Different Techniques For Body Composition Assessment
Nayera E. Hassan,
Sahar A. El-Masry,
Nadia L. Soliman
Mona M. EL-Batran
The inter-relationships and alter nativity of body composition variables
derived from simple anthropometry [BMI and Skin Folds (SFs) prediction
equations] and Bioelectrical Impedance Analysis (BIA) with dual energy
x-ray (DXA) of healthy sixty nine children (37 boys and 32 girls) aged
9.24±1.73 years old were evaluated. The children recruited from
public schools in Giza governorate. All of them had BMI ranged between
15th and 85th percentile and were assessed for body composition [percentage
body fat (%BF), fat free mass (FFM; kilograms) and body fat mass (BFM;
kilograms)] using Slaughter Skin Folds (SFs) prediction equations, BIA
and DXA. Repeated ANOVA showed significant differences among the three
methods used for the studied variables (p<0.001). In general, Slaughter
and BIA are significantly underestimated measured %BF. There is a high
correlation between the BMI and both the estimated %BF and BFM (r = 0.67-0.91
for boys and r = 0.87 to 0.97 for the girls). Partial correlation among
the estimated % BF derived from the three different methods in both genders
revealed a high significant correlations between the estimated %BF derived
from DXA and Slaughter equations (r = 0.76 for boys and 0.97 for girls).
While the correlation between the estimated %BF derived from DXA and BIA
was 0.77 for boys in contrast to girls where it is low significant correlation
(r = 0.387). Results suggest that BIA has limited utility in estimating
body composition, where as BMI and SFs seem to be more useful in estimating
body composition. In conclusion all methods are significantly under estimated
body fatness as determined by DXA and the various methods are not interchangeable.
Children's body composition had recently received a great attention as
a useful and fundamental tool for a careful evaluation of their nutritional
and metabolic status (Butte et al., 1999; Eisenmann et al.,
2004; Wells and Fewtrell, 2006; Wang et al., 2006; Pietrobelli
et al., 2007). It can be measured by different methods varying
in their sophistication, accuracy, feasibility, cost and availability.
Since the body composition is influenced by age and maturation, some procedures
are clearly unsuitable for children e.g., under water weighing, others
are limited because of availability and cost e.g., Magnetic resonance
imaging and dual-energy X-ray absorptiometry (DXA)], while the third offer
good feasibility and reasonable cost e.g., Bio-electrical Impedance Analysis
(BIA) and formula based on skin fold thickness (Garn et al., 1986;
Lohman, 1989; Ellis, 1996; Gutin et al., 1996; Treuth et al.,
2001; Fors et al., 2002). A major issue in the interpretation of
body composition analysis is that different methods may yield different
results for the same variable in the same child. However, the value of
any approach is greatly enhanced by the availability of the reference
data (Cole et al., 2002; American Academy of Pediatrics, 2003;
Aviva et al., 2004).
Among pediatric population, little is known about what is the simple,
suitable and more available method used to measure and assess body composition
compared with the gold criterion methods such as dual energy x-ray absorpiometry
(DXA) as it is the closest representation of true body composition (Aviva
et al., 2004). So, there is a need to develop an approach where
the body composition could be assessed and followed overtime in the individual.
Hence, our aim is to investigate the inter-relationships between body
composition variables derived from simple anthropometry BMI and Slaughter
skin folds prediction equations and Bio-electrical Impedance Analysis
(BIA) with dual-energy X-ray absorpiometry (DXA) and to find an alternative
method to DXA for assessment of body composition in young Egyptian children
that provide insight into accuracy and utility of various methods for
measuring the body composition.
