Asia Pac J Clin Nutr 2013;22(4):522-529

522

Original Article

Development and validation of anthropometricprediction equations for estimation of body fat inIndonesian men
Janatin Hastuti MKes1,2, Masaharu Kagawa BSc(Hons), PhD3, Nuala M Byrne MAppSc, PhD4,
Andrew P Hills MSc, PhD, FASMF5
1

School of Exercise and Nutrition Sciences, Institute of Health and Biomedical Innovation, Queensland
University of Technology, Australia
2
Faculty of Medicine, Gadjah Mada University, Indonesia
3
Institute of Nutrition Sciences, Kagawa Nutrition University, Japan
4
School of Exercise and Nutrition Sciences, Institute of Health and Biomedical Innovation, Queensland
University of Technology, Australia
5
Mater Mothers’ Hospital, Mater Research and Griffith Health Institute, Griffith University, Australia

Body composition of 292 males aged between 18 and 65 years was measured using the deuterium oxide dilution
technique. Participants were divided into development (n=146) and cross-validation (n=146) groups. Stature,body weight, skinfold thickness at eight sites, girth at five sites, and bone breadth at four sites were measured andbody mass index (BMI), waist-to-hip ratio (WHR), and waist-to-stature ratio (WSR) calculated. Equations weredeveloped using multiple regression analyses with skinfolds, breadth and girth measures, BMI, and other indicesas independent variables and percentage body fat (%BF) determined from deuterium dilution technique as thereference. All equations were then tested in the cross-validation group. Results from the reference method werealso compared with existing prediction equations by Durnin and Womersley (1974), Davidson et al (2011), andGurrici et al (1998). The proposed prediction equations were valid in our cross-validation samples with r=0.770.86, bias 0.2-0.5%, and pure error 2.8-3.6%. The strongest was generated from skinfolds with r=0.83, SEE 3.7%,
and AIC 377.2. The Durnin and Womersley (1974) and Davidson et al (2011) equations significantly (p<0.001)
underestimated %BF by 1.0 and 6.9% respectively, whereas the Gurrici et al (1998) equation significantly(p<0.001) overestimated %BF by 3.3% in our cross-validation samples compared to the reference. Results suggest that the proposed prediction equations are useful in the estimation of %BF in Indonesian men.

Key Words: anthropometry, prediction equation, deuterium oxide dilution, body fat, Indonesia

INTRODUCTION
Body composition is an important indicator of nutritional
status. However, precise and accurate body compositionassessment techniques such as the deuterium dilution
technique (D2O) and dual-energy X-ray absorptiometry(DXA) are generally challenging and require expensive
instruments as well as specifically trained technicians.
Consequently, these techniques are only suitable for laboratory-based studies. Many studies have reported theestimation of body composition including percentage
body fat (%BF) using anthropometry (eg skinfolds, circumferences).1-6 While these studies use prediction equations, these equations are mostly developed from Caucasian populations. Since body composition prediction
equations are population-specific, it is inappropriate toapply such approaches in the estimation of body composition in different ethnic groups.7,8 For example, the formula of Durnin and Womersley9 has been shown to underestimate %BF in Indonesian populations using a threecompartment model10 and D2O technique.11,12 Davidson etal 2 modified the formula of Durnin and Womersley9 fordifferent racial groups, including Asians, however nostudies have cross-validated this equation using an Indonesian population. The equation by Gurrici et al 11 is the
only equation developed from an Indonesian population
to predict body fatness using BMI, however this equation
has not been cross-validated since its development.
The D2O dilution technique can accurately predict total
body water (TBW)13 from which fat-free mass (FFM) can
be calculated assuming a constant hydration of FFM in
the two-compartment model.13,14 D2O has been used as a
Corresponding Author: Janatin Hastuti, Faculty of Medicine,
Gadjah Mada University, Medika Street, Sleman, Yogyakarta
55281, Indonesia.
Tel: +62 274 552577; Fax: +62 274 552577
Email: janatin.hastuti@student.qut.edu.au;
janatin.hastuti@ugm.ac.id
Manuscript received 27 February 2013. Initial review completed
15 April 2013. Revision accepted 6 July 2013.
doi: 10.6133/apjcn.2013.22.4.14

