Applicability of a segmental bioelectrical impedance analysis for predicting the whole body skeletal muscle volume

Abstract

This study aimed to test the hypothesis that a segmental bioelectrical impedance (BI) analysis can predict whole body skeletal muscle (SM) volume more accurately than a whole body BI analysis. Thirty males (19-34 yr) participated in this study. They were divided into validation (n = 20) and cross-validation groups (n = 10). The BI values were obtained using two methods: whole body BI analysis, which determines impedance between the wrist and ankle; and segmental BI analysis, which determines the impedance of every body segment in both sides of the upper arm, lower arm, upper leg and lower leg, and five parts of the trunk. Using a magnetic resonance imaging method, whole body SM volume was determined as a reference (SMV(MRI)). Simple and multiple regression analyses were applied to (length)(2)/Z (BI index) for the whole body and for every body segment, respectively, to develop the prediction equations of SMV(MRI). In the validation group, there were no significant differences between the measured and estimated SMV and no systematic errors in either BI analysis. In the cross-validation group, the whole body BI analysis produced systematic errors and resulted in the overestimation of SMV(MRI), but the segmental BI analysis was cross-validated. In the pooled data, the segmental BI analysis produced a prediction equation, which involves the BI indexes of the trunk and upper thigh as independent variables, with a SE of estimation of 1,693.8 cm(3) (6.1%). Thus the findings obtained here indicated that the segmental BI analysis is superior to the whole body BI analysis for estimating SMV(MRI).

Full text

doi: 10.1152/japplphysiol.00255.2007
103:1688-1695, 2007. First published 30 August 2007;J Appl Physiol
Kanehisa
Noriko I. Tanaka, Masae Miyatani, Yoshihisa Masuo, Tetsuo Fukunaga and Hiroaki
volume
analysis for predicting the whole body skeletal muscle
Applicability of a segmental bioelectrical impedance
You might find this additional info useful...
17 articles, 10 of which you can access for free at: This article cites
http://jap.physiology.org/content/103/5/1688.full#ref-list-1
including high resolution figures, can be found at: Updated information and services
http://jap.physiology.org/content/103/5/1688.full
can be found at: Journal of Applied Physiology about Additional material and information
http://www.the-aps.org/publications/jappl
This information is current as of June 8, 2013.
http://www.the-aps.org/.
© 2007 the American Physiological Society. ISSN: 8750-7587, ESSN: 1522-1601. Visit our website at
year (monthly) by the American Physiological Society, 9650 Rockville Pike, Bethesda MD 20814-3991. Copyright
physiology, especially those papers emphasizing adaptive and integrative mechanisms. It is published 12 times a
publishes original papers that deal with diverse area of research in appliedJournal of Applied Physiology
by guest on June 8, 2013http://jap.physiology.org/Downloaded from

Applicability of a segmental bioelectrical impedance analysis for predicting
the whole body skeletal muscle volume
Noriko I. Tanaka,
1
Masae Miyatani,
2
Yoshihisa Masuo,
3
Tetsuo Fukunaga,
3
and Hiroaki Kanehisa
4
1
Department of Sport System, Kokushikan University, Tokyo, Japan;
2
Rehabilitation Engineering Laboratory, Lyndhurst
Centre Toronto Rehabilitation Institute, Toronto, Ontario, Canada;
3
Department of Sport Sciences, School of Human
Sciences, Waseda University, Saitama, Japan; and
4
Department of Life Sciences (Sports Sciences), University of Tokyo,
Tokyo, Japan
Submitted 5 March 2007; accepted in final form 23 August 2007
Tanaka NI, Miyatani M, Masuo Y, Fukunaga T, Kanehisa H.
Applicability of a segmental bioelectrical impedance analysis
for predicting the whole body skeletal muscle volume. J Appl
Physiol 103: 1688–1695, 2007. First published August 30, 2007;
doi:10.1152/japplphysiol.00255.2007.—This study aimed to test
the hypothesis that a segmental bioelectrical impedance (BI) analysis
can predict whole body skeletal muscle (SM) volume more accurately
than a whole body BI analysis. Thirty males (19 –34 yr) participated
in this study. They were divided into validation (n⫽20) and
cross-validation groups (n⫽10). The BI values were obtained using
two methods: whole body BI analysis, which determines impedance
between the wrist and ankle; and segmental BI analysis, which
determines the impedance of every body segment in both sides of the
upper arm, lower arm, upper leg and lower leg, and five parts of the
trunk. Using a magnetic resonance imaging method, whole body SM
volume was determined as a reference (SMV
MRI
). Simple and mul-
tiple regression analyses were applied to (length)
2
/Z(BI index) for the
whole body and for every body segment, respectively, to develop the
prediction equations of SMV
MRI
. In the validation group, there were
no significant differences between the measured and estimated SMV
and no systematic errors in either BI analysis. In the cross-validation
group, the whole body BI analysis produced systematic errors and
resulted in the overestimation of SMV
MRI
, but the segmental BI
analysis was cross-validated. In the pooled data, the segmental BI
analysis produced a prediction equation, which involves the BI in-
dexes of the trunk and upper thigh as independent variables, with a SE
of estimation of 1,693.8 cm
3
(6.1%). Thus the findings obtained here
indicated that the segmental BI analysis is superior to the whole body
BI analysis for estimating SMV
MRI
.
