Asia Pac J Clin Nutr 2017;26(4):650-656

650

Original Article

Determination of new anthropometric cut-off values forobesity screening in Indonesian adults
Janatin Hastuti SSi, MKes, PhD1, Masaharu Kagawa BSc(Hons), PhD2, Nuala M Byrne
3
3
MAppSc, PhD , Andrew P Hills MSc, PhD, FASMF
1Lab. of Bioanthropology and Paleoanthropology, Faculty of Medicine, Universitas Gadjah Mada,
Yogyakarta, Indonesia
2
Institute of Nutrition Sciences, Kagawa Nutrition University, Japan3
School of Health Sciences, University of Tasmania, Australia
Background and Objectives: Body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR),
and waist-to-stature ratio (WSR) have increasingly been used as screening tools for obesity. However, optimal
cut-off values may be different between populations. The current study determined the optimum cut-off values
for BMI, WC, WHR, and WSR for obesity screening in Indonesian adults using receiver-operating characteristic(ROC) analysis. Methods and Study Design: Stature, body weight, waist and hip circumferences were measured
on 600 Indonesians aged 18-65 years (males, n=292; females, n=308) and BMI, WHR, and WSR calculated. Percentage of body fat (%BF) was determined using the deuterium isotope (D2O) dilution technique. Some existing
cut-off points for obesity determination were evaluated for sensitivity and specificity. Results: The existing cutoff values showed low sensitivity in our sample (between 18.4 and 71.1%) and new proposed cut-offs increased
the sensitivity to reach 66.7 to 88.5%. The new cut-offs for BMI, WC, WHR, and WSR for determination of obesity were 21.9 (kg/m2), 76.8 (cm), 0.86, and 0.48, respectively, for males and 23.6 (kg/m2), 71.7 (cm), 0.77, and0.47, respectively, for females. Conclusions: WC and WSR are the most predictive both for males and females,
and therefore are considered as better screening tools for obesity in this population.

Key Words: anthropometric measures, cut-off values, obesity screening, percentage of body fat, Indonesian adults

INTRODUCTION
Body mass index (BMI) has been extensively used for thedetermination of obesity,1 however, body fatness (regional and total), has been regarded as a better indicator ofincreased risk for obesity.2 As the accurate measurementof body fat is challenging, anthropometry has commonly
been used to determine both regional and total body fat.
Recent studies have indicated that central obesity presents a greater health risk than general obesity assessedwith BMI. 3 Widely recognized as markers for central
obesity, measurements of waist circumference (WC),3,4
hip circumference (HC), waist-to-hip ratio (WHR),5-8 andwaist-to-stature ratio (WSR) or waist-to-height ratio(WHtR)9 have increasingly been used to assess healthrisks associated with obesity. However, in some quarters
there is controversy regarding the superiority of someobesity indicators in the discrimination of health risks
associated with obesity.10 A meta-analysis10 involving 17
prospective and 35 cross-sectional studies showed that
BMI, WC, WSR, and WHR were equally able to discriminate type 2 diabetes in prospective studies. In contrast,
WC or WHR could discriminate type 2 diabetes risk inindividuals in cross-sectional studies. A systematic review and meta-analysis which included more than
300,000 people from multi-ethnic backgrounds supported
previous studies that measures of abdominal obesity using
WHtR or WSR could better discriminate obesity-related

cardiometabolic risk than BMI and WC.9
However, associations differ across age, gender andethnicity which suggests the importance of age-, genderand ethnic-specific cut-off points.6,11-13 South Asians havebeen reported to be at higher risk of developing cardiometabolic conditions compared with Caucasians of asimilar size and shape14-16 suggesting the need to lower
the optimal cut-off values for these populations. Determination of cut-off points for WC, WHR, and WSR for obesity indicators is critically important for research and clinical practice. To date, an increasing number of investigations have proposed optimal cut-off values in different
populations, including Asians.4,5,9,17 However, no studies
to date have reported specific cut-off values for obesity
determination in Indonesian populations, except by
Guricci et al18 using BMI only. This study aimed to examine the application of BMI, WC, WHR, and WSR for
Corresponding Author: Dr Janatin Hastuti, Lab. of Bioanthropology and Paleoanthropology, Faculty of Medicine, Universitas Gadjah Mada, Jalan Medika, Sekip, Yogyakarta, 55281,
Indonesia.
Tel: +62 274 552577; Fax: +62 274 552577
Email: janatin.hastuti@ugm.ac.id
Manuscript received 03 February 2016. Initial review completed
10 March 2016. Revision accepted 18 March 2016.
doi: 10.6133/apjcn.072016.09

