Paper Fat Mass Estimates in Obesity

1. Original Research Communications-general
Body composition in Pima Indians:
validation of bioelectrical resistance1’2
Russell Rising, Boyd Swinburn, Karen Larson, and Eric Ravussin
ABSTRACF To assess the validity of bioelectrical resistance specific equations (9) increased correlations between FFM de-
(BR) in an obese population, body composition was determined termined by hydrostatic weighing and FFM predicted by resis-
by both hydrostatic weighing and by BR in 156 Pima Indian tance from 0.90 to 0.94 and from 0.89 to 0.93 for men and
volunteers representing a wide range of body weight (46.1-202.6 women, respectively.
kg) and body composition (1 1-52% fat). A predictive equation The degree ofobesity observed in the Pima Indian community
was derived by use of data on height, BR, weight, age, and sex is far greater than that measured in the populations previously
from 130 randomly selected volunteers and was applied to the used to validate the BR method. To validate this method in
remaining 26 volunteers. When compared with the manufac- Pima Indians, FFM was estimated by both hydrostatic weighing
turer’s software, the new equation increased correlations with and BR in 156 Pima Indians representing a wide range of body
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hydrostatic weighing for predicting percent body fat and fat-free weight (46. 1-202.6 kg) and body composition (1 1-52% body
mass (FFM) from 0.70 to 0.92 and 0.79 to 0.97, respectively. fat). A new equation was derived in 130 Pima Indian volunteers
The manufacturer’s software underestimated FFM by 5.3 ± 8.6 and was tested on the remaining 26 volunteers. To test its validity
kg (P < 0.05) when compared with FFM derived from hydro- in field conditions, the effect offood and fluid ingestion on body
static weighing whereas the new equation improved the accuracy composition by BR was also assessed in another 1 7 Pima Indian
to -0. 1 ± 3.3 kg (NS). There were no significant effects of fluid volunteers.
intake (700 mL) or breakfast consumption on body composition
as determined by BR. BR represents a simple and accurate way
Methods
to assess body composition in Pima Indians with our newly de-
rived equation. Am J C/in Nuir 199 1;53:594-8. Subjects
KEY WORDS Bioelectrical impedance, obesity, fat-free One hundred fifty-six Pima Indian volunteers (92 males, 64
mass, fat mass females) were admitted to the Clinical Diabetes and Nutrition
Section ofthe National Institutes of Health in Phoenix, AZ, for
various studies of carbohydrate and energy metabolism over a
Introduction 3-y period. All volunteers were fed a weight-maintenance diet
(50% ofenergy as carbohydrate, 30% as fat, and 20% as protein)
The Pima Indians are a relatively genetically homogeneous
during their stay in the metabolic ward. Upon admission, all
group of people living southeast of Phoenix, AZ. They have the
volunteers were determined to be in good health by means of
highest incidence of non-insulin-dependent diabetes in the world
medical history, physical examination, electrocardiogram, blood
and a very high prevalence of obesity (1). In metabolic studies
screening, and urine testing. Volunteers were screened for dia-
an accurate assessment of body composition to estimate fat-free
betes mellitus by an oral-glucose-tolerance test, according to the
mass (FFM) and fat mass is important so that results can be
procedure of the National Diabetes Data Group (10), but were
expressed in terms ofthe volunteer’s metabolic size. Bioelectrical-
not excluded from the study if they were diabetic. None was
resistance (BR) analysis (2-5) offers an inexpensive, rapid, and
taking any medication or had clinical evidence of illness apart
safe method for estimating body composition without the need
from obesity.
for a hydrostatic weighing facility.
