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1 Division of Endocrinology, ABSTRACT Roemmich, James N., Pamela A. Clark, Arthur Weltman, and
Alan D. Rogol. Alterations in growth and body composition during
puberty. I. Comparing multicompartment body composition models.
J. Appl. Physiol. 83(3): 927-935, 1997.
children; adolescents; body fat; hydrostatic weighing; bioelectrical impedance; skinfolds
INTRODUCTION ACCURATE ASSESSMENT of body composition in children is
necessary to examine the risk of obesity and related health issues as
well as to investigate the influence of various interventions on
changes in adipose and fat-free tissues. Until recently,
most assessments of body composition in children were based on the two-compartment (2C) model of Siri (26), which significantly overestimates percent body fat (%BF). The overprediction in %BF is
due to the chemical immaturity of children, who have a lower density of
the fat-free mass (FFM) due to a higher proportion of water and lower
proportions of mineral and protein in their FFM than in the FFM of the
adult cadavers used to derive the 2C model (reviewed in Ref.
4).
The inaccuracy of 2C body composition estimates in children resulted in
the development multicompartment models of body composition measurement
(14). In a four-compartment (4C) model, the FFM is divided into its
constituent parts: water, mineral, and protein. Two three-compartment
(3C) models have also been developed, each of which combines two
constituents of the FFM into one compartment. In the 3C water-density
(3C-H2O) model,
protein and mineral are combined as solids. In the 3C
mineral-density (3C-min) model, water and protein are combined to form
the lean soft tissue (4). Although the multicompartment models of body
composition should offer improved accuracy, they have not been widely
validated in children and adolescents.
In addition to multicompartment models, several less-technical and
less-expensive techniques for body composition assessment in children
have been developed to correct for the maturational changes in the
density of the FFM. Lohman (13) has published age-adjusted constants
for the Siri equation (27). Once the body density is
determined from underwater weighing, it can be used in the age-adjusted
Siri equations to calculate the 2C model of body composition. Several
investigators have utilized these age-adjusted equations as a criterion
method to validate and cross-validate skinfold and bioelectrical
impedance (BIA) prediction equations (12, 16). However, the validity of
the Lohman age-adjusted equations has not been determined. Furthermore,
although a limited number of skinfold and BIA equations have been
developed based on multicompartment models (2, 11, 28), these equations have not been adequately cross-validated against multicompartment models in diverse subject samples. In the present study, we
investigated the agreement between 2C, 3C, and 4C models of body
composition in children and adolescents.
METHODS
A four-compartment (4C) model of body composition was used as a
criterion to determine the accuracy of three-compartment (3C) and
two-compartment (2C) models to estimate percent body fat (%BF) in
prepubertal and pubertal boys (genital I & II,
n = 17; genital III & IV,
n = 7) and girls (breast I & II, n = 8; breast III & IV,
n = 15). The 3C water-density (3C-H2O) and 3C mineral-density
models, dual-energy X-ray absorptiometry, the Lohman age-adjusted
equations, the Slaughter et al. skinfold equations, and the Houtkooper
et al. and Boileau bioelectrical impedance equations were
evaluated. Agreement with the 4C model increased with the
number of compartments (i.e., body water, bone mineral) measured.
Except for the 3C-H2O model, the
limits of agreement were large and did not perform well for
individuals. The mean %BF by dual-energy X-ray absorptiometry (23.6%)
was greater than that of the criterion 4C method (21.7%).
For the field methods, the Slaughter et al. skinfold equations
performed better than did the Houtkooper et al. and Boileau
bioimpedance equations. The hydration of the fat-free mass decreased
(genital I & II = 75.7%, genital III & IV = 74.8%, breast I & II = 75.5%, breast III & IV = 74.4%) and the mineral content increased
(genital I & II = 4.9%, genital III & IV = 5.0%, breast I & II = 5.1%, breast III & IV = 5.7%) with maturation. The density
of the fat-free mass also increased (genital I & II = 1.084 g/ml,
genital III & IV = 1.087 g/ml, breast I & II = 1.086 g/ml, breast III & IV = 1.091 g/ml) with maturation. All of the models reduced the %BF overprediction of the Siri 2C model, but only the 4C and
3C-H2O models should be used as
criterion methods for body composition validation in children and
adolescents.