Equations for %BF estimation in Indonesian males

reference method to assess body composition in manystudies, including the accurate determination of FFM
against the four-compartment (4C) model in Mexican
youth15 and %BF against the three-compartment (3C)model in Indonesian females.10
To date, no prediction equations using anthropometry
exist for Indonesian adults, except for the BMI equation
by Gurrici et al.11 In addition, some anthropometricmeasures and indices such as waist circumference (WC),waist-to-stature ratio (WSR), and waist-to-hip ratio(WHR) are also suggested as better indicators of obesity
compared with BMI.16 Hence, it is important to develop%BF prediction equations that utilize anthropometricvariables other than BMI, therefore the aim of the present
study was to develop and validate %BF prediction equations using anthropometric variables with D2O technique
as a reference method. The study also evaluated the validity of a number of existing prediction equations.

523

make a 10% deuterium solution. Participants were given
the 10% deuterium solution based on their body weightafter collecting a 10 mL pre-dose urine sample. A second
10 mL urine sample was collected 6 hours later. Urine
samples were subsequently analysed using an isotope
ratio mass spectrometry (IRMS-Hydra 20-20 SerCon
Mass Spectrometry) at the Institute of Health and Biomedical Innovation of Queensland University of Technology, Australia. TBW, FFM, and fat mass (FM) were
determined using the equation proposed by the IAEA.14
The hydration coefficient for adults (0.732), based on theclassic work of Pace and Rathburn14,20 was applied.

Anthropometry
Stature was taken using a microtoise (Johnson and Johnson
Co Ltd) to the nearest 0.1 cm. Body weight was measured
with the participant wearing light clothing using a Seca
weight scale (Seca 803, Seca Deutschland) to the nearest 0.1
kg. Skinfold thickness was measured at eight sites (triceps,
subscapular, biceps, iliac crest, supraspinale, abdominal,
front thigh, and medial calf) using a Harpenden calliper.
Breadth measurements at biacromial, biiliocristal, humerus,
and femur were taken using sliding callipers (GPM Swiss).
Five girth measures (arm relaxed, arm flexed and tensed,
minimum waist, gluteal, and maximum calf) were takenusing an anthropometric tape (Holtain Rinehart Co. Ltd.).
All measurements were conducted using the standard protocol of the International Society for the Advancement ofKinanthropometry (ISAK)18 by an accredited ISAK level 1
anthropometrist. Technical error of measurement (TEM)
was calculated for the first 20 participants and indicated that
all measures were within target intra-tester TEM values (below 7.5% for skinfolds and below 1.5% for othermeasures).19

Statistical analysis
Participants were divided randomly into development andcross-validation groups of the same size (n=146). Outliers
from anthropometric measurements and %BF ≥3.3 SD were
removed from each group. Independent t-test was used todetect possible differences in age and physical characteristics
between the development and validation groups. Stepwise
multiple regression analyses were performed to predict %BF
via various models using %BF as a dependent variable andanthropometric variables as independent variables. Different
sets of anthropometric variables (individual skinfold sites,
sum of four skinfolds [biceps, triceps, subscapular, iliac
crest], sum of eight skinfolds, girth and breadth measures[girths at arm relaxed, arm flexed and tensed, minimum
waist, gluteal, and maximum calf; breadths at biacromial,biiliocristal, humerus, and femur] and anthropometric index[BMI, WHR, WSR, and acromio-iliac index]) were enteredas independent variables along with age, body weight, andstature. The coefficient of correlation (r), coefficient of determination (r2), standard error of the estimate (SEE), and the
Akaike Information Criterion (AIC) were presented to evaluate the precision of the equations. Equations that have ahigh r2, a small SEE, and the smallest AIC value was chosen
for the best “fit” models.
The estimated %BF values using the proposed equations were compared with the values obtained from thereference method using a paired sample t-test in the crossvalidation groups. The pure error (PE) was calculated as
the square root of the mean of squares of differences between measured and predicted body composition. A
smaller pure error value indicated greater accuracy of theequation. The proposed equations were also evaluatedusing scatter plots and Bland and Altman plots.21 In addition, %BF estimated from existing equations of Durnin
and Womersley,9 Davidson et al,2 and Gurrici et al11 werecompared with %BFD2O using paired-sample t-test and theBland and Altman plots.21 All statistical analyses wereconducted using the SPSS program (version 19, SPSS Inc,
2010, Chicago, IL) and significance was determined at
p<0.05.