SUBJECTS AND METHODS
The participants in the research were sample of Egyptian primary school
children (485 boys and 456 girls), aged 7-12 years. The students were
recruited from two public schools in Giza governorate, from October, 2003
to April 2004. Permission to perform the study was granted by the Ministry
of Education. A local consultation with the directors of schools was done
for any concerns and ethical issues of the research. An informed consent
form was delivered to the parents or the Legal Authorized Representative
(LAR) of National Research Centre for their approval and signatures prior
the conduction of the research. The order of the procedures was as follows:
a questionnaire, anthropometry, BIA, general overview of the study and
A questionnaire was designed to collect personal, social and medical
data from each child. Those having chronic disease or any situation that
impair their normal growth were excluded. The socioeconomic status of
the student was characterized by scoring the parental educational level,
occupation and crowding index and only the medium leveled students were
enrolled in this study (the majority of the students in these two public
Anthropometric measurements for each student were attempted including
body weight (Wt), body height (Ht) and skin fold thickness at triceps
and sub scapular areas, following the recommendations of the International
Biological program (Hiernaux and Tanner, 1969). Three consecutive readings
were obtained for each measurement; by one researcher and another one
assisted to him, who was well trained on performing these measurements
before starting this piece of research and the mean was recorded. The
body weight was measured using a standardized Seca Beam balance to the
nearest 10 g with minimal clothes for which no correction was made. The
body height was measured using Holtain portable anthropometer to the nearest
0.1 cm. The BMI (kg m-2) was derived and accordingly the children
were classified to exclude those below 15th and above 85th percentiles
(malnourished), so the normal children only (429 boys and 390 girls) continue
in the study. Triceps and sub scapular skin fold thickness were measured
using Holtain Caliper to the nearest 0.2 mm and their sum was calculated.
When the sum of skin fold thickness was below 35 mm, percentage body
fat (% BF), Fat Free Mass (FFM) and Body Fat Mass (BFM) were calculated
for each student by the following equations according to Slaughter (1988):
Estimated body fat % (%BF)
For boys: %BF = 1.21 (TRI + Subscap SF) - 0.008 (TRI + Subscap
For girls: %BF = 1.33 (TRI + Subscap SF) - 0.013 (TRI + Subscap.
Fat-free-mass (FFM): FFM = Wt.-(Fat % /100. Wt.) kg
Body Fat Mass (BFM): BFM = Wt.-FFM (kg)
BIA: Whole body resistance and reactance (capacitance) were measured
using a bioelectrical impedance analyzer (HOLTAIN LIMITED). As specified
by the manufacturer, the unit was calibrated before testing using 400-ohm
resistor and electrodes were placed on wrist and ankles. By using child
sex, age, weight and height approximated to the nearest unit, the BFM,
%BF and FFM were derived.
As a general overview of the study, the estimated % BF and BFM by Slaughter
equations and BIA in evaluation of body composition of school children
were investigated using Pearson`s correlation tests. Highly significant
correlations were found (p>0.001).
Then, DXA was used as a criterion measure for 69 children: 37 boys and
32 girls (those only who respond to our recall) for its availability and
cost. Whole body DXA scans were performed using a Lunar DPX-L densitometer
(Lunar Radiation Corporation, Madison, WI) with the child in minimal clothing
while lying supine. The DXA machine creates a series of transverse scans
by directing a very weak, but focused, pencil beam X-ray systematically
inch-by-inch across the child's body differentiating the body tissue into
bone mineral, lean mass (non bone) and fat mass. In this study, % BF,
BFM and FFM were determined using the pediatric medium scan mode (software
Quality control procedures were followed in accordance with manufacture's
recommendations. Routine daily calibration was done using the standard
applied by the manufacture.
Data analysis: Descriptive statistics (mean±standard deviation)
were calculated for the anthropometric and body composition measures and
independent student's t-tests were carried out to examine sex differences.
The difference between the mean values of the body composition variables
(%BF, FFM, BFM) derived from the three different methods (Slaughter equations,
BIA and DXA) in both genders were calculated by repeated measures ANOVA.
To eliminate age factor, partial correlation was attempted to investigate
the interrelationships between the different body composition variables.
The Bland-Altman procedure was used to examine the pair-wise comparison
between %BF measured by DXA and the other methods (BIA and Slaughter equation).
Error score were computed by subtracting the estimated values (% BF derived
from Slaughter and BIA) from the criterion value DXA. One sample Student's
t test was performed to examine whether the mean error scores were significantly
different from zero. These error scores were also graphically illustrated
according to the procedures of Bland and Altman (1986) and were plotted
against the mean value of the two respective methods e.g., (DXA+BIA)/2.
The mean error scores were illustrated by a solid horizontal line. Statistical
analysis was conducted using SPSS version 10 for windows (Statistical
Package for Social Sciences). The charts were drawn using Microsoft Excel
Physical characteristics of the sample showed no significant gender difference
regarding the age (9.06 for boys and 9.36 for girls), anthropometric and
body composition measures (Table 1).