20°C
until analysis by gas-isotope-ratio mass spectrometry (Metabolic
Solutions, Merrimack, NH). The
2H2O
pool size was calculated from the baseline and equilibrium urine
samples as described by Prentice (19). A factor of 1.04 was used to
correct for the incorporation of deuterium into nonaqueous tissues
(23). The TBW was converted from liters to kilograms by dividing by
0.9937, the density of water at body temperature (10).
Body density.
Body density was measured by hydrostatic weighing by using the
procedures previously described by Sinning (25). Residual volume (RV)
was measured on land by nitrogen washout (32) with the subject seated
in the same position as that utilized during the underwater weighing.
The RV measurements were repeated until two trials were within ±50
ml.
Bone mineral content (BMC).
Soft tissue composition of the total body and measurement of BMC was
made by dual-energy X-ray absorptiometry (DEXA) by using a Hologic QDR
2000 bone densitometer (Hologic, Waltham, MA). The subjects removed all
metal and clothes containing metal before the scan. The subjects were
placed in a supine position and asked to remain still. A series of
transverse scans were made with a pencil beam from head to toe of the
subject at 1-cm intervals. All scans were then analyzed with
Hologic-enhanced whole body software version 5.64 by a single trained
technologist. The BMC was divided by 0.88 to correct for fractional
lowering of the volume of bone mineral density by absorptiometry
compared with the volume of bone ash mineral density (22, 24). The BMC
was then converted to total mineral by dividing by 0.824 (14).
4C model.
The 4C model equation of Lohman (14) was used as the criterion method
to which all other models and equations were compared. The equation
is
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(4) |
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1 = female).
Proportional composition of FFM.
The proportional contribution of water, mineral, and protein to the FFM
was calculated by dividing the TBW, osseous mineral (OM), nonosseous
mineral (NOM), and protein by the FFM. The NOM was obtained by
multiplying M by 0.176 (14). The protein/FFM was calculated as
protein/FFM = 1 (TBW/FFM + OM/FFM + NOM/FFM). The density of
the FFM (DFFM) was calculated by
assuming a density of 0.9937 for water, 2.982 for OM, 3.317 for NOM,
and 1.34 g/ml for protein. The carbohydrate (assumed to be 0.6% of the
FFM) was added to the protein fraction (8).
Statistical analyses.
Each model and prediction equation was compared for agreement to the 4C
model with Bland-Altman pairwise comparisons (1). In this approach,
based on graphical representations of the data, the mean difference
(bias) between two models was plotted against the value obtained from
the 4C model. According to Bland and Altman, if all of the differences
are within ±2 SD of the mean and there is not a significant
relationship between the differences and the average values, the
methods are considered equivalent. However, in some circumstances,
±2 SD of the mean may include clinically relevant differences. In
this case, the tolerable levels of disagreement can be set to a more
stringent level (e.g., ±1 SD). Two-way analysis of variance
[(2) gender × (2) maturation] was used to compare physical characteristics and differences in bias. When
the groups were combined, a one-way analysis of variance was used to
compare the mean estimates of %BF for the 2C, 3C, and 4C
models. Pearson-product moment correlations were computed
between the criterion 4C and other models. The constant error
(CE) and total error (TE) were computed as described by Thorland et al.
(30).
RESULTS
The physical characteristics of the subjects are shown in Table 2. There were no gender-by-maturation interactions. There were several gender differences: the boys had a greater Db and lower amounts of %BF and fat mass than did the girls. The gender differences in TBW (P = 0.08) and FFM (P = 0.07) approached significance. As expected, there were also several differences due to pubertal maturation. The more pubertally advanced boys and girls were older, taller, more skeletally mature, heavier, and had greater amounts of body water, %BF, and FFM than did the prepubertal boys and girls. The maturational differences in body density (P = 0.08) approached significance.
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Table 3 shows the proportional contribution of water, mineral and protein to the FFM. The pubertal subjects tended to have a smaller (P = 0.09) water fraction of the FFM (TBW/FFM) and a greater (P = 0.05) total mineral proportion of the FFM (M/FFM). The M/FFM was also greater (P = 0.01) in the female than in the male subjects. The pubertal boys and girls had a greater (P = 0.06) DFFM than did the prepubertal groups.