Body composition of each volunteer was determined by hy-
In general, studies validating BR analysis for determining body
drostatic weighing (1 1), and percent body fat was calculated ac-
composition used sample populations of various ages and in-
cording to the equation of Keys and Brozek (12). FFM was
cluding many racial groups (6, 7). These sample populations did
not include obese individuals
extremely (> 50% fat) or individ-
uals from Native American Indian populations. Total body water
I From the Clinical Diabetes and Nutrition Section, National Institute
was found to be similar in young black and white men (8) al-
of Diabetes and Digestive and Kidney Diseases, National Institutes of
though blacks had a higher body density, possibly because of Health, Phoenix, AZ.
greater bone mineral content in their FFM. In people with var- 2 Address reprint requests to R Rising, National Institutes of Health,
ious degrees of obesity, fat-specific regression equations (9) were 4212 North 16th Street, Room 541-A, Phoenix, AZ 85016.
derived by use of anthropometry to classify men and women Received April 27, 1990.
into categories of> 20% and < 30% fat, respectively. These fat- Accepted for publication July 18, 1990.
594 Am iC/in Nuir 199 1;53:594-8. Printed in USA. © 1991 American Society for Clinical Nutrition

2. BIOELECTRICAL RESISTANCE IN PIMA INDIANS 595
TABLE I Predictive equation
Physical characteristics of 156 Pima Indians in whom a prediction
equation to estimate body composition by bioelectrical resistance was
Ofthe 156 volunteers whose characteristics are shown in Table
derived and tested 1 ( 123 nondiabetic, 33 diabetic), 1 30 (74 males, 56 females)
were randomly selected (13) for the derivation ofa new predictive
Predictive equation Testing of equation equation, whereas 26 (18 males, 8 females) were utilized for
(n = 130) (n = 26) testing the equation. Percent body fat was calculated by use of
the new prediction equation by subtracting FFM from the vol-
Sex ratio (M:F) 74:56 18:8
unteer’s body weight to determine fat mass, then dividing fat
Age(y) 30 ± 8 32 ±10
mass by total body weight, multiplied by 100. There were no
Weight (kg) 99.3 ± 26.7 106.8 ± 24.8
significant differences in age, height, body weight, and body
Height (cm) 166 ± 9 168 ± 9
Fat, hydrostatic (%) 34 ± 9 34 ± 8 composition between the subjects used to derive the new equa-
Fat, resistance (%) 38 ± 9 39 ± 9 tion and those utilized for testing this equation.
FFM, hydrostatic (kg) 63.5 ± 13.4 69.3 ± 13.5
FFM, resistance (kg) 58.6 ± 1 1.7 64.0 ± 12.9
Effect offluid intake
To test the effect of hydration, resistance measurements were
S SD.
obtained according to the above protocol on another eight male
Pima Indian volunteers (aged 35 ± 14 y, with 25 ± 10% body
fat by hydrostatic weighing) 1 h before and 1 h after consuming
calculated from the difference between the volunteers’ body 700 mL of fluid (water or diet soda). Percent body fat was cal-
weight and their fat mass. BR was measured between 0700 and culated by use of the new prediction equation. The volunteers’
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0800 after an 1 1-12-h fast. For the resistance measurement vol- physical characteristics are presented in Table 2. To test the
unteers were instructed to lie flat with their hands at their sides reproducibility of the resistance measurements, this procedure
and with their thighs maintained apart. Electrodes (Conmed was repeated for 5 consecutive days.
The mean CVs of the per-
Corp, Utica, NY) were placed on the subjects and resistance cent body fat determinations over the 5-d period were 3.6% and
measurements were obtained with a bioelectrical impedance an- 3.8%, before and after fluid intake, respectively. Because of the
alyzer (model BIA-l03, RJL Systems, Inc, Detroit) according small variability ofthe repeated measurements, data were pooled
to instructions provided by the instrument manufacturer. Percent for the 5 d (Table 2).
