Vol. 281, Issue 5, E1005-E1014, November 2001
New equations for estimating body cell mass
from bioimpedance parallel models in healthy older
Germans
Manuela
Dittmar1 and
Helmut
Reber2
1 Department of Biology, Institute of Anthropology,
Gutenberg University, 55099 Mainz; and 2 Department of
Nuclear Medicine, Gutenberg University Hospital, 55101 Mainz, Germany
 |
ABSTRACT |
The objectives of this study
were to assess for elderly Germans the validity of existing equations
for predicting body cell mass (BCM) and to develop from single- and
multifrequency bioimpedance (SFBIA, MFBIA) models new
prediction equations. In a data-splitting approach, validation and
cross-validation were performed in 160 healthy elderly (60- to 90-yr)
subjects. BCM was determined using a tetrapolar bioimpedance analyzer
(800 µA; 4 fixed frequencies: 1, 5, 50, and 100 kHz; electrodes
placed to hand, wrist, ankle, and foot) and whole body 40K
counting as a reference method. New prediction equations were derived
by multiple stepwise regression analysis. The Bland-Altman procedure
was used for methods comparison. Relative to whole body counting, the
manufacturer's equation overestimated BCM by 9% in men
(P < 0.0001, paired t-test) and 4% in
women (P = 0.002). Compared with the manufacturer's
equation, the newly derived equations (r = 0.92, RMSE = 6-9%) improved accuracy (pure error = 13 vs. 7-8%) and
reduced bias and limits of agreement. SFBIA and MFBIA equations did not
differ in precision or accuracy. We conclude that the newly derived
equations improved BCM estimates in the elderly compared with existing
equations. There was no advantage of MFBIA over SFBIA equations.
body composition; whole body potassium-40 counting; validation; cross-validation; European ancestry
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INTRODUCTION |
THE HUMAN BODY MASS
can be partitioned into two main compartments, the fat-free mass and
the fat mass. The fat-free mass comprises the cellular mass, bone mass,
and extracellular water. The body cell mass (BCM) can be defined as the
fat-free mass without bone mineral mass and extracellular water. The
concept of BCM as the sum of oxygen-exchanging, glucose-oxidizing,
work-performing, potassium-rich cellular components of the human body
has been formulated by Moore et al. (31). BCM is the most
metabolically active human body compartment, whose main fractions are
the cellular components of muscles and viscera. It comprises those
tissues that are most likely to be affected by physical activity,
nutrition, or disease. The assessment and quantification of BCM is
particularly important in elderly subjects, because BCM decreases in
the course of natural aging processes (3, 11), primarily
due to a loss in skeletal muscle mass. Because the amount of BCM
decline is closely related to immobility, morbidity, disease, and
mortality (37, 42), the accurate assessment of BCM has
considerable clinical relevance.
For this reason, valid and reliable techniques for determining BCM are
needed. BCM is considered difficult to quantify. Commonly used
methods are the counting of naturally occurring 40K in a
whole body counter, the determination of total exchangeable potassium
by isotopic dilution techniques with the use of the exchangeable
sodium-to-potassium ratio (Nae/Ke)
(43), and, recently, bioelectrical impedance analysis (BIA).
Whole body counting of 40K is considered a criterion method
for measuring BCM (34). This method enables by noninvasive
nuclear technique the in vivo determination of the naturally occurring radioactive isotope 40K in the human body. Because
40K is 0.012% of natural potassium, it provides the value
of total body potassium (TBK). Potassium is almost exclusively an
intracellular cation (95%), found chiefly in muscle and viscera
(hence, essentially not found in fat, bone, or extracellular water), so
that the TBK measurement serves as an index of BCM. However, this
method is not suited for epidemiological and clinical studies, because
access to whole body counters is limited, specialized teams are
required, and measurement is sophisticated and costly.
In contrast, the new, noninvasive BIA is well suited for determining
BCM in larger population groups, because the relatively inexpensive
equipment is portable, simple, and quick to operate, and the
measurement has little interobserver error, requires minimal cooperation from the subject, and can be done at bedside. This method
is based on the principle that various tissues have different conductive and resistive properties at different frequencies of an
electrical current (28). Bioimpedance analyzers apply a
low-level, 500- to 800-µA alternating current at one or several
frequencies to the human body and measure the impedance of the body to
the electrical flow. The impedance of the body is composed of two components, 1) the resistance of the tissues, which is
proportional to the fluid volume in the body, and 2) the
reactance, which is the reciprocal of the capacitance of cell
membranes, tissue interfaces, and nonionic tissues (21).
