2.
SEGAL ET AL
(lab D). After all experimental procedures were explained to the tions to predict densitometrically determined LBM (LBMd) for
subjects their written informed consent was obtained. The test each sample. Resistance, reactance, height2/resistance, weight,
protocol was reviewed and approved by the institutional review height2, age, and sex (dummy coded with males, = 0, females
board at each of the participating institutions. Each subject = 1) were offered as possible predictors. LBMd was used as the
completed all measurementS on the same morning. Most of the dependent variable. The regressions were carried out in stepwise
subjects were studied after an overnight (12 h) fast and those fashion. Before pooling the data from males and females, the
who were not tested after an overnight fast were at least 3 h equality of the slopes for males and females was tested for sta
postabsorptive. tistical significance (1 1).
A quadruple crossvalidation of the equations for predicting
Densitometry LBMd from BIA was carried out according to the procedure
Body fat content and LBM were determined by densitometry. described by Lord and Novick (12): the bestfitting equation
At lab B, body density was determined by hydrostatic weighing derived from each data set was applied to the other three data
in a stainless steel tank in which a swing seat was suspended sets. For each data set statistical significance ofthe deviation of
from a Chatillon 15kg scale (Chatillon, New York, NY). At the regression of LBMd on LBM, crosspredicted with use of
labs A, C, and D, the underwater weighing systems were mod the equations derived from the other three laboratories, from
ifications ofthe method ofAkers and Buskirk (5), which makes the line of identity was tested. This procedure was followed to
use of force transducers. The subjects submerged beneath the test the significance of differences in the bestfitting regression
surface of the water while expiring maximally and remained as lines among laboratories (1 1).
motionless as possible at the point of maximal expiration for Additional statistical analyses ofthe data are described in the
, 5 5 while underwater weight was recorded. After several prac results section. The 0.05 level of significance was used for all
tice trials to familiarize the subjects with the test procedure, 10
data analyses.
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trials were performed except in lab A where only 4 trials were
performed. The estimated underwater weight was the highest Results
value that was reproduced three times (6). In labs A, B, and C
residual lung volume was estimated by means of the closed The characteristics of the subjects are shown in Table
circuit oxygen dilution method of Wilmore (7) with use of a I The population
. varied widely with respect to age and
Collins spirometer (Warren E Collins, Braintree, MA) and a
body composition. The same predictor variables were se
Hewlett Packard Model 47302A (HewlettPackard, Cupertino,
lected by the stepwise regression procedure in all four sets
CA) or a MedScience Model 505D nitrogen analyzer (Fiske
MedScience, St Louis, MO). In lab D, residual volume was of data: R, ht2, wt, age, and sex. The data from the men
measured by the closedcircuit helium dilution method (8) with and women were treated separately because the regression
use of a Collins Model 3002 modular lung analyzer (Warren E coefficients ofthe bestfitting regression lines were signif
Collins). In labs B, C, and D, two trials were performed while icantly different for men and women. The bestfitting
the subjects assumed a sitting position that duplicated body po equations for each laboratory are shown in Table 2. Re
sition in the tank during underwater weighing. Residual volume sistance and height2, individually, were better predictors
at lab A was measured in the water at the time ofthe underwater of LBMd than the calculated height2/resistance, as deter
weighing. Body density was calculated from the formula of mined by greater correlation coefficients and smaller SEES.