human body composition; magnetic resonance imaging; muscle dis-
tribution; validation; cross-validation
THE QUALITATIVE ASSESSMENT of human skeletal muscle (SM)
mass helps us to evaluate physical resources in relation to
physical performance in daily life and/or sporting activities
(16). There is increasing interest in the use of bioelectrical
impedance (BI) analysis to estimate SM mass because it is
safe, noninvasive, convenient, easy, and inexpensive (3). How-
ever, little information on the validity of BI analyses for
estimating whole body SM mass is available. To our knowl-
edge, only Janssen et al. (14) have tried to estimate whole body
SM mass using a BI analysis in which the BI value between the
right wrist and right leg was obtained. In their results, however,
the developed prediction equation produced a systematic error
and overestimated whole body SM mass. The BI analysis taken
in the prior study has been referred to as “whole body BI
analysis” (3, 5, 9, 14, 19), although the electric current in this
technique has been shown to be passed thorough the whole
trunk and one side of the extremities (9). When a whole body
BI analysis is used to estimate whole body SM mass, the
human body is assumed to be a cylindrical and isotrophic
conductor with a uniform cross-sectional area (CSA). How-
ever, the whole body BI value depends strongly on the varia-
tion in the CSA of the lower arm and lower leg (4, 8, 9).
Moreover, it has been reported that the change in the trunk SM
volume hardly affects the whole body BI value (9). Consider-
ing these points, it is hypothesized that the BI value obtained
by the whole body BI analysis may be mostly affected by SM
mass in the distal parts of limbs, and so this would be a reason
for the systematic error in the estimates of whole body SM
mass with the whole body BI analysis (14). However, no study
has examined this assumption.
As another technique of the BI analysis, Organ et al. (19)
developed various combinations of electrodes to determine
the BI value of every body segment, i.e., a segmental BI
analysis. A prior study (12) that used a subject sample with
a large variation in muscularity found that, compared with
the whole body BI analysis, a segmental BI analysis that
measured BI values from proximal segments of the human
body (i.e., upper arm, upper leg, and whole trunk) could
predict lean body mass without influence from differences in
the lean tissues between the proximal and distal parts (lower
arms and lower legs) of the body segments. The segmental
BI analysis can be used to estimate the limb SM volume
through comparison with that determined by magnetic res-
onance imaging (MRI) (2, 17, 18). In addition, Ishiguro
et al. (13) indicated that the segmental BI analysis could be
applicable to the estimation of trunk SM volume. Taking
these findings into account, it may be assumed that the
prediction equation developed from a segmental BI analysis,
which involves the BI indexes of the upper arm, upper leg,
and trunk as the independent variables, can predict whole
body SM mass with a higher degree of accuracy compared
with that developed from a whole body BI analysis. The
present study aimed to test this hypothesis. To this end, we
measured BI values using the whole body and segmental BI
analyses in young adult men, including athletes, who formed
a heterogeneous sample with respect to body physique and
muscular development. Some data on the physical charac-
Address for reprint requests and other correspondence: N. I. Tanaka, Dept.
of Sport System, Kokushikan Univ., 7-3-1 Nagayama, Tama-shi, Tokyo
206-8515, Japan.
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.
J Appl Physiol 103: 1688–1695, 2007.
First published August 30, 2007; doi:10.1152/japplphysiol.00255.2007.
8750-7587/07 $8.00 Copyright ©2007 the American Physiological Society http://www. jap.org1688
by guest on June 8, 2013http://jap.physiology.org/Downloaded from

teristics of subjects and the trunk SM volume have been
reported elsewhere (13).
METHODS
Subjects. Thirty healthy Asian males (19 –34 yr) voluntarily par-
ticipated in this study. Fourteen of the subjects were athletes (8
American football players, 3 power lifters, 1 weight lifter, 1 triathlete,
and 1 baseball player) who had participated in competitive meets in
their own events at the college level within a year preceding the
measurements. The remainder were either sedentary or mildly active,
but none was currently involved in any type of exercise program (ⱖ30
min/day, ⱖ2 days/wk). To confirm the cross-validity of the predicting
equation, the subjects were randomly separated into a validation group
(n⫽20) and a cross-validation group (n⫽10), in which the
percentage of the number of athletes to the total number of subjects
was almost the same, i.e., 10 athletes in the validation group and 4
athletes in the cross-validation group. Physical characteristics of each
subject group are listed in Table 1. Data for the athletes were collected
during preseason training. Therefore, none of the athletes were dehy-
drated to control their body mass for competition. All measurements
for the athletes were performed more than 40 h after completion of a
training session. This study was approved by the ethics committee of
the Department of Life Sciences, Graduate School of Arts and
Sciences, University of Tokyo, and was consistent with their require-
ments for human experimentation. The subjects were fully informed
about the procedures and the purpose of this study. Written informed
consent was obtained from all participants.
Anthropometric measurements. Body height was measured to the
nearest 0.1 cm on a standard physician’s scale. The body mass was
measured to the nearest 0.1 kg on a calibrated electric scale. The
lengths of the limb on the right side of the body were measured to the
nearest 0.5 cm with a flexible metal tape (Flat rule, KDS). In this
study, the length of every body segment was defined as the distance
between the electrodes placed to determine the segmental BI values in
accordance with the prior study (11): upper arm, distance between the
acromion process and the lateral epicondyle of the humerus (L
upper arm
);
lower arm, distance between the head of the radius and the processus
styloideus (L
lower arm
); upper leg, distance between the greater trochanter
of the femur and articular cleft between the femoral and tibial condyles
(L
upper leg
); lower leg, distance between the malleolus lateralis and the
articular cleft between the femoral and tibial condyles (L
lower leg
). The
distance between the acromion process of the right shoulder and the
greater trochanter of the right femur was measured from MRI images
and defined as the trunk length (L
TR
).