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The r2, CE, and TE for %BF for each of the body composition models compared with the 4C model are shown in Table 4. The r2 ranged from 0.990 (3C-H2O) to 0.402 (Boileau BIA). As shown by the CE, the Houtkooper et al. and the Boileau BIA equations underpredicted the %BF by a mean of 0.68 and 2.18 %BF, respectively. All of the other models and equations overpredicted the %BF by varying amounts. As expected, the CE of the Siri 2C model was significantly greater than all other models. The CE of the Boileau BIA equation was significantly different from all other equations except the Houtkooper et al. BIA equation. The TE reflects the difference between the actual (criterion 4C) and predicted %BF values (i.e., reflects dispersion around the line of identity) (30). The TE ranged from 0.88 %BF (3C-H2O) to 6.07 %BF (Boileau BIA). The TE of the 3C-H2O equation was 2.36- to 5.89-fold lower than the TE values derived from the other models and equations.
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The Siri 2C model overpredicted the %BF by 5.15% and had a large
amount of variation around the mean bias (Fig.
1A). The error of some
predictions was beyond the large ±2 SD limits of agreement. The
overprediction of %BF by the Siri 2C model was dependent on the %BF
(r2 = 0.15, P = 0.007) and produced larger
overpredictions at lower %BF. Furthermore, the bias was greater in the
male than in the female subjects (Table 5).
Use of the age-adjusted constants of Lohman in the Siri 2C model (Fig.
1B) reduced the bias to a mean
overprediction of 1.14 %BF. The limits of agreement (Fig. 1B) were still large, ranging from a
5% underestimation to an 8% overestimation of %BF. Several data
points were near or beyond the large ±2 SD limits of agreement.
Similar to the Siri 2C model, the bias was greater in the male than in
the female subjects, and the overprediction of %BF was dependent on
the %BF (r2 = 0.08, P = 0.06). At the
upper end of the range for %BF, only one estimate was above the mean
difference. The maturational difference in bias approached
(P = 0.09) significance (Table 5).
Correction for the proportional mineral composition (3C-min, Fig.
2A) also reduced the mean overprediction bias (0.40 %BF) but did not improve the agreement beyond that of the age-adjusted equations of Lohman. The
Siri 3C-H2O model (Fig.
2B) had a mean overprediction of
0.75 %BF and the narrowest limits of agreement (2 SD = ±0.99
%BF). The limits of agreement of the
3C-H2O equation were 5.16-fold lower than that of the 3C-min model, which had the next smallest limits
of agreement. All but nine of the %BF difference scores for the
3C-H2O model were within ±1
SD. The bias tended to be larger in the prepubertal subjects
(P = 0.08, Table 5). The DEXA model
(Fig. 2C) produced a mean
overprediction bias of 1.88 %BF and, except for the skinfold
and BIA models, the limits of agreement (2SD = + 8.30 %BF) were larger
than any of the other models tested. When the estimates of %BF for all
subjects were combined into one group (data not shown), the %BF by
DEXA was significantly greater than by the 4C model.
) in percent
body fat (%BF) from 4-compartment (4C) model Siri
2-compartment (2C) model vs. 4C model %BF.
B: in %BF from 4C model 2C Lohman age-adjusted equations vs. 4C model %BF. Dashed lines, mean
; solid lines, ±2 SD of mean ; dotted line, regression
between %BF and 4C model %BF. 
, Breast I & II female subjects;
, breast III & IV female subjects; 
, genital I & II male
subjects; 
, genital III & IV male subjects.
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in %BF from 4C model 3-compartment mineral density (3C-min) model vs. 4C model %BF.
B: %BF from 4C model 3C
water density (3C-H2O) model vs. 4C model %BF.
C: in %BF from 4C model dual-energy X-ray absorptiometry (DEXA) vs. 4C model %BF. Dashed lines, mean ; solid lines, ±2 SD of mean ; dotted lines,
regression between %BF and 4C model %BF. , breast I & II
female subjects; , breast III & IV female subjects; , genital I & II male subjects; , genital III & IV male subjects.
The skinfold equations also reduced the bias relative to the 2C Siri
model. The Slaughter T+C and Slaughter T+S equations (Fig.