Deuterium oxide (D2O) dilution
The D2O dilution technique was used to assess TBW following the protocols of Heyward and Wagner14 and theInternational Atomic Energy Agency (IAEA).20 Thismethod measures the degree of dilution of a known doseof deuterium after it has equilibrated in body fluid. A 100
mL D2O (99.9 atom % D; Aldrich Chemistry, SigmaAldrich Pty Ltd) was added into 900 mL tap water to

RESULTS
Physical characteristics of the groups are presented inTable 1. There were no significant between-group differences in any physical characteristics. Table 2 shows the
proposed prediction equations. The best fit prediction
equation was obtained from skinfold variables with
r=0.83. Age made a significant contribution to the prediction equation using skinfolds. The proposed equations

MATERIALS AND METHODS
ParticipantsParticipants were recruited from Javanese populations living
in Yogyakarta Special District, Indonesia. Two hundred andninety-two healthy males aged 18-65 years participated in
the study. Sample size was determined using the formula ofWhitney and Ball.17 Ethical approval was obtained from the
Human Research Ethics Committee of Queensland University of Technology, Australia and Gadjah Mada University,
Indonesia. All participants provided written informed consent. Participants were instructed to fast overnight and avoid
physical exercise and excessive sweating prior to the day ofthe measurement.

524

J Hastuti, M Kagawa, NM Byrne and AP Hills

Table 1. Characteristics of the study groups
Development Group(n=146)39.0 ± 11.6
58.2 ± 10.0
165.0 ± 7.0
21.3 ± 3.1
21.1 ± 6.6
9.7 ± 4.6
15.1 ± 8.5
45.0 ± 22.1
75.0 ± 7.8
89.7 ± 6.5
6.9 ± 0.4
45.5 ± 4.9

Age (years)
Body weight (kg)
Stature (cm)BMI (kg/m2)%BFD2O
Triceps skinfold (mm)Abdominal skinfold (mm)Sum of 4 skinfolds† (mm)Waist girth (cm)Gluteal girth (cm)
Humerus girth (cm)WSR

Validation Group(n=146)38.7 ± 12.0
59.5 ± 10.4
165.3 ± 5.9
21.6 ± 3.3
21.7 ± 7.5
10.2 ± 5.1
15.6 ± 8.6
48.2 ± 24.5
76.0 ± 8.8
90.3 ± 6.9
6.9 ± 0.4
45.9 ± 5.3

p
0.824
0.309
0.735
0.449
0.466
0.385
0.616
0.242
0.291
0.414
0.317
0.480

Note: † sum of skinfold thicknesses at triceps, biceps, subscapular, and iliac crest; p: p values

Table 2. Percentage body fat prediction equations developed using anthropometric variables
Dependent variables
Skinfold sites

Regression equation%BF = 8.000 + 0.402 (abdominal) + 0.486 (triceps) + 0.059 (age)

r
0.831

r2
0.691

SEE
3.680

AIC
377.2

Sum 4 skinfolds†%BF = 7.579 + 0.237 (sum of 4 skinfolds) + 0.073 (age)

0.804

0.647

3.919

399.8

BMI

%BF = -6.971 + 1.318 (BMI)

0.631

0.398

5.100

472.5

Girth and breadth
measures%BF = -14.533 + 0.363 (waist girth) + 0.474 (gluteal girth) 4.955 (humerus breadth)

0.740

0.547

4.455

432.2

Anthropometric index%BF = -16.849 + 0.553 (WSR) + 0.219 (body weight)

0.680

0.462

4.837

461.3

2

Note: † sum of skinfold thicknesses at triceps, biceps, subscapular, and iliac crest; r: coefficient of correlation; r : coefficient of determination; SEE: standard error of the estimate; AIC: the Akaike Information Criterion