MRI measurements. With the use of MRI scans taken with a body
coil (Airis, Hitachi Medco), a series of transverse images from the
acromion process to the malleolus lateralis was obtained. The image
condition was T1 weighted, spin-echo, multislice sequences with a
slice thickness of 10 mm and a slice interval of 20 mm, with a
repetition time of 200 ms and an echo time of 20 ms. Each subject lay
supine in the body coil with his arms and legs extended and relaxed.
We defined the whole body SM volume as the sum of trunk and limb
SM volumes (4). The trunk SM was separated from limbs by using
slices between specific landmarks, the acromion process of the shoul-
der and the greater trochanter of the femur (4). Therefore, some SM
located in the shoulder and/or gluteus (i.e, triangular and/or gluteal
muscle) were partially analyzed as the trunk SMs. From each cross-
sectional image, outlines of tissues (SM, subcutaneous fat, bone,
visceral, and others) were traced and digitized by personal computer
(Power Macintosh G4, Apple) to calculate the anatomic CSA of every
tissue. Adipose and tendinous tissues, which were imaged in different
tones from the muscle tissue, were excluded when digitizing. We
removed as much of intramuscular adipose tissue areas as possible
from the SM and categorized those as “others.” By summing the
anatomic SM CSA and then multiplying the sum by the interval of 20
mm, whole body SM volume was determined and referred to as
SMV
MRI
.
The test-retest variability of SMV
MRI
was assessed with 10 men
(22–26 yr) on two separate days. The intraclass correlation coefficient
for the test-retest measurements was 0.990 and the coefficient of
variation (CV) was 1.8. There was no significant difference between
the mean values of the two tests. Again, the intraobserver reproduc-
ibility was assessed by analyzing the MRI images of 5 men (22–26 yr)
two times. The intraclass correlation coefficient and the CV of
SMV
MRI
values from the two trials were 0.951 and 2.9, respectively.
There was no significant difference between the mean values of the
two trials.
BI measurements. A BI acquisition system (Muscle ␣, Art Haven
9) and the disposable electrodes (Red Dot 2330, 3M) were used to
determine the BI values of the whole body and each body segment.
This system applies a constant current of 500 ␮A and frequency of 50
kHz through the body. The measured BI value was referred to as Z.
The BI measurements were performed on different days from the MRI
measurements with an interval of 1 or 2 days. The subjects refrained
from vigorous exercise and alcohol intake for 24 h, and from taking
a meal for 4 h, preceding the experiments. All BI measurements were
carried out in the supine position, with the arms relaxed at the side but
not touching the body and the legs separated at least 25.0 cm at the
ankles so that there was no contact between the upper legs. The
subjects were instructed to keep breathing quietly because the respi-
ratory cycle affected the trunk Z(7). During the measurements, room
temperature was kept at 23°C (8).
The electrode placement is shown in Fig. 1. The source electrodes
were placed at the dorsal surface of the third metacarpal bone of the
right hand and the dorsal surface of the third metatarsal bone of
the right foot for the whole body BI analysis, and the dorsal surface
of the third metacarpal bone of both hands and the dorsal surface of
the third metatarsal bone of both feet for the segmental BI analysis.
The detector electrode placement was as follows: for the measurement
of whole body Z(Z
whole body
), at the dorsal surface of the right wrist
at the level of the hand of radial and ulnar bones and anterior surface
of the right ankle between the protruding portions of the tibial and
fibular bones; for the upper arm Z(Z
upper arm
), at the dorsal surface of
both elbows between the lateral epicondyles of the humerus and the
head of the radius and the acromion process of both shoulders; for
the lower arm Z(Z
lower arm
), at the dorsal surfaces of both wrists at the
level of the head of radial and ulnar bones and the dorsal surface of
both elbows between the lateral epicondyles of the humerus and the
head of the radius; for the upper leg Z(Z
upper leg
), at the articular cleft
between the femoral and tibial condyles of both legs and the greater
trochanter of both femurs; and for the lower leg Z(Z
lower leg
), at the
Table 1. Descriptive data on physical characteristics
and MRI-measured tissue volume of subjects
Variables
Validation Group
(n⫽20)
Cross-
Validation
Group
(n⫽10) Total (n⫽30)
Mean SD Mean SD Mean SD
Age, yr 24.5 2.8 24.2 4.1 24.4 3.2
Height, cm 175.4 5.0 174.2 5.9 175.0 5.2
Body mass, kg 77.8* 10.7 73.0 7.3 76.2 9.8
BMI, kg/m
2
25.3 3.3 24.1 2.4 24.9 3.1
Segment length, cm
Upper arm 33.2 1.2 32.9 1.7 33.1 1.4
Lower arm 24.4 1.0 24.2 1.2 24.3 1.0
Upper leg 40.9 1.4 40.9 1.9 40.9 1.6
Lower leg 40.4 1.7 40.2 2.0 40.3 1.8
Trunk 61.0 2.8 58.4 3.0 60.1 3.1
n⫽no. of men/group. BMI, body mass index. *Mean value is significantly
different from that for the cross-validation group at P⬍0.05.