Anthropometric cut-offs for obesity screening

obesity determination in Indonesian adults and to evaluate
the optimum cut-off values of indices as screening tools
for obesity.
METHODS
ParticipantsParticipants were 600 Javanese (292 men and 308 women)
adults aged 18-65 years living in Yogyakarta Special District, Indonesia. The study was approved by the Human
Research Ethics Committee of Queensland University ofTechnology, Australia and Universitas Gadjah Mada,
Indonesia. Signed informed consents were obtained fromthe participants.
Anthropometric measures
Stature, body weight, minimum WC, and HC were measured. Stature was measured 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 (Seca803, Seca Deutschland) tothe nearest 0.1 kg. Circumferences were measured using
an anthropometric tape (Holtain Rinehart Co. Ltd.) at thelevel of the minimum waist circumference or at the midpoint between the lower costal (10th rib) border and theiliac crest if there was no obvious narrowing.19 All measurements followed the standard protocol of the International Society for the Advancement of Kinanthropometry(ISAK)19 and BMI, WHR, and WSR were calculated
from those measures.
Percentage of body fat
Participants were instructed to fast overnight and avoid
physical exercise and excessive sweating prior to the dayof the measurement. Percentage of body fat (%BF) was

651

obtained from the deuterium oxide (D2O) dilution technique using an isotope ratio mass spectrometer (IRMSHydra 20-20 SerCon Mass Spectrometer) following theguidelines of Heyward and Wagner20 and the International Atomic Energy Agency.21 Details of the procedures
have been described previously.22 (Table 1)
Statistical methods
Obesity prevalence was defined from the overweight andobese categories on BMI, WC, WHR, WSR and %BF.
Details of the classifications are presented in Table 1. The
BMI cut-off points used were those recommended by the
Ministry of Health, Republic of Indonesia, with a similar
cut-off for determination of overweight to the WHO International BMI cut-off (BMI ≥25 kg/m2).23 The difference between these two categories is in the cut-off points
for obese - the Indonesian Ministry of Health specifies a
BMI ≥27.0 kg/m2 and WHO specifies a BMI ≥30.0
kg/m.24 Chi-square testing was performed to find differences in category distribution of the obesity classifications between males and females. Sensitivity and specificity were calculated to evaluate the accuracy of the BMI,
WC, WHR, and WSR classifications in the determinationof obesity based on %BF as the reference.
The cut-off points of the BMI, WC, WHR, and WSR
were determined using receiver-operating characteristic(ROC) curve analysis as a screening tool for obesity defined by %BF. The ROC curve is a plot of the sensitivity(true positive rate) against 1-specificity (false positiverate) for each anthropometric index. The area under the
curve (AUC) is an indicator of how precise an anthropometric index can distinguish a positive outcome. The
AUC value can be between 0 and 1, with 0.5 (diagonalline) demonstrating that the anthropometric index has no

Table 1. Obesity classification

BMI†

%BF‡
Men
Women
WC§
Men
Women
WHR‡
Men
Women
WSR¶
Men
Women

Category
Severe underweight
Mild underweight
NormalMild overweight
Severe overweight

CompleteLimit<1717.0–18.4
18.5–24.9
25.0–27.0>27NormalHigh
Very highNormalHigh
Very high<25.0
25.0–30.0
>30.0
>35.0
35.0–45.0
>45.0

Obese
Obese

≥90.0
≥80.0

Obese
Obese

>0.89
>0.81

Obese
Obese

≥0.51
≥0.53

Category
NormalObese

Dichotomous
Limit
<25
≥25

Normal
Obese

<25.0
>25.0

Normal
Obese

<35.0
>35.0

%BF: percentage of body fat; WC: waist circumference; WHR: waist-to-hip ratio; WSR: waist-to-stature ratio.
†
Cut-off points for Indonesians (Ministry of Health Republic of Indonesia, 1994).
‡
Category defined by WHO.
§
Category by International Diabetes Federation (IDF).
¶
Category recommended for Asians (Liu et al., 2011).