showed the SEE ranged from 3.7 to 5.1%. The BMI equation showed the poorest performance with r, r2, and SEE
values of 0.63, 0.40, and 5.1%, respectively.
The predicted %BF values from the proposed equations
were compared with %BFD2O using the cross-validationgroup (Table 3). The %BF values of the proposed equations were comparable to the %BFD2O with mean difference 0.2-0.5%, PE of 3.7-5.1%, and r between 0.63 and0.83. The variables included in the prediction equations
were responsible for about 40-69% of the variance in themodels. The accuracy of each prediction equation was
further assessed in the Bland and Altman plots presented
in Figure 1 with the widest limits of agreement between 9.2 (lower limit) and 8.7 (upper limit) from the BMI prediction equation. A slight tendency to underestimate %BF
was observed in all prediction equations as %BF increased.
Table 4 presents the differences between %BFD2O andselected existing prediction equations. The equation by
Durnin and Womersley9 showed the closest value to the%BFD2O with a slight but significant (p<0.05) underestimation (0.8%), while equation proposed by Davidson etal 2 underestimated %BF at about 6.7% in our crossvalidation samples (Table 4). Application of the BMI
equation by Gurrici et al 11 resulted in overestimation by
3.4%. Agreement between the predicted %BF and thereference method as evaluated with the Bland and Altman
plots showed limits of agreement (mean difference ± 1.96
SD) between 7.0 and 9.0%.

DISCUSSION
The present study developed and validated %BF prediction equations for Indonesian males. The proposed prediction equations generated from both individual and sumof four skinfolds displayed good precision according to
Heyward and Wagner’s14 recommendation (r>0.80 and
SEE<4.0%). In addition, cross-validation analysis forthese equations indicated very good precision with PE
less than 3.0%.
The proposed equations showed comparable accuracy
and precision to the equation developed by Gurrici et al 11
in 110 Indonesian adults living in Sumatra and was consistent with findings in a previous study by Kagawa andcolleagues 4 in 45 Japanese adult males living in Perth. It
is possible that the reference technique used to measure%BF would be more precise in estimating %BF than the
2C model the present study and others used.15,22
Equations which used an individual skinfold site andthe sum of four skinfolds showed the strongest correlation
with both equations having r=0.87; r2=0.76; and SEE=
3.7%. Subscapular, triceps, and abdominal skinfold sites
made significant contributions to the model, indicatingthat fat deposition in participants was predominantly inthe upper trunk region. Our findings are similar withthose from previous studies that %BF from skinfolds
gave the best performance in terms of correlation, bias,
and error.22-24 The inclusion of age as a predictor further
improved the skinfold equations as found in previous
studies by van der Ploeg et al 22 and Kagawa et al.4
Prediction equations generated from girth and breadthEquations for %BF estimation in Indonesian males

525

Table 3. Comparison of %BF from the reference method and anthropometric prediction equations in the crossvalidation samples
Measures
Skinfold sites
Sum of 4 skf†
BMI
Girth & breadth measures
Anthropometric index

Reference††
Mean ± SD
21.3 ± 7.1
21.4 ± 7.2
21.3 ± 6.9
21.6 ± 7.3
21.4 ± 7.2

Prediction equation
Mean ± SD
Bias ± SD
PE ± SD
21.5 ± 5.5-0.2 ± 3.8
2.8 ± 2.6
21.9 ± 5.9-0.5 ± 3.7
2.8 ± 2.4
21.5 ± 4.3-0.3 ± 4.6
3.6 ± 2.8
21.7 ± 5.6-0.2 ± 4.2
3.2 ± 2.8
21.6 ± 4.9-0.2 ± 4.3
3.3 ± 2.8

Paired testrt
0.841**-0.4780.858**-1.6580.768**-0.6830.818**-0.4480.805**-0.575

Limits ofagreement-7.7 – 7.4-7.7 – 6.7-9.2 – 8.7-8.5 – 8.2-8.7 – 8.3

Note:* p<0.05; ** p<0.01; †: sum of skinfold thicknesses at triceps, biceps, subscapular, and iliac crest; ††: the reference values are different from each variable due to the different dropped outliers; r: coefficient of correlation; t: t values

Figure 1. Agreement between measured %BF and %BF estimated using prediction equations based the analysis of Bland and Altman
technique. The difference between %BF obtained from D2O and %BF predicted from skinfold site (a), sum of four skinfold (b), BMI (c),girth and breadth measures (d), and anthropometric index (e) prediction equations is plotted against mean %BF. Note: equation 1: %BF =8.000 + 0.402 (abdominal) + 0.486 (triceps) + 0.059 (age); 2: %BF = 7.579 + 0.237 (sum of 4 skinfolds) + 0.073 (age); 3: %BF = -6.971+ 1.318 (BMI); 4: %BF = -14.533 + 0.363 (waist girth) + 0.474 (gluteal girth) - 4.955 (humerus breadth); 5: %BF = -16.849 + 0.553(WSR) + 0.219 (body weight); R: the coefficient of correlation of the trend line; ** p<0.01

