1420 PHILLIPS ET AL.
method for evaluating and comparing the results of longitudi- that FFM is 73% water (14). We will refer to FFM calculated from
nal models (“slope-intercept” plots). The objectives of these TBW as FFMref to indicate FFM by the reference method. The
analyses were as follows: 1) to develop our own equation to percentage body fat estimated from TBW was calculated from the
predict FFM using a reference measure of TBW collected in measures of body weight and FFMref according to the following
equation: %BFref [Weight (kg) – FFMref (kg)]/Weight (kg) 100.
our sample; and 2) to use longitudinal data analysis approaches BMI was calculated as weight (kg) divided by height (m) squared. A
to evaluate our equation as well as other published equations BMI Z-score was calculated using the revised Centers for Disease
to assess their performance against reference measures of body Control and Prevention growth reference standards (15). The im-
composition. pedance index (H2/R) was calculated as height (cm squared) divided
by resistance ( ).
SUBJECTS AND METHODS
Participants. Between September 1990 and June 1993, we en-
rolled 196 girls in the Massachusetts Institute of Technology (MIT) Cross-sectional development of MIT equation. Using our data,
Growth and Development Study. The criteria for enrollment were we ﬁrst developed a regression equation, which we will refer to as the
premenarcheal status and a triceps skinfold less than the 85th per- MIT equation, to predict FFMref from BIA and anthropometric
centile for age and sex (10). Premenarcheal girls aged 8 –12 y were measures. Previous analyses conducted in this cohort suggested that
recruited from the Cambridge and Somerville public schools in Mas- the accretion of both FFM and body fat was related to maturational
sachusetts, the MIT summer day camp, and friends and siblings of status (16). Therefore, we modeled FFMref separately pre- and post-
enrolled participants. All participants were healthy, free of disease menarche, resulting in two prediction equations, one for premen-
and were not taking any medication at study entry. Participants were arche and one for postmenarche (16). Generating separate equations
followed annually until 4 y postmenarche. The racial composition of based on maturational status also helped avoid problems of collinear-
the cohort was 72% white, 15% black and 13% from other races. At ity between age and other model predictors.
baseline, 63% of the girls were prepubertal (Tanner stage 1) and 37% Our data were structured using a “long” format, such that a girl’s
were pubertal but premenarcheal (Tanner stages 2 and 3). The study measurements at a given visit represented one row in the data set. For
protocol was approved by both the Committee on the Use of Humans example, if a girl had three measurements of TBW and BIA con-
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as Experimental Subjects at MIT and the Human Investigation ducted at three different visits (and hence three different ages), she
Review Committee of the New England Medical Center. would provide three “rows” of data to the analysis. Using this format,
Study protocol. TBW measurements at both baseline and exit the 196 participants provided 422 measurements (or rows) that were
were available for 69% of participants. In addition, within the cohort, available for analysis. The distribution of measurement numbers was
some girls participated in supplementary hypothesis-driven substudies as follows: 52 participants had one measure of TBW, 84 participants
in which TBW was measured at additional time points. In one had two measures of TBW, 42 participants had three measures of
substudy, girls who were 12 y old at the second follow-up visit were TBW, 14 participants had four measures of TBW and 4 participants
invited to participate in a longitudinal study of energy expenditure. In had ﬁve measures of TBW.
the other, girls at menarche were invited to participate in a study of In the ﬁrst step of the model development process, participants
changes in visceral fat measured by MRI at menarche and 4 y were randomly assigned to one of two data sets: a model development
postmenarche. Girls were admitted to the General Clinical Research data set and a model validation data set. Each participant appeared in
Center at MIT for an overnight visit. At the time of their initial visit, only one of the two data sets. Because we had participants with
a physician obtained a medical history and examined each participant varying numbers of measurements, the selection of participants into
to ensure that she was in good health. On the evening of admission, one of the two data sets was done randomly and in blocks, so that
participants consumed no food or beverages after 1800 h. At 2000 h, participants with one, two, three, four or ﬁve TBW measurements
the staff obtained baseline urine and each participant was given 0.25g were equally distributed between the two data sets.