Most single-frequency (SF)BIA analyzers operate at a fixed frequency of
50 kHz, it being the frequency of maximal reactance for most muscle
tissues (41), and provide separate measurements of
resistance and reactance. In contrast, multifrequency (MF)BIA analyzers
work at several fixed frequencies or across a spectrum of frequencies
(bioelectrical impedance spectroscopy) and are based on the
principle that low-frequency impedance measures extracellular fluid
whereas high-frequency impedance measures both extra- and intracellular fluids.
BIA predicts BCM from the measured resistance and reactance data by use
of equations that are based on specific models (23, 27).
The existing equations and the underlying assumptions were developed
primarily from younger and middle-aged population groups and,
therefore, might not be applicable to elderly groups. Consequently, there is a need to test the validity of the existing prediction equations for their applicability to the elderly and, if necessary, to
develop new age-specific equations. Deurenberg et al. (15) found that prediction equations that were developed in younger populations overestimated fat-free mass in the elderly by ~6 kg (8%
of body wt). Bussolotto et al. (8) reported that the
fat-free mass hydration variability in the elderly might be the
principal variable that explains errors obtained in fat-free mass
estimation by BIA equations in the elderly, emphasizing the need for
more validation studies. So far, age-specific predictive BIA equations for elderly groups have been developed for estimating fat-free mass
(2, 15, 30, 38), whereas validation studies for estimating
BCM are still, to the authors' knowledge, missing.
The primary objectives of this study were 1) to evaluate in
relatively healthy elderly men and women the accuracy of existing prediction equations for estimating BCM from BIA and 2) to
develop new population-, age-, and sex-specific prediction equations
for estimating BCM from SF- and MFBIA models by use of whole body counting of 40K as a reference method.
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STUDY POPULATION AND METHODS |
Study Population
The elderly participants of this study were recruited by M. Dittmar from local community centers and associations for elderly people in the area of Mainz from October 1998 to July 1999. Their health status was evaluated by means of a medical questionnaire and by
their private physicians. Their nutritional status was determined using
a standardized food record based on a 7-day food diary. Subjects were
selected for this study if they met entry criteria as follows:
1) no amputations, 2) no diseases and medications that affect body composition and potassium homeostasis, 3)
no diarrhea or edema, 4) no current taking of diuretics,
laxatives, or oral potassium supplements, 5) no alcohol
abuse, 6) no malnutrition, 7) no adiposity [body
mass index (BMI) <30 kg/m2]. Obese subjects
were excluded because on the one hand, fat tissue tends to absorb
-rays to a small extent so that 40K emission would be
underestimated (12) and as a consequence BCM would be
underestimated, and on the other hand, BIA overestimates fat-free mass,
and thereby BCM, in obese subjects (3, 40). Of the 192 volunteers that met the entry criteria, 31 refused to participate in
whole body counting of naturally occurring 40K because of
claustrophobia. There was no statistically significant difference in
weight, height, BMI, and physical activity level of the elderly who
agreed to participate compared with those who decided not to
participate in the study. Finally, 160 apparently healthy, free-living,
retired men (n = 81) and women (n = 79) of German ancestry, aged 60-90 yr, volunteered to participate in
this study. Data on their physical activity level and dietary potassium
intake were published in an earlier study (17). One of the
original 161 participants who entered that study was excluded from this
report because of a missing bioimpedance measurement. Written informed
consent was obtained from each participant before investigation. The
study protocol was approved by the ethics committee of the Medical
Association of Rhineland-Palatinate.
Measurement Methods
Study design.
To validate BIA in the elderly group, all subjects had BIA and whole
body 40K counting in a set order on the same day in the
morning after an overnight fast of 
12 h. Subjects were instructed to
refrain from ingesting alcohol for 48 h and from strenuous
physical activity for 24 h preceding the testing day to minimize
perturbation of body fluid.
Anthropometry.
