with data collection.
FOOTNOTES
Address for reprint requests: S. F. Siconolfi, Space Biomedical Research Institute SD3/Space Biomedical Research Institute, NASA Johnson Space Center, Houston, TX 77058.
Received 21 November 1994; accepted in final form 11 July 1996.
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S. F. Siconolfi, R. J. Gretebeck, W. W. Wong, S. S. Moore, and J. H. Gilbert III Determining bone and total body mineral content from body density and bioelectrical response spectroscopy J Appl Physiol, October 1, 1998; 85(4): 1578 - 1582. [Abstract] [Full Text] [PDF]
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ABSTRACT