1689ESTIMATION OF WHOLE BODY SKELETAL MUSCLE VOLUME
J Appl Physiol •VOL 103 •NOVEMBER 2007 •www.jap.org
by guest on June 8, 2013http://jap.physiology.org/Downloaded from

anterior surface of both ankles between the protruding portions of the
tibial and fibular bones and the articular cleft between the femoral and
tibial condyles of both legs. For the trunk BI measurement, the
detector electrodes were placed at the acromion process of both
shoulders and the greater trochanter of both femurs. This combination
of electrodes can measure Zfrom five regions: both sides of the upper
trunk (Z
TRur
and Z
TRul
), the middle trunk (Z
TRm
), and both sides of the
lower trunk (Z
TRlr
and Z
TRll
) (13). The whole trunk Z(Z
TRwhole
) can
be calculated with the following equation using each BI measurement
ZTRwhole ⫽(ZTRur ⫻ZTRul)/(ZTRur ⫹ZTRul)
⫹ZTRm ⫹(ZTRlr ⫻ZTRll)/(ZTRlr ⫹ZTRll)
The BI indexes of the whole body and each body segment were
calculated as follows
BI index of whole body ⫽(height)2/Zwhole body
BI index of upper arm ⫽(Lupper arm)2/
[Zupper arm (right side) ⫹Zupper arm (left side)]
BI index of lower arm ⫽(Llower arm)2/
[Zlower arm (right side) ⫹Zlower arm (left side)]
BI index of upper leg ⫽(Lupper leg)2/
[Zupper leg (right side) ⫹Zupper leg (left side)]
BI index of lower leg ⫽(Llower leg)2/
[Zlower leg (right side) ⫹Zlower leg (left side)]
BI index of trunk ⫽(LTR)2/ZTRwhole
The test-retest variability of the Zvalues and BI indexes was assessed
with 23 men (19 –30 yr) on two separate days. The intraclass corre-
lation coefficients and the %CV were 0.839 – 0.978 and 1.6 –2.9% for
each Zvalue. There were no significant differences in each Zvalue
between the two tests.
Data analysis. Descriptive values were presented as means and
standard deviations (SDs). In the validation group, first, the equations
were developed for predicting the measured SMV
MRI
with the use of
the BI indexes as independent variables, determined in each of the
whole body and the segmental BI analyses. For the whole body BI
analysis, a simple regression analysis was applied to develop a
prediction equation for SMV
MRI
with (height)
2
/Z
whole body
as an
independent variable. For the segmental BI analysis, the multiple
regression analysis was used to develop the prediction equation for
SMV
MRI
using the BI indexes in the upper arm, upper leg, and trunk
as the independent variables. The estimated whole body SM volume
was referred to as SMV
BI
; SMV
whole body BI
refers to the whole body
BI analysis and SMV
segmental BI
for the segmental BI analysis. For
every independent variable selected, the product of the standard
regression coefficient in the multiple regression equation and the
simple correlation coefficient in the relationship with SMV
MRI
,ex
-
pressed as a percentage, was calculated as an index presenting its
relative contribution to the estimation of SMV
MRI
. Second, it was
confirmed that the regression slope and intercept for the relationship
between the SMV
MRI
and SMV
BI
values did not significantly differ
from 1 and 0, respectively. Again, the significance of the difference
between SMV
MRI
and SMV
BI
was confirmed using Student’s paired
t-test. The SE of the estimate (SEE) was calculated to evaluate the
accuracy of SMV
BI
. The SEE was expressed as an absolute value and
relative to the mean of SMV
MRI
. Third, the residual (SMV
MRI
⫺
SMV
BI
) was plotted against the mean SMV for the two methods to
examine for systematic error, as described by Bland and Altman (6).
When the three conditions mentioned above were satisfied, SMV
BI
was calculated for the individuals of the cross-validation group using
the equation derived from the validation group. The cross-validity of
the prediction equation was examined by the same three steps as used
for the validation group. If either or both of the prediction equations
were cross-validated, the data from the two groups were pooled to
generate the final equation, and the standard regression coefficient of
each independent variable was calculated. With regard to the final
equation, too, the accuracy was confirmed by the same three steps as
mentioned above. A simple linear regression analysis was used to
calculate the correlation coefficient (r). The probability level for
statistical significance was set at P⬍0.05.
RESULTS
Baseline characteristics of the validation and cross-valida-
tion groups. Table 1 shows the descriptive data on the physical
characteristics in the validation and cross-validation groups.
There were no significant differences between the two groups
in any variables except for body mass.
Figure 2 shows the distribution of the measured SM CSA in
every body segment, plotted at every 10% of the segment
length. The largest SM CSA was observed at 10% L
TR
, and the
second one at 90% L
TR
.
The SM volumes of the whole body and every body segment
determined by MRI did not differ between the validation and
cross-validation groups (Table 2). Moreover, there were no
significant differences between the groups in the measured Zs
and BI indexes (Table 3).
Prediction equation derived from the validation group. The
whole body BI index was significantly correlated to the
SMV
MRI
(r⫽0.883, P⬍0.05) in the validation group. This
relationship produced an equation, SMV
whole body BI
⫽422.2 ⫻
[(height)
2
/Z
whole body
]⫺1,201.4, with R
2
and SEE values of
0.779 and 2,180.6 cm
3
(7.7%), respectively.
In the segmental BI analysis, the BI indexes of the upper leg
and trunk were selected as significant contributors to predict
SMV
MRI
(Fig. 3) and produced an equation, SMV
segmental BI
⫽
Fig. 1. Schematic representations of the positions of electrodes for bioelectri-
cal impedance (BI) analyses.
1690 ESTIMATION OF WHOLE BODY SKELETAL MUSCLE VOLUME
J Appl Physiol •VOL 103 •NOVEMBER 2007 •www.jap.org
by guest on June 8, 2013http://jap.physiology.org/Downloaded from

129.1 ⫻[(L
TR
)
2
/Z
TRwhole
⫹1,241.3 ⫻(L
upper leg
)
2
/Z
upper leg
]⫺
6,844.1, with R
2
and SEE values of 0.852 and 1,866.0 cm
3
(6.6%), respectively. The relative contribution of each of the
two BI indexes to the prediction of SMV
MRI
was 51.9% for the
upper leg and 33.7% for the trunk. Even if the BI index of
the upper arm was entered as the predictive variable, R
2
(0.856)
and SEE (1,844.8 cm
3
, 6.5%) were similar as those in the
equation using the BI indexes of the upper leg and trunk.