Goldman and Buskirk (9) and percent body fat was derived
The residuals were analyzed and found to be randomly
from body density by use ofthe Siri equation (10): percent body
fat = (4.95/density)  4.5. LBM is the difference between total
body weight and fat weight, where fat weight equals total body
weight multiplied by percent body fat. TABLE 1
Characteristics of the subjects (mean ± SD)
Bioe!ectrical impedance analysis
LabA LabB LabC LabD
Total body resistivity was measured with a fourterminal por
(n = 96) (n = 99) (n = 490) (n = 404)
table impedance analyzer (RJL Systems, Detroit, MI). Mea
surements were made while the subjects lay comfortably on a Men
stretcher with the limbs abducted from the body. Currentinjector Age 32± 9 26± 8 34± 8 32± 7
electrodes were placed just below the phalangeralmetacarpal Weight(kg) 75±12 79±12 79±12 88±13
joint in the middle of the dorsal side of the right hand and just Height(cm) 178± 8 179± 7 175± 7 179± 7
below the transverse (metatarsal) arch on the superior side of LBM(kg)* 61± 8 66± 7 62± 8 67± 8
the right foot. Detector electrodes were placed on the posterior Percent fat 18 ± 7 16 ± 8 22 ± 7 23 ± 8
side of the right wrist, midline, with the prominent pisiform Resistance(Q) 485±63 459±47 442±55 432 ±49
bone on the medial (fifth phalangeal) side and ventrally across
LabA LabB LabC LabD
the medial ankle bone ofthe right ankle with the foot semiflexed. (n = 64) (n = 81) (n = 224) (n = 141)
Resistance (R) to the flow ofa 50kHz injected current was mea
sured on a 0 l000( scale and reactance (Xc) was measured on Women
a 020041 scale. Empirically derived formulas provided by the Age 35± 9 29±10 24± 5 27± 6
manufacturer of the instrument were used to calculate es Weight(kg) 59± 8 71±23 61± 8 63± 9
timated LBM. Height(cm) 165± 8 165± 7 163± 6 164± 7
LBM (kg)* 43 ± 6 48 ± 7 44 ± 6 45 ± 5
Statistical analyses Percent fat* 27 ± 8 29 ± 12 28 ± 6 27 ± 8
Resistance(Q) 587±58 551±68 554±62 559±68
Multiple regression analyses were applied to the data from
each laboratory to derive bestfitting multiple regression equa S Determined from hydrodensitometry. LBM = lean body mass.
3.
LEAN BODY MASS ESTIMATION BY IMPEDANCE 9
TABLE 2 ofidentity. Reductions in the correlation coefficients and
Bestfitting equations for predicting lean body mass for each lab and increases in the SEEs resulting from application of the
all labs pooled equations derived at other laboratories compared with
vnab1e Lab A Lab B Lab C Lab D All labs the bestfitting equations were minimal. However, as
Men
shown in Table 3, differences in regression equations were
Height2 0.00109 0.00124 0.00122 0.00140 0.00132 found among some ofthe laboratories. For the men these
Resistance 0.01607 0.06626 0.03736 0.06336 0.04394 differences were attributable to differences among the lab
Weight 0.41004 0.26261 0.31973 0.26079 0.30520 oratories in body fat content: The lab C and lab D men
Age 0. 15407 0.22776 0.13038 0. 15634 0.16760
were significantly fatter than the lab A and lab B men.
Intercept 8.14874 41.35041 19.77883 32.29519 22.66827
R 0.9$ I 0.907 0.882 0.896 0.898 When adjustment was made statistically for differences
SEE 3.28 2.91 3.62 3.49 3.61 in body fat content among the four labs, differences among
regression equations
were eliminated. Specifically, LBMCI
vuiable Lab A Lab B Lab C Lab D All Labs
was regressed on body
fat and residualized LBMd values
Women
were obtained. The residualized LBMd, purged of any
Height2 0.00112 0.00114 0.000942 0.00103 0.00108
Resistance 0.03797 0.02502 0.01410 0.02578 0.02090
relationship with body fat, was used as the dependent
Weight 0.21110 0.18856 0.31153 0.22280 0.23199 variable and stepwise regressions were carried out for each
Age 0. 12953 0.06498 0. 14505 0.01802 0.06777 lab using resistance, height2, weight, and age as the in
Intercept 27.16729 19.25955 10.91436 18.29870 14.59453
dependent variables. The resulting regression equations
R 0.891 0.942 0.876 0.861 0.889
were analyzed for statistical differences among labs. For
SEE 2.51 2.31 2.15 2.42 2.43
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both men and women (even though the differences among
labs in the women’s body fat did not achieve statistical
significance) no statistical differences among labs in the
distributed and were uncorrelated with the predicted LBM regression coefficients were obtained when this adjustment
values. for body fat was made. This confounding effect of body
fatness on the prediction of LBMd supports a previous
Quadruple crossvalidation finding that the error in predicting LBMd from BIA was
significantly related to obesity (4).