526

J Hastuti, M Kagawa, NM Byrne and AP Hills

Table 4. Differences between %BF obtained from D2O and various prediction equations
Mean ± SD%BF from D2O%BF from DW%BF from D%BF from Gurrici

21.7 ± 7.5
20.9 ± 7.1
15.0 ± 6.3
25.1 ± 4.9

Paired correlationr

Paired difference
Mean diff ± SD

Limits of agreement

0.86**
0.88**
0.81**

0.8 ± 3.9*
6.7 ± 3.6**-3.4 ± 4.6**

7.7 (-6.9 – 8.5)
7.0 (-0.3 – 13.7)
9.0 (-12.3 – 5.6)

Note: * p<0.05; ** p<0.01; r: coefficient of correlation; DW: %BF predicted using BD formula of Durnin and Womersley (1974) and %BF
formula of Siri (1962); D: %BF predicted using formula of Davidson et al. (2011); Gurrici: %BF predicted using formula of Gurrici et al.
(1998)

measures and anthropometric indices indicated fairly
good in the development group, however evaluation in
the cross-validation group showed good precision withPE less than 3.5%. Whereas, the prediction equation using BMI was poor in the development group but fairly
good in the cross-validation analysis. Even though waist
girth, gluteal girth, and WHR may be useful to evaluate
fat distribution,25 race and gender gave significant differences in the distribution of visceral adipose tissue in relation to those measures.26 Moreover, Lei et al 27 consideredthat those measures are highly related and therefore it ishard to be modelled in multiple regression analysis. BMI
is the most commonly used tool for evaluating excessive
body weight and the use of BMI to determine %BF is
criticized because of the quadratic relation and the variability in the association between BMI and %BF resulted
in a significant error in the prediction of %BF using BMI
regression equation.28
A cross-validation study of the new prediction equations indicated high correlation with the measured %BF,
however, a slight tendency to overestimate %BF at a lower %BF and a tendency to underestimate %BF at a higher%BF. The most prominent deviation was observed in the
BMI equation as reported in other studies.10,11,23,29
Deurenberg et al 23 suggested that the bias of prediction
equations was related to level of body fatness with underestimation at higher levels of body fatness. Incorrect assumptions can partly explain this phenomenon, for example, the relative contribution of the increased fat mass tobody weight becomes greater with increased BMI, whilst
the prediction of %BF from BMI assumes that it is constant. Similarly, the skinfold equation assumes that the
subcutaneous fat is representative of total body fat,whereas, with the increase in total body fat, the relative
amount of internal fat increases, leading to an underestimation of %BF at higher levels of fatness.23
Application of prediction equations developed from
Caucasians were inappropriate in our samples, particularly the equation by Davidson et al.2 The Durnin andWomersley 9 equation showed a significant underestimation of %BF, but was closer to the measured %BF with a
0.8% bias. This finding confirms the results of previous
studies by Küpper et al 10 and Gurrici et al 11 in Indonesian adults with differences potentially due to race or ethnicity.12,30,31 A meta-analysis of %BF associated withBMI in Asian populations indicated a different relationship among Asians living in different countries.32 Evenamong Indonesian populations, different relationships
have been reported between those of Malay and Chinese
ancestry.12 In addition, Durnin and Womersley’s predic-