The regression analyses indicated that the slopes and inter-
cepts of the regression equations for the relationship between
SMV
MRI
and SMV
BI
were not significantly different from 1
and 0, respectively, in the whole body and segmental BI
analyses (Fig. 4, Aand B). In addition, there were no significant
differences between the measured and estimated SMVs in the
two BI analyses. Again, no significant systematic errors were
found in the Bland-Altman plots for the whole body [r⫽
0.257, nonsignificant (NS)] and segmental (r⫽0.204, NS) BI
analyses (Fig. 4, Cand D).
Cross-validation of the prediction equation. The prediction
equation derived from the validation group was used to
estimate SMV
MRI
in the cross-validation group. The slopes
and intercepts of the regression equations for the relation-
ships between SMV
MRI
and either SMV
whole body BI
or
SMV
segmental BI
were not significantly different from 1 and 0,
respectively (Fig. 5, Aand B). However, the Bland-Altman plot
for the whole body BI analysis indicated that SMV
whole body BI
tended to be influenced by the magnitude of SMV
MRI
(r⫽
⫺0.635, P⬍0.05) (Fig. 5C). SMV
segmental BI
(26,031.4 ⫾
3,312.2 cm
3
) did not significantly differ from SMV
MRI
(26,738.3 ⫾3,120.8 cm
3
), but SMV
whole body BI
(27,498.3 ⫾
3,694.3 cm
3
) was significantly greater (Fig. 6). Consequently,
the data obtained by the whole body BI analysis were omitted
from the analysis for developing the prediction equation using
the pooled data.
Prediction equation derived from the pooled data.Inthe
pooled data, too, the BI indexes of the upper leg and trunk were
selected as significant contributors to predict SMV
MRI
and
produced an equation, SMV
segmental BI
⫽116.1 ⫻[(L
TR
)
2
/
Z
TRwhole
⫹1,220.8 ⫻(L
upper leg
)
2
/Z
upper leg
]⫺4,913.1, with R
2
and SEE values of 0.842 and 1,693.8 cm
3
(6.1%), respectively.
The relative contribution of two BI indexes to the prediction of
the SMV
MRI
was 52.6% for the upper leg and 32.8% for the
trunk. The regression analysis indicated that the slope and
intercept of the regression equation for the relationship be-
tween SMV
MRI
and SMV
segmental BI
were not significantly
different from 1 and 0, respectively (Fig. 7A). There was no
significant difference between SMV
MRI
and SMV
BI
. In addi-
tion, no significant systematic error (r⫽0.239, NS) was found
in the Bland-Altman plot (Fig. 7B).
DISCUSSION
The present study is the first to compare the accuracy of
SMV
BI
between whole body and segmental BI analyses. In
the validation group, the whole body and segmental BI
analyses produced equations with a similar accuracy for
estimating SMV
MRI
. In the cross-validation group, however,
SMV
whole body BI
was significantly greater than SMV
MRI
, and
Fig. 2. Distribution of skeletal muscle cross-sectional area (CSA) in the whole
body. }, Sum of the CSAs in both sides of the body; {, CSAs in right side of
the body.
Table 2. Descriptive data on MRI-measured skeletal muscle volume of subjects
Variables
Validation Group
(n⫽20)
Cross-Validation Group
(n⫽10) Total (n⫽30)
Mean SD Mean SD Mean SD
Skeletal muscle volume, cm
3
Whole body 28,429 5,256 26,451 3,077 27,770 4,684
Upper arm 2,642 565 2,482 601 2,589 572
Lower arm 1,349 350 1,283 265 1,327 321
Upper leg 9,542 1,964 9,107 1,213 9,397 1,740
Lower leg 2,946 505 3,062 524 2,985 505
Trunk 11,950 2,256 10,518 1,259 11,472 2,073
n⫽no. of men/group.
1691ESTIMATION OF WHOLE BODY SKELETAL MUSCLE VOLUME
J Appl Physiol •VOL 103 •NOVEMBER 2007 •www.jap.org
by guest on June 8, 2013http://jap.physiology.org/Downloaded from

so only the segmental BI analysis was cross-validated. The
SEE value (6.3%) obtained from the application of the seg-
mental BI analysis to the pooled data was lower than that (9%)
reported in a prior study (14) that used the whole body BI
analysis to estimate whole body SM mass. The present results
indicated that the segmental BI analysis could predict SMV
MRI
more accurately than the whole body BI analysis.
Janssen et al. (14) reported that the prediction equation
derived from data on Caucasians obtained using the whole
body BI analysis overestimated the whole body SM mass in an
Asian cohort. They speculated that the biological differences
between Caucasians and Asians would influence the relation-
ship between Zvalue and the whole body SM mass. Mean-
while, the present study indicated that the whole body BI
analysis overestimated SMV
MRI
even though Asians were used
as the subjects to develop the prediction equation. Certainly,
there is a possibility that the poor performance of the whole
body BI analysis in the cross-validation group might be attrib-
uted to the subject sample size. However, if the volume units
are converted to mass units by multiplying the volumes by the
assumed constant density for adipose-free SM (1.04 kg/l) (20),
one can find a similar average value (27.5 ⫾5.9 kg) for the
subjects in the present study as that (26.4 ⫾7.6 kg)
examined by Janssen et al. (14). Regardless of the subject
sample size taken in the present study, therefore, it seems
that the whole body BI analysis itself has a potential to
overestimate SMV
MRI
.