Height and weight were measured following the technique of Martin (see
Ref. 26). Standing height was measured, with subjects unshod and wearing light indoor clothes, to the nearest 0.01 m, by
means of an anthropometer. Weight was determined on a calibrated electronic scale accurate to 0.1 kg. BMI was calculated as body weight
divided by height squared (kg/m2).
Whole body counting of 40K.
TBK was determined by whole body counting of the radioactive isotope
40K by H. Reber at the Department of Nuclear Medicine,
University Hospital, Mainz. The 40K activity was measured
in a counting chamber with 33-cm-thick barite-concrete outer walls and
15-cm-thick steel inner walls from pre-World War II battleships; inside
dimensions of the chamber are 1.2 m wide × 1.2 m
high × 2.2 m long. The whole body counter is a fixed-array
counter consisting of 12 NaI (T1) detectors mounted in pairs below the
measuring coach, where the subject lies still in a supine position for
10-min counting time in the enclosed counting chamber after removal of
eyeglasses, jewelry, and watches. Voice communication with the subject
and ventilation are provided in the chamber. Analysis of the measuring
data was computer based. TBK was calculated from the
background-corrected 40K counts as TBK (g) = Cfi × 40K counts, where Cfi
is the specific calibration factor for the geometry of the whole body
counter. For calibration, a homogeneous anthropomorphically shaped
phantom filled with a known concentration of potassium chloride
solution of known composition was used. The relative counting error of
the TBK measurement, governed by the 40K counts, is 2.1%.
BCM was calculated from TBK by use of the formula of Moore et al.
(31) as BCM (kg) = 0.00833 × TBK (mmol). It has
been shown that the potassium concentration in both the BCM and the
intracellular compartment is relatively constant in normal subjects,
independent of age and sex (11).
MFBIA.
BCM was determined by M. Dittmar at the Institute of Anthropology,
University of Mainz, by using a tetrapolar whole body multifrequency bioelectrical impedance analyzer (BIA 2000-M, Data Input, Hofheim, Germany) that is derived from the RJL analyzers. The device was calibrated each morning by means of a standard resistor supplied by the
manufacturer. Measurements were performed in the subjects, within 30 min of voiding, in a supine position on a flat, nonconductive couch,
with their limbs abducted from the trunk. Glasses, jewelry, and watches
were removed. A tetrapolar arrangement of gel electrodes was placed at
defined anatomical sites to one hand, wrist, ankle, and foot of each
participant, following the instructions of the manufacturer. The
minimal distance between the electrodes was 5 cm to avoid interactions
between source and sensor electrodes. The BIA 2000-M analyzer applies
an 800-µA alternating current and measures resistance (R,
), reactance (Xc, ), impedance (Z, ),
and phase angle (PA, °) at four fixed frequencies (1, 5, 50, 100 kHz). At 1 kHz, only R and Z are measured,
because Xc always equals zero. The precision error of the
analyzer for the measurement of R is ±0.5%, for
Xc ±2.0%, and for the PA ±0.5°. R and
Xc are the two vectors of the impedance that the human body
opposes to the applied alternating electrical current, where
Z is defined as Z2 = R2 + Xc2. To
differentiate between R and Xc, the BIA 2000-M
device uses phase-sensitive electronics. Capacitors in the circuit of
the alternating current cause a time shift, where the maximum of the voltage lags behind the maximum of the current. Because alternating current is sinusoidal, the phase shift is measured in degrees and is
called the PA. The PA was calculated as a function of the ratio of the
resistance of body fluid volumes to the reactance (capacitance) of cell
membranes as PA = arc tangent
(Xc/R)0.5 × (180/
), with = 3.1416 (19). Studies have shown that the PA at 50 kHz
1) enables discrimination between intra- and extracellular current distribution (1, 20) and 2) is
proportional to the amount of BCM in the lean body mass (LBM)
(19). Therefore, the manufacturer's analysis software
(Body software version 4) calculated BCM as BCM (kg) = LBM × ln (PA50) × 0.29. The LBM was calculated from
total body water (TBW) by assuming 73% hydration of LBM
(33) as LBM = TBW/0.732. TBW was calculated from the
resistivity index Ht2/R at 50 kHz and weight
using the published bioimpedance equation of Kushner and Schoeller
(29), modified by the manufacturer's empirical constants.
Bioimpedance Models
In this study, new equations for predicting BCM were developed
from SFBIA and MFBIA models. Following De Lorenzo et al.