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J Hastuti, M Kagawa, NM Byrne and AP Hills

predictive performance and 1 indicating ideal performance. The optimal cut-off value for each anthropometricindex (BMI, WC, WHR, and WSR) was determined by
the value of the largest sum of sensitivity and specificity.
All statistical analyses were conducted using the SPSS
program (version 20, SPSS Inc., 2011, Chicago, IL) andsignificance was determined with p<0.05.
RESULTS
Characteristics of participants are presented in Table 2.
There was no difference in average age between males
and females, however males were significantly (p<0.01)
taller, heavier, and with greater WC. In contrast, HC,%BF, BMI and WSR were significantly greater in females, p<0.05 and p<0.01, respectively, for the latter two
measures, whereas WHR was greater in males (p<0.01).
Differences in obesity prevalence among several obesitycategories displayed in Table 3 showed all were significantly different between category and gender (p<0.01). In
males, 30.0% were obese according to %BF category
while only half (14.0%) were reported as obese in the
BMI category. A higher proportion of females were classified as obese according to %BF category (46.4%),
whereas using a BMI category about a quarter of the females were considered obese. Compared with the BMI
category recommended by the Department of Health,Republic of Indonesia, BMI cut-offs for increased risk for
Asian populations provided a greater prevalence of individuals who were obese. The lowest prevalence of obesity
was obtained by the WC category - 5.8% of males and17.9% of females.
Sensitivity and specificity of BMI and waist girth categories for obesity toward a %BF obesity category aredetailed in Table 4. Regardless of gender, WC confirmed
the highest specificity (99.5% and 97.0% in males andfemales, respectively). However, this classification
showed low sensitivity i.e. 18.4% in males and 35.2% infemales. BMI category for obesity as recommended by
the Indonesian Health Department was able to correctly
identify obese individuals at rates of 41.4% in males and51.4% in females.
The ROC curves for BMI, WC, WHR, and WSR in
males and females, as illustrated in Figure 1, showed that
WC was the best indicator of %BF in both genders with
WHR the poorest indicator (with the smallest area under
the ROC curve). The values of the AUC for all anthropometric indices can be seen in Table 5. The optimal cutoffs for determination of overweight or obesity by %BF
in males in the present study, therefore, were 21.9 kg/m2,
76.8 cm, 0.86, and 0.48 for BMI, WC, WHR, and WSR,

Table 2. Characteristics of participantsMen(mean±SD)292
38.8±11.8
165.2±6.5
59.1±10.675.7±8.6
90.8±6.9
21.6±3.5
0.84±0.05
0.46±0.05
21.4±7.0

N
Age (years)
Stature (cm)Body weight (kg)WC (cm)HC (cm)BMI (kg/m2)
WHR
WSR
%BF

Women
(mean±SD)308
39.3±11.0
153.1±5.3**
52.5±9.6**
72.0±8.8**
92.7±7.5**22.4±3.8*
0.78±0.06**
0.47±0.06**
33.3±7.7**

WC: waist circumference; HC: hip circumference; WHR: waistto-hip ratio; WSR: waist-to-stature ratio; %BF: percentage ofbody fat.
*p<0.05; **p<0.01.

Table 3. Comparison of obesity prevalence using%BF and different categories of BMI%BF category†
NormalObese
BMI‡
NormalObese
WC§
NormalObese
WHR†
NormalObese
WSR¶
Normal
Obese

Men
N (%)

Women
N (%)

χ2

203 (70.0)
87 (30.0)

164 (53.6)
142 (46.4)

16.94**

251 (86.0)
41 (14.0)229 (74.4)
79 (25.6)

12.62**

275 (94.2)
17 (5.8)

253 (82.1)
55 (17.9)

20.56**

252 (86.3)
40 (13.7)

232 (75.3)
76 (24.7)

11.58**

236 (80.8)
56 (19.2)

251 (81.5)
57 (18.5)

0.04

WC: waist circumference; WHR: waist-to-hip ratio; WSR:
waist-to-stature ratio; %BF: percentage of body fat.
†
Category defined by WHO.
‡
Cut-off points for Indonesian (Ministry of Health Republic ofIndonesia, 1994).
§
Category by International Diabetes Federation (IDF).
¶
Category recommended for Asians (Liu et al 2011).
**
p<0.01.

