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Body Composition and Physical Performance 1992.
Pp. 223-235. Washington, D.C.
National Academy Press
14
Body Composition Measurement
Accuracy, Validity,
and Comparability
foe! A. Grinker
BACKGROUND
Because estimates of body composition may vary as a function of gen-
der, age, or ethnicity, their universal applicability needs to be considered
with care. Current military standards include both gender- and age-specific
norms. Are they sufficient? Are norms for older women more restrictive
than for comparable men? Should norms be adjusted for race as well?
Should norms be based not on total body composition, but on fat distribu-
tion patterns? Finally, should performance rather than body composition be
the major determinant? The substitution of tests of health and physical
capacity is possible, such as submaximal treadmill test performance, blood
pressure test to rule out hypertension, spirometry to check lung health,
Cybex to check quadriceps strength, hand grip dynamometer for hand
strength, and evaluation of endurance via field performance or mini mara-
thon. Would these tests provide more information than arbitrary standards
based on changing norms? How relevant is physical appearance to effective
military service, and how well correlated are arbitrary standards of body
composition with preferred physical appearance?
To assess these questions, it is necessary to document a number of
factors. The applicability of different methods of assessing body composi-
tion can be compared in relationship to assumptions of universal applica-
bility. Secular, gender, and age-related differences in body composition
and fatness can be documented. Ethnic or racial differences both in body
composition and in age-related effects can also be documented.
223
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224
JOEL A. GRINKER
METHODS AND ASSUMPTIONS
Is the two-compartment model (lean body mass ELBM] and body
fat LBFi) still useful? Should the four-compartment model (LBM, BF, body
water, and bone mineral) be used? How well do multiple anthropometric
measures mirror body composition, body density, and ethnic, racial, and
age-related differences in fat distribution or changes in bone density?
Body composition can be measured directly by chemical analysis of
animal or human carcasses or cadavers. Indirect measures include densito-
metry via hydrostatic weighing; anthropometric measures of skinfolds/
circumferences; and the more recent procedures of isotope dilution, neutron
activation analysis, and potassium-40 counting (Boring et al., 1962; Brozek
and Henschel, 1961; Forbes and Hursh, 1963; Lukaski, 1987~. However, it
is important to realize that the use of any indirect method of assessing
human body composition results in errors of prediction. The usual errors
range from 2.5 percent for predicting BF from densitometry to 3 to 9 per-
cent by anthropometry (Lohman, 19811. An early comparison of ultrasonic
and skinfold measurements to evaluate subcutaneous fat thickness and to
predict total BF weight suggested that skinfolds were the more effective and
less costly procedure (Borkan et al., 1982b).
The prevalent use of anthropometric measures (that is, height, weight,
skinfolds and circumferences, and associated nomograms) is based on ease
of application, simplicity, and reasonable correspondence with other tech-
niques. Skinfolds of major interest include biceps, triceps, subscapular,
suprailiac, abdomen, thigh, and medial calf. However, systematic errors
can be introduced if the differential compressibility of skinfolds with age
and skinfold thickness are not controlled (Himes et al., 19791. This tech-
nique depends on two assumptions: that selected skinfold thicknesses are
representative of the total subcutaneous adipose tissue mass and that subcu-
taneous adipose tissue has a known relationship with total BF. However,
the relationships between skinfold thickness and total BF reportedly differ
with ethnicity, gender, and age (Chumlea et al., 1984; Durnin and Womers-
ley, 1974; Jones et al., 1976; Wilmore and Behnke, 1970; Yuhasz, 1962~.
In addition, these measurements are highly susceptible to experimenter
bias or error leading to wide variability among experimenters.
Densitometry has generally been considered the gold standard or crite-
rion against which other techniques have been validated (Lohman, 1984;
Roche, 19871. This technique assumes the two-compartment model: fat
and fat-free mass (FFM; lipid-free) (Behnke et al, 19421. Fat is assumed to
have a constant density of 0.9, although interstitial muscle fat is slightly
higher (Mendez et al., 1960; Morales et al., 1945~. However, the density of
FFM is not constant (Lohman, 1986; Roche, 19871. Until middle age, bone
mineral mass and muscle mass increase, and extracellular fluid decreases
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BODY COMPOSITION MEASUREMENT
225
within the FFM. In old age, these differences are reversed for bone mineral
and muscle. The density of FFM is also increased with marked physical
activity due to the greater percentage of bone mineral (Mendez and Keys,
1960; Morales et al., 19451. Negative estimates of percent BE for some
athletes are probably due to a greater density of FFM than allowed in the
usual calculations (Roche, 19871.