(13), BCM was considered a concept that could be defined
by intracellular water (ICW), because impedance is more closely related
to the volume of intracellular ion-containing water fluids than to the corresponding body masses. For predicting ICW, one SFBIA and four MFBIA
models were considered on the basis of parallel models described by
Kotler et al. (27) and Gudivaka et al. (23),
respectively. The SFBIA model was included to evaluate whether MFBIA
predicts BCM better than SFBIA.
Single-frequency 50-kHz parallel model.
This model uses resistance (R) and reactance (Xc)
as measured at 50 kHz. The choice of predicting BCM on the basis of
Xc at 50 kHz was performed in view of the results of Kotler
et al. (27) and Gudivaka et al. (23). A
parallel model was used because the 1997 follow-up to the 1994 Technology Conference of the National Institutes of Health (NIH) on the
assessment of bioimpedance analysis technology for body composition
measurement has stated that the parallel SFBIA model provides more
acceptable estimates of BCM than the serial model (20).
The parallel model is based on the assumption that the human body
reacts as if the resistance-capacitance circuits were arranged in
parallel (32). For this, the Xc value at 50 kHz
is converted to its parallel value (Xcp) as
Xcp50 = Xc50 + (R502/Xc50).
The parallel reactance is linearly related to ICW as
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(1)
|
where m and c are constants derived from
linear regression analysis (m is slope and c is
intercept), and Ht means height, which estimates circuit length.
Multifrequency parallel models: 1/50, 1/100, 5/50, and 5/100 kHz.
Multifrequency models are based on the observation that the injected
current passes almost exclusively through extracellular water at low
frequencies <10 kHz, because it cannot pass the cell membranes, and
through extra- and intracellular water at high frequencies >10 kHz. In
this study, the 1-, 5-, 50-, and 100-kHz frequencies were used, because
these four frequencies were available on the BIA 2000-M bioimpedance
analyzer. Parallel models were applied because Gudivaka et al.
(23) stated that these models consider "that the
specific resistivities (resistance per unit length of a conductor with
a cross-sectional area of 1 cm2) are quite different for
extra- and intracellular fluids and that the resistance should be
segregated into the extracellular (Rec) and
intracellular (Ric) components by use of a
parallel model." Four models were evaluated in this study, all based
on the models described by Gudivaka et al., where
Rf is resistance at f kHz
Multifrequency 1/50-kHz parallel model
|
(2)
|
where Rec = R1 and Ric = (R1 × R50)/(R1 
R50)
Multifrequency 1/100-kHz parallel
model
|
(3)
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where Rec = R1 and Ric = (R1 × R100)/(R1 R100)
Multifrequency-5/50-kHz parallel
model
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(4)
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where Rec = R5 and Ric = (R5 × R50)/(R5 R50)
Multifrequency-5/100-kHz parallel
model
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(5)
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where Rec = R5 and Ric = (R5 × R100)/(R5 R100)
Statistical Methods
All statistical analyses of the data were performed by M. Dittmar using the SPSS/PC software package for MS Windows, release 8.0 (SPSS, Chicago, IL). For the analyses, an 
-level of 0.05 was used as
the criterion for statistical significance.
Development and Cross-Validation of New Predictive Equations
New population-, age-, sex-, and device-specific equations for
estimating BCM from BIA were developed following the statistical methods described by Guo and Chumlea (24). BCM (kg) was
chosen as the dependent variable estimated from TBK measured by whole body counting of 40K. Validation and cross-validation were
performed by a computer-based data-splitting approach. The prediction
equation for BCM was derived from a random subset of the study
population (model-building sample, 55 men, 55 women) and was internally
validated on the remaining subset (cross-validation sample, 26 men, 24 women). Multiple linear regression analysis (stepwise method) was
performed to identify the best predictors of BCM by using as
independent variables the bioimpedance indexes relating to the five BIA
models (Ht2/Xcp50,
Ht2/Ric1/50,
Ht2/Ric1/100,
Ht2/Ric5/50,
Ht2/Ric5/100), sex (as a dummy
variable; 1 man, 0 women), weight, age, and further bioimpedance
measures. The assumption of normality of the response variable was
tested using the Kolmogorov-Smirnov test. The homogeneity of the
response variable was tested by checking residual plots for trends or
patterns. Presence of a linear relationship between the response
variable and each predictor variable was tested by using scatterplots.