The quadruple crossvalidation of the equations for The relationship between LBM predicted from height2,
predicting LBMd is shown in Table 3. The purpose of the resistance, weight, and age and LBMd for men and women
quadruple crossvalidation was to determine the repro (all labs pooled but separate equations for men and
ducibility across laboratories of the relationship between women) is shown in Figures 1 and 2. When the data are
LBMd and LBM predicted from BIA and other variables. expressed as percent body fat, the correlations between
LBMd was regressed on LBM predicted by each of the densitometrically determined percent body fat and pre
equations in order to determine whether the slopes and dicted percent body fat are r = 0.809 and r = 0.852 for
intercepts differed from 1 and 0, respectively, indicating men and women, respectively, with SEEs of4.44% fat for
that the regression lines differed significantly from the line men and 3.98% fat for women.
TABLE 3
Quadruple crossvalidation ofequations for predicting lean body mass (LBM): correlations (and SEE) between densitometrically determined LBM
(LBMd) and LBM predicted by bestfitting equation from each lab and all labs pooled
LabA LabB LabC LabD
Men
LBM (lab A equation) 0.911 (3.23) 0.854 (3.54)t 0.872 (3.75) 0.854 (4.08)ff
LBM (lab B equation) 0.832 (4.34)t1 0.907 (2.86) 0.860 (3.92)tf 0.892 (3.54)
LBM (lab C equation) 0.896 (3.47) 0.883 (3. 19) 0.882 (3.61) 0.884 (3.66)
LBM (lab D equation) 0.853 (4.09)ff 0.902 (2.94) 0.871 (3.77)t 0.896 (3.48)
LBM (all labs equation) 0.886 (3.62) 0.893 (3.06) 0.88 1 (3.63) 0.889 (3.58)
LabA L.abB LabC LabD
Women
LBM (lab A equation) 0.891 (2.45) 0.936 (2.38)tf 0.834 (2.45)tt 0.841 (2.55)tf
LBM (lab B equation) 0.878 (2.58) 0.942 (2.27) 0.854 (2.30) 0.856 (2.44)
LBM (lab C equation) 0.859 (2.76) 0.932 (2.44)ff 0.876 (2.15) 0.832 (2.62)
LBM (lab D equation) 0.868 (2.68) 0.936 (2.38)ff 0.853 (2.32) 0.861 (2.40)
LBM (all labs equation) 0.872 (2.64) 0.940 (2.30) 0.866 (2.22) 0.856 (2.44)
S Bestfitting results for each lab are indicated by italics.
t Intercept significantly different from 0; p < 0.05.
:l:Slope significantly different from 1; p < 0.05.
4.
SEGAL ET AL
90 and vice versa were significantly different from LBMd
000 5 0 (Table 6).
DO DO
C 0 0 D*C* C Figures 3 and 4 show the relationship between LBMd
CC0O* 0* 0 0
80 O ODD 0 0
and LBM predicted with use of the fatnessspecific equa
*000*CDS 11
CS *0000000*C
*0000000000 C *
tions. The dispersion ofdata points is considerably smaller
DDDO000000000* DO
than when the generalized equations were applied (Figs
0’
70 * *A000IHSO*ODCDO*
DSS0N*0000000 C
#{149}000000000000*OC 1 and 2). For men, the R value increased from 0.896 to
C CC00000000* CC*
A 00*0*0*00*000 0.938 and the SEE decreased from 3.62 to 2.84 kg with
SI A 0*110000000S0* *
E 60 *CC*0*00000*0* use of fatnessspecific equations. For women, the multiple
0 *DC00000CXOC000C
SD
CDA C000*000110
ACC0000000COC C
C
A I k A correlation coefficient R increased from 0.889 to 0.930
C
0
SI
5Q C
ACAC0000COC
ACC*D*AC B=LabB and the SEE decreased from 2.43 to 1.95 kg with use of
CCC
A C *CCC 

fatnessspecific equations. For the men when the data are
J CCC DLabD expressed as percent body fat, the correlation between
C C * = Multiple data
40 densitometrically determined and predicted percent body
points
fat increases from 0.809 to 0.896 and the SEE decreases
C r.896
from 4.44% fat to 3.35% fat with use ofthe fatnessspecific
30 SEE =3.62kg
equations. For the women the correlation between den
I I I I
sitometrically determined percent body fat and predicted
.. 40 50 60 70 80 90 percent body fat increases from 0.852 to 0.909 and the
Downloaded from www.ajcn.org by on January 11, 2008
LBM(BIA) (kg) SEE decreases from 3.98% fat to 3. 18% fat with use of the
(Best fitting equation for all men) fatnessspecific equations.