A prior study (12) suggested that the application of the
whole body BI analysis to the estimation of the lean body mass
did not reflect the relative development of lean tissue mass in
the upper arms and upper legs within the arms and legs,
respectively, to the BI measurements. In general, SM volume is
less in the distal than the proximal segment in each of the arms
and legs. From the findings of Kanehisa and Fukunaga (15), the
SM CSA of the upper leg was greater in the strength-trained
athletes than in the untrained subjects, but that of the lower leg
was similar between the two groups, when the difference in
lean body mass was normalized. In the subject sample includ-
ing athletes, therefore, it was expected that the relative differ-
ence in the SM volume between the segments in either arms or
legs would be a factor explaining the residual of the whole
body BI analysis. In the pooled data of the present study,
however, there were no significant relationships between the
Table 3. Descriptive data on Z values and BI indexes of subjects
Variables
Validation Group
(n⫽20)
Cross-Validation Group
(n⫽10) Total (n⫽30)
Mean SD Mean SD Mean SD
Z value, ⍀
Whole body BI analysis
Z
whole body
447.0 61.7 454.0 61.4 449.3 60.6
Segmental BI analysis
Z
upper arm
(right side) 75.1 13.9 79.5 15.1 76.5 14.2
Z
upper arm
(left side) 73.9 15.0 78.7 14.7 75.5 14.9
Z
lower arm
(right side) 117.7 19.0 119.3 20.8 118.3 19.3
Z
lower arm
(left side) 120.4 19.7 121.0 20.9 120.6 19.8
Z
upper leg
(right side) 51.7 7.5 53.4 6.3 52.3 7.1
Z
upper leg
(left side) 51.2 7.2 53.3 8.1 51.9 7.5
Z
lower leg
(right side) 141.5 10.3 140.0 18.0 141.6 18.6
Z
lower leg
(left side) 142.2 21.0 143.5 17.8 142.7 19.7
Z
TRwhole
33.2 21.0 33.7 3.0 33.3 3.7
BI index, cm
2
/⍀
Whole body BI analysis
Height
2
/Z
whole body
70.2 11.0 67.8 8.7 69.4 10.2
Segmental BI analysis
(L
upper arm
)
2
/Z
upper arm
7.7 1.6 7.1 1.7 7.5 1.7
(L
lower arm
)
2
/Z
lower arm
2.6 0.5 2.5 0.4 2.5 0.5
(L
upper leg
)
2
/Z
upper leg
16.5 2.6 15.9 1.8 16.3 2.4
(L
lower leg
)
2
/Z
lower leg
5.9 0.9 5.8 0.6 5.8 0.8
(L
TR
)
2
/Z
TRwhole
114.2 18.0 102.1 13.3 110.2 17.4
n⫽no. of men/group. BI, bioelectrical impedance; Z, measured BI; L, segment length; TR, trunk.
Fig. 3. Selected electrode positions in the segmental BI analysis.
1692 ESTIMATION OF WHOLE BODY SKELETAL MUSCLE VOLUME
J Appl Physiol •VOL 103 •NOVEMBER 2007 •www.jap.org
by guest on June 8, 2013http://jap.physiology.org/Downloaded from

residual of the whole body BI analysis and the SM volume
ratios of the upper arm to the arm (r⫽⫺0.117, NS) and
the upper leg to the leg (r⫽0.271, NS). This implies that the
accuracy of the whole body BI analysis in the estimates of
SMV
MRI
was independent of the differences in SM distribution
between the proximal and distal parts in each of the upper and
lower extremities. On the other hand, the percentage of the sum
of SM volumes of the upper arm, upper leg, and trunk to the
SMV
MRI
was 84.4%. Compared with the SM CSAs and vol-
umes of these segments, those of the lower arm and lower leg
were considerably smaller as shown in Fig. 2 and Table 2.
Therefore, if the Zvalue measured by the whole body BI
analysis would reflect the SM volume of these distal segments
rather than that of the upper arm, upper leg, and trunk, it might
Fig. 4. Relationship between the measured skeletal muscle vol-
ume (SMV
MRI
) and estimated SMV (Aand B) and between the
residual (difference between the measured and estimated SMV)
and mean SMV determined by 2 methods (Cand D)inthe
validation group. Aand Cindicate the corresponding relationship
for the whole body BI analysis, and Band Dfor the segmental BI
analysis. SEE, SE of estimate. Solid lines, regression lines.
Dashed lines in Aand Bare lines of identity. Horizontal dashed
lines in Cand Dare lines of ⫾2SD.
Fig. 5. Relationship between measured and estimated SMV
(Aand B) and between the residual (difference between the
measured and estimated SMV) and mean SMV determined by
2 methods (Cand D) in the cross-validation group. Aand C
indicate the corresponding relationship for the whole body BI
analysis, and Band Dfor the segmental BI analysis. Solid lines:
regression lines. Dashed lines in Aand Bare lines of identity.
Horizontal dashed lines in Cand Dare lines of ⫾2SD.
1693ESTIMATION OF WHOLE BODY SKELETAL MUSCLE VOLUME
J Appl Physiol •VOL 103 •NOVEMBER 2007 •www.jap.org
by guest on June 8, 2013http://jap.physiology.org/Downloaded from

be a reason why the predicting equation was not cross-vali-
dated.