Although the two-compartment model has been considered adequate for
young White men, it is not as useful for different ages, women, other ethnic
groups, or even the extremely active (Lohman, 1986; Parizkova, 1977; Roche,
1987; Womersley et al., 1976~. Because of variations in the density of the
FFM the correct model requires assessment of total body water and skeletal
mass, in addition to measurement of body density. Physical training may
also alter the fat-free body mass, suggesting that the new gold standard
include separate measures of water, muscle, and bone mineral content. Greater
delineation of lean body components that is, total body nitrogen, total
body water, potassium, and so on have emerged. Newer technologies
such as photon absorptiometry and neutron activation analysis are among
the more quantitative means of measuring mineral content. The technique
of dual-energy x-ray absorptiometry (DEXA), although as yet unverified,
holds promise for its ability to measure accurately total body as well as
regional bone and soft tissue composition (Mazess et al., 1990; Peppier and
Mazess, 1981).
Measurement and Definitions in Body Composition
The application of limits in allowable body composition in the military
depends on several assumptions. The first and primary assumption is that a
single arbitrary point on the continuum of body fatness represents a "revers-
ible abnormality". Overfatness or obesity is assumed to be a distinct abnor-
mality that can be treated. Treatments consist of various procedures to
induce "temporary" weight loss. Another assumption is that patterns of fat
distribution at specific ages are less important or critical to overall health
than is absolute fatness. Also implicit in the application of restrictive age-
specific standards is the assumption that overweight/obesity at all ages is
equivalently associated with increased health risks and/or poorer perfor-
mance. Definitions of overweight and obesity, however, are population
specific and subject to pronounced secular influences. Application to indi-
viduals may often be arbitrary or inappropriate. Second, reversal of over-
weight or obesity may be not only difficult to maintain but may itself be
correlated with increased health risks (Williamson and Levy, 19881.
Estimates of the population prevalence of overweight or overfatness are
dependent both on the criteria and the measures used (Bray, 1987; Garrow,
1983;Simopoulos and Van Itallie, 19841. Among the most commonly used
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226
JOEL A. GRINKER
criteria are relative weights (adjusted for height and gender) corresponding
to specific percentiles for a specific population, ideal body weight, or body
mass index (BMI; typically, weight in kg per height in m24. One common
external standard for overfatness is based on a BMI above 26 while a fre-
quently employed standard of ideal body weight is based on the Metropoli-
tan Life Insurance mortality results (1959, 19831. National Center for Health
Statistics (NCHS) surveys reported that 29 percent of the 1960-1962 and 26
percent of the 1977 U.S. adult population were overweight based on the
1959 Metropolitan Life Insurance norms of ideal weight-for-height (NCHS,
1966, 19801.
More direct measures of fatness such as those derived from the sum of
various skinfolds have also been used in large population-based studies
with criteria based on population distributions. Norms are based on data
from national health surveys such as the National Health and Nutrition
Evaluation Survey (NHANES) I or II or data from insurance companies.
The use of even multiple skinfolds or nomograms based on skinfolds and
circumferences poses several problems. In overweight and obese subjects,
these measurements show poor reliability (Forbes, 19641. Skin thickness
and skinfold compressibility vary as a function of age, site, and gender
(Brozek and Kinzey, 1960; Clegg and Kent, 1967; Garn and Gorman, 1956;
Himes et al., 1979; Lee and Ng, 1965; Martin et al., 1985; Ruiz et al., 1971;
Millar and Stephens, 19871.
Discrepancies in reports of the prevalence of obesity have also been
the result of applying different criteria for defining obesity (for example,
NHANES I versus NHANES II or Metropolitan Life Insurance norms for
1959 versus 1983~. In addition, differences in sampling (for example, ran-
domized census tract selection versus random digit telephone dialing) or
measurements (for example, telephone self-report versus direct measures)
have produced differences in reported obesity prevalence.