respectively; in females, the values were 23.6 kg/m2, 71.7
cm, 0.77, and 0.47, respectively (Table 5). These new cutoff values increased the sensitivity of the BMI, WC,
WHR, and WSR in males to reach 83.9%, 88.5%, 66.7%,
and 79.3%, respectively. In females, the sensitivity of theanthropometric indices increased to 90.2%, 81.0%, 71.1%,

Table 4. Sensitivity and specificity of some available categories for obesity
Males
BMI†
WC‡
WHR§
WSR¶

Specificity %
97.5
99.5
95.6
96.6

FemalesSensitivity%
41.4
18.4
35.6
56.3

Specificity%
96.3
97.0
87.8
96.3

WC: waist circumference; WHR: waist-to-hip ratio; WSR: waist-to-stature ratio; %BF: percentage of body fat.
†Cut-off points for Indonesians (Ministry of Health Republic of Indonesia, 1994).
Category by International Diabetes Federation (IDF).
§
Category by WHO.
‡Sensitivity%
51.4
35.2
39.4
35.9

Anthropometric cut-offs for obesity screening

a)

653

b)

Figure 1. Receiver-operating characteristic (ROC) curves for body mass index (BMI), waist circumference (WC), waist-to-hip ratio(WHR), and waist-to-stature ratio (WSR) in males (a) and in females (b).

Table 5. Optimal cut-off, sensitivity, specificity, SEE, and area under the ROC curves for anthropometric indices in
predicting %BF in males and females
Men
BMI
WC
WHR
WSR
Women
BMI
WC
WHR
WSR

Cut-off

Sensitivity

Specificity

SEE

Area

95% CI

21.9
76.8
0.86
0.48

83.9
88.5
66.7
79.3

76.8
81.8
78.3
88.2

0.023
0.021
0.029
0.021

0.878
0.902
0.797
0.896

0.833–0.923
0.861–0.943
0.741–0.854
0.854–0.938

23.6
71.7
0.77
0.47

67.6
81.0
71.1
73.9

90.2
78.7
61.0
79.3

0.021
0.021
0.030
0.022

0.866
0.860
0.707
0.840

0.826–0.906
0.820–0.901
0.648–0.765
0.797–0.883

SEE: standard error of the estimate; WC: waist circumference; WHR: waist-to-hip ratio; WSR: waist-to-stature ratio.

and 73.3% for BMI, WC, WHR, and WSR, respectively.
The most significant increase appears in WC by which
the sensitivity is fourfold in males and more than twofoldin females.
Table 6 indicates that by using the new cut-off points,
the prevalence of obesity (including overweight) ranged
from 30.8 to 41.1% in males and from 36.4 to 48.7% infemales. The closest predicted obesity prevalence in bothgenders was obtained from WSR classifications (30.8 and46.1% in males and females, respectively).
DISCUSSION
The present study showed that the available BMI, WC,
WHR, and WSR cut-offs for defining obesity proposedby WHO, IDF, and some previous investigators, were notappropriate for Indonesian adults. We determined that
cut-off values of 21.9 kg/m2, 76.8 cm, 0.86, and 0.48 for
BMI, WC, WHR, and WSR, respectively, were the optimum for obesity determination for Indonesian adult males
and 23.6 kg/m2, 71.7 cm, 0.77, and 0.47, respectively, foradult females. Of these, WC and WSR were the most
predictive for both males and females. In addition, BMI
should not be used in preference to other methods but
always be used in conjunction with other anthropometricindicators for obesity such as WC, WHR, and WSR, as
screening tools to classify individuals at risk of obesity.
Our findings indicated that approximately 20% males

and 30% females who had normal weight according to
BMI actually had high or very high body fat. On the other
hand, approximately 10% of individuals with normal%BF were identified as obese by BMI. Both types of individuals may potentially have health risks. Those who
were obese by BMI classification but with a normal %BF
may engage in weight loss behaviours that put them athealth risk. It might be that individuals with high BMI but
low %BF have greater muscularity,25 however participants in the present study were not specifically selected,
and were not involved in high levels of physical activities,
indeed, athletes were excluded. Similarly, normal weightindividuals with a high %BF may be at risk of healthproblems pertaining to obesity.
The Indonesian and WHO international BMI classifications could only identify overweight or obese individuals
in about 50% of the sample (41.4% of males and 51.4%
females). The current study defined cut-off values of BMI
for the obese category including overweight as has beenused by the Ministry of Health, Republic of Indonesia.23
Many studies have observed the sensitivity and specificityof the BMI category for obesity, however the use of different obesity cut-off values for BMI and %BF madecomparisons difficult. For example, Chen et al26 reported
that the WHO BMI-obesity criterion (BMI ≥25 kg/m2)
showed good sensitivity (75% and 71%, respectively)
compared with the %BF obesity cut-off (%BF ≥40%) in

