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Modern Nutrition in Health and
Disease
10th Edition
2006 Lippincott Williams & Wilkins
49
Body Composition andAnthropometry1Steven B. Heymsfield
Richard N. Baumgartner
A persons body composition reflects his or her total lifetime
nutrient and energy balance. Maintaining optimum health
requires the maintenance of adequate tissue levels of
essential nutrients and a source of energy. More than 40
syndromes develop if tissue levels of these are either too
low or too high (1). Anthropometry is the science of
estimating or predicting body composition based on
measurements of weight, stature, body circumferences, and
subcutaneous fat thicknesses. This chapter describes
methods for predicting protein and energy stores fromanthropometry because most patients seen for clinical
evaluation have a disorder of protein-energy balance (2,3).
This is prefaced by a discussion of the principles underlying
energy and protein balance and body composition models.
ASSESSMENT COMPONENTS
Steady-State Relations
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The concept of steady-state relations between energy
exchange and protein stores is important because
disruptions result in disordered body composition and
associated pathologic features. The difference betweenenergy intake and expenditure affects three main body
composition components, the small storage carbohydrate
glycogen pool, the larger structural and functional protein
pool, and the variable lipid or fat storage pool (Fig. 49.1).
Taken together with associated water and minerals, the
collective energy compartment is reflected by and changes
parallel with body mass. Body weight is therefore a
fundamental measurement in nutritional assessment
because it is an indirect marker of protein mass and energy
stores.
The consequences of weight change depend on initial body
composition. Weight loss in a frail elderly patient with
sarcopenia (low muscle mass), whether voluntary or
involuntary, conveys very different risks than weight loss in
a middle-aged overweight patient with adequate muscle
mass. Weight loss involves the depletion of body nutrient
stores. The excessive depletion of protein stores, whether
from wasting or cachexia, results in the loss of specific
cellular and tissue functions, with consequences ranging
from loss of cell-mediated immunity to cognitive
impairment. Conversely, weight loss in an overweight or
obese person with adequate protein stores mainly consists
of loss in fat mass, which improves certain cellular and
tissue functions, for example by reducing levels of oxidative
stress and improving insulin sensitivity and glucose and
lipid metabolism.
A major portion of tissue function can be attributed to
proteins that are activated by energy derived from
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metabolism of organic fuels (4). As an organic compound,
protein is also a metabolic fuel, and, under conditions of
weight stability, oxidation of amino acids provides about
15% of daily energy requirements (5). The energy-producing reactions take this general form:
Urea is not metabolized further and is excreted unchanged
in urine. During periods of nutritional deprivation,
approximately half the total body protein mass can
P.752
be used as metabolic fuel (6). A greater loss of protein is
incompatible with survival. Therefore, when food intake is
less than nutrient losses, amino acids from proteins are
oxidized to provide energy, various tissue functions are
altered, and, ultimately, protracted negative protein balance
results in a rapid rate of lean tissue depletion and death.The extent to which this occurs depends on the availability
of other nonprotein energy stores.
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Figure 49.1. Interrelations between energy intake, output,
and stores. (Data from Heymsfield SB, Baumgartner RN, Pan
S-F. Nutritional assessment of malnutrition by
anthropometric methods. In: Shils ME, Olson JA, Shike M,
eds. Modern Nutrition in Health and Disease. 9 t h ed.
Baltimore: Williams & Wilkins, 1999:90321; and
Heymsfield SB, Hoffman DJ, Testolin C et al. Evaluation of
human adiposity. In: Bjrntorp ed. International Textbook
of Obesity. Chichester, UK: John Wiley & Sons, 2001:85
97.)
The main sources of nonprotein energy are glycogen and fat
or triglyceride. Glycogen is stored primarily in liver and
skeletal muscle (7). Glycogen stores are small (
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and liver cells (9). Fat stores vary widely in humans, with
fatty acid oxidation representing about 35% of energy
production in the average US diet (5). Both glycogen and fat
are oxidized in reactions similar to that for protein(Equation 1), except urea is produced only with amino acid
oxidation.
The sum of protein, glycogen, and fat constitutes total body
energy content. These fuels account for more than 90% of
the nonaqueous portion of body weight (10). Generalizations
can be made on how body weight, protein, glycogen, fat,
and energy stores relate to each other. Glycogen and
protein are both solubilized by water and electrolytes. About
2 to 4 g water will bind to 1 g of either glycogen or protein
(11). Changes in glycogen or protein balance are thus
associated with greater changes in body weight than can be
attributed to loss of the actual chemical component. For
example, oxidation and loss of 100 g glycogen would result
in approximately a 0.5-kg reduction in body weight.
The main remaining chemical components exclusive of fat
are minerals, found primarily in the skeleton (8,10). The
total fat-free portion of body weight thus consists of
protein, glycogen, water, and minerals (Fig. 49.2). In
healthy adults, the steady-state fractional contribution of
three of these components to total fat-free mass (FFM) is
reasonably constant: protein = 0.195, water = 0.725, and
mineral = 0.08, respectively. Glycogen levels vary
throughout the day and represent a fraction of FFM in the
range of 0.01 to 0.02. With long-term weight loss, the
change in FFM is approximately the same as the relative
reduction in protein (1). Acute changes in body weight and
FFM may also reflect alterations in glycogen and fluid
balance.