Average fatness and prevalence rates for overweight/obesity can also
vary markedly as a consequence of socioeconomic status (SES), age, race,
and gender (Cronk and Roche, 1982; NCHS, 1986, 1987; Forman et al.,
19864. Overweight and level of education or SES are inversely associated
(Baecke et al., 1983; Forman et al., 1986; Garn and Clark, 1976; Garn, 1985;
Moore et al., 1962; NCHS, 1980; Silverstone et al., 1969~. Within each of the
four National Health and Education Surveys (NHES) surveys, even younger
adults (18 to 35 years old; especially those above the median of the distribu-
tion) had higher BMIs at progressively older ages (Harlan et al., 19881. The
prevalence of overweight and obesity increases until individuals are approxi-
mately 50 years of age, then levels and declines (Jeffrey et al., 1984; NCHS,
1966; Ross and Mirowsky, 1983; Stewart and Brook, 19831.
Secular trends in the American population have been recognized in
increased values in the criteria for defining obesity in the recent Metropoli
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BODY COMPOSITION MEASUREMENT
227
tan Life Insurance tables (1983) based on changes in measured fatness of
sampled populations (eighty-fifth percentile) and risk. However, this latest
version failed to include age as a variable, and consequently, the recom-
mended weights are reported to be too liberal for young adults to accurately
reflect total mortality for 40 year olds, and may be too restrictive even for
50 or 60 year olds (Andres et al., 19851.
Obesity Prevalence, Age Effects and Weight Fluctuations
Population Based Data
Cross-sectional studies have documented differences in fatness as a
function of gender, age, race, and secular influences (Abraham et al, 1983;
Garn, 1985; NCHS, 1965, 1986, 1980; Malina et al., 1983; Wong and Trowbridge,
1984; Zillikens and Conway, 1990~. The U.S. population has reportedly
gained weight over the last 2 decades, and the prevalence of obesity has
increased (Simopoulos, 1987) even in childhood and adolescence (Dietz et
al., 1985; Gortmaker et al., 19874. Overweight among adults of varying
ages has increased within the last 10 years despite widespread health con-
cerns and dieting (Fisher and Bennet, 19851. Recent statistics suggest that
in 1986, 28.4 percent of U.S. adults 25 to 74 years of age were 20 percent
or more overweight as judged by BMI greater than 27.8 for men and greater
than 27.3 for women (NCHS, 19861.
Cross-sectional studies in England, Canada, the United States, and Hol-
land report that in both men and women, relative weight increases during
adulthood, is maintained in middle age, and decreases in old age (Baecke et
al., 1983;Bray, 1987;Jeffreyetal., 1984;Khosla and Lowe, 1968;Millar
and Stephens, 1987; Montoye et al., 1965; NCHS, 1980; Rosenbaum et al.,
1985; Stewart and Brook, 1983~. Although such associations between age
and overweight could be due in part to a confusion between cohort and age
effects possible in cross-sectional studies, data from prospective studies
support these general findings. These longitudinal studies suggest age-
related trends in relative weight (Friedlaender et al., 1977; Hsu et al., 1977;
Kannel et al., 19791.
Individual-Based Data
At present, little is known about patterns of individual weight change
within the population during adult years. When and to what extent does
weight loss or gain occur? Is stability in BE related to pattern of fat distri-
bution? It has recently been suggested that stability in body habitue may be
related to a lower risk for chronic disease such as coronary heart disease
(CHD) (Hamm et al., 19891. Whether the risk of other chronic diseases,
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228
..
JOEL A. GRINKER
such as cancer or noninsulin dependent diabetes (NIDD) are also related to
weight fluctuations is unknown. The few existing prospective studies sug-
gest relative consistency in body weight patterns over time. (See, for exam-
ple, Kramer et al., 1989.)
Changes in weight, BMI, and skinfold thickness (triceps and subscapu-
lar) were studied after intervals of 4 to 7 years in over 17,000 Finnish adults
as part of a recent health survey (Rissanen et al., 19881. Average weight
and BMI increased with age in men and women below age 50 at entry,
changed little in men aged 50 to 70 (women aged 50 to 60), and declined at
later ages. Both moderate overweight (BMI = 27.0 to 29.9 kg/m2) and
severe overweight (BMI 2 30 kg/m2) increased in successive age cohorts of
men and women until age 70. A relatively high proportion of Finnish
adults, approximately 24.7 percent of all men and 33.7 percent of all wom-
en were considered overweight, and 8.3 percent of men and 17.4 percent of
women were estimated to be severely overweight.