The practical application of the fatnessspecific equa
FIG 1. Relationship between densitometrically determined lean body
tions is questionable since their use depends on prior
mass (LBMd) and lean body mass (LBM) predicted from bioelectrical
knowledge of an individual’s Further body fat content.
impedance analysis (BIA) and other indices with use of the bestfitting
equation for the men in all four laboratories.
more, the fatnessspecific equations for predicting LBMd
are the basis of categorizing the subjects with respect to
densitometrically determined percent body fat, and it is
important to note that percent fat and LBM are not truly
Fatnessspeafic prediction equations independent since both are derived from hydrodensitom
etry. Although for subjects who are obviously lean or obese
Additional analyses were applied to the data to char there would be no question as to which prediction is most
acterize further the relationship between body fatness and appropriate, for subjects who are neither clearly lean nor
the prediction of LBMd by BIA and to determine the
appropriateness of fatnessspecific equations. The total
populations of men and women were divided randomly
into two subsets for the purpose of crossvalidation and
each subset was divided into normal and obese groups 65 S S
based on whether the subjects were less than or greater
55
than 20% and 30% body fat for men and women, respec 60 S SB
tively. For each of the two sets of normal and obese men 0’
S S
A S
and normal and obese women (total of eight subsets), an 55 
CD.
0 C DOD
equation for predicting LBMd was derived. The same set *C *C* C
O * *D*C*OSD S
of variables (height2, resistance, weight, and age) entered SI
E 50  SCC
#{149}0*0*00*SC
*B** IHI C D
0
into all equations except that age did not enter into the *S**C*C*SCDS
(I) C A***IHHI*** S
equations for the two sets ofnormal women. The fatness C **IHHI***C * A = Lab A
. 45  D *0*5*1155CC CD C
specific equations and the crossvalidation ofthese equa C*****000**C
B Lab B
#{149}
S*C*0000 SO C C = Lab C
tions between the two randomly generated sets for each 40 
S*S**IIOOS*0C
*005*5000CC 0 = Lab D
I
sex and level of fatness are shown in Tables 4 and 5. Use C
*B*DC*CDCC
*C**CSCC
11 : Multiple data
of these fatnessspecific equations greatly improved the AACC *SC points
35  #{149}CDC C
accuracy of predicting LBMd: the multiple correlation CCC D
r =889
coefficients were significantly increased and the SEES were SEE =2.43 kg
30
significantly reduced. No differences were found in the ii I I I I I I I I I
regression coefficients between set 1 and set 2 of normal 30 40 50 60 70
men, obese men, normal women, or obese women, in
LBM(BIA) (kg)
dicating the validity and reproducibility of fatnessspecific
equations (Table 5). However, significant differences were ( Bestfitting equation for all women)
observed between the regression equations for the normal FIG 2. Relationship between densitometrically determined lean body
and obese subjects for both men and women. Further mass (LBMd) and lean body mass (LBM) predicted from bioelectrical
more, the mean predicted LBM values obtained by ap impedance analysis (BIA) and other indices with use of the bestfitting
plying the normal group’s equations to the obese subjects equation for the women in all four laboratories.
5.