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Figure 49.2. The first four of the five levels of human body
composition. Components related to fatness are identifiedby bold enclosure. ECS and ECF are extracellular and
intracellular solids, respectively. (From Bistrian BR,
Blackburn GL, Vitale J et al. JAMA 1976;235:156770, with
permission.)
Fat maintains a relatively constant, although more complex
relation to fat-free components. Figure 49.3shows a plot of
total body fat(TBF)/height2 versus body weight/ height 2 in
414 women. Fat was measured in the women using a four
component model (8). The ratio body weight/ height 2,
referred to as body mass index (BMI, kg/m2), is
P.753
discussed later in more detail. Two important points arerelated to this figure. First, the intercept for zero TBF is a
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BMI of approximately 13, which represents a woman without
any fat and minimal protein stores, a condition incompatible
with survival. Second, the slope of the regression line (i.e.,
the change in fat adjusted for stature/the change in bodyweight adjusted for stature) of approximately 0.74 indicates
that body weight added above a BMI of about 13 is
predicted to be about three fourths fat and one fourth FFM.
The composition of excess weight, however, may differ
between men and women and vary with race, age, and
disease (12).Figure 49.4shows how the relationship
between BMI and percentage of body fat changes with age
in healthy women and men. With increasing age, any
measured BMI corresponds to a higher level of body fat,
owing to the slow, age-related loss of muscle mass, or
sarcopenia (13,14).
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Figure 49.3. Relationship between total body fat (measured
by four component model [8]) adjusted for stature and body
mass index (BMI) in 414 healthy women (R 2 = 0.91, p 0.85); in
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women, hip or thigh circumferences may have slightly
higher correlations. Correlations of upper arm, thigh, and
calf circumferences with measures of body fat are somewhat
lower, and these circumferences tend to be more stronglyinfluenced by variation in appendicular skeletal muscle.
Waist circumference and ratios of waist to hip or thigh
circumference are widely used to grade or estimate visceral
adiposity, which is recognized as the main aspect of adipose
tissue distribution that is associated with increased risk of
chronic disease (64). Waist circumference is now used in
conjunction with BMI to classify persons into risk levels for
chronic disease. Risks are increased for BMIs greater than
25 kg/m2 when waist circumference is greater than 108 cm
in men and 88 cm in women (65). Zhu and colleagues (66)
also developed cutpoints for grading chronic disease risk
specifically from waist circumference. Their analysis
indicated that a waist circumference greater than 100 cm in
men and 93 cm in women was associated with a disease risk
equivalent to a BMI greater than 30 kg/m 2, indicating the
need for clinical weight loss.
Combining a limb skinfold thickness with a corresponding
circumference allows calculation of limb fat areas using the
following general equation:
where C is a limb circumference measurement (i.e., upper
arm, midthigh, calf), and SF is a skinfold measurement
taken at the same level as the circumference measurement
following standard methods. Most of the problems related to
a single skinfold measurement also occur with the limb fat
area. The advantage generally ascribed to area calculations
is that the result includes the contribution of limb
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circumference; two limbs with equal skinfolds but unequal
circumferences will have different amounts of fat.
Many prediction equations are available for calculating TBF
from measured skinfold thicknesses, circumferences, bodyweight, and stature. All the present methods in use are
descriptive in that measured anthropometric dimensions
are converted to TBF or other components using statistically
derived equations in the absence of an underlying theory or
mechanism. In contrast, some body composition methods
are based on theoretic or mechanistic models (e.g., BCM is
calculated from exchangeable potassium using a model that
assumes a constant intracellular potassium concentration).
All descriptive methods, including anthropometry, share in
common the following: development in a well-defined
subject group, use of a criterion method for estimating TBF,
and a prediction model formulated using regression
analysis. Some methods, for convenience and speed, are
based only on gender, body weight, stature, and
circumferences (67,68). As all prediction formulas are
population specific, they should be cross-validated in new
subject groups before application. Ideally, the fat-prediction
formula would be used in a group similar to the population
on whom it was developed.
A good example and most widely applied TBF prediction
formula was developed by Durnin and Womersley using
underwater weighing as the criterion for fat estimation (69)
(Table 49.3). The sample consisted of 209 white men and
272 women who were less than 68 years of age and on
average were normal or slightly overweight. Once TBF is
known, it can be subtracted from body weight to provide a
value for FFM.
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TABLE 49.3. CALCULATION OF FAT AND FAT-FREE BODY
MASS ACCORDING TO THE METHOD OF DURNIN AND
WOMERSLEY
1. Determine the patients age and weight (kg)
2. Measure the following skinfolds in mm: biceps,
triceps, subscapular, suprailiac
3. Compute the sum () of these skinfolds
4. Compute the logarithm of
5. Apply one of the following age- and sex-specific
equations to compute body density (D, g/mL)
AGE RANGE
(y) MEN WOMEN
1719 D = 1.1620
0.0630 (log)
D = 1.1549 -
0.0678 (log)
2029 D = 1.1631
0.0632 (log)
D = 1.1599 -
0.0717 (log)
3039 D = 1.1422
0.0544 (log)
D = 1.1423 -
0.0632 (log)
4049 D = 1.1620 D = 1.1333 -
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0.0700 (log) 0.0612 (log)
50+ D = 1.1715
0.0779 (log)
D = 1.1339 -
0.0645 (log)
6. Fat mass (kg) is then calculated as: FM = Body
weight (kg) [4.95/D - 4.5]
7. Fat-free mass (FFM) (kg) = Weight (kg) - Fat mass
(kg)
P.761
A literature search will turn up many fat -prediction formulas
that are applicable in specific populations and that vary inuse of measurement type (i.e., c ircumferences and
skinfolds) and anatomic location. Some examples of
methods in current use for female subjects are presented in
Table 49.4. In most of these formulas, the dependent (i.e.,
predicted) variable is Db. These methods were developed
using underwater weighing as a reference for Dbestimation
and anthropometric dimensions along with other covariates
such as age were set in regression models as independent
variables. The anthropometric predicted density can be
converted to percentage of fat using traditional two
component body composition models, as outlined in Table
49.4.