Absence of multicollinearity of the predictor variables in the
equations was examined by the condition number (CN) computed for
standardized residuals with the intercept included. Absence of
autocorrelation was judged by the Durbin-Watson test. The coefficient
of determination (r2) was used as a measure of
goodness of fit of the generated equations. The precision of the
predictive equations in the model-building sample was determined using
the root mean square error (RMSE) as RMSE = [
(X1 X2)2/(n p 1)]0.5, where
X1 and X2 are the
observed and predicted BCM values for an individual, n is
the number of subjects in the model-building sample, and p
is the number of predictor variables. To measure the performance of the
predictive equations on cross-validation, the pure error (PE) between
the TBK and BIA methods in the internal cross-validation sample was
calculated as PE = [(Y1 Y2)2/n]0.5,
where Y1 is BCM predicted by BIA,
Y2 is BCM observed by the TBK method, and
n is the number of subjects in the internal cross-validation sample.
Measurement Methods Comparison
Student's paired t-test and the statistical
technique of Bland and Altman (6) were employed to examine
the degree of agreement between the TBK and BIA methods to predict BCM.
The latter technique concentrates on individual differences and
calculates bias and 95% limits of agreement between the TBK and BIA
measures. The use of correlation coefficients or regression
analysis has been shown not to be appropriate for the comparison of
measurement methods (6, 44).
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RESULTS |
Table 1 summarizes the
anthropometric and bioimpedance characteristics, measured TBK, and
predicted BCM for the entire elderly sample and the validation and
cross-validation subsamples. BMI ranged for the entire sample from 19.0 to 29.9 kg/m2. BIA using the manufacturer's equation
significantly overestimated BCM relative to the TBK method by 2.4 kg
(8.9%) in older men (t = 8.131, P < 0.0001) and by 0.8 kg (4.1%) in women (t = 3.149, P = 0.002; paired t-tests, entire sample).
To reduce the bias between the BIA and TBK methods in estimating
BCM, new prediction equations for estimating BCM from BIA were
developed.
Development of New Predictive Equations for Estimating BCM from
BIA
Selection of predictor variables for the best fitting prediction
equation for estimating BCM from BIA.
For each of the five bioimpedance models under consideration, a
multiple stepwise linear regression analysis was performed to develop
prediction equations for BCM in the model-building subsample. Strongest
correlations with BCMTBK were found for the SFBIA
index Ht2/Xcp50 (model 1)
and for the MFBIA indexes
Ht2/Ric5/50 (model 4) and
Ht2/Ric5/100 (model 5),
whereas the MFBIA indexes
Ht2/Ric1/50 (model 2) and
Ht2/Ric1/100 (model 3)
displayed only weak correlations (Table
2). In addition, sex was strongly
correlated with BCMTBK. Separate stepwise regression
analyses showed that the inclusion of sex as a predictor variable
improved significance for the indexes Ht2/Xcp50,
Ht2/Ric5/50, and
Ht2/Ric5/100, whereas the indexes
Ht2/Ric1/50 and
Ht2/Ric1/100 were removed from the
analyses. The additional inclusion of weight improved significance for
the SFBIA index but not for the remaining MFBIA indexes. Further
inclusion of bioelectrical impedance variables (R at 1, 5, 50, and 100 kHz; Xc and PA each at 5, 50, and 100 kHz) as
predictors did not significantly alter the precision of the MFBIA
equations. The resulting best fitting regression equations for
estimating BCM from SFBIA (model 1) and MFBIA
(models 4 and 5) were
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Table 2.
Comparison of the five bioimpedance models for predicting BCM (ICW) in
the elderly: results of 5 separate stepwise multiple regression
analyses (validation sample, n = 110)
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SFBIA model
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(6)
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MFBIA models
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(7)
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(8)
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where Ht is in cm, Wt is in kg, sex is coded as 1 for men and 0 for women, Xcp50 is parallel reactance at 50 kHz
in ohms, Ric5/50 is
[(R5 × R50)/(R5 R50)] in ohms, and
Ric5/100 is [(R5 × R100)/(R5 R100)] in ohms. The precision of the three new
equations is indicated by the RMSE that reached from 1.71 to 1.73 kg
(6% for men, 9% for women).