LEAN BODY MASS ESTIMATION BY IMPEDANCE 11
TABLE 4
Fatnessspecific equations for predicting LBM
Normal Obese
SetI Set2 Setl Setl Set2 SetI
Variable (n=244) (n=228) +set2 (n=295) (n=302) +set2
Men
HCight 0.00060171 0.00071366 0.00066360 0.00072092 0.001020 0.00088580
Resistance 0.01959 0.02319 0.02 1 17 0.0542 1 0.08245 0.02999
Weight 0.65940 0.59597 0.62854 0.48291 0.38179 0.42688
Age 0. 14244 0. 10545 0. 12380 0.0542 1 0.08245 0.07002
Intercept 8.73968 10.64701 9.33285 11.48504 16.69512 14.52435
R 0.948 0.943 0.946 0.937 0.939 0.937
SEE 2.50 2.44 2.47 2.97 3.04 3.03
Normal Obese
Seti Set2 SetI SetI Set2 Sell
Variable (n= 146) (n= 177) +set2 (n=99) (n=76) +set2
Women
Height2 0.00060098 0.00066464 0.00064602 0.000955 14 0.00077596 0.00091186
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Resistance 0.018 17 0.01 121 0.01 397 0.01420 0.01 560 0.01466
Weight 0.40328 0.43868 0.42087 0.3 1 134 0.282 16 0.29990
Age    0.07187 0.06215 0.07012
Intercept 15.26646 7.18338 10.43485 7.47371 14.20227 9.37938
R 0.839 0.903 0.907 0.953 0.954 0.952
SEE 1.90 2.00 1.97 2.06 1.81 1.97
obese, there is a need for a technique to determine which pendent of densitometrically determined body fatness,
prediction equation is most applicable. The validity of would significantly improve the prediction of LBMd, an
fatnessspecific equations for predicting LBMd requires a independent group ofsubjects was studied. These subjects
method of categorizing subjects that is objective and in (88 men, 72 women) underwent hydrostatic weighing, BIA
dependent ofdensitometry. Body mass index (BMI), body measurement, and skinfold thickness measurements.
surface area (BSA), and percent ofdesirable body weight Percent fat was derived
body from the sum of the biceps,
according to the 1959 Metropolitan Life Insurance stan triceps, suprailiac crest, and subscapular skinfolds with
dards (13)
were tested as possible classification criteria. use of the tabled values of Durnin and Womersely (14).
Classification of the subjects by these indices did not sig The subjects were divided into normal and obese groups
nificantly improve the prediction ofLBMd: categorization based on whether anthropometrically determined percent
ofthe subjects according to whether they had a BMI less fat was less than or greater than 20% for men and less
than or greater than 26, were below or above the 50th than or greater than 30% for women. Compared with
percentile ofBSA for their sex, or were less than or greater densitometrically determined percent fat, 82 of 88 men
than 120% of desirable body weight did not lead to sig (93%) and 66 of 72 women (92%) were correctly catego
nificant increases in the multiple correlation coefficients rized with respect to their level of fatness. To determine
or significant reductions in the SEEs. Each of these three the effectiveness of this classification, for each subgroup
criteria also was tested as a continuous variable: in separate (normal and obese men, normal and obese women) LBMd
analyses, each ofthe indices was entered forcibly into the was compared with LBM estimated with use of the gen
regression equation to predict LBMd at the first step, and eralized equations and LBM estimated with use of the
height2, resistance, weight, and age were entered block fatnessspecific equations. These results are shown in
wise at the second step (1 1). Separate analyses were carried Table 7. Use ofthe generalized BIA equations significantly
out for men and women. The resulting correlations and underestimated LBMd for both the normal men and nor
SEEs were not significantly different from those obtained mal women. However, the question remained as to
by use ofthe generalized equations derived from all men whether use of the BIA fatnessspecific equations signifi
and all women (individually) pooled together (Figs 1 and cantly improved the prediction of LBMd over an
2), indicating that BSA, BMI, and percent desirable body thropometry alone. To answer this question, LBM
weight did not significantly improve the prediction of values (LBMa) were derived from anthropometrically
LBMd. determined percent body fat: weight  (weight X percent
fat). LBMd was regressed on LBMa and LBM determined
Use ofanthropometry to categorize subjects with use of the fatnessspecific equations with LBMa en
To test whether categorization ofsubjects into two levels tered forcibly at the first step. The significance ofthe entry
of fatness on the basis of anthropometry, which is inde of LBM derived with the fatnessspecific BIA equations
6.
SEGAL ET AL
TABLES for men and from 2.63 to 2.09 kg for women). Thus,
Crossvalidation of fatnessspecific equations for predicting lean body mass* anthropometrically determined percent fat can be used
(LBM) reliably as the criterion for determining which fatness
specific BIA equation to apply. There is obviously no need
Normal Normal All normal
to prescreen subjects who are extremely lean or extremely
seti set2 men
obese. However, for subjects who are neither clearly lean
Men nor obese, anthropometry may be useful in determining
Normal men
LBM (normal set I eq) 0.948 (2.50) 0.942 (2.45) 
the optimal BIA prediction equation. It is important to
LBM (normal set 2 eq) 0.947 (2.51) 0.943 (2.44)  note, however, that the fatnessspecific BIA equations sig
LBM (obese men’s eq)   0.939 (2.60)t nificantly improve the prediction of LBMd over anthro
Obese Obese All obese pometry alone.