Small changes in individual weights were reported, with two-thirds of
these Finnish participants maintaining their weight within 5 kg of their
original weight classification (lean, normal, moderately overweight, or se-
verely overweight). A weight gain of 10 kg or more occurred in 9 percent
of the men and 4 percent of the women, and a 10-kg weight loss occurred in
only 2 percent of the men and 4 percent of the women. Both weight loss
and weight gain occurred among overweight subjects. Weight loss was
associated with old age and higher initial BMI, whereas weight gain was
most common in young adults, even among those with high initial BMI.
Men aged 20 to 29 at entry gained an average 3.3 kg/5 years. Weight gain
was less common among older subjects. Among 40 to 69-year-old men,
there were negligible changes, with 15 percent losing or gaining 5 kg. BMI
increased until age 50 and decreased thereafter.
Results from the normative aging study (NAS) (Borkan et al., 1983,
1986; P. Vokonos, Boston Veterans Administration, pers. com.) illustrate
strong age, cohort, and secular effects in fatness among healthy male adult
volunteers. During the 20 years of this study, the average weight reportedly
increased until age 55, with subsequent stability and then reduction. Pat-
terns of central fat distribution have been examined in a small group of
selected subjects from the NAS using CAT scans. Great variability among
individuals in the redistribution of fat with increased age leading to an
uneven thinning of subcutaneous fat and increased intra-abdominal fat has
been documented (Borkan et al., 1982a; Borkan and Norris, 1977; Mueller,
19821. Estimates of internal abdominal fat appear to be poorly correlated
with overall estimates of fatness and not well correlated with estimates such
as the waist-hip ratio (Shimokata et al., 1989~. Abdominal fat and internal
depots have been closely associated with cardiovascular disease (CVD).
Data from the NAS have also been used to assess the effects of weight
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BODY COMPOSITION MEASUREMENT
229
change and age on coronary disease risk factors (Borkan et al., 19861. Even
after controlling for age, smoking status, initial weight, and initial levels of
the risk factor, increases in weight were significantly related to increases in
most risk factors (for example, cholesterol levels, fasting glucose, triglycer-
ides). However, data from at least one other longitudinal study suggest a
curvilinear relationship between fatness and mortality (Andres et al., 19851.
Recently, several studies have focused on the potential deleterious con-
sequences of weight changes and have reported greater morbidity and mor-
tality solely as a consequence of weight fluctuations (Hamm et al., 1989;
Hoffman and Kromhout, 19891. Recent reports from the MRFIT, Goteborg,
and Framingham populations suggest an association between weight
cycling (individual variations in body weight) and coronary heart disease
and mortality, which are reportedly independent of BMI or age (Lissner et
al., 1988, 1990, 1991~. Whether undiagnosed illness is also a factor is
under discussion. The factors related to success or failure in dieting and
thus in promoting weight stability such as gender, ethnicity, intentionality,
use of exercise, degree and duration of overweight/fatness, and fat distri-
bution patterns need to be clarified.
The appropriateness of age-specific criteria, however, remains some-
what controversial. With affluence, fatness increases regularly with age,
but it is unclear whether this is biologically desirable or inevitable. Per-
haps, as with losses in muscle mass and strength, adequate exercise and
attention to diet can prevent age-associated increases in total fatness but
not, perhaps, changes in fat distribution. Although certain preindustrial
societies may not demonstrate age-related increase in weight (Dietz et al.,
1989), the documentation of shifts in the pattern of fat distribution suggests
that ideal body weight and body composition are in fact age dependent.
Andres (1990) has argued persuasively that modest increases in weight with
increasing age (10 pounds/decade) are associated with minimum mortality
among healthy, insured individuals. However, many analyses of these epi-
demiological data sets have included "healthy" smokers.
A recent study (Must et al., 1991) reports data from NHANES I on
persons ranging in age from childhood to 74 years during 1971 to 1974.
Population- and race-specific percentiles of BMI for obesity and super-
obesity were obtained. Significant variability as a function of age, gender,
and race were reported. In women, racial differences in the eighty-fifth and
ninety-fifth percentiles of BMI emerged in the teens and persisted into
adulthood with a continued divergence with age. The BMI at the fiftieth
percentile was also higher in Black women starting in the teen years. In
men, Whites had greater BMI at the eighty-fifth percentile until age 35;
afterwards BMIs for Black men were greater. Black men had greater BMIs
at the ninety-fifth percentile throughout adulthood with a continued diver-
gence with age.