The advantages of calculating TBF are that (a) more than
one skinfold site is usually included in the calculation and
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(b) the result (in kilograms) can be used directly to
calculate energy reserves as fat. The latter values can then
be integrated with estimates of energy balance calculations,
thus providing a more physiologic description of thepatients nutritional state. A cautionary note is that, as with
all prediction equations, results are most accurate on
populations on which the equation was derived. The
accuracy of the Durnin-Womersley equation and those
presented in Table 49.4is unknown in patients with severe
weight loss, and the techniques should not be applied when
a gross distortion in body habitus or obvious fluid
accumulation is present (69). As emphasized by Damon and
Goldman, skinfold thicknesses describe, but do not measure,
TBF (70). The error of p rediction ofTBF from skinfolds may
be considerable in some persons even when group means
are accurate. More accurate methods of measuring fat are
therefore usually applied in research studies of body
composition.
TABLE 49.4. ANTHROPOMETRIC EQUATIONS THAT
PREDICT BODY DENSITY IN THE FEMALE POPULATION a
AUTHOR
S
(DATE;
REF.) EQUATION n
MEAN
OR
RANG
E r SEE
Katch &
McArdl
e
(1973;
Density =
1.09246 -
[0.00049
(scapula
69 25.6
6.4%
0.8
4
0.008
6
(3.6
%)
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106) SF)] -
[0.00075
(iliac SF)] +
[0.00710
(ED)] -
[0.00121
(thigh C)]
Jackson
et al.
(1980;
107)
Density =
1.1470 -
[0.0004293
(chest SF +
midaxillary
SF + triceps
SF +
subscapular
SF + abd SF
+ suprailiac
SF + thigh
SF)] +
[0.00000065
(7SF)2] -
[0.00009975(A) -
[0.00062141
5 (gluteal
C)]
24
9
4
44%
0.8
7
0.007
9
(3.6
%)
Wright Density = 18 2 0.7 (4.1
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et al.
(1980;
108)
[1.051
(biceps C)] -
[1.522
(forearm C)]
- [0.879
(neck C)] +
[0.326 (abd2
C)] + [0.597
(thigh C)] +
0.707
1 37% 3 %)
Hodgdo
n &
Beckett
(1984;
67)
Density = -
(0.35004
[log10 (waist
C + hip C -
neck C)] +
(0.22100
[log10 (H)])
+ 1.29579
21
4
10
47%
0.8
0
0.008
0
(3.7
%)
Vogel
et al.
(1988;109)
% Body fat
= [0.173
(hip C)] +(105.328
[log10 (Wt)])
- 0.515 (H)]
- [1.574
(forearm C)]
-
26
6
5
50%
0.7
7
(3.9
%)
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[0.533(neck
C)] - [0.200
(wrist C)] -
35.6
Tran &
Weltma
n
(1989;
110)
Density =
1.168297 -
[0.00284
(abd C)] +
[0.00001220
98 (abd2)] -
[0.00073312
8 (hip C)] +
[0.00051047
7 (H)] -
[0.00021616
(A)]
40
0
35.9
7.7
%
0.8
9
0.009
5
(4.2
%)
aA, age (y); Abd, average waist and abdomen at naval
(cm); C, circumference (cm); ED, elbow diameter (cm);
H, height (cm); SF, skinfold (mm); Wt, weight (kg).
Correlations are show for test group samples unless
otherwise specified. The interested reader shouldconsult original source for information regarding
application of specific equation.
It is customary to express TBF estimates as a percentage of
body weight. A problem in interpreting this approach is
P.762
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Figure 49.6. Percentage of body weight as fat in 413
healthy women versus body mass index (BMI). Fat was
measured by four component model (8).
Lean Tissues
Lean tissues refer in general to the following sequence of
components at the five levels of body composition: atomic:
nitrogen, potassium, and calcium; molecular: FFM, water,
and protein; cellular: BCM; tissue-system: skeletal muscle,
skeleton, and visceral organs; whole-body: anthropometric
measurements (e.g., skinfolds/circumferences) (see Table
49.1). These various components are associated with the
major portion of whole-body metabolic activity and biologic
functions.
Semistarvation.