Repeated measures ANOVA of the estimated body composition variables (%BF,
FFM, BFM) derived from the three different methods (Slaughter, BIA, DXA),
showed a significant differences among the three methods for the studied
variables (p<0.001). In both genders, it is obvious that the BIA methods
significantly showed the least values for the estimated %BF and BFM, consequently
the highest values were seen in the FFM, followed by Slaughter equation,
then by DXA for both sexes (Table 2, 3).
Bland-Altman plots: The overall mean difference and 95% confidence
intervals for each body composition method were as follow. BIA was 5.82
(3.26, 8.39), 9.78 (6.88, 12.68) for boys and girls respectively and 7.66
(5.73, 9.59) for the total sample. Regarding Slaughter values, it was
4.05 (1.79, 6.30) for boys and 3.29 (2.74, 3.84) for girls while the total
sample was 3.70 (2.48, 4.91). The two methods used (Slaughter, BIA) are
significantly under estimated measured percentage body fat (%BF) where
p<0.001 (Fig. 1).
Partial correlations between BMI and body composition variables (%BF,
BFM and FFM) derived from various methods were significant in both genders
(p<0.05, p<0.001). In general, correlations between the BMI and
both estimated %BF and BFM were strong (for boys r = 0.67-0.91 and for
girls r = 0.87-0.97, p<0.001) except with those estimated by BIA for
girls, the correlation was moderate (r = 0.56, 0.62, p<0.001). The
lowest correlations (weak to moderate) were recorded between BMI and estimated
FFM (for boys r = 0.34 to 0.46 and for girls r = 0.20-0.56, p<0.001)
except for FFM-DXA, the correlation was insignificant for girls (Table 4).
Partial correlation among the estimated body fat percentage derived from
the three different methods for both genders, revealed high significant
correlations between the estimated % BF derived from DXA and
Physical characteristics of the sample
||Comparison of body composition measured by Slaughter
equations, BIA and DXA in boys (n = 37)
|Values represent mean±SD and minimum-maximum.
Significant differences among methods were proved by ANOVA test (p<0.001)
||Comparison of body composition measured by Slaughter
equations, BIA and DXA in girls (n = 32)
|Values represent mean±SD and minimum-maximum.
Significant differences among methods were proved by ANOVA test (p<0.001)
||Partial correlations, controlling for age, between the
BMI and body composition measures in both sexes
|All correlations were significant (**: p<0.001, *:
p<0.05) except with DXA-FFM in girls
Partial correlations, controlling for age, among
body composition measures in both sexes
|Upper, girls; lower, boys. *: p<0.005, **: p<0.001
Slaughter methods where r = 0.763 for boys and 0.971 for girls. As regard
the correlation between the estimated % BF derived from DXA and BIA showed
a high significant correlation (r = 0.772) for boys and low significant
correlation (r = 0.387) for girls. Also, the estimated % BF derived from
BIA and Slaughter methods showed a high significant correlation (r = 0.730)
for boys and low insignificant correlation (r = 0.284) for girls (Table 5).
Human body composition, particularly the content of fat tissue and its
distribution, has been extensively measured in healthy, diseased and obese
subjects (Nancy et al., 2000). A variety of non-invasive methods
have been applied for these studies (Bolanowski and Nilsson, 2001). Skin
fold thickness prediction equations and bioelectrical impedance (BIA)
are readily available and commonly used techniques in patient monitoring
for Body Composition Analysis (BCA) in clinical practice (Erselcan et
al., 2000). Bioelectrical impedance analysis (BIA) is a commonly used
method, based on the conduction of electrical current in the body and
the differences in the ability to conduct electricity between the fat
and water components of the body. Recently, dual-energy x-ray absorptiometry
(DXA) has been introduced for bone mass, bone mineral density and body
composition studies. Unlike other methods, DXA measures three components
of the body: bone mineral content, fat tissue mass and lean tissue mass
and additionally regional fat distribution. Despite some minor bias, DXA
is considerably less expensive and easier to administer in pediatric subjects
than other established gold standard reference methods for assessing body
composition (Pietrobelli et al., 2003). Its results have been reported
to be quite accurate and precise, in comparison with in vivo or
in vitro multiple component reference methods (Erselcan et al.,
This research provides an evidence of convergent validity for various
methods and prediction equations of body composition in young children
across the primary school age range which considered as a critical period
for the adiposity development. Furthermore, the results will provide insight
into the ability to compare results between studies using different body
composition methodologies. Overall, the %BF and BFM estimated by the Slaughter
prediction equations show high correlations among themselves and the BMI
(r = 0.88 to 0.91 for boys and r = 0.87 to 0.97 for girls) and those estimated
by DXA show similar correlations (r = 0.68 to 0.83 for boys and r = 0.88
to 0.91 for girls), while the BIA estimates of %BF and BFM show high correlations
for boys (r = 0.76 to 0.85) and moderate for girls (r = 0.56 to 0.62).