body fat was calculated by using the manufacturer’s software
(Bodycomp II, version 1. 1, RJL Systems, Inc). The instrument Effect ofbreakfast ingestion
was calibrated periodically with a 50041 resistor provided by the Resistance measurements were also obtained on another nine
manufacturer, and measurements were continued as long as the Pima Indian volunteers (eight males, one female) (aged 30 ± 10
instrument readings remained within 2 2 of the calibrated re- y, with 28 ± 9% body fat by hydrostatic weighing) 1 h before
sistor. The experimental protocol was approved by the NIDDK and 1 h after eating breakfast. Their physical characteristics are
Clinical Research Subpanel and the Tribal Council of the Gila shown in Table 3. The measurements were repeated for 2 con-
River Indian Community. Written informed consent was ob- secutive days. Percent body fat was calculated by use ofthe new
tamed from the volunteers. All physical characteristics of the prediction equation, according to the above procedure. Because
volunteers are expressed as mean ± SD. the mean CVs ofthe percent body fat were only 2. 1% and 2.8%
TABLE 2
Physical characteristics and mean changes in resistance (AR) and percent body fat (iWat) for eight volunteers before and after 700 mL of fluid
intake
Fluid status
Before After
Subject Height Weight R Fat R Fat iR Fat
cm kg l % Q % (1 %
I 177 122.6 380 41 392 42 +12 +1
2 169 131.6 393 39 402 39 +9 0
3 165 73.5 490 18 511 19 +21 +1
4 170 80.5 449 15 460 16 +11 +1
5 173 91.6 482 25 489 25 +7 0
6 170 76.8 503 15 493 15 -10 0
7 163 52.7 604 3 616 3 +12 0
8 172 105.0 477 34 481 34 +4 0
i±SD 170±4 91.7±26.5 472±70 24± 13 481±70 24±13 8±9t 0±1
C Results are pooled from five repeated measurements.
t Significantly greater than the value before fluid, P < 0.05 (paired I test).

3. 596 RISING ET AL
TABLE 3
Physical characteristics and mean changes in resistance (SR) and percent body fat (Fat) for nine volunteers before and after breakfast5
Breakfast consumption
Before After
Subject Height Weight R Fat Weight R Fat R Fat
cm kg % kg t % t %
1 178 118.4 500 41 118.8 490 41 -10 0
2 177 123.2 382 42 123.8 377 42 -5 0
3 168 59.6 588 7 59.3 609 8 +21 +1
4 164 85.2 499 22 85.4 500 22 +2 0
5 172 118.7 435 41 118.3 429 40 -6 -1
6 159 86.6 606 35 87.0 612 35 +6 0
7 176 92.0 560 27 92.4 530 26 -30 -1
8 170 116.7 384 39 117.4 385 39 +1 0
9 173 138.1 439 53 138.6 427 53 -12 0
i±SD 171 ±6 104.3±24.7 488±84 34± 14 104.5±24.9 484±88 34± 13 -4± 14 0± 1
S Results are pooled from two repeated measurements.
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before and after the standardized breakfast, respectively, mean determined by hydrostatic weighing. Statistical significance of
results over the 2 d are presented in Table 3. differences between pooled means before and after hydration
and breakfast, respectively, were assessed by paired I test.
Data analysis
All data were analyzed by use ofthe StatisticalAnalysis System Results
package (SAS Institute, Cary, NC). Stepwise multiple-regression
analysis (forward selection technique) was used to derive the Figure 1 shows the relationship between percent body fat (by
best predictive equation with FFM, determined by hydrostatic resistance) calculated by use ofthe manufacturer’s software, and
weighing as the dependent variable, and height (cm), body weight percent body fat (by hydrostatic weighing) in the 1 56 volunteers
(kg), height/resistance (cm2/2), reactance (t1), body mass index used to derive and test a new predictive equation. At any given
(kg/m2), age (y), body weight#{176}”, and sex (class variable; male = percent body fat measured by hydrostatic weighing, there is a
1 female
, = 0) were entered as independent variables. Indepen- large range ofpercent body fat estimated from BR. In addition,
dent variables were eliminated if they did not meet the signifi- BR overestimated percent body fat. From the multiple-regression
cance level of P < 0. 1 5 for entry into the model. analysis with use of FFM (by hydrostatic weighing) as the de-
To test the new prediction equation, percent fat and FFM pendent variable, Ht2/R, body weight, age, and sex were signif-
values from both the manufacturer’s software and from the new icant determinants of FFM in Pima Indians. The following pre-
prediction equation were regressed on percent fat and FFM, diction equation provides the best estimate of FFM (Table 4):
FFM (kg) = 13.74 + 0.34 X (Ht2/R)
60 + 0.33 X (body weight) - 0.14 X (age) + 6.18 X (sex)
C
a
where height is expressed in cm, resistance in , weight in kg,
. 5
a) and age in y; a value of 1 is given for males and 0 for females.