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J Hastuti, M Kagawa, NM Byrne and AP Hills

Table 6. Prevalence of overweight/obesity based on the new cut-off values and reference %BF
Indicator
%BF
BMI
WC
WHR
WSR

Men
Normal-weight203 (70.0%)172 (58.9%)
178 (61.0%)
189 (64.7%)
202 (69.2%)

Women
Obese
87 (30.0%)120 (41.1%)
114 (39.0%)
103 (35.3%)
202 (30.8%)

Normal-weight
164 (53.6%)
196 (63.6%)
158 (51.3%)
143 (46.4%)
166 (53.9%)

Obese
142 (46.4%)
112 (36.4%)
150 (48.7%)
165 (53.6%)
142 (46.1%)%BF: percentage of body fat; WC: waist circumference; WHR: waist-to-hip ratio; WSR: waist-to-stature ratio.

Chinese females. Whereas, Dudeja et al27 reported in an
Indian population that the WHO BMI-obesity criterion(BMI ≥25 kg/m2) resulted in a sensitivity of 34% andspecificity of 97% when the %BF-obesity criterion of%BF used was ≥25% for males and ≥30% for females.
Another study by Ko28 in a Chinese population, using cutoffs for %BF similar to the present study (%BF ≥25% in
males and ≥35% in females), reported a sensitivity of
90% and a specificity of 83.4% using the BMI cut-offs of≥23.8 kg/m2 in males and ≥24.2 kg/m2 in females for obesity. This suggests that lowering the obesity criterion may
improve the sensitivity and specificity.
Gurrici et al18,29 have suggested lowering the BMI cutoff points for obesity in Indonesia from 30 kg/m2 to 27
kg/m2 for obesity and from 25 kg/m2 to 22 kg/m2 foroverweight. The study however, did not differentiate
males from females. The present study indicated that an
application of the international BMI cut-off points recommended by WHO (BMI ≥25 kg/m2) which have been
adopted in Indonesia, was also not appropriate. Our study
found that BMI ≥21.9 kg/m2 and BMI ≥23.6 kg/m2 were
the optimum cut-off values for Indonesian males and females, respectively. These new cut-off values increased
the sensitivity up to 42% in males (to reach 83.9%) and
16% in females (to reach 67.6%) compared with the
WHO international BMI cut-off points.
The WC cut-offs for obesity as recommended by the
IDF for Asians (WC ≥90 cm for males and WC ≥80 cm
for females), which have also been applied in the National Health Survey in Indonesia, showed the poorest sensitivity (18.4% in males and 35.2% in females) among all
the anthropometric indices. This is contrary to a study
reported by Vasudevan et al30 in South Asian Indians
which found that the IDF WC cut-offs could identify approximately 75% of obese adults in that population. Similarly, Misra and Khurana16 indicated that WC cut-offs for
Asian Indians as low as 90 cm in males and 80 cm in females were the most appropriate. Some other studies haveproposed different cut-off values for WC, however, thevalues vary across gender and ethnicity. Based on a large
prospective cohort study involving more than 69 000 US
adults, Flint et al11 reported that WC cut-off values of
84.0 cm in males and 71.0 cm in females may be useful in
identifying individuals at increased risk of developing
coronary heart disease (CHD) in the US. Zaher et al31
proposed that a WC cut-off value of 83 cm is appropriate
for identifying increased risk of non-communicable disease (NCD) in Malaysian males and females. Liu et al32
proposed WC cut-off values for the Chinese population
were 91.3 cm for males and 87.1 cm for females.
Our data indicated that WC ≥76.8 cm in males and≥71.7 cm in females were the optimum cut-offs for identifying obesity in Indonesian adults. These cut-off values
were close to those generated by Misra et al15 for Asian
Indians, i.e. WC ≥78 cm for males and ≥72 cm for females. The results of the present study indicated that WC
is a better indicator of obesity in both males and females
in comparison with other anthropometric indices, as reflected in the largest AUC (Figure 1) and the high sensitivity (Table 5). The new proposed WC cut-offs can improve the sensitivity more than fourfold in males (to88.5%) and more than twofold in females (to 81.0%). The
specificity of this index, however, was slightly lower
compared with other indices but still approximately 80%.
This is consistent with other reports that WC may be abetter indicator for obesity.31,33 Individuals with high WC
have been reported to have more than a fivefold increased
likelihood of multiple cardiometabolic risk factors andover half the greater likelihood of having high CHD risk
status, even after adjusting for BMI in a US population.12
Research also indicated that those of South Asian origin
have a higher risk of cardiovascular diseases than those ofEuropean origin at lower WC.14 Our new WC cut-off
values are of importance given that WC is regarded ashighly related with abdominal obesity and that abdominal
obesity is associated with cardiovascular risk factors.
However, given that the association varies by ethnicity
and is independently associated with high cardiovasculardisease (CVD) risk status, further validation of its clinicalsignificance is needed for its application to different ethnic groups.
Cut-off values for anthropometric indices of obesity
may differ between countries. This may be partly due todifferent ethnicities having different relationships between anthropometric measures and body composition.34,35 Flegal et al36 demonstrated BMI, WC, and WSR
perform similarly as indicators of body fatness measured
with DXA in a large US population sample from the National Health and Nutrition Examination Survey(NHANES). Furthermore, evidence suggested that WHR
was more strongly associated with the mortality index
than BMI.37 However, Taylor et al38 demonstrated that
central adiposity measurements were positively associated with all-cause mortality as was BMI, but only whenthose individuals with a BMI less than 22.5 kg/m2 were
removed from the analysis, indicating that those adiposity
measures could not replace BMI in clinical or publichealth practice. Our findings indicated that previous
WHR categories were better than WC categories but not
as good as BMI or WSR categories. The new cut-offs for
the WHR (WHR ≥0.86 for males and 0.77 for females)
can improve the sensitivity by about 30% to become