Furthermore, Partial correlation among the estimated body fat percentage
derived from the three different methods,
||Bland Altman tests depicting error scores between DXA
and the various measures and estimates of percentage of BF. The solid
line represents the mean error
revealed high significant correlations between the %BF estimated by DXA
and those estimated by Slaughter prediction equations for both genders
and by BIA for boys only (r = 0.73 to 0.97, p<0.001)). However, for
girls, the BIA estimates of %BF exhibited low correlation with DXA (r
= 0.39, p<0.05) and insignificant correlation with % BF- Slaughter
(r = 0.28). Despite the moderate to high correlations among methods, results
comparing the mean values from the different methods or Slaughter prediction
equations revealed some systematic error. All methods were found to be
significantly underestimating the values of %BF from DXA. The mean bias
ranged from 4.1 to 5.8 for boys, from 3.1 to 9.8 for girls and from 3.7
to 7.7 for the total sample.
In this study, larger discrepancies were found with the BIA procedure.
It appears that the discrepancy in the BIA measures is a function of weight
status. As indicated in Fig. 1, BIA underestimated %BF from DXA in boys
tended to be leaner or fatter and in girls tended to be leaner. At the
same time, it overestimated %BF in girls tended to be fatter. This can
also be seen in the range of values (Table 2, 3). BIA showed the lowest
minimum and maximum values for %BF in boys and the lowest minimum and
highest maximum values for %BF in girls compared with the other methods.
These results are in agreement with others that show a high bias in %BF
between BIA and DXA. For example, Treuth et al. (2001) found a
mean bias of 7.6% with limits of agreement of 7.6% in prepubertal girls.
Eisenkolbl et al. (2001), in Austria, concluded that %BF measured
by BIA compared to DXA method in 6-18 years old obese children is three
times higher with boys than with girls. The reverse was estimated in other
studies on prepubertal children in Japan and USA (Okasora et al.,
1999; Elberg et al., 2004) where they stated that change in %BF
was systematically overestimated by BIA equations. While, Sun et al.
(2005) in Canada, stated that BIA is a good alternative for estimating
%BF when subjects are within a normal body fat range. BIA tends to overestimate
%BF in lean subjects and underestimate %BF in obese subjects.
In general, Slaughter prediction equations also underestimated %BF from
DXA in both sexes of this sample. However, the overall results for anthropometric
indices of adiposity (i.e., BMI and Skin folds prediction equations) are
encouraging because the techniques are relatively simple (assuming proper
training), cost effective and widely used. Specifically, the results show
that Slaughter prediction equations can be used with reasonable accuracy
to monitor the age-related changes in body composition during the adiposity
rebound. The errors in BF estimates that were observed are not surprising
considering that the adiposity rebound may be an artifact of growth in
FFM, as indicated by the association between BMI and DXA FFM where the
lowest correlations were recorded between BMI and estimated FFM (for boys
r = 0.34 to 0.46 and for girls r = 0.20 to 0.56, p<0.001 except for
FFM-DXA, the correlation was insignificant for girls). The subjects here
represented the spectrum of ages in which the adiposity rebound occurs.