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JOEL A. G8lNKER
RESEARCH APPLICATIONS
Data from routine physicals in the military should provide both pro-
spective data as well as cross-sectional data. The incidence and prevalence
of weight shifts or changes in body composition in the military population
can be documented. The existence of long-term trends in weight/fatness
stability or cycling in individuals differing in body habitue can be explored.
Initial anthropometric measures and pattern of fat deposition as well as
estimates of percent BE or BMI can be compared with routine periodic
measures of body composition and the incidence or degree of weight fluctu-
ations individually determined. Secular and generational trends as well as
relative stability in weight and fatness can be explored among different
ethnic and racial groups. Retrospective case-cohort analysis can also be
performed to determine the overall pattern of weight fluctuation; the initial
fatness patterns of subjects subsequently exceeding specific fatness criteria
can be contrasted against the entry status of a random selection of all partic-
ipants at entry (Sorensen and Sonne-Holm, 19881.
Weight and fatness stability can be defined as weight plus 5 pounds of
starting weight per year. Weight stability can also be estimated by the
intraindividual variability in body weights or fat distribution patterns, that is,
the coefficient of variation (CV) of at least three consecutive body weights
taken at regular intervals (3 to 5 years). Weight change can be defined as at
least a 5-kg loss or gain; and weight cycling can be defined as two or more
weight changes within the last 15 years. Comparisons can be made among
current weight, initial weight, and "cycled" weights. Current and prior anthro-
pometric measures can be used to provide estimates and adjustments of body
composition and fat deposition and to estimate gender, ethnic, race, and age
effects. It would also be of interest to measure adipose tissue in selected
subjects for lipolysis and conduct VO2max testing or measure total metabolic
rate by doubly labeled water technique in selected subjects with high or low
weight fluctuations. These data would allow estimates of individual differenc-
es in rates of lipolysis or energy utilization. These latter relationships might
begin to provide partial answers to the major question of the relationships
among body composition, body fatness, and performance.
SUMMARY
The body composition criteria for entrance and for retention in the
military services especially the Army, are not identical. Screening criteria
are primarily based on weight/height for age with retention criteria based on
body composition standards that are only moderately related to performance.
This paper discussed several key issues of measurement which influence
both the accuracy and the reliability of measures of body composition. Fur
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BODY COMPOSITION MEASUREMENT
231
ther research is necessary to examine the relationships among the various
methods of measuring body composition and various performance criteria.
Major issues that were addressed in this discussion of criteria included
those related to validity or accuracy and precision as well as issues of
reliability. These factors are related not only to technical measurement
error but also to issues concerning stability in body composition in adult
men and women and differences in body composition among various sub-
groups for example, racial or ethnic. Body composition and the adequacy
(validity and reliability) of measurements were discussed in relation to age,
pattern of fat distribution, gender, and ethnic or racial differences. The
prevalence and significance of weight shifts with aging or dieting were also
discussed. Finally, the relationship between standards of body composition
and performance in relation to differences among age, ethnic, and gender
groups was addressed. Additional research should address these remaining
issues:
· What should be used as the true "gold standard" in determining body
composition? Is the two- or four-compartment model more useful?
· How accurate are the large scale screening techniques versus experiment-
al procedures? How reliable? What are the correlations among measurements?
· What corrections in weight or fatness should be allowed for gender,
race and ethnic origin? How should ethnic differences in fatness distribu-
tion patterns be translated into body composition standards?
· How stable are the weights and body compositions of adults? Are
age associated corrections desirable or necessary?
· If certain patterns of fat distribution (centripetal or abdominal depots)
are more likely to occur with older age and be more closely linked with
morbidity/mortality, should body composition recommendations and standards
be differentially aimed at specific subgroups, i.e. especially men (and women)
with centripetal fat distribution patterns? Should standards of acceptable
weight/fatness be relaxed for women (or those meeting lower waist/hip
ratios)? Since smoking (in women) is related to higher waist/hip ratios should
fatness/appearance recommendations include restrictions on cigarettes?
Standards of measurement (validity and reliability) must be considered along
with issues of applicability to military needs.
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Representative terms from entire chapter:
body weight