Checking for violation of assumptions.
The assumptions for performing linear regression analysis were
fulfilled. The response variable was normally distributed in both
sexes, as shown by the Kolmogorov-Smirnov test. The assumption of
homogeneity of the response variable was fulfilled, because the
variance of the response variable was constant for all values of the
predictor variables, as indicated by the absence of trends or patterns
in the residual plots. Scatter plots demonstrated linear relationships
between the response variable and the predictor variables (shown for
Eqs. 6 and 7 in Fig.
1). A scatter plot also indicated linear
relationship among the independent variables Xcp50 and weight. Absence of multicollinearity
of the predictor variables in the equation has been indicated by the
condition numbers (CN) that were smaller than 30. The CN were computed
for standardized residuals with the intercept included. According to
Belsley et al. (5), a CN of 30 indicates probable
collinearity in the model. Absence of autocorrelation of the residual
values was fulfilled, as shown by the Durbin-Watson test.
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Fig. 1.
Relationship between body cell mass (BCM) measured by
40K and 2 indexes calculated from single- and
multifrequency bioimpedance parallel models, in men ( )
and women ( ) (validation subsample). Ht, height;
Xcp, parallel reactance; Ric,
intracellular resistance.
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Cross-Validation of the New Developed Equations
The three new equations derived from the data set in the
validation subsample were applied to the data set of the
cross-validation subsample. The accuracy of the equations for
predicting BCM from BIA in the cross-validation subsample, as indicated
by the PE, was 1.61 kg (6.8%) for the SFBIA equation
(BCMBIANEW1), and 1.82 kg (7.7%) and 1.78 kg (7.5%) for
the MFBIA equations (BCMBIANEW2 and
BCMBIANEW3), respectively (Table
3). The PEs were similar to the
corresponding RMSEs of the same equations. The accuracy of the newly
derived equations was further compared with the accuracy of the
manufacturer's equation and two published equations of Kotler et al.
(27) for estimating BCM from BIA (equations are given in
full in Table 3). Because the equations of Kotler et al. calculated
TBK, they were multiplied by 0.00833 by use of the formula of Moore et
al. (31) to convert TBK to BCM. The PEs of the
manufacturer's and Kotler's linear equations were larger than the PEs
found for the new generated equations. The manufacturer's equation
significantly overestimated BCM relative to the TBK method, whereas the
linear equation of Kotler et al. significantly underestimated BCM.
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Table 3.
Cross-validation and significance of the difference (bias) between BCM
calculated from TBK and from various BIA equations (cross-validation
sample, n = 51)
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Measurement Method Comparison at an Individual Level
The Bland and Altman technique was applied to determine the level
of agreement between the two methods TBK and BIA for predicting BCM at
an individual level (Fig. 2). Comparisons
were restricted to the three newly derived equations and the
manufacturer's equation because they relate to the same bioimpedance
analyzer. The limits of agreement (±2 SD from mean bias) were for the
manufacturer's BIA equation for men, ±5.18 kg (19.3%), and for
women, ±4.52 kg (23.3%). The new equations BIANEW1,
BIANEW2, and BIANEW3 showed smaller limits of
agreement, indicating better agreement between the BIA and TBK methods
for predicting BCM: for men ±3.84 kg (14.3%), ±3.98 kg (14.8%), and
±3.92 kg (14.6%), respectively; for women ±2.74 kg (14.1%), ±2.94
kg (15.1%), and ±2.96 kg (15.3%), respectively.
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Fig. 2.
Bland-Altman analysis of the difference between the total
body potassium (TBK) and bioelectrical impedance analysis (BIA) methods
for predicting BCM against the average measurement of both methods by
use of the manufacturer's and the new BIA equations, on the basis of
single-frequency (SF)BIA and multifrequency (MF)BIA models. Horizontal
lines indicate the mean difference (bias, kg) ± 2 SD (limits of
agreement, kg) of the difference. Data are shown separately for men
(, solid line) and women (, broken
line).
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The difference of BCM predicted from the TBK and BIA methods reached
from 0.002 to 0.3 kg (0.01 to 1.1%) for the three new BIA equations.
