Composição corporal e antropometria

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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


43/80

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

%)


44/80

[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


45/80


46/80

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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