3, A and
B) overpredicted %BF by 0.31 and
0.09 %BF, respectively, and limits of agreement (±2 SD) by 8.10 and 9.88 %BF, respectively. The Slaughter T+C equation
did not predict well when the %BF exceeded 30%. The Slaughter T+C
equation overpredicted %BF more in the boys than girls
(P = 0.03, Table 5). The Slaughter T+S
equation tended to underpredict body fat in the female subjects and
overpredict body fat in the male subjects
(P = 0.08; Table 5). The Houtkooper et
al. and Boileau BIA equations (Fig. 4,
A and
B, respectively) produced a mean bias
in %BF of 0.68 and 2.18%, respectively. The limits of agreement
(±2 SD) for the Houtkooper et al. and Boileau BIA equations were
11.04 and 12.02 %BF, respectively. The bias of the Houtkooper et al.
(r2 = 0.22, P = 0.001) and Boileau
(r2 = 0.14, P = 0.01) BIA equations were dependent
on the %BF. The Houtkooper BIA equation also tended to overestimate
the %BF in the pubertal boys and underestimate the %BF in the pre-
and early-pubertal girls (P = 0.07, Table 5). The bias in the Boileau BIA equation estimate of %BF was
dependent on both the gender and the maturation of the subjects (Table
5).
in %BF from 4C model Slaughter T+C (Slaughter et al. equations that utilize triceps and calf
skinfolds) vs. 4C model %BF. B: in %BF from 4C model Slaughter T+S (Slaughter et al. equations
that utilize triceps and subscapular skinfolds) vs. 4C model %BF.
Dashed lines, mean ; solid lines, ±2 SD of mean ; dotted
lines, regression between %BF and 4C model %BF. , breast I & II female subjects, , breast III & IV female subjects; , genital
I & II male subjects; genital III & IV male subjects.
in %BF from 4C model Houtkooper et al. bioelectrical impedance (BIA) vs. 4C model %BF.
B: %BF from 4C model Boileau BIA vs. 4C model %BF. Dashed lines, mean ; solid lines,
±2 SD of mean ; dotted lines, regression between %BF and 4C
model %BF. , breast I & II female subjects, , breast III & IV
female subjects; , genital I & II male subjects; , genital III & IV male subjects.
DISCUSSION
The ability of existing body composition equations to predict %BF was tested in children and adolescents. All were validated against a 4C model of body composition that reduces error by accounting for individual and maturational differences in the proportions of water and mineral to the FFM. Except for the Siri 2C equation, all of the equations tested were based directly on, or originally validated against, a multicompartment model of body composition. Our results indicate that the mean estimates of %BF from the Siri 2C model and DEXA were significantly greater than the criterion 4C model. Comparison of the limits of agreement (±2SD of the mean bias, Figs. 1, 2, 3, 4) demonstrated that the 3C-H2O model agreed with the 4C model at least 5.16-fold better than did the other models and equations. Except for the 3C-H2O model, the limits of agreement with the 4C model were large and the models did not perform well for the individual.
The proportional composition of the FFM and DFFM data presented here (Table 3) compare favorably with previous investigations of children and adolescents and support the validity of our 4C model and the results presented herein. The TBW/FFM of prepubertal boys and girls has been reported to range from 73.1 to 76.2% and from 72.2 to 77.0%, respectively, while the TBW/FFM of pubertal boys and girls ranges from 74.2 to 75.0% and from 74.0 to 75.5%, respectively (3, 8-10, 13, 15, 31). The M/FFM has also been previously reported to be greater in girls (5.0%) than in boys (4.9%) at age 10.5 yr and to not increase substantially in boys between ages 10.5 and 13.5 yr (8, 9, 31). The M/FFM of prepubertal girls increases from 5.3% at 12.5 yr to as much as 7.0% by 14.5 yr (31).
The mean DFFM of the present study also agrees with previously reported ranges. The DFFM of boys and girls aged 9-10.5 yr ranges from 1.084 to 1.089 g/ml and from 1.082 to 1.087 g/ml, respectively. The DFFM of boys and girls aged 13-14 yr ranges from 1.087 to 1.094 g/ml and from 1.092 to 1.093 g/ml, respectively (2, 3, 8, 9, 14).