tion equations were developed for the estimation of bodydensity. The conversion from body density to %BF may
have resulted in an error in the prediction of %BF. Variability in activity levels,33 body build,34,35 muscularity,32,33
and frame size may also contribute to these differences.36
The BMI equation by Gurrici et al 11 overestimated %BF
by 3.4% in the current investigation. This result may partly due to differences in methodology, including sample
characteristics.
The present study has several limitations. Participantsin the current study were not completely representative ofthe total Indonesian population due to the heterogeneityof the population. However, participants were chosenfrom a major ethnic group (ie Javanese ethnicity) and thesemi-stratified sampling used enriched the variability ofthe sample. The use of a two-compartment model for theestimation of FFM also limits the accuracy of the FFM
estimation and the use of a multi-compartment approach
would have enabled an independent assessment of thevarious components of FFM.15,29,37 However, the deuterium dilution technique was the most suitable approach foruse in a large free-living sample. In addition, other studies
have indicated that the validity of the deuterium dilution
technique in body fat estimation is comparable with multi-compartment models.15 Future studies are recommended to include multiple ethnic groups to develop generalprediction equations which are representative of the entire
Indonesian population and also use a multi-compartment
model as the reference for body composition.
In conclusion, our findings highlighted that earlier anthropometric prediction equations (ie prediction equationsof Durnin and Womersley,9 Davidson et al,2 and Gurrici
et al 11) are inappropriate when applied to participants inthe current study. The prediction equations developed in
the study can accurately predict %BF. The significance of
this finding is equations were developed using a criterion
method of body composition assessment and crossvalidated with large samples across a wide age range (1865 years).
ACKNOWLEDGEMENTS
The current study was supported by the Directorate General ofHigher Education Republic of Indonesia, Gadjah Mada University, Indonesia, and Queensland University of Technology, Australia. We are grateful to the participants in the current study andthe data collection staff. For the laboratory assessment wewould like to thank Ms Connie Wishart.
AUTHOR DISCLOSURESThe authors report no conflict of interests.

Equations for %BF estimation in Indonesian males

REFERENCES
1. Bellisari A, Roche AF. Anthropometry and ultrasound. In:
Heymsfield SB, Lohman TG, Wang Z, Going SB, editors.
Human Body Composition. 2nd ed. Champaign, IL: Human
Kinetics; 2005. pp. 109-28.
2. Davidson LE, Wang J, Thornton JC, Kaleem Z, Silva-Palacios
F, Pierson RN et al. Predicting fat percent by skinfolds in racial
groups: Durnin and Womersley revisited. Med Sci Sport Exerc.
2011;43:542-549. doi: 10.1249/MSS.0b013e3181ef3f07
3. Deurenberg P, Weststrate JA, Seidell JC. Body mass index as ameasure of body fatness: Age- and sex-specific prediction
formulas. Br J Nutr. 1991;65:105-14. doi: 10.1079/BJN199100
73
4. Kagawa M, Kerr D, Binns CW. New percentage body fat
prediction equations for Japanese males. J Physiol Anthropol.
2006;25:275-9. doi: 10.2114/jpa2.25.275
5. Ramírez E, Valencia ME, Bourges H, Espinosa T, Moyacamarena SY, Salazar G et al. Body composition prediction
equations based on deuterium oxide dilution method in Mexican
children: a national study. Eur J Clin Nutr. 2012;66: 1099-103.
doi: 10.1038/ejcn.2012.89
6. Norton K. Anthropometric estimation of body fat. In: Norton K,
Olds T, editors. Anthropometrica. New Delhi: CBS Publishers;
2009. pp. 171-98.
7. Deurenberg P, Deurenberg-Yap M. Validity of bodycomposition methods across ethnic population groups. Acta
Diabetol. 2003;40:S246-9. doi: 10.1007/s00592-003-0077-z
8. Malina RM. Variation in body composition associated with sex
and ethnicity. In: Heymsfield SB, Lohman TG, Wang Z, GoingSB, editors. Human Body Composition. 2nd ed. Champaign, IL:
Human Kinetics; 2005. pp. 271-98.
9. Durnin JVGA, Womersley J. Body fat assessed from total bodydensity and its estimation from skinfold thickness:
Measurements on 481 men and women aged from 16 to 72
years. Br J Nutr. 1974;32:77-97. doi: 10.1079/BJN19740060
10. Küpper J, Bartz M, Schultink JW, Lukito W, Deurenberg P.
Measurements of body fat in Indonesian adults: Comparison
between a three-compartment model and widely used methods.
Asia Pac J Clin Nutr. 1998;7:49-54.
11. Gurrici S, Hartriyanti Y, Hautvast JGAJ, Deurenberg P.
Relationship between body fat and body mass index:
Differences between Indonesians and Dutch Caucasians. Eur J
Clin Nutr. 1998;52:779-83. doi: 10.1038/sj.ejcn.1600637
12. Gurrici S, Hartriyanti Y, Hautvast JGAJ, Deurenberg P.
Differences in the relationship between body fat and body massindex between two different Indonesian ethnic groups: The
effect of body build. Eur J Clin Nutr. 1999;53:468-72. doi: 10.1
038/sj.ejcn.1600778
13. Schoeller D. Hydrometry. In: Heymsfield S, Lohman T, Wang
Z, Going S, editors. Human Body Composition. 2nd ed.
Champaign, IL: Human Kinetics; 2005. pp. 35-50.
14. Heyward V, Wagner D. Applied Body Composition
Assessment. 2nd ed. Champaign, IL: Human Kinetics; 2004.
15. Ramírez E, Valencia M, Moya-Camarena S, Alemán-Mateo H,
Méndez R. Four-compartment model and validation of
deuterium dilution technique to estimate fat-free mass in
Mexican youth. Nutr. 2009;25:194-9. doi: 10.1016/j.nut.2008.0
8.007
16. Lee CMY, Huxley RR, Wildman RP, Woodward M. Indices ofabdominal obesity are better discriminators of cardiovascularrisk factors than BMI: A meta-analysis. J Clin Epidemiol. 2008;
61: 646-53. doi: 10.1016/j.jclinepi.2007.08.012
17. Whitley E, Ball J. Statistics review 4: Sample size calculations.
Critical Care. 2002;6:335-41.
18. International Society for the Advancement of Kinanthropometry.
International Standards for Anthropometric Assessment.
Canbera: ISAK; 2006.