Semistarvation results in negative balances of energy,protein, water, and minerals, a reduction in FFM and BCM,
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and atrophy of tissues and organs (1,72). Not all lean
components change at the same rate during periods of
negative balance. At the molecular level, cellular proteins
are depleted rapidly, and connective tissue proteins are lostat a slower rate (1). Similarly, at the cellular level, rapid
changes can occur in BCM, whereas extracellular fluid is lost
more slowly or may even increase in volume (72). Organs
and tissues also differ in their rate of weight loss during
semistarvation. Liver mass decreases rapidly and brain
weight changes very little if at all in uncomplicated
semistarvation; liver and other visceral organs may be
preserved in chronic catabolic conditions such as metastatic
malignant diseases (50). Skeletal muscle is a major
reservoir of amino acids for acute-phase protein synthesis
and can decrease by up to 75% in weight during protein-
energy malnutrition (6). The malnourished patient with a
reduced body weight therefore has a different composition
at each of the five levels compared with his or her normally
nourished counterpart. This explains why anthropometric
equations developed in physiologically normal subjects may
not predict a specific component with equal accuracy in an
undernourished seriously ill patient.
In anthropometrically assessing the severity of malnutrition,
an important goal is to define the amount and rate of
change in total body or skeletal muscle protein ( 6). The
main anthropometric indices used for this assessment are
FFM (molecular level) and limb muscle areas (tissue-system
level). Because lower limits compatible with survival are
known for both types of measurement, the severity of
protein-energy malnutrition is usually judged as the
patients value relative to the normal range on the one hand
and the minimal range on the other (1). In terms of
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prognostic value, these measurements will provide some
index of potential survival time; given the patients
anthropometric FFM or muscle index and nitrogen balance,
progression toward or away from potentially lethalstarvation can be established. During nutritional therapy
and follow-up, the anthropometric FFM indices are used as
measures of nitrogen balance, and specific details regarding
interpretation are presented in the following paragraphs.
Measurement and Interpretation.
Measuring FFM is accomplished by anthropometric methods,
such as those described earlier in the section on fat. The
same cautions in measurement technique and selection of
patients noted in the earlier discussion of fat also apply to
FFM. With regard to interpretation, in theory multiplying
FFM (in grams) by 0.195 and 1.02 provides the amount of
total body protein in grams and metabolizable energy in
kilocalories. Of the metabolizable energy in the healthy
subject, about half of that in FFM is available during
prolonged periods of semistarvation (1). When combined
with balance data and information on TBF, these bedside
calculations often provide an interesting insight into a
P.763
patients course. Unfortunately, the information needed foraccurate prediction of total body protein cannot be derived
from anthropometric FFM because of the changes in body
hydration and variability of skinfold measurements
described earlier. A large tumor burden or organomegaly of
any cause may also add mass (as water, protein, and
mineral) to FFM that is metabolically unavailable. In
patients without serious derangements in body composition,
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FFM can be used to calculate RMR as presented earlier in
Equation 5. This FFM-based calculation ofRMR is largely
independent of sex and age although evidence is
accumulating that ethnic RMR differences exist even aftercontrolling for body composition (36).
Calculating the amount of limb muscle tissue from
anthropometric data requires only two measurements: the
limb circumference and the corresponding skinfold
thickness. The midportion of the upper limb is usually
studied, and little additional information is gathered by also
measuring thigh and calf muscle areas (46). Calf muscle
measurement would, of course, be useful in subjects whose
upper extremities are burned, amputated, edematous, or
immobilized by casts or traction devices. The upper arm
muscles tend to atrophy slightly more rapidly during
semistarvation than the muscles of the thigh or calf, but the
differences are not large (46). Equations for estimating limb
muscle area from anthropometry take the following general
form:
where C is a limb circumference (e.g., upper arm, mid-
thigh, calf), and SF is a skinfold taken at the same level as
the circumference using standardized methods.
The primary application of limb muscle measurements is to
obtain a measure of the amount and rate of change in
skeletal muscle protein. The following three factors should
therefore be considered:
The mass of a skeletal muscle represents a three-dimensional measurement (i.e., volume), whereas limb
muscle area and circumference are two- and one-
dimensional indices, respectively (1,46). As the muscle
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changes volume, the corresponding proportional
changes in muscle area and circumference will be
smaller than the change in volume. For example, a
50% decrease in muscle volume will correspond to atheoretic decrease in muscle area and circumference of
37 and 21%, respectively. As a rule, the relative
change in muscle area will be larger than the change in
muscle circumference.
The equations for calculating limb muscle indices arebased on simple theoretic assumptions regarding arm
geometry (1,46). Actually, the calculated arm muscle
area overestimates the amount of skeletal muscle by
15 to 25% in relatively young, nonobese subjects. Half
of this overestimate results from the inclusion of bone
in the calculated area, and the remainder results from
errors in the assumptions and the inclusion of
nonmuscle tissue (e.g., neurovascular bundle) in the
result (73). Two methods of correcting this
overestimate of muscle area are available. The fir st is
to express results as a percentage of standard,
because the standard value will also contain these
nonskeletal muscle components. The second approach
is to calculate bone-free arm muscle area by
subtracting a constant value (10 cm2 for men; 6.5 cm2
for women) from the arm muscle area estimate
obtained by the general equation. Studies by Forbes
and Baumgartner and their colleagues suggested that
arm muscle area assumptions are also inaccurate in
obese and elderly subjects, respectively (74,75).
Martin and colleagues developed an an thropometric
prediction formula for total body skeletal muscle mass,
although the accuracy of this method in monitoring
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changes in muscle mass and associated protein content
has not been reported (52). Further studies are
therefore needed to improve our understanding of the
relationship between anthropometric muscle estimatesat the whole-body level of body composition and actual
skeletal muscle mass at the tissue-system level.