To test the assumption mentioned above, we applied a
multiple regression analysis using the whole body BI value as
the dependent variable and the BI values in the upper arm,
lower arm, upper leg, lower leg, and trunk as the independent
variables in the pooled data. As a consequence, the relative
contribution of the BI values in the lower arm and lower leg for
determining the whole body BI was 60.0%. In addition, the
residual in the estimate of SMV
MRI
using the BI indexes of the
lower arm and lower leg as the independent variables was
significantly correlated with that of the whole body BI analysis
(r⫽0.831, P⬍0.05) in the pooled data (Fig. 8). These results
indicate that the whole body BI value is largely influenced by
the distal extremities, and consequently it may be a factor
producing the error in the estimate of SMV
MRI
by the whole
body BI analysis. On the other hand, it may be that the
segmental BI analysis used in the present study resulted in a
higher accuracy for estimating SMV
MRI
compared with the
whole body BI analysis by selecting the BI indexes of the
upper leg and trunk, which have higher percentages of the SM
volume in the whole body (33.8% and 41.3%, respectively, in
the pooled data).
From the finding of Ishiguro et al. (12), the BI indexes of the
upper arm, upper leg, and trunk were selected for estimating
the lean body mass by segmental BI analyses. At the start of
the present study, it seemed that the BI index of the upper arm
would also be a significant contributor for predicting the whole
body SM volume. However, the present results indicated that
SMV
MRI
could be predicted by measuring the BI indexes of the
trunk and upper leg only. Adding the BI index of the upper arm
as a predictive variable did not improve the accuracy of the
estimates of SM volume. One reason for this result may be the
procedure used for measuring the trunk Zvalues. The present
study measured the Zvalues of the trunk in five regions (both
sides of the upper region, the middle region, and both sides of
the lower region). On the other hand, Ishiguro et al. (13)
assumed the trunk to be one cylinder and obtained the Zvalue
using a network circuit model with the detector electrodes on
both sides of knee and elbow. In their results, the contribution
of the trunk BI index for predicting lean body mass was only
7.1%. This value was considerably different from the substan-
tial percentage of the trunk lean tissue mass to that of the whole
body, ⬃50% (19). We cannot directly compare the contribu-
tion of the trunk BI index of the present study to that of the
prior study (12) because the reference value (SMV
MRI
vs. the
lean body mass) and the subjects are different. In the present
results, however, the contribution of the trunk BI index [(L
TR
)
2
/
Z
TRwhole
] indicated a relatively high (32.8%) and closer value
to the average in the percentage of the trunk SM volume
(41.3 ⫾2.7%) to SMV
MRI
in the pooled data. On the other
hand, the percentage of the upper arm SM volume to the
SMV
MRI
was lower (9.3 ⫾1.0%) than that of upper leg
(33.8 ⫾1.9%) and trunk. The SM volume in the upper arm was
significantly correlated to that of the trunk (r⫽0.888, P⬍
0.05) and the upper leg (r⫽0.816, P⬍0.05). Therefore, it
may be assumed that the application of the electrode place-
ments that enabled us to obtain Zvalues from the five regions
of the trunk improved the contribution of the trunk BI index for
estimating SMV
MRI
and eliminated the need to enter the upper
Fig. 8. Relationship between the residuals (difference between the measured
and estimated SMV) in the predicting equation using the BI indexes of the
lower arm and lower leg as independent variables (y-axis) and in the whole
body BI analysis (x-axis) with the pooled data. Solid line, regression line.
Fig. 6. Measured and estimated SMV in both BI analyses. *Significantly
different from MRI.
Fig. 7. Relationship between the measured and estimated SMV (A) and
between the residual (difference between the measured and estimated SMV)
and mean SMV determined by 2 methods (B) with the pooled data. Both
indicate the corresponding relationship for the segmental BI analysis. Solid
line, regression line. Dashed line in Ais line of identity. Horizontal dashed
lines in Bare lines of ⫾2SD.
1694 ESTIMATION OF WHOLE BODY SKELETAL MUSCLE VOLUME
J Appl Physiol •VOL 103 •NOVEMBER 2007 •www.jap.org
by guest on June 8, 2013http://jap.physiology.org/Downloaded from

arm BI index into the prediction equation as the predictive
variable.
In estimating the trunk SM volume from the segmental BI
analysis, however, the influence of the visceral tissue volume
on the accuracy cannot be excluded. Particularly, the visceral
tissue volume at 41–50% L
TR,
which has high conductivity
because it is mainly made up of smooth muscle and water, has
a low but significant negative correlation between the residual
of the trunk SM volume estimates, expressed as a percentage of
the trunk SM volume (13). Meanwhile, a regression analysis
for the pooled data of this study indicated that the residual of
SMV
MRI
in the segmental BI analysis did not significantly
correlate to the percentage of the visceral tissue volume to the
SM volume in each part of the trunk (r⫽⫺0.259 to 0.011,
NS). In contrast to the relatively high percentage of the visceral
tissue volume to the total tissue volume (31.0%) in the trunk
(13), the corresponding value is 7.1% of the whole body in the
pooled data. This relatively low percentage might be assumed
to have less influence on the accuracy of the SMV
MRI
estima-
tion. However, the subjects examined here were healthy young
men. With regard to the influence of visceral tissue volume on
the estimate of the whole body SM, further investigation using
obese and/or elderly individuals is needed.