0
In the 26 volunteers for whom the above new equation was
U 40
applied, the coefficient of correlation and SEE between percent
U
a) body fat from resistance and percent body fat from hydrostatic
a)
30
0
.
.%
> 20
.0 TABLE 4
0
Coefficient and SEE for the determinants of FFM (kg), by bioelectrical
>,
10
0 resistance, in I 30 Pima Indians
0
r SEE F P
0 10 20 30 40 50 60
% Body fat by hydrostatic weighing Intercept(kg) 13.74 1.52 - 0.0001
Ht2/R (cm2/tI) 0.34 0.05 42.8 0.0001
FIG 1. Variation of percent body fat, by bioclectrical resistance (by use
Body weight (kg) 0.33 0.02 324.8 0.0001
of the manufacturer’s software), compared with percent body fat, by Age (y) -0.14 0.03 18.3 0.0001
hydrostatic weighing, for 156 Pima Indians (P < 0.0001), SEE 5.04,
Sex (0 or I ) 6. 18 0.9 1 44.4 0.0001
r = 0.83). Points plotted along the line of identity.

4. BIOELECTRICAL RESISTANCE IN PIMA INDIANS 597
weighing increased from 0.70 (SEE 6.89) when the manufac- 100
turer’s software was used, to 0.92 (SEE 3.22) when the new C
0
90
equation was used (Fig 2). Similar improvements in coefficients .
0
of correlation with the new equation were found when FFM 80
a.
(predicted from resistance or determined by hydrostatic weigh- 0’ w
. 5) 70
ing) were compared (Fig 3). Correlation coefficients for percent 5)
L
body fat and FFM, between resistance (new equation) and hy- C)
C
60
#{149}S
0 U
drostatic weighing, were not equal (0.92 and 0.97, respectively) 0
U) 50
because the variables used for the comparison (kg FFM and In C
a) 0
percent body fat) were not directly proportional to each other. 40
0
For the 26 test volunteers, the new prediction equation im- U
L
30
proved the mean accuracy of predicting FFM from -5.3 ± 8.6 0 0 50 60 70 80 90100
C)
kg (P < 0.05 for difference from 0) to -0. 1 ± 3.3 kg (NS from a)
a)
0) of the mean value (69.3 ± 1 3.5 kg) obtained by hydrostatic 0
.0 1(
weighing. A similar magnitude of improvement was found for >‘
S
.0
percent body fat with values decreasing from an overestimate
In
of 5 ± 7% (P < 0.05 for comparison with 0) to 0 ± 3% (NS for U)
C
0
comparison with 0) of the mean value (34 ± 8%) obtained with E 0
hydrostatic weighing. a) 0 S
a)
L
0
In the eight subjects who participated in the fluid intake study,
w
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the individual CV for five repeated resistance measurements 0 a)
varied from 2.0% to 4.3% before fluid intake and from 2.8% to z
5.1% after fluid intake. Mean resistance increased slightly from
472 ± 70 121 h before fluid intake to 481 ± 70 12 (P < 0.05) 1
h after fluid intake (Table 2). This increase did not result in a
40 50 60 70 80 90 100
Fat-free moss by hydrostatic weighing (kg)
FIG 3. Fat-free mass (kg), by bloelectrical resistance, (by use ofthe man-
S ufacturer’s software)
(upper panel) and the new prediction equation (lower
C
0 panel) vs fat-free mass (kg), by hydrostatic weighing, in 26 Pima Indians
a (P < 0.0001 ), SEE 8. 10 (r = 0.79) and 3.30 (r = 0.97) for the upper and
lower panels, respectively). Points plotted along the line of identity.
U)
a)
a)
0
C
0
U
a
significant change in percent body fat when estimated by resis-
C’, tance.
‘I) C
a) 0
For each volunteer in the breakfast study, mean resistance,
0
and therefore estimate of percent body fat, did not change in
0
response to breakfast consumption (Table 3).