527

19. Gore C, Norton K, Olds T, Whittingham N, Birchall K, CloughM, et al. Accreditation in anthropometry: An Australian model.
In: Norton K, Olds T, editors. Anthropometrica. New Delhi:
CBS Publishers 2009. pp. 395-421.
20. International Atomic Energy Agency Human Health Series No
3. Assessment of Body Composition and Total Energy
Expenditure in Humans Using Stable Isotope Techniques.
Vienna: International Atomic Energy Agency; 2009.
21. Bland JM, Altman DG. Measuring agreement in method
comparison studies. Stat Method Med Res. 1999;8:135-60. doi:
10.1191/096228099673819272
22. van der Ploeg GE, Gunn SM, Withers RT, Modra AC. Use ofanthropometric variables to predict relative body fat determinedby a four-compartment body composition model. Eur J Clin
Nutr. 2003;57:1009-16. doi: 10.1038/sj.ejcn.1601636
23. Deurenberg P, Deurenberg-Yap M, Wang J, Lin FP, Schmidt G.
Prediction of percentage body fat from anthropometry andbioelectrical impedance in Singaporean and Beijing Chinese.
Asia Pac J Clin Nutr. 2000;9:93-8. doi: 10.1046/j.1440-6047.
2000.00149.x
24. Leahy S, O'Neill, Sohun R, Toomey C, Jakeman P. Generalised
equations for the prediction of percentage body fat byanthropometry in adult men and women aged 18-81 years. Br J
Nutr. 2013:109:678-85. doi:10.1017/S0007114512001870.
25. Taylor RW, Brooking L, Williams SM, Manning PJ, Sutherland
WH, Coppell KJ et al. Body mass index and waist
circumference cutoffs to define obesity in indigenous New
Zealanders. Am J Clin Nutr. 2010;92:390-7. doi: 10.3945/ajcn.
2010.29317
26. Lee SY, Kuk JL, Hannon TS, Arslanian SA. Race and genderdifferences in the relationships between anthropometrics andabdominal fat in youth. Obes. 2008;16:1066-71. doi: 10.1038/
oby.2008.13
27. Lei S-F, Liu M-Y, Chen X-D, Deng F-Y, Lv J-H, Jian W-X etal. Relationship of total body fatness and five anthropometricindices in Chinese aged 20–40 years: Different effects of age
and gender. Eur J Clin Nutr. 2006;60:511-8. doi: 10.1038/sj.
ejcn.1602345
28. Frankenfield DC, Rowe WA, Cooney RN, Smith JS, Becker D.
Limits of body mass index to detect obesity and predict bodycomposition. Nutr. 2001;17:26-30. doi: 10.1016/S0899-9007(00)00471-8
29. Peterson MJ, Czerwinski SA, Siervogel RM. Development andvalidation of skinfold-thickness prediction equations with a 4compartment model. Am J Clin Nutr. 2003;77:1186-91.
30. Deurenberg P, Deurenberg-Yap M. Differences in bodycomposition assumptions across ethnic groups: Practicalconsequences. Curr Opinion Clin Nutr Metab Care. 2001;4:
377-83. doi: 10.1097/00075197-200109000-00007
31. Kagawa M, Binns CW, Hills AP. Body composition andanthropometry in Japanese and Australian Caucasian males andJapanese females. Asia Pac J Clin Nutr. 2007;16:31-6.
32. Deurenberg P, Deurenberg-Yap M, Guricci S. Asians aredifferent from Caucasians and from each other in their bodymass index/body fat per cent relationship. Obes Rev. 2002;3:
141-6. doi: 10.1046/j.1467-789X.2002.00065.x
33. Luke A, Durazo-Arvizzu R, Rotimi C, Prewitt E, Forrester T,
Wilks R, et al. Relation between BMI and body fat in black
population samples from Nigeria, Jamaica, and United States.
Am J Epidemiol. 1997;145:620-8. doi: 10.1093/oxfordjour
nals.aje.a009159
34. Deurenberg P, Deurenberg-Yap M, Wang J, Lin F, Schmidt G.
The impact of body build on the relationship between bodymass index and percent body fat. Int J Obes. 1999;23:537-42.
doi: 10.1038/sj.ijo.0800868
35. Norgan NG. Population differences in body composition in
relation to BMI. Eur J Clin Nutr. 1994;48:S10-S27.