Although the adiposity rebound is represented by the change in the BMI,
the change in skin folds also indicates somewhat of a rebound (Tanner,
1962; Malina et al., 2003). Overall, the BMI, Slaughter prediction
equations and DXA show similar correlations (r = 0.83 to 0.88 for boys
and r = 0.87 to 0.91 for girls). These results suggest that the BMI and
skin folds data prediction equations may represent the adiposity rebound
Some comment should be made regarding the Slaughter prediction equations
used here. Overall, the Slaughter prediction equations show high correlations
among the other methods (r = 0.88 to 0.91 for boys and r = 0.87 to 0.97
for girls). The equation of Slaughter et al. (1988), used in this
research is suitable as it is a common equation used in the pediatric
literature and were derived from 8- to 18-year-old subjects. On other
hand, Dezenberg et al. (1999), stated in their study of body composition
prediction from anthropometry in preadolescent children that the Slaughter
prediction equations were not valid even though the correlation between
Slaughter prediction equations-BFM and DXA-BFM as 0.90. It is important
to remember that skin fold thicknesses represent subcutaneous fatness
at selected anatomical sites; therefore, one would not expect a correlation
much higher than that found here.
These results suggest that BIA has limited utility in estimating body
composition, whereas BMI and Slaughter prediction equations (derived from
skin fold thickness) seem to be more useful in estimating children body
composition. So, when DXA is not available, use of Slaughter in estimating
body composition is more informative than BIA. However, all methods significantly
underestimated body fatness as determined by DXA. These findings, along
with rather large limits of agreement derived from the Bland-Altman procedure,
suggest that the methods should not be used interchangeably. This was
supported by other studies in different countries (Gutin et al.,
1996; Ellis, 1996; Eisenmann et al., 2004; Fors et al.,
2002; Minderico et al., 2007).
American Academy of Pediatrics, 2003.
Prevention of pediatric over weight and obesity. Pediatrics, pp: 112.
Aviva, B.S., C.T. John, W. Jack, N.P. Richard, B.H. Steven and H. Mary, 2004.
Measurement of percentage of body fat in 411 children and adolescents: A comparison of dual energy X- ray absorptiometry with a four compartment model. Pediatrics, 113: 1285-1290.Direct Link |
Bland, J.M. and D.G. Altman, 1986.
Statistical methods for assessing agreement between two methods of clinical measurement. Lancet, 1: 307-310.CrossRef | PubMed | Direct Link |
Bolanowski, M. and B.E. Nilsson, 2001.
Assessment of human body composition using dual-energy x-ray absorptiometry and bioelectrical impedance analysis. Med. Sci. Monit., 7: 1029-1033.Direct Link |
Butte, N., C. Heinz, J. Hopkinson, W. Wong, R. Shypailo and K. Ellis, 1999.
Fat mass in infants and toddlers: Comparability of total body water, total body potassium, total body electrical conductivity and dual-energy X-ray absorptiometry. J. Pediatr. Gastroenterol. Nutr., 29: 184-189.Direct Link |
Cole, T.J., M.C. Bellizzi, K.M. Flegal and W.H. Dietz, 2000.
Establishing a standard definition for child overweight and obesity worldwide: International survey. Br. Med. J., 320: 1240-1243.CrossRef | PubMed | Direct Link |
Dezenberg, C.V., T.R. Nagy, B.A. Gower, R. Johnson and M.I. Goran, 1999.
Predicting body composition from anthropometry in preadolescent children. Int. J. Obes., 23: 253-259.Direct Link |
Eisenkolbl, J., M. Kartasurya and K. Widhalm, 2001.
Underestimation of percentage fat mass measured by bioelectrical impedance analysis compared to dual energy X-ray absorptiometry method in obese children. Eur. J. Clin. Nutr., 55: 423-429.Direct Link |
Eisenmann, J.C., K.A. Heelan and G.J. Welk, 2004.
Assessing body composition among 3- to 8-year-old children: Anthropometry, BIA and DXA. Obes. Res., 12: 1633-1640.CrossRef | Direct Link |
Elberg, J., J.R. McDuffie, N.G. Sebring, C. Salaita, M. Keil, D. Robotham, J.C. Reynolds and J.A. Yanovski, 2004.
Comparison of methods to assess change in children's body composition. Am. J. Clin. Nutr., 80: 64-69.Direct Link |
Ellis, K.J., 1996.
Measuring body fatness in children and young adults: Comparison of bioelectric impedance analysis, total body electrical conductivity and dual-energy X-ray absorptiometry. Int. J. Obes. Relat. Metab. Disord., 20: 866-873.PubMed | Direct Link |
Erselcan, T., F. Candan, S. Saruhan and T. Ayca, 2000.