0
a)
a)
0
n
Discussion
>‘
BR, a simple and inexpensive method, does not accurately
0
estimate body composition in Pima Indians when the equations
>
0
C
0
contained in the manufacturer’s software are used. Use of an
0
In 0 equation specific for Pima Indians improved the average accu-
L4J
racy of estimating FFM and percent body fat to within -0.1
± 3.3 kg and 0 ± 3%, respectively, of the values obtained by
a)
z hydrostatic weighing. This new equation will provide a reason-
ably accurate determination of body composition in this pop-
ulation.
Hydrostatic weighing is the most widely used standard for
10 20 30 40 50 60 determining body composition in humanc and is considered to
% Body fat by hydrostatic weighing be the reference standard against which new methods are tested
(1 1). We found hydrostatic weighing to be satisfactory (CV 7%)
FIG 2. Percent body fat, by bioelectrical resistance (by use of the man-
ufacturer’s software, upper panel) and by the new prediction equation
(14). Therefore, the large variation in percent body fat estimated
(lower panel) vs percent body fat, by hydrostatic weighing, in 26 Pima by BR at any given percent body fat, derived from hydrostatic
Indians (P < 0.0001, SEE 6.89 (r = 0.70) and 3.22 (r = 0.92) for the weighing, indicates that the method is not suitable in Pima In-
upper and lower panels, respectively). Points plotted along the line of dians when the manufacturer’s software is used. Indeed, other
identity. equations have been derived from data on groups of subjects

5. 598 RISING ET AL
from other races (6-8), but more importantly, not representing corporated, Detroit, for providing the equipment for the study; and most
the degree of obesity found in Pima Indians. Therefore, a new importantly, the volunteers.
equation needed to be derived for Pima Indians to easily estimate
body composition in field conditions. We found that such a new
References
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The improvement in the accuracy ofdetermining body corn- in Pima Indians: contribution ofobesity and parental diabetes. Am
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S. Barnett A, Halsey RH. Radiocardiograms-the
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3. Thomasett A. Bioelectrical properties of tissue impedance. Lyon
total body water content (8). Also, the accuracy of predicting
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FFM from resistance analysis was improved when subjects were
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There was no effect of fluid intake or breakfast ingestion on 5. Nyboeri, Liedtke Ri, Reid KA, Gessert WA. Nontraumatic electrical
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significant increase in BR observed 1 h after fluid intake was l983;l5:38l-4.
unexpected an increase
because in the conductive medium (wa- 6. Segal KR, Gutin B, Presta E, Wang J, Van Italic TB. Estimation
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flow (5). It is possible that remeasuring resistance only 1 h after
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7. Lukaski HC, Johnson PE, Bolonchuk WW, Lykken 01. Assessment
fluid ingestion does not allow enough time for complete fluid
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unteers rehydrated after an 18-h fast (15). On the other hand, CG. Density oflean body mass is greater in blacks than in whites.
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l979;28: 1039-57.
changes in resistance. Another prediction equation (not shown)
1 1. Goldman RF, Buskirk ER. A method of underwater weighing and
was derived with a separate coefficient for resistance to determine
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ifthis type ofequation would show changes in percent body fat Techniques for measuring body composition. Washington, DC: Na-
in response to fluid or breakfast intake. This latter equation did tional Academy of Sciences, 196 1:78-89.
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shortly before resistance measurements does not affect the out-
man. Am J Physiol l985;248 (Endocrinol Metab 1 l):E286-9 1.
come of body composition determinations.
15. Stump CS, Houtkooper LB, Hewitt Mi, Going SB, Lohman TG.
We thank Carol Lamkin and the nursing staff of the Clinical Diabetes Bioelectric impedance variability with dehydration and exercise. Med
and Nutrition Section for their professional care of the volunteers and Sci Sports Exerc l987;20:S82 (abstr).
for their assistance in performing the resistance measurements; Tom 16. Gray DS. Changes in bioelectrical impedance during fasting. Am J
Anderson for his help with the hydrostatic weighing; RJL Systems In- Clin Nutr l988;48:l 184-7.