sell set2 men
Obese men Manufacturer’s prediction equation
LBM (obese set 1 eq) 0.937 (2.97) 0.934 (.318) 
LBM (obese set 2 eq) 0.933 (3.06) 0.939 (3.05) 
Equations are provided with the BIA instrument for
LBM (normal men’s eq)   0.932 (3.l3)t
the prediction of LBM: for men LBM = 6.493
Normal Normal All normal + 0.4936(height2/resistance) + 0.332(weight); and for
set 1 set 2 women
women LBM 5.09 1 + 0.6483(height2/resistance)
Women + 0. 1699(weight). The correlation between LBMd and
Downloaded from www.ajcn.org by on January 11, 2008
Normal women
LBM predicted with use of the manufacturer’s equation
LBM (normal set I eq) 0.916 (1.90) 0.897 (2.05) 
LBM(normalset2eq) 0.911 (1.93) 0.903(2.00) 
was r = 0.857 (SEE = 3.70 kg) for men and r = 0.800
LBM (obese women’s UI)   0.897 (2.06)t (SEE = 3.18 kg) for women. However, the mean predicted
LBM was significantly greater than LBMd for both men
Obese Obese Allobese
set 1 set 2 women (69.2 ± 8.52 kg predicted LBM vs 64.0 ± 8.2 kg LBMd)
and women (47.3 ± 6.2 kg predicted LBM vs 44.6 ± 5.30
Obese women
LBM (obese set 1 eq) 0.953 (2.05) 0.953 (1.97) 
kg LBMd), indicating that the manufacturer’s equation
LBM (obese set 2 eq) 0.952 (2.06) 0.954 (1.81)  systematically overestimated LBMd for both sexes. This
LBM (normal women’s ui)   0.938 (2.20)t overestimation of LBMd by the manufacturer’s equation
was most apparent in the obese men and women (Ta
11 Bestfining equations for predicting LBM for normal and obese men and
women denved from set 1 were applied to set 2 and vice versa. Measured LBM ble 6).
(LBMd) was regressed on LBM predicted with use ofthe equation developed from
the opposite t and correlations between measured and crosspredicted LBM and
SEES are shown. Effect of fitnessspecific equations is indicated by regression of
Discussion
LBMd on LBM cross predicted by applying obese subjects’ equation to normal
subjects and vice versa.
t Slope significantly different from 1, p < 0.05; intercept significantly different The results ofthis study confirm the validity ofthe BIA
from 0, p< 0.05. method for predicting LBM in large heterogeneous sam
ples of men and women. In contrast to previous reports
(24), height2 and resistance individually rather than
at the second step was then determined. For both men height2/resistance were selected by the stepwise regression
and women the addition ofLBM derived from the fatness process. This finding was consistent among the four labs.
specific BIA equations significantly improved the predic Also, whereas in previous studies (3, 4) the slopes of the
tion of LBMd: the change in the correlation
coefficients prediction equations were not reliably different between
(from R = 0.934 to 0.943 for men and from R = 0.923 men and women, in the present study significant differ
to 0.952 for women) was highly significant (p < 0.0001) ence in the regression coefficients were found between
as were the reduction in the SEEs (from 2.53 to 2.22 kg men and women. It is possible that discrepancies between
TABLE 6
Comparison of mean (±SD) densitometrically determined lean body mass (LBMd) with lean body mass (LBM) crossp redicted with use of fatness
specific equations and RJL System’s equation
Normal men Obese men Normal women Obese women
LBMd 64. 12 ± 7.56 63.95 ± 8.63 44.54 ± 4.64 44.71 ± 6.36
LBM (manufacturer’s eq) 66.60 ± 7. 18 7 1 . 19 ± 8.95 46.56 ± 5.44 48.87 ± 7.04
LBM (obese men’s eq) 57.98 ± 5.90
LBM (normal men’s eq) 71.51 ± 10.1 1
LBM (obese women’s eq) 41.38 ± 3.91
LBM (normal women’s eq) 49.63 ± 7.79
S
p < 0.01 vs LBMd.
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