Atrophic skeletal muscle differs in chemicalcomposition from normal tissue. Per gram of muscle,
the amounts of water, total lipid, and collagen are
increased, whereas the noncollagen proteins are
reduced. Thus, the concentration of functional proteins
per unit arm muscle area or circumference is relatively
lower in the atrophied muscle. Another chemical
consideration is that muscle size can abruptly change
by 5 to 10% in response to rapid changes in muscle
glycogen as a result of the water-binding properties of
glycogen (46).
Thus, both anthropometric FFM and muscle indices are truly
indirect markers of the active protein component of body
weight. The two lean tissue indices should be considered
approximate bedside guides to the amount of total body
protein. Despite their approximate nature, anthropometric
muscle estimates correlate with more complex methods of
estimating skeletal muscle (e.g., total limb muscle area
versus total body skeletal muscle volume by magnetic
resonance imaging; Fig. 49.7) over the broad biologic range
of muscle mass in humans.
Small changes in total body protein cannot be detected by
anthropometry, and nitrogen balance and other techniques
must be used for this assessment.
Bioimpedance Analysis
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Nutritional assessment using anthropometry is now
increasingly augmented through the additional measurement
ofBIA. This technique is used to predict body composition
based on the electrical conductive properties of the humanbody. The ability of the body to conduct an electric current
is the result of the presence of free ions, or electrolytes, in
the body water. The amount of electricity
P.764
that can be conducted is determined mainly by the total
volume of electrolyte-rich fluid in the body. Measures of
bioelectric conductivity are therefore proportional to TBW
and to body composition components with high water
concentrations such as the FFM and skeletal muscle mass.
As a result, these methods predict TBW, FFM, and total body
skeletal muscle mass. TBF must be derived as the difference
between body weight and predicted FFM (76).
Figure 49.7. Correlation between total limb muscle area
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(sum of arm, calf, and thigh muscle areas in cm 2) and total
body skeletal muscle volume in L by whole-body magnetic
resonance imaging in healthy subjects. N = 79, Total muscle
area (cm2) = 7.6 Skeletal muscle (L) + 115.4, R 2 =
0.6423, p < 0.001.
Many factors other than the amount and electrolyte
concentration of body water, however, influence
measurements of electrical conductivity. These include body
temperature, distribution of fluids between intracellular and
extracellular spaces, body proportions or geometry, the
amounts and structures of different conductive, as well as
nonconductive, tissues, and technical issues such as correct
calibration and application of the equipment. The net result
is that exact functional relationships between measurements
of bioelectric conductivity and TBW or other fat-free
components cannot be derived from either physiochemical
models or experimentally. Thus, relationships between
conductivity measurements and body composition
components are established indirectly by statistical
calibration against criterion measures (e.g., estimates of
TBW from deuterium dilution analysis or FFM from DXA) in a
sample of subjects (76).
Measurement and Interpretation.The most commonly used BIA method injects a high-
frequency, low-amplitude alternating electric current (50
kHz at 500 to 800 A) into the body using distally placed
electrodes and measures the voltage drop caused by
resistance with proximal electrodes. Conventionally, surface
gel electrodes are used with standardized placements on the
right ankle and hand, although other electrode
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arrangements have been described that allow estimation of
segmental (e.g., arm or leg) electrical properties (76).
Stainless steel contact electrodes are also now used in some
systems in place of the conventional gel electrodes.The amount of resistance measured (R) is inversely
proportional to the volume of electrolytic fluid in the body.
It is also dependent on the proportions or geometry of this
volume (i.e., ratio of length [L] to cross-sectional area [A],
or R L/A). These relationships have led to the use of the
simple formula V = L 2/R as the theoretic basis of most BIA
applications, where V is conductive volume (e.g., TBW), L is
a measure of body length (usually stature), R is measured
resistance, and is an estimate of the specific resistivity
of the conductive material (76).
The validity of this simple formula has certain limitations.
The formula is accurate only for a cylindric conductor with
uniform cross-sectional area and homogenous composition
(e.g., a wire). The human body could be described as a
series of roughly cylindric conductors with variable cross-
sectional area and heterogeneous, highly structured
composition. The value of is influenced by all these factors
and consequently cannot be deduced directly. As a result,
equations for predicting body composition must be
developed based on independent measurements of
resistance, stature, and other anthropometric variables, and
TBW or FFM in a sample of subjects. Least-squares
regression techniques are applied to the data to derive an
equation of the basic form:
where a is intercept, b is slope, and e is residual error or
unexplained variation in V caused by random measurement
errors and/or misspecification of the parameters (a and b)
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in the equation. It is not possible to interpret the parameter
b in this equation as an estimate of in the formula V =
L2/R, unless the intercept (a) and residual error (e)
approach zero. These conditions are rarely, if ever, met forthe reasons given earlier. BIA prediction equations usually
include additional parameters for age, gender, body
circumferences, and skinfold thickness. Thus, this method
can be considered a supplement to anthropometry for
predicting body composition in patients. Some equations for
predicting TBW, FFM and total body skeletal muscle mass
are given in Table 49.5(77,78,79).