H218O/kg estimated TBW. After administering the isotope, the study To develop the MIT prediction equation, stepwise multiple linear
staff collected all urine voided until 0600 h the next morning to regression analysis was conducted in the model development and
determine the loss of isotope in the urine. The second void of the model validation data sets using FFMref as the outcome. Within each
morning was collected at 0800 h to measure 18O enrichment above data set, separate models were generated pre- and postmenarche.
baseline values. Isotopic analyses for assessment of body composition Possible predictors considered included height2/resistance, weight,
were conducted at the USDA Human Nutrition Research Center at height and reactance. Race, coded as two dummy variables for black
Tufts University (Boston) on two isotope ratio mass spectrometers and “other,” with white as the reference category, was included in all
(Hydra Gas; PDZ Europa LTD, Northwich, UK, and SIRA 10; models. These two equations developed in our data set will be referred
Micromass, Altrincham, UK). Urinary enrichment of 18O was deter- to as FFMMITpre and FFMMITpost. It is important to note that al-
mined by mass spectroscopy as described elsewhere (11). The CV of though we developed two prediction equations, one for premenarche
TBW determined from 18O dilution in urine was 2% (12). Oxygen and one for postmenarche, for a given measurement for a speciﬁc girl,
dilution space was calculated according to Halliday and Miller (13). a single predicted FFM value was generated. Thus, predicted values
We assumed that the 18O dilution space was 1% higher than TBW for FFM were generated in the model development and model vali-
(12). dation data sets. The model parameters, R2 and residual plots were
Height and body weight were measured in the morning. Height compared from the model development and model validation data
was measured to 0.1 cm with a wall-mounted stadiometer. Fasting sets. An independent-samples t test was used to test for signiﬁcant
weight was measured in a hospital gown using a Seca scale (Seca differences in predicted FFM between the two data sets.
Corporation, Hanover, MD) accurate to 0.1 kg. The data from the two sets were then recombined and the step-
Bioelectrical impedance analysis was conducted at the time when wise regression procedure was repeated. The parameters for height2/
TBW was measured. Resistance (R) and reactance were measured resistance, height, weight and race (categorized as white, black,
after an overnight fast with the participant supine (Bioelectrical “other”) generated from this full set (n 422) constituted our ﬁnal
impedance analyzer, BIA 101, RJL, Clinton Township, MI). The model. Then, using FFM predicted from our ﬁnal model, we calcu-
accuracy of the machine was checked before the measurements with lated %BFMIT according to the following equation: [Weight (kg)
a 500- resistor supplied by the manufacturer. Electrodes were placed – FFMMIT (kg)]/Weight (kg) 100. In addition, predicted FFM and
on the dorsal surface of the right foot and ankle, and right wrist and %BF were calculated using equations published by Deurenberg et al.
hand. A current was applied at a frequency of 50 kHz. Previous (4,6,17), Houtkooper et al. (7), Kushner et al. (18) and Schaefer et
analyses conducted in this cohort indicated that FFM based on a al. (19) (Table 1). FFM calculated by the equation of Houtkooper et
measure of BIA is highly reproducible (3). al. (7) was adjusted by 4% to correct for the overestimation of TBW
Study variables. Both isotopic dilution and BIA provide a mea- by deuterium dilution.