Before summarizing the present results, we should comment
on the limitations of the experimental design in the present
study. The sample size was relatively small. Also, only young
adult males were examined. In general, the distribution of the
SM of females differs from that of males (1). Moreover, the
accuracy of the predicting body composition from BI analysis
is influenced by the body fat percentage (4) and age (3). Hence,
we cannot deny that the accuracy of the equation developed in
the present study would vary when subject samples involving
females, obesity, and/or elderly are taken for analysis. In
addition, the method used to analyze the MRI scans for
regional areas was a bit primitive and did not exploit more
advanced segmentation software being used in this research
field. There remains a possibility that smaller islands of adi-
pose tissue within the skeletal muscle bundle are not fully
excluded, as they would be using newer approaches, and so the
SM volume might be overestimated. Especially, the use of the
software would be heightened to examine the elderly, because
they have three times higher accumulation of the intramuscular
fat compared with the young men (11). Further study, to clarify
the influences of differences in the subject samples and the
method used to analyze the MRI scans on the estimate of SM
volume, is needed to generalize the findings obtained in the
present study.
In summary, the findings obtained here indicated that the
validity and cross-validity of the segmental BI analysis that
measures Zvalues from both sides of the upper arm and upper
leg, and five regions (both sides of the upper, the middle, and
both sides of the lower region) of the trunk was confirmed. On
the other hand, the whole body BI analysis significantly over-
estimated the whole body SM volume. The development of
segmental BI technique predicting the whole body SM volume
will be of benefit to lean, obese, or long-term hospitalized
individuals as well as athletes for evaluating conventionally
their own muscularity.
REFERENCES
1. Abe T, Kearns CF, Fukunaga T. Sex differences in whole body skeletal
muscle mass measured by magnetic resonance imaging and its distribution
in young Japanese adults. Br J Sports Med 37: 436 – 440, 2003.
2. Bartok C, Schoeller DA. Estimation of segmental muscle volume by
bioelectrical impedance spectroscopy. J Appl Physiol 96: 161–166, 2004.
3. Baumgartner RN. Electrical impedance and total body electrical conduc-
tivity. In: Human Body Composition, edited by Roche AF, Heymsfield
AB, Lohman TG. Champagne, IL: Human Kinetics, 1996.
4. Baumgartner RN, Ross R, Heymsfield SB. Does adipose tissue influence
bioelectric impedance in obese men and women? J Appl Physiol 84:
257–262, 1998.
5. Baumgartner RN, Chumlea WC, Roche AF. Estimation of body com-
position from bioelectric impedance of body segments. Am J Clin Nutr 50:
221–226, 1989.
6. Bland JM, Altman DG. Statistical methods for assessing agreement
between two methods of clinical measurement. Lancet 1: 307–310, 1986.
7. Bracco D, Thiebaud D, Chiolero RL, Landry M, Burckhardt P,
Schutz Y. Segmental body composition assessed by bioelectrical imped-
ance analysis and DEXA in humans. J Appl Physiol 81: 2580 –2587, 1996.
8. Caton JR, Mole PA, Adams WC, Heustis DS. Body composition
analysis by bioelectrical impedance: effect of skin temperature. Med Sci
Sports Exerc 20: 489 – 491, 1988.
9. Foster KR, Lukaski HC. Whole body impedance: What does it measure?
Am J Clin Nutr 64, Suppl 3: 388S–396S, 1996.
10. Fuller NJ, Elia M. Potential use of bioelectrical impedance of the “whole
body” and of body segments for the assessment of body composition:
comparison with densitometry and anthropometry. Eur J Clin Nutr 43:
779 –791, 1989.
11. Kent-Braun JA, Ng AV, Young K. Skeletal muscle contractile and
noncontractile components in young and older women and men. J Appl
Physiol 88: 662– 668, 2000.
12. Ishiguro N, Kanaehisa H, Miyatani M, Masuo Y, Fukunaga T. A
comparison among three bioelectrical impedance analyses for predicting
lean body mass in a population with a large difference in muscularity. Eur
J Appl Physiol 94: 25–35, 2005.
13. Ishiguro N, Kanaehisa H, Miyatani M, Masuo Y, Fukunaga T. Appli-
cability of segmental bioelectrical impedance analysis for predicting trunk
skeletal muscle volume. J Appl Physiol 100: 572–578, 2006.
14. Janssen I, Heymsfield SB, Baumgartner RN, Ross R. Estimation of
skeletal muscle mass by bioelectrical impedance analysis. J Appl Physiol
89: 465– 471, 2000.
15. Kanehisa H, Fukunaga T. Profiles of musculoskeletal development in
limbs of college Olympic weightlifters and wrestlers. Eur J Appl Physiol
Occup Physiol 79: 414 – 420, 1999.
16. Lukaski HC. Estimation of muscle mass. Human Body Composition,
edited by Roche AF, Heymsfield SB, Lohman TG. Champaign, IL: Human
Kinetics, 1996.
17. Miyatani M, Kanehisa H, Fukunaga T. Validity of bioelectrical imped-
ance and ultrasonographic methods for estimating the muscle volume of
the upper arm. Eur J Appl Physiol 82: 391–396, 2000.
18. Miyatani M, Kanehisa H, Masuo Y, Ito M, Fukunaga T. Validity of
estimating limb muscle volume by bioelectrical impedance. J Appl Physiol
91: 386 –394, 2001.
19. Organ LW, Bradham GB, Gore DT, Lozier SL. Segmental bioelectrical
impedance analysis: theory and application of a new technique. J Appl
Physiol 77: 98 –112, 1994.
20. Snyder WS, Cooke MJ, Manssett ES, Larhansen LT, Howells GP,
Tipson IH. Report of the Task Group on Reference Man. Oxford, UK:
Pergamon, 1975.
1695ESTIMATION OF WHOLE BODY SKELETAL MUSCLE VOLUME
J Appl Physiol •VOL 103 •NOVEMBER 2007 •www.jap.org
by guest on June 8, 2013http://jap.physiology.org/Downloaded from