TABLE 49.5. EQUATIONS FOR PREDICTING BODY
COMPOSITION FROM BIOELECTRIC IMPEDANCE AND
ANTHROPOMETRY
Males 1280 ya
TBW= 1.203 + 0.176(Weight) + 0.449(Stature 2/R)
FFM= -10.678 + 0.262(Weight) +
0.652(Stature2/R)
Females 1280 ya
TBW= 3.747 + 0.113(Weight) + 0.45(Stature 2/R)
+ 0.015(R)
FFM= -9.529 + 0.168(Weight) + 0.696(Stature2/R)
+ 0.016(R)
Adults 1886 yb
Total skeletal muscle mass = 5.102 +
0.401(Stature2/R) + 3.825(gender) -0.071(age)
Weight (kg); Stature (cm); R, resistance (ohms); TBW,
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total body water; FFM, fat-free mass. TBF, total body
fat = Weight FFM.
aData from reference 77.
bData from reference 78.
P.765
Similar to anthropometric prediction methods, BIA equations
tend to lose accuracy when applied to subjects who do not
resemble those included in the sample from which the
equations were developed. Thus, their generalizability may
be limited and all equations should be prevalidated in a
subsample against an accepted reference method before
general extension to an entire study population. In clinical
application, the user should be aware that large
disturbances in body fluid distribution between intracellular
and extracellular compartments, for example edema or
ascites, may affect the accuracy ofBIA equations for
predicting body composition. Conversely, methods have
been developed that take advantage of the sensitivity ofBIA
to alterations of body water distribution in patients with
various disorders (80).
Reference Values
Body Weight
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The patients body weight is evaluated using two reference
sources. The first reference values are those of the patient,
and these include a usual weight by history or previous
measured weight. This is important because many obesepatients who lose weight during an illness and are thus
potentially malnourished will still be overweight by
conventional standards. The second reference source is the
healthy population. In this approach, the individual persons
actual body weight is compared with that of a gender-,
stature-, and age-appropriate reference or desirable body
weight (see Appendix Tables A-12, A-14, and A-15 and their
subdivisions). The subjects actual body weight is expressed
as a percentage of desirable. The normal range for desirable
body weight varies among different sources, but it usually is
set between 90 and 120%. A body weight below or above
these levels is consistent with undernutrition and obesity,
respectively.
Another method of comparing the patients weight with that
of a reference population is to calculate a body weight
(BW)-stature (S) index (81). Most weight-stature indices in
present use take the form W/S p (69). The term p indicates
how stature is to be scaled. The main assumptions of
weight-stature indices are that they are independent of
height, represent an indirect index of body composition,
correlate with health outcomes, and can be generalized
across different populations.
The use ofBMI, calculated as BW/height 2, has gained wide
acceptance as a weight-stature index for use in diagnosing
both protein-energy malnutrition and obesity (82,83,84).
Most of the assumptions of weight-stature indices are
fulfilled by BMI, although several limitations should be
noted. First, although the correlation between BMI and TBF
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is relatively strong (r = 0.5 to 0.8), individual variation is
large and some subjects can be misclassified as
undernourished or obese (81). For example, some athletic
subjects have a large skeletal muscle mass and a high BMIbut are not obese. Thus, man with a BMI of 27 can have TBF
ranging from 10 to 31% of body weight (85). This variability
in percentage of body fat may also increase in old age (86).
In studies such as these, some of the observed error may be
in the reference method for estimating body fa t. BMI may
also have a small stature dependence because persons with
short legs for their height have higher BMI values
independent of fatness (82). Finally, Gallagher and
colleagues, using a four-component model as the criterion
for body fat estimation, reported that BMI as a measure of
fatness in their healthy cohort was age and sex dependent
but independent of ethnicity in their African-American and
white adults (87). Figure 49.4shows how the relationship
between BMI and body fat changes with age, as noted
earlier.
Generally accepted BMI ranges for classifying patients as
normal, overweight, and obese are presented in Table 49.6
(88) (see also Appendix Tables A-13 and A-18 and their
subdivisions). Table 49.6also gives three BMI levels for
grading chronic protein-energy malnutrition that are less
well accepted (89). The diagnosis of protein-energy
malnutrition or obesity and of their associated risks is often
multifactorial and may require additional estimates including
body composition, energy expenditure, organ and ti ssue
function, and biochemical markers.
Fat and Lean
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Two methods are used to process anthropometric data other
than body weight. The first method is to express the
individual persons values relative to a healthy reference
population. This method provides the anthropometriccomponent used to assess whether and to what extent the
patient is malnourished. The anthropometric reference
tables present the results of large surveys and usually
describe the general population. Reference data are now
available for estimates from bioelectric impedance. The data
in Appendix Tables A-15-e-1,2, and3were adapted from
Chumlea and associates and are based on data from the
Third National Health and Nutrition Examination Survey
(79). The estimates ofTBW, percentage of fat, and FFM in
these tables were derived from bioelectric resistance (R)
measurements using standard prediction equations.
TABLE 49.6. BODY MASS INDEX AND GRADES OF
OBESITY AND PROTEIN-ENERGY MALNUTRITION
BODY MASS
INDEX GRADE
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2529.9 Overweight
3034.9 Class I obese
3539.9 Class II obese
>40 Class III severe obesity
P.766
The reference tables usually present data in three forms:
(a) as a mean value; (b) as mean and standard deviation
(SD); and (c) as percentiles. Describing a population in
terms of mean and SD assumes that the measurement under
study is symmetrically (normally) distributed. If data fit this
model, then the mean 2 SD includes 95% of the
population. An abnormal value is more than 2 SD above or
below the mean. Some tables provide only a mean, and the
patients value is then expressed as a percentage of the
standard or reference value. A weakness of this approach is
that tables of this type do not provide the observer with a
method of determining whether the result is within the
normal range. The second type of table includes the SD, or
95% range of the healthy population, thus indicating
whether and to what degree the patients results are
abnormal. The third mode of expression is in terms of
percentiles (e.g., see Appendix Tables A-12 to A-16, and A-
18-b). The advantage of expressing results as a percentile
rather than as a percentage of standard is that the
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reference population need not be symmetrically distributed.