sure of total body water. Because we were interested in FFM and %BF, Longitudinal analysis. After our prediction equation was devel-
it was necessary to convert TBW into FFM, applying the assumption oped, our goal was to apply our equation and other published equa-
COMPARISON OF BODY COMPOSITION BY TBW AND BIA 1421
Equations for determining fat-free mass (FFM) by bioelectrical impedance and anthropometry1
Deurenberg et al. (17) FFM 12.72 (0.456 height2/resistance) (0.292 weight) (0.102 height)
Deurenberg et al. (6)
Age 10 y FFM 4.83 (0.640 height2/resistance)
Age 10–12 y FFM 14.7 (0.488 height2/resistance) (0.221 weight) [(12.77 height) 0.01]
Age 13 y FFM 6.5 (0.258 height2/resistance) (0.375 weight) [(10.5 height) 0.01] (0.164 age)
Deurenberg et al. (4)
Age 16 y FFM 6.48 (0.406 height2/resistance) (0.36 weight) [(5.58 height) 0.01]
Age 16 y FFM 12.44 (0.340 height2/resistance) (0.273 weight) [(15.34 height) 0.01] (0.127 age)
Kushner et al. (18)2 FFM [0.04 (0.593 height2/resistance) (0.065 weight)]/0.73
Houtkooper et al. (7) FFM 1.31 (0.61 height2/resistance) (0.25 weight)
Schaefer et al. (19) FFM 0.15 (0.65 height2/resistance) (0.68 age)
1 Weight in kg, height in cm, age in years, FFM in kg, TBW (total body water) in kg.
2 Kushner equation predicted TBW; division by 0.73 to convert TBW to FFM.
tions to our data and assess their performance over time using lon- the model results for both the slope and intercept simultaneously. All
gitudinal data analysis approaches. To accomplish this, we ﬁrst analyses were performed using SPSS (Version 10.1, SPSS, Chicago,
calculated the difference between the reference estimates of FFMref IL), SAS (Version 8.0, SAS Institute, Cary, NC) and S-PLUS
Downloaded from jn.nutrition.org by on June 16, 2008
and %BFref and the estimates of FFM and %BF generated from the (Version 4.5, MathSoft, Seattle, WA).
BIA prediction equations. Next, this difference was plotted against
age using nonparametric robust local smoothing procedures (20).
Although this technique ignores the correlation structure in repeated RESULTS
measurements, these plots allowed us to visualize the general pattern Equation development. At study entry, we had complete
of the relations between age and error in estimating FFM and %BF.
Second, linear mixed-effects modeling (LME) was used to evaluate
data for 196 participants. As expected, FFM and %BF in-
the change in FFM and %BF over time. In these models, age was creased between baseline and exit (Table 2; paired t test, P
treated as a random effect and a ﬁxed effect to express the trend of 0.001). By either H218O or BIA, the mean %BF at study
change over time in the whole population. This approach character- entry was 23% (n 196) and the mean %BF at 4 y postmen-
izes individual variation relative to the population mean while taking arche was 27% (n 133) (Table 2). We observed no signif-
into account the correlation between repeated measurements on the icant difference between FFM values predicted in the model
same participant as well as different numbers of measurements per development and model validation data sets (t test; P 0.05).
participant. Using both reference and predicted values of FFM and We evaluated the predictive ability (R2) and standard error of
%BF as the outcome, the LME models generated a set of slopes and the estimate (SEE) of four multiple regression models that
intercepts associated with each model, along with their corresponding predicted FFMref from our set of predictors (Table 3). Height2/
error measures. The width of the 95% conﬁdence intervals (CI)
around the slopes and intercepts reﬂects the variation around indi- resistance entered both models ﬁrst and accounted for 91% of
vidual slopes and intercepts and was used to assess the precision of the the variation in FFM premenarcheally and 79% of the varia-
various equations. tion in FFM postmenarcheally. Weight and height entered
The results of the LME models were visualized using slope-inter- next in both models, adding a small amount of explanatory
cept plots, which allow one to summarize and compare graphically capability over height2/resistance alone. Reactance entered
Characteristics of the girls at study entry and at 4 y postmenarche (MIT Growth and Development Study, 1990 –2002)1
Baseline (n 196) Exit2 (n 133)