When the difference in BCM was plotted against age and weight, no
systematic bias was detected for the new equations. In contrast,
positive and negative relationships resulted when the difference in BCM
was plotted against BCMTBK and BMI, respectively, in any of
the three new equations (shown for equation BIANEW1 in Fig.
3).
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Fig. 3.
Relationship between the prediction error of BCM (difference in BCM
from TBK and BIA) from the developed prediction equation
BIANEW1 plotted against BCMTBK and body
mass index (BMI) for men () and women
().
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DISCUSSION |
This study developed, and cross-validated for the first time,
equations for predicting BCM from BIA in relatively healthy elderly
Germans aged 60-90 yr. Whole body counting of 40K was
used as a reference method. To obtain precise results by the TBK
method, elderly volunteers with potassium-wasting conditions or taking
oral potassium supplements were excluded from this study. For precise
BIA results, elderly with altered electrolyte concentration or
hydration status were excluded. In addition, obese elderly were
excluded because the TBK method may underestimate BCM and the BIA
method may overestimate BCM in the obese.
The development and cross-validation of the new equations were
performed in a data-splitting approach, subdividing the entire sample
into a validation and a cross-validation subset. In the validation
sample, three new prediction equations for estimating BCM were
developed from five single- and multifrequency bioimpedance parallel
models. Results show that BCM was best predicted from the
single-frequency 50-kHz model (parallel reactance) and the multifrequency 5/50- and 5/100-kHz models, whereas the multifrequency 1/50- and 1/100-kHz models displayed much lower correlations with BCM
from TBK. The lower correlations found for multifrequency models
involving the 1-kHz frequency compared with those involving 5 kHz could
be explained in part by the larger within-subject variation of
resistance at 1 kHz compared with 5 kHz in the present elderly group,
as can be seen from the larger standard deviation for 1 kHz in Table 1.
This agrees with findings obtained in other studies in elderly groups
where a larger standard deviation of the impedance at 1 kHz compared
with other frequencies has been reported (43). The
precision for each of the three new equations, as indicated by the
RMSE, was 6% for men and 9% for women. These percentages are
consistent with those reported from BIA validation studies that used
parallel reactance models at 50 kHz and TBK as a reference method
(27). The accuracy of the three new equations, as
indicated by the PE in the cross-validation sample, was similar to the
RMSE of the same equation in the validation sample. The new equations
improved accuracy compared with the manufacturer's equation, reducing
the PE in BCM from 13.0 to 6.8 (SFBIA equation), 7.5, and 7.7% (MFBIA equations).
The predictive precision and accuracy were similar for the three new
equations in the healthy elderly group. With regard to the
multifrequency bioimpedance equations, this implies that the use of 100 kHz over 50 kHz had no advantage. This further gives evidence that the
equations derived from multifrequency bioimpedance did not perform
better than the equation derived from the single-frequency 50-kHz data
in the present elderly group. This indicates that single-frequency
bioimpedance analyzers, operating at 50 kHz, may be well suited for
predicting BCM in healthy elderly groups when the appropriate equation
is used. Similarly, authors who performed validation studies in
patients with HIV (39) and cystic fibrosis
(36) did not observe an advantage of multifrequency over
single-frequency BIA in predicting BCM. This can be explained by the
observation that relations between extra- and intracellular fluid
spaces are relatively constant in healthy and diseased subjects who are
not characterized by altered body hydration. Nevertheless, in elderly
with altered states of hydration, multifrequency bioimpedance might be
superior to single-frequency bioimpedance (35). Future studies should address this question.
The newly derived BIA equations reduced the mean difference in BCM
between TBK and BIA to 0.01-1.1% in both sexes in the
cross-validation subsample. In contrast, the manufacturer's equation
significantly overestimated BCM in the elderly men and women by 9.3 and
5.0%, respectively. The new equations also improved the validity of bioiompedance analysis at an individual level, reducing the limits of
agreement (±2 SD from mean) for BCM estimates from ±19-23% (manufacturer's equation) to ±14-15%. The difference in
accuracy between the new BIA equations and the manufacturer's equation can be explained by the use of different predictor variables and by
possible effects of sample characteristics, particularly by differences
in age ranges, on the development of the equations. The gender
difference characterized by higher overprediction of BCM in men by the
manufacturer's equation can be explained on the one hand by the larger
decline of BCM in men with advancing age being attributable to a higher
loss of muscle mass compared with women (11, 22, 25). As a
consequence, older men probably differ in BCM more strongly from young
and middle-aged reference populations than older women. On the other
hand, body height is used in the equation for estimating BCM from BIA.