528

J Hastuti, M Kagawa, NM Byrne and AP Hills

36. Deurenberg P, Yap M, van Staveren W. Body mass index andpercent body fat: A meta analysis among different ethnic groups.
Int J Obes. 1998;22:1164-71. doi: 10.1038/sj.ijo.0800 741
37. Sun SS, Chumlea WC, Heymsfield SB, Lukaski HC, Schoeller

D, Friedl K et al. Development of bioelectrical impedance
analysis prediction equations for body composition with the useof a multicomponent model for use in epidemiologic surveys.
Am J Clin Nutr. 2003;77:331-40.

Equations for %BF estimation in Indonesian males

529

Original Article

Development and validation of anthropometricprediction equations for estimation of body fat inIndonesian men
Janatin Hastuti MKes1,2, Masaharu Kagawa BSc(Hons), PhD3, Nuala M Byrne MAppSc, PhD4,
Andrew P Hills MSc, PhD, FASMF5
1

School of Exercise and Nutrition Sciences, Institute of Health and Biomedical Innovation, Queensland
University of Technology, Australia
2
Faculty of Medicine, Gadjah Mada University, Indonesia
3
Institute of Nutrition Sciences, Kagawa Nutrition University, Japan
4
School of Exercise and Nutrition Sciences, Institute of Health and Biomedical Innovation, Queensland
University of Technology, Australia
5
Mater Mothers’ Hospital, Mater Research and Griffith Health Institute, Griffith University, Australia

對印尼男性體脂肪估計之體位預測方程式的發展和驗證
利用氧化氘稀釋法測量 292 位，年齡 18-65 歲男性之體組成。將所有參與者分成
發展組(146 位)和交叉驗證組(146 位)，測量其身高、體重、8 處皮脂厚度、5 處圍
長以及 4 處骨寬，並計算身體質量指數、腰臀比和腰圍身高比。利用多元迴歸分析，將皮脂厚度、骨寬、圍長、身體質量指數及其他指標當做自變項，由氧化氘
稀釋法測得的體脂肪百分比(%BF)當參考值，來發展方程式。所有方程式皆用交叉驗證組的指標數值進行驗證。由參考方法得到的結果亦和 3 個現有的預測方程式-Durnin 和 Womersley (1974)、Davidson 等(2011)和 Gurrici 等(1998)進行比較。
本研究所提出的預測方程式經交叉驗證樣本證明為有效，相關系數 0.77- 0.86、偏差 0.2-0.5%、純誤差 2.8-3.6%。由皮脂厚度產生之方程式預測能力最佳，相關系數 0.83、估計標準誤 3.7%、赤池信息量準則 377.2。Durnin 和 Womersley(1974)、Davidson 等(2011) 2 個方程式皆顯著低估(p<0.001)體脂肪百分比分別達
1.0 和 6.9%；而 Gurrici 等(1998)的方程式則顯著高估(p<0.001)體脂肪百分比達
3.3%。以上結果顯示，本研究提出的預測方程式對於印尼男性體脂肪百分比的預
測是可行的。
關鍵字：體位測量、預測方程式、氧化氘稀釋、體脂肪、印尼