Often anthropometric surveys of populations produce
skewed distributions, and therefore the easiest option is
to present results in percentiles (90). In this approach, thevalues of the subject exactly in the middle of the group are
at the fiftieth percentile. If the patients value is between
the fifth and the ninety-fifth percentile, the result is
considered normal; a result below or above these respective
values is abnormal.
No simple method of judging the severity or potential
morbidity of protein-energy malnutrition from
anthropometric data is available. Studies in adults have not
yet clearly defined the risk of a subnormal anthropometric
index, especially for results falling just below the normal
range. Combining anthropometric data with the results of
other components of the nutritional assessment provides
some measure of potential morbidity (91).
The second method of expressing anthropometric data is in
terms of the ind ividual persons total body energy content,
TBF, and FFM. When estimated energy and nitrogen balance
are combined with these body composition data, a whole
spectrum of potential calculations is possible. Of course,
these are approximations, but their application in teaching
and solving simple clinical questions often proves useful.
CLINICAL APPLICATIONS
Suggested applications of anthropometry are the following:
Weight and height should be recorded in the chart ofevery hospitalized patient. Weight indices, such as
recent weight loss, should be added to the data base
for all patients who have a history of weight change.
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The weight of all patients undergoing short-term
nutritional support should be measured daily.
The uses of one skinfold measurement, limb fat area,and limb muscle area are helpful:
o When body weight is an invalid index of energyreserves because of edema or massive tumor
burden. The upper limb is usually not affected by
dependent edema.
o When body weight is unmeasurable because ofimmobilizing devices, such as a cast or respirator.
o When patients are seen for nutritionalconsultation or are seen at rounds removed from
the bedside. Anthropometric estimates provide a
quantitative description of what is usually visible
at the bedside. Although weight alone is useful in
this regard, two patients of the same height and
weight may differ in body composition.
o During the initial evaluation of hospitalizedpatients who are prescribed short -term nutritional
support. Although changes in fat and lean tissue
will most likely not be detected over a 1- to 2-
week period, the baseline anthropometric data
will become a permanent component of the
nutritional data base. This information will then
be available if a future reevaluation is needed.
TBF and FFM are useful indices:o In patients who are undergoing long-term
nutritional follow-up over months or years. Limb
muscle area measurements, preferably of the
upper arm, should also be included in this group
to complete the body composition data base.
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o In groups of subjects forming the basis ofnutritional studies, when a more critical
assessment of body composition is often useful
and more accurate techniques of evaluating bodycomposition are not available.
o In estimating RMR based on FFM.o For teaching purposes, when the interrelations of
metabolic balance, body composition, and
nutritional therapy are the subject of discussion.
AGING AND ANTHROPOMETRICINDICES
Body composition changes throughout the adult life span,
and this must be considered when evaluating
anthropometric indices (92). Height declines and, assuming
body weight remains unchanged, there is more fat and less
FFM in an elderly person than in a younger one of the samesex (92,93,94). Most of the loss in FFM can be accounted for
by a decrease in skeletal muscle (95). A summary of how
body composition changes with age and how anthropometric
measures are affected is presented in Table 49.7.
Because of these changes in body size, shape, and
composition of the FFM, investigators now advocate
geriatric-specific anthropometric and bioimpedance body
composition prediction equations (96,97,98).
A difficult problem in the elderly is estimation of height,
especially in wheelchair-bound, bedridden, or kyphotic
P.767
persons. Specialized approaches such as recumbent
anthropometry may be useful in hospitalized or nursing
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home patients (99). Another useful approach is to measure
knee height or arm span (see Table 49.2) to predict adult
stature. Knee height and arm span undergo little change
with age in adults and provide estimates of stature that aredifficult to obtain by conventional methods. The estimated
value for height can then be used in calculating other
assessment indices and for comparison of these results to
height-adjusted reference values. Alternatively, knee height
can be used in place of stature in indices such as fat/knee
height2 (100).
TABLE 49.7. EFFECTS OF AGING ON BODY
COMPOSITION AND ANTHROPOMETRIC MEASUREMENTS
ANTHROPOMETRIC
MEASUREMENT AGE-EFFECT
Weight The average population valueincreases until the fifth decade
and then plateaus or decline.
Height The average population value
decreases by 0.5 to 1.5 cm per
decade after maturity; the rate
of decline is sex and race
dependent
Fat Fat increases as a percentage of
body weight up to about 50 y
and then plateaus or declines in
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old age after 70 y;
redistribution occurs from limb
to truncal subcutaneous sites
and from subcutaneous to
internal, visceral, and
interstitial sites
FFM FFMdecreases after age 40 y
owing to decreases in skeletal
muscle and bone; rates of loss
in muscle are higher in men and
accelerate after age 70 y; rates
of loss in bone are higher in
women and accelerate during
menopause; the mass of visceral
organs decreases slightly in old
age; the hydration ofFFM
becomes more variable
Skinfolds The compressibility of skinfolds
changes with age owing to a
loss in elastic recoil of skin and
an increase in the viscoelasticrecovery time; skinfolds in the
elderly are often pendulous and
difficult to measure additionally
owing to loss of underlying
muscle tone
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Circumferences Pendulous skinfolds can make
circumferences more difficult to
measure in the elderly; it is
more difficult to locate bony
landmarks in the obese
FFM, fat-free mass.