Characteristic Mean SD Range Mean SD Range (n 133), P-value
Age, y 10.0 0.95 4.4 16.9 1.1 5.0 0.001
Height, cm 140.4 8.5 47.4 166.4 6.5 36.6 0.001
Weight, kg 32.9 6.2 32.6 59.3 7.9 42.9 0.001
BMI Z-score 0.27 0.89 6.0 0.05 0.79 4.3 0.001
Resistance, 729 76 485 639 66 355 0.001
Height2/resistance, cm2/ 27.5 5.5 29.9 43.8 5.8 29.4 0.001
FFM by 18O dilution, kg 25.2 4.4 21.6 42.9 4.7 22.5 0.001
FFM by BIA,4 kg 25.1 4.2 21.8 42.8 4.7 22.9 0.001
%BF by 18O dilution 23.2 5.2 25.7 27.4 4.6 23.3 0.001
%BF by BIA4 23.5 4.7 22.6 27.6 3.8 19.7 0.001
1 Abbreviations: FFM, fat-free mass; BIA, bioelectrical impedance; %BF, percentage body fat.
2 Subjects with both BIA and TBW at study exit, 4 y postmenarche.
3 Paired-samples t test.
4 Calculated using the MIT equations.
1422 PHILLIPS ET AL.
TABLE 3 equations was calculated (Table 4). In general, the published
predictive equations underestimated FFM and overestimated
Regression models to predict fat-free mass (FFM) from %BF. As expected, the MIT equation performed the best for
bioelectrical impedance (BIA) and anthropometry in girls over both FFM and %BF. In this cross-sectional analysis, the equa-
adolescence (in order of variable entry into equation) tions of Kushner et al. (18) and Houtkooper et al. (7) had the
smallest mean prediction errors. Overall, the variability in the
Premenarche Postmenarche error estimates was smaller for FFM than for %BF.
Next, we visualized the pattern of change in prediction
Equation1 R2 SEE R2 SEE
error with age (Figures 1A and B), which provides more
information than the crude mean error estimates (Table 4).
FFM Height2/resistance 0.91 1.4 0.79 2.4 These ﬁgures show that although the equation of Kushner et
FFM Height2/resistance al. (19) had the smallest mean error overall (Table 4), it
weight 0.94 1.1 0.90 1.7 performed well at the younger ages for FFM and %BF, but the
*FFM Height2/resistance error increased at older ages. The error associated with the
weight height 0.95 1.1 0.91 1.6
equation of Houtkooper et al. (7) was similar to that of
weight height reactance 0.95 1.0 0.91 1.5 Kushner et al. (19), whereas the three equations of Deurenberg
et al. (4,6,17) performed better at older ages than at younger
1 FFM (fat free mass) in kg; height in centimeters; weight in kg. ages. The equation of Schaefer et al. (19) performed poorly
* Indicates ﬁnal model selected. after age 11 y. These results highlight the importance of
considering age and the value of a longitudinal approach.
Longitudinal assessment. Our ﬁnal step, as described in
the models, but because its inclusion did not substantially the Subjects and Methods section, was to use LME models to
improve R2 values, the more parsimonious model was chosen generate a set of slopes and intercepts for FFM and %BF
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(Table 3). For any given set of explanatory variables, the R2 (Table 5). These coefﬁcients represent the populational slopes
values were higher and SEE were lower before menarche than and intercepts, which are the weighted average of the slopes
after menarche, indicating increased variability in FFM post- from the 196 participants. As expected, the MIT equation was
menarcheally. The best equations to predict FFMref in our closest to the reference in terms of both slope and intercept for
dataset pre- and postmenarche were as follows: FFMMITpre FFM. Similarly, the MIT equation was closest to the reference
5.508 (0.420 height2/resistance) (0.209 in terms of both slope and intercept for %BF. For estimates of