Comparison of body composition analysis methods in clinical routine. Ann. Nutr. Metab., 44: 243-248.Direct Link |
Fors, H., L. Gelander, R. Bjarnason, K. Albertsson-Wikland and I. Bosaeus, 2002.
Body composition, as assessed by bioelectrical impedance spectroscopy and dual-energy X-ray absorptiometry, in a healthy pediatric population. Acta Paediatr., 91: 755-760.Direct Link |
Garn, S.M., W.R. Leonard and V.M. Hawthorne, 1986.
Three limitations of the body mass index. Am. J. Clin. Nutr., 44: 996-997.
Gutin, B., M. Litaker, S. Islam, T. Manos, C. Smith and F. Treiber, 1996.
Body-composition measurement in 9-11-y-old children by dual-energy X-ray absorptiometry, skinfold-thickness measurements and bioimpedance analysis. Am. J. Clin. Nutr., 63: 287-292.Direct Link |
Hiernaux, J. and J.M. Tanner, 1969.
Growth and Physical Studies. In: Human Biology: A Guide to Field Methods, Weiner, J.S. and S.A. Lourie (Eds.). International Biological Programme by Blackwell Scientific, London, UK Direct Link |
Lohman, T.G., 1989.
Assessment of body composition in children. Pediat. Exerc. Sci., 1: 19-30.Direct Link |
Malina, R.M., C. Bouchard and O. Bar-Or, 2003.
Growth, Maturation and Physical Activity. 2nd Edn., Human Kinetics Publisher, Illinois, ISBN: 0-88011-882-2, pp: 30-35
Minderico, C.S., A.M. Silva, K. Keller, T.L. Branco and S.S. Martins et al
Usefulness of different techniques for measuring body composition changes during weight loss in overweight and obese women. Br. J. Nutr., 99: 432-441.CrossRef | Direct Link |
Nancy, F.B., M.H. Judy, W.W. William, E.O. Brain Smith and J.E. Kennth, 2000.
Body composition during the first 2 years of life: An updated reference. Pediatr. Res., 47: 578-585.CrossRef | Direct Link |
Okasora, K., R. Takaya, M. Tokuda, Y. Fukunaga, T. Oguni , H. Tanaka, K. Konishi and H. Tamai, 1999.
Comparison of bioelectrical impedance analysis and dual energy X-ray absorptiometry for assessment of body composition in children. Pediatr. Int., 41: 121-125.CrossRef | Direct Link |
Pietrobelli, A., A. Andreoli, V. Cervelli, M.G. Carbonelli, D.G. Peroni and A. De Lorenzo, 2003.
Predicting fat-free mass in children using bioimpedance analysis. Acta Diabetol., 40: S212-S215.CrossRef | Direct Link |
Pietrobelli, A., M. Malavolti, N. Fuiano and M.S. Faith, 2007.
The invisible fat. Acta Pediatr. Suppl., 96: 35-38.Direct Link |
Slaughter, M.H., T.G. Lohman, R.A. Boileau, C.A. Horswill, R.J. Stillman, M.D. Van Loan and D.A. Bemben, 1988.
Skinfold equations for estimation of body fatness in children and youth. Hum. Biol., 60: 709-723.Direct Link |
Sun, G., C.R. French, G.R. Martin, B. Younghusband and R.C. Green et al
Comparison of multifrequency bioelectrical impedance analysis with dual-energy X-ray absorptiometry for assessment of percentage body fat in a large, healthy population. Am. J. Clin. Nutr., 81: 74-78.CrossRef | Direct Link |
Tanner, J.M., 1962.
Growth at Adolescence. 2nd Edn., Blackwell, Oxford, UK
Treuth, M.S., N.F. Butte, W.W. Wong and K.J. Ellis, 2001.
Body composition in prepubertal girls: Comparison of six methods. Int. J. Obes. Relat. Metab. Disord., 25: 1352-1359.Direct Link |
Wang, J., D. Gallagher, J.C. Thornton, Y.U.W.M. Horlick and F.X. Pi-Sunyer, 2006.
Validation of a 3-dimensional photonic scanner for the measurement of body volumes, dimensions and percentages body fat. Am. J. Clin. Nutr., 83: 809-816.
Wells, J.C.K. and M.S. Fewtrell, 2006.
Measuring body composition. Arch. Dis. Child, 91: 612-617.Direct Link |