Anthropometric measurements may be useful in diagnosingmalnutrition in hospitalized elderly subjects. Lansey and
colleagues examined 47 consecutive geriatric patients
admitted to an acute care facility and found that
approximately 45% of the patients had two anthropometric
measures (i.e., midarm circumference and muscle area;
subscapular and triceps skinfold) less than the fifth
percentile, indicating severe malnutrition (101). In contrast,
only 28% of patients were at less than 90% of ideal body
weight. Anthropometric measurements may therefore
supplement and be more sensitive than body weight and
stature in the evaluation of malnutrition in hospitalized
elderly patients.
Anthropometric measures may also predict disability and
mortality in elderly populations. Rolland and associates
reported that the simple measurement of calf circumference
predicted self-reported disability in a population of elderly
women (102). Campbell and colleagues reported that low
arm muscle area and triceps skinfold thickness were
associated with significantly increased mortality risk in 758
subjects who were more than 70 years old (103). The Mini-
Nutritional Assessment, which is now widely used to screen
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elderly patients for malnutrition, incorporates midarm and
calf circumferences and BMI (104).
EVALUATING AND CONTROLLING
ERROR SOURCES
As with all measurements, anthropometric evaluations
include error. In this section, we provide an introductory
discussion on anthropometric error sources. The interested
reader is referred to comprehensive reviews for an advanced
discussion of this important topic (8,20,22).
Error can be considered in the context of the fundamental
body composition methodology equation as shown in Figure
49.8. The equation in the figure indicates that
P.768
error in the estimation of a body composition component
(e.g., TBF) from anthropometric measurements is a function
of two main errors sources, measurement of a quantity (Q)
and mathematic function (i.e., descriptive or mechanistic).
The figure also notes that errors of measurement may be
either or both systematic (nonrandom) and random. Errors
resulting from misspecification of mathematical functions
are always systematic. The following discussion first
considers measurable quantity errors and then proceeds to
an overview of mathematic function errors.
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Figure 49.8. Sources of error in anthropometric methods.
A subjects anthropometric dimensions can be evaluated at a
single point in time or on repeated occasions over time.
Measurement error is the main concern with a single
evaluation and measurement error combined with normal
biologic variation must be considered with repeated
measurements. Measurement error can be caused by
instrument error and observer error. Methods of minimizing
instrument error are reviewed in earlier sections. In
summary, these mainly resolve to the correct choice of
appropriate measurement instruments and accurate
calibration. Observer error is related to three factors:
precision, reliability, and accuracy.
Accuracy is the level of agreement between the measured
value and the true dimension. Accuracy of an
anthropometric dimension is usually established by
comparison to a reference method. For example,
subcutaneous adipose tissue thickness estimated using a
skinfold caliper can be compared with corresponding
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estimates by computed tomography or magnetic resonance
imaging. Of course, any such analysis also includes
instrument and observer errors. In clinical situations,
measurements by an anthropometrist are usually comparedwith those of a designated expert (20).
Precision, as distinct from accuracy, defines the quality of a
measurement in terms of being sharply defined or exact. In
this sense, precision refers to the scale of measurement; for
example, a skinfold measured to the nearest 1 mm is more
precise than one measured to the nearest 0.5 cm. A highly
precise measurement (e.g., body weight measured to the
nearest gram) is not necessarily accurate if the weight scale
used is improperly calibrated. The definition of precision
overlaps to some extent with that of reliability. Reliability is
the degree to which a measurement is replicable using the
same instrument by the same or a different observer (105).
The linkage to precision comes in that it is difficult for a
measurement to be precise or exact if it is unreliable.
The precision of an anthropometric measurement can be
quantified as the variability among repeated measurements
over a short time in the same subject (22). One way of
expressing precision is the technical error of measurement,
which is the standard deviation of repeated measurements
on the same subject by the same or different observers. The
technical error of measurement, which is expressed in the
same units as the measured quantity, can also be expressed
in percent as a coefficient of variation (i.e., SD/mean
100) (20).
Reliability is also referred to as reproducibility or
repeatability (105). Reliability, as distinct from precision,
is more commonly expressed in terms of the intraclass
correlation among repeated measurements, sometimes
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called the reliability coefficient. Measures of reliability often
include both measurement error and physiologic variation.
The total variation of an anthropometric measurement
monitored over time includes measurement variation andbiologic variation. Biologic variation occurs even in the
healthy person as weight and fluid balance fluctuate over
time. This aspect of measurement variability is the
difference between total anthropometric dimension variation
over time and that caused by measurement error. Some
measures, such as stature, are extremely stable in adults,
whereas others, such as selected skinfold thicknesses, are
moderately variable over time. In practice, this biologic
component of variability is often included in estimates of
the reliability on anthropometric measurements.
Anthropometric dimensions are often used directly, as for
example trice
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