weight) (0.08593 height) (0.515 black race) FFM, the slope for age was relatively consistent across all of
(0.02273 other race); FFMMITpost 11.937 (0.389 the equations; the values for the intercept, however, varied
height2/resistance) (0.285 weight) (0.124 height) considerably. For %BF, the values for both intercept and slope
(0.543 black race) (0.393 other race). The pre- and varied to differing degrees from the reference estimates. The
postmenarche equations differ mainly in the intercept, i.e., the 95% CI around the slopes and intercepts for both FFM and
premenarche intercept is about –5.5 and the postmenarche %BF indicated that the precision around the estimates was
intercept is about –12. relatively good for all of the equations examined (i.e., CI were
Cross-sectional assessment. Before assessing the perfor- fairly narrow). The range of CI widths around the age slope
mance of the MIT and other published equations longitudi- was very similar for both FFM (range 0.13– 0.25) and %BF
nally, we evaluated the prediction errors cross-sectionally. For (range 0.15– 0.23). For intercept values, the range of widths
each measurement, the difference between FFMref or %BFref was slightly narrower for FFM (range 1.7–2.7) than for %BF
and the estimates of FFM and %BF predicted from the various (range 2.1–3.5). Although the precision of the various
Mean differences at baseline and exit between fat-free mass (FFM) and percentage body fat (%BF) estimated from H218O dilution
and FFM and %BF estimated using bioelectrical impedance (BIA) equations in girls during adolescence
Baseline (n 196) Exit (n 133)
Outcome Equation Mean difference (95% CI) Mean difference (95% CI)
FFM, kg MIT Equation 0.09 ( 0.05, 0.23) 0.11 ( 0.16, 0.38)
Houtkooper et al. (7) 0.13 ( 0.29, 0.03) 1.71 (1.41, 2.00)
Kushner et al. (18) 0.20 ( 0.40, 0.004) 1.92 (1.56, 2.29)
Deurenberg et al. (4) 0.77 (0.61, 0.93) 0.87 (0.59, 1.15)
Deurenberg et al. (17) 1.39 (1.22, 1.56) 1.32 (1.03, 1.60)
Deurenberg et al. (6) 1.68 (1.48, 1.88) 1.13 (0.84, 1.41)
Schaefer et al. (19) 0.27 (0.09, 0.45) 2.74 (2.34, 3.14)
%BF MIT Equation 0.21 ( 0.63, 0.21) 0.23 ( 0.69, 0.23)
Houtkooper et al. (7) 0.28 ( 0.18, 0.73) 3.00 ( 3.53, 2.47)
Kushner et al. (18) 0.41 ( 0.16, 0.99) 3.33 ( 3.98, 2.69)
Deurenberg et al. (4) 2.69 ( 3.19, 2.19) 1.54 ( 2.02, 1.05)
Deurenberg et al. (17) 4.71 ( 5.28, 4.13) 2.36 ( 2.87, 1.86)
Deurenberg et al. (6) 5.31 ( 5.88, 4.74) 1.99 ( 2.47, 1.51)
Schaefer et al. (19) 0.51 ( 1.06, 0.04) 4.43 ( 5.06, 3.79)
COMPARISON OF BODY COMPOSITION BY TBW AND BIA 1423
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FIGURE 1 Difference in fat-free mass (FFM) estimated from 18O
and FFM estimated by bioelectrical impedance analysis (BIA) prediction
equations (A) and difference in percentage body fat (%BF) estimated
from 18O and %BF estimated by BIA prediction equations (B) vs. age in
girls during adolescence
equations varied little, the validity of the equations did. For
example, for FFM, the Schaefer equation (20) has the narrow-
est CI around the intercept and age slope but it was farthest
from the reference estimate of FFM in terms of validity. FIGURE 2 Results of mixed effects models to estimate changes
To better evaluate the performance of the LME models in in fat-free mass (FFM) (A) and percentage body fat (%BF) (B) over time
terms of both slope and intercept simultaneously, we con- in girls during adolescence
structed slope-intercept plots for FFM and %BF (Figures 2A
and B). To evaluate how well the model performs over time, and %BF changes over time quite well. The equation of