Generally, height will be underestimated in elderly subjects, because
there often occurs an age-related decline in height, due to senile
kyphosis and vertebral collapse. Broekhoff et al. (7)
reported that underestimation in height by five centimeters can cause
an underestimation of fat-free mass of ~0.7-1.9 kg, in case of
its use in a prediction equation. Because BCM is a fraction of the
fat-free mass and because the age-related decline in height is larger
in women than in men, the lower BCM values in women estimated by the
manufacturer's BIA equation could partly be attributed to the higher
underestimation of height in women. A further explanation for the
differences in accuracy between the sexes is provided by Deurenberg et
al. (16), who reported a higher ratio of extra- to
intracellular water in women resulting in a lower body impedance by BIA.
This study showed that the difference between the BIA and TBK methods
in predicting BCM was positively related to BCM from TBK for each of
the new equations. This indicates that the prediction error of BCM
depends on the amount of BCM, in that the equations overestimated low
BCM and underestimated high BCM in the elderly. Such a positive
relationship has also been reported for intracellular water
(14) and has been explained with the observation that, at
higher levels of intracellular water, the extracellular water is
overestimated. Subsequently, the difference between predicted total
body water and extracellular water changes at increasing levels of
intracellular water or BCM. Further factors that might influence the
prediction error are altered cell membrane integrity and vascular
permeability in the elderly. Future studies remain to be undertaken to
investigate cell membrane capacitance in elderly groups.
Whole body counting of 40K was used as a reference method
to predict BCM. Cohn et al. (11), on the basis of a
cross-sectional study in normal individuals aged 20 to 79 yr, found
that the TBK-to-BCM ratio did not change significantly with age and
that the intracellular potassium concentration was not affected by age,
being similar in both sexes. Nevertheless, there might be a potential
limitation to this study. The calculation of BCM from TBK assumes for
adults an average K-to-N ratio (K/N) of 3 mmol potassium per gram of nitrogen, and a 0.04-g nitrogen content/g wet wt for lean tissues, resulting in the equation BCM (kg) = 0.00833 × TBK (mmol)
(31), as used in this study. This poses the question
whether K/N changes with age. Cohn et al. (9) reported
that both TBK and total body nitrogen (TBN) decrease with age in both
sexes. They found that the TBN-to-TBK ratio (TBN/TBK) tended to
increase for males aged 50-79 yr, reflecting the more rapid loss
of TBK with age compared with the corresponding loss of TBN
(10). In women, TBN/TBK showed only a slight tendency to
increase with age. However, the K/N ratio of the entire body may not be
instructive, because the various human body tissues vary widely in
terms of their K/N ratios, and BCM comprises mainly skeletal muscle
mass and viscera. Future long-term research with the use of appropriate
methodology should address this question by quantifying age-related
changes of the K/N ratio in the respective tissues in elderly groups. This study excluded subjects with altered hydration status due to
diseases, medications, diarrhea, and edema. However, the hydration status of the subjects was not estimated; this is a potential source of error.
In conclusion, the newly derived age-specific regression equations
improve prediction of BCM in elderly aged 60-90 yr compared with
the generalized equation of the manufacturer. The new equations should
be used for normal-weight and overweight elderly (BMI 19.0 to 29.9 kg/m2) where they have been developed. They might not be
applicable to the obese elderly, because this study excluded subjects
with a BMI >30 kg/m2. Because the new equations were
internally cross-validated, future studies are needed that will
externally cross-validate the equations in independent population groups.
| |
FOOTNOTES |
Address for reprint requests and other correspondence: M. Dittmar, Institute of Anthropology, Dept. of Biology (21),
Johannes Gutenberg-University, Saarstr. 21, 55099 Mainz, Germany
(E-mail: Manuela.Dittmar{at}gmx.de).
The costs of publication of this
article were defrayed in part by the
payment of page charges. The article
must therefore be hereby marked
"advertisement"
in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Received 18 October 2000; accepted in final form 22 June 2001.
| |
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ABSTRACT
INTRODUCTION