The overestimation of %BF in prepubertal and pubertal boys and girls by the Siri 2C equation is due to an assumed constant TBW/FFM of 73.2% and M/FFM of 6.8%, resulting in a constant DFFM of 1.1 g/ml (4). For instance, the Siri 2C model overpredicted the %BF more for boys than for girls but comparison of the DFFM data (Table 3) shows the girls had DFFM closer to the assumed 1.1 g/ml. Several 3C models of body composition have been developed to correct for individual and maturational differences in the TBW/FFM and M/FFM. However, these models require cross-validation in diverse subject populations (2). Correction for M (3C-min model, Fig. 2) reduced the bias to 0.40 %BF compared with 5.15 %BF for the Siri 2C model. However, the TE of the 3C-min model was 2.36-fold higher than the 3C-H2O equation and, in toto, the 3C-H2O model performed much more accurately than did the 3C-min model (Fig. 2). The 3C-min model (Fig. 2) did not appreciably improve the agreement (2SD = + 6.06 %BF) beyond that of the Lohman age-adjusted equations (Fig. 1, discussed below). Thus the 3C-min model, which requires a radiation exposure (albeit small), does not perform well at the individual level and does not improve the prediction accuracy of body composition in children beyond the less-invasive age-adjusted equations of Lohman.
Correction for the TBW may more accurately assess %BF than correction for M because water accounts for 74-79% and mineral a relatively small 4-7% of the FFM (26). Viewed from another perspective, the SD of the CE between the 4C and 3C-H2O models was 0.49 %BF, which is the reduction in error variability by including a mineral component in the 4C model (24). The SD of the CE between the 4C and 3C-min model was 3.02 %BF.
Despite the high agreement between the 3C-H2O and criterion 4C models (Fig. 2), the bias was dependent (P = 0.08) on the maturation of the subjects (Table 5). This may be due to the 3C-H2O model not correcting for the M/FFM. The M/FFM of the prepubertal subjects was farther from the assumed constant than in the pubertal subjects. However, the mean bias was small in all subject groups, and the ±2SD limits of agreement were <1 %BF. Thus our data support the conclusions of Siconolfi et al. (24) and Lohman (14) that the 3C-H2O model is a valid predictor of %BF. The data do not support use of the 3C-min model as a criterion method in children and adolescents.
Although the multicompartment models offer advantages in accuracy, many investigators cannot utilize them because of cost and technical constraints. Thus Lohman (13) published age-adjusted constants for the Siri 2C equation. These equations have been described as a criterion method (4, 13) and have been used to validate (12, 16) other methods. The Lohman age-adjusted equations reduced the overprediction by the Siri 2C model (Fig. 1). However, the limits of agreement were large and very similar to the Siri 2C equation (Fig. 1). The Lohman equations assume a greater DFFM than found in the boys in the present study, which resulted in the overestimation of the %BF (Table 5). In addition, the bias of the Lohman equations tended (r = 0.28, P = 0.06) to be dependent on the %BF and generally overestimated the %BF of males with <12 %BF.
Thus the Lohman age-adjusted equations should not be used to validate new or preexisting equations because, as shown in Fig. 1, if the TBW/FFM or M/FFM of a child differs from the average for that age and gender, a large prediction error can occur (13). As suggested by Lohman (13), to further refine these equations, future investigations should continue to report the water and mineral fractions of the FFM. Refined equations will allow medical professionals and coaches to simply and more accurately estimate the minimal weight of adolescent wrestlers, gymnasts, and distance runners. The overprediction of %BF in the leanest subjects of the present study by the Lohman equations would have reduced the minimal weight prescription for these individuals.
There have been no other studies that have cross-validated the DEXA method against a 4C model in children and adolescents. The mean DEXA estimates of %BF were higher than the 4C model. The Hologic DEXA overestimates %BF, in part, because it assumes a relatively low constant TBW/FFM of 73.2% (see Table 3). The large limits of agreement (Fig. 2) and large TE suggest DEXA has limited validity in children and adolescents. DEXA has been used as a criterion method in body composition validation studies of children(5, 18). However, this should be avoided until the DEXA software can account for maturational differences in the proportional contribution of water to the FFM.