the model parameters should be close to the reference in terms Deurenberg et al. (4) also performed very well in terms of both
of both slope and intercept. The MIT model approximated FFM slope and intercept for FFM, and better than the other pre-
Results from linear mixed-effects models predicting fat-free mass (FFM) and percentage
body fat (%BF) in girls during adolescence
FFM, kg n 422 %BF n 422
Intercept (95% CI) Age slope (95% CI) Intercept (95% CI) Age slope (95% CI)
H218O 0.63 ( 1.81, 0.55) 2.61 (2.51, 2.72) 17.33 (15.79, 18.88) 0.60 (0.50, 0.71)
MIT equation 0.48 ( 1.65, 0.68) 2.60 (2.50, 2.71) 17.14 (15.87, 18.42) 0.63 (0.54, 0.71)
Deurenberg et al. (4) 1.16 ( 2.31, 0.02) 2.60 (2.51, 2.69) 21.29 (20.25, 22.34) 0.45 (0.38, 0.53)
Deurenberg et al. (17) 2.01 ( 3.23, 0.80) 2.62 (2.52, 2.72) 25.16 (23.89, 26.45) 0.26 (0.18, 0.34)
Deurenberg et al. (6) 3.16 ( 4.51, 1.82) 2.72 (2.60, 2.84) 27.35 (25.60, 29.11) 0.08 ( 0.02, 0.18)
Houtkooper et al. (7) 2.23 (1.08, 3.39) 2.34 (2.25, 2.44) 11.98 (10.78, 13.19) 1.10 (1.02, 1.18)
Kushner et al. (18) 2.78 (1.55, 4.01) 2.30 (2.20, 2.39) 11.08 (9.45, 12.72) 1.17 (1.06, 1.28)
Schaefer et al. (19) 2.72 (1.83, 3.61) 2.24 (2.17, 2.31) 12.06 (10.35, 13.78) 1.19 (1.08, 1.31)
1424 PHILLIPS ET AL.
dictive equations for %BF. For FFM, the equations of Hout- differ signiﬁcantly from the reference in estimating FFM.
kooper et al. (7), Kushner et al. (18) and Schaefer et al. (19) Other equations considered were based on smaller samples,
formed a cluster farther from the other equations. The MIT ranging from 94 to 246 male and female subjects. The age
equation and that of Deurenberg et al. (4) did not differ ranges in the other studies varied, with some including both
signiﬁcantly from the reference in terms of either slope or children and adults (4,18), and others including only children
intercept (based on the overlap of 95% conﬁdence limits). and young adults (6,7,19). The discrepancy among published
Estimates from the other equations of Deurenberg et al. (6,17) equations may be due to different methodologies used to
did not differ signiﬁcantly from the reference in terms of slope, determine FFM. Schaefer et al. (19) based their estimates on
but were signiﬁcantly different from the reference in terms of K counting, Deurenberg et al. (4,6,17) on hydrodensitom-
intercept. etry, and Houtkooper et al. (7) and Kushner et al. (18) on
Similarly, for %BF, the equation of Deurenberg et al. (4) isotopic dilution with deuterium and H218O, respectively.
was closest to the reference of the published equations con- Each of these methods has its own set of assumptions.
sidered. The equations of Houtkooper et al. (7), Kushner et al. Several authors have cautioned that the ability of BIA to
(18) and Schaefer et al. (19) formed a cluster farther from the accurately assess body composition varies with the prediction
other equations. The values of slope and intercept for the equation used. In a longitudinal assessment of body composi-
equations of Deurenberg et al. (4,15,17) did differ signiﬁcantly tion change in adults with acquired immune deﬁciency syn-
from the reference estimates in terms of both slope and inter- drome, Paton et al. (28) found that different conclusions were
cept. drawn about the proportion of FFM change depending on the
BIA equation used. Our experience lends support to others
DISCUSSION who have recommended that researchers avoid applying pre-
diction formulas from the literature to their own data without
Although it is well established that cross-sectional esti- ﬁrst validating the formulas in their subjects (4).