An important element of body composition research is to develop field methods such as anthropometry and BIA that permit accurate body composition assessments in clinical, educational, and health club settings. Slaughter et al. (28) published skinfold equations that were validated against a 4C model. However, they have not been adequately cross-validated by using a multicompartment model of body composition. In the present study, the skinfold equations did not perform well for the individual. Reilley et al. (20) cross-validated the Slaughter T+S equation against a modification of the Siri 2C model and also found large limits of agreement. For male subjects, the Slaughter T+S equation overestimated %BF by ~3.5% with + 2SD limits of agreement of ~7% BF. For female subjects, Slaughter T+S underestimated %BF by ~2.5% with limits of agreement of 12% BF. Thus the Slaughter et al. (28) skinfold equations, which were thought to be the most accurate for children and adolescents, require further refinement. Unfortunately, there are few, if any, appropriate skinfold equations for use with children, and new skinfold equations should be developed and validated by using either the 3C-H2O or 4C models of body composition.
The lack of agreement for %BF between the Houtkooper et al. (11) and Boileau (2) BIA equations and the 4C model (Fig. 4) is surprising because they were validated against a 4C model. In addition to large limits of agreement, the bias of both BIA equations was dependent on the %BF. Both equations overestimated the %BF of lean subjects (males) and underestimated the %BF of fatter individuals. When originally validated, the Houtkooper et al. BIA equation predicted the FFM with an r2 of 0.95, SE of estimate (SEE) of 2.1 kg, and a CV of 5.1%. With use of FFM as the prediction variable in the present study, the r2 was 0.91, SEE was 2.8 kg, and the TE was 2.43 kg FFM. The Houtkooper et al. BIA equation overpredicted the FFM by a mean of 0.36 kg with a ±2SD limit of agreement of 5.06 kg. Some of the interstudy differences could be due to the use of the Valhalla bioimpedance analyzer in the present study while Houtkooper et al. used an RJL analyzer. The importance of choosing the same analyzer that was used to develop the prediction equations has been previously discussed (6). Boileau (2) reported separate equations for Valhalla and RJL instruments. For the Boileau Valhalla equation, the SEE was 1.75 kg FFM. In the present study, the r2, SEE, and TE were 0.88, 2.3 kg FFM, and 2.62 kg FFM, respectively. The Boileau equation produced a mean overestimation of 0.58 kg FFM and the ±2 SD limits of agreement were 5.64 kg. The Boileau equation produced more bias in girls, especially prepubertal girls.
In conclusion, all of the models and equations tested reduced the bias of the Siri 2C model for the determination of %BF in children and adolescents. However, except for the 3C-H2O model, on a child by child basis there is generally low agreement between the 4C model estimates of %BF and estimates that 1) correct for the M/FFM (3C-min model), 2) correct for average amounts of TBW/FFM and M/FFM (Lohman age-adjusted equations), 3) are based on the DEXA method and, 4) are based on skinfolds and BIA. Investigators can have high confidence when using the 3C-H2O model, and it can be used as the criterion method in validation studies. The same conclusion cannot be made for the 3C-min model. The Lohman age-adjusted equations predict as well as DEXA and the 3C-min model, so the expense and radiation of the necessary DEXA scan can be avoided. Because of general lack of agreement, investigators and clinicians must be careful when estimating %BF in children. New equations must be developed and validated against a criterion 4C model. Because of the inability of the current models to predict %BF of children on an individual basis, the practice of making %BF predictions must be addressed. Until more accurate equations are developed, investigators and health care professionals who do not have access to the 3C-H2O model may want to consider using a sum of skinfolds in conjunction with the height and weight rather than reporting an errant %BF.
ACKNOWLEDGEMENTS
We acknowledge Sandra Jackson and the nursing staff at the General Clinical Research Center, who provided patient care, and Becky Nelson and Sharon Anderson for assistance with data collection. We acknowledge the subjects for continued support of the research program for the past two years.
FOOTNOTES
Address for reprint requests: J. N. Roemmich, Univ. of Virginia Health Sciences Center, Div. of Endocrinology, Dept. of Pediatrics, Box 386, Charlottesville, VA 22908 (E-mail: jr5n{at}virginia.edu).
Received 27 February 1997; accepted in final form 13 May 1997.
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ABSTRACT
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