mates of body composition by BIA correlate well with body Our study has some noteworthy limitations. Both isotopic
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composition assessed by reference methods, there is some dilution and BIA assess TBW, not FFM. We assumed that the
debate concerning whether BIA can accurately assess changes hydration of FFM is 73% water. Variability in the hydration
in body composition. Several studies have examined the abil- constant may introduce errors in the calculation of FFM and
ity of BIA to accurately detect the changes in body composi- %BF. This variability reﬂects both individual variation and
tion that occur during weight loss programs in adults. The variability due to the age-related decline in hydration, be-
results are conﬂicting, with some studies supporting the accu- lieved to be decreasing during childhood toward the adult
racy of BIA in detecting body composition changes (21–23), value of 0.73. Studies that have used a multicompartmental
and others ﬁnding that changes in body composition assessed model to determine the hydration status of fat-free mass have
by BIA differ signiﬁcantly from the reference method used had varying results. Roemmich et al. (29) used a four-compo-
(24 –26). However, such studies generally involve small num- nent model to estimate water content of FFM. They found a
bers of overweight or obese subjects and are conducted over hydration factor of 0.755 0.067 in the prepubertal girls
time periods ranging from 4 to 32 wk. As reviewed by van der (mean age 10.4 y) and a hydration factor of 0.744 0.052 in
Kooy et al. (26), discrepancies may be due to the timing of the pubertal girls (mean age 13.3 y). Another study that used
impedance measurements, changes in water and glycogen a multicompartmental approach found a FFM water constant
stores, or the duration of the weight loss period. In children, of 0.722 0.014 in prepubertal children (mean age 8.5 y)
Wabitsch et al. (27) developed and evaluated an equation for compared with 0.707 0.013 in young women (30). Al-
the prediction of TBW from BIA in obese children and ado- though these hydration factors differ from the 73% determined
lescents before and after weight loss over a 40-d treatment from cadaver analyses, they also differ among studies. Al-
period. They found that the equation developed gave an though it seems clear that hydration factors vary among indi-
accurate prediction of individual values of TBW before and viduals, it is not clear that the variability within subjects
after weight loss, but that prediction of small changes in TBW exceeds the variation with age. Thus, the exact hydration
during weight loss was not possible. factor of children and adolescents has not been established. In
We had an opportunity to examine how well BIA estimates a study that included almost 1200 children, the hydration
changes in FFM and %BF as measured by H218O in a relatively factor appears to be stable over the age range in our study (M.
homogeneous, initially nonobese premenarcheal cohort of 196 Horlick, Columbia University College of Physicians and Sur-
girls followed through adolescence. There is very little infor- geons, personal communication). Therefore, any error in the
mation concerning whether BIA can detect changes in body hydration factor would be constant over adolescence and
composition during the rapid period of adolescent growth. Our would not affect the comparison of longitudinal model param-
ﬁndings indicate that BIA is a useful technique for estimating eters.
change in body composition over time. Our prediction equa- In summary, we developed a speciﬁc BIA equation for use
tion, based on a reference measurement of TBW to estimate in our population of girls throughout the adolescent period,
FFM, performed signiﬁcantly better cross-sectionally and over using H218O as the reference standard. We then applied our
time in this cohort of girls than other published prediction equation and various other published equations developed
equations. Furthermore, our results are strengthened by the use among external populations to our data. Using longitudinal
of longitudinal analysis techniques that allow the changes that analysis techniques, we examined the ability of these equa-
occur over time to be characterized most accurately. tions to estimate changes in FFM and %BF over time com-
The ﬁnding that the prediction equation developed in our pared with measures from the reference method. The slope-
cohort performed better than other published prediction equa- intercept plots introduced represent a useful way to summarize
tions highlights the importance of equation selection when and compare the results of longitudinal linear models. Our
using BIA. Of the published equations examined in our anal- results show that BIA provides accurate estimates of the pat-
ysis, the equation of Deurenberg et al. (4), developed in a large tern of change in FFM and %BF over time, but that the
cohort of 827 male and female subjects between 7 and 83 y of accuracy depends on the equation chosen. Our experience
age, performed best in terms of both FFM and %BF and did not suggests that researchers who use longitudinal designs to study
COMPARISON OF BODY COMPOSITION BY TBW AND BIA 1425
changes in body composition with indirect methods must be 14. Pace, N. & Rathbun, E. N. (1949) Studies of body composition III. The
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