Anthropometric cut-offs for obesity screening

66.7% in males and 71.1% in females. However, the
AUC of this index in our study showed the poorest ability
to determine obesity classified by %BF compared to otheranthropometric indices.
The available WSR classifications showed a higher
sensitivity compared with the WHO international BMI
cut-off points but lower than the WHO BMI cut-offs
modified for Asians. WSR cut-off points for Asians defined by Hsieh and colleagues39,40 showed better results ascompared to those defined by Liu et al32 in our participants. However, our new WSR cut-off values (WSR
≥0.48 for males and ≥0.47 for females) can improve thesensitivity of the index up to 23% and 38% in males andfemales respectively to reach 79.3% in males and 73.9%
in females. Findings of the current study are consistent
with some previous reports that WSR could better predict
obesity health risk than WHR and BMI in some populations.41,42 However, our study indicated WSR as a better
predictor than BMI in females only.
The importance of our findings is that our newly proposed cut-off values of selected anthropometric indicesimprove the sensitivity in determining obesity in Indonesian adults. The strength of our study is the use of a reference method for determination of body fatness. However,
our study did not include assessment of disease risk factors associated with obesity, which may have different
associations with each anthropometric measure or index.
Further study regarding the role of central adiposity indisease outcomes is required to provide sufficient information regarding these indices as obesity indicators. This
study also contributes to the empirical literature by fillingthe existing gaps on the comprehensive anthropometry
and body composition data in Indonesian adults.43 However, participants in our study were limited to those ofJavanese ethnicity living in Yogyakarta Province; futurestudies involving other ethnicities and larger sample sizes
are recommended to provide national representative data.
In conclusion, our study showed that obesity defined by%BF as a reference is likely to be under-diagnosed using
the previously recommended cut-off anthropometric indices. This may indicate that these classifications may not
be adequate for risk prediction. Our new cut-off values
for anthropometric measures and indices were highly correlated with body fatness measured with D2O and wereable to be used to determine obesity more sensitively in
the Indonesian population sampled. We recommend theuse of WC or WSR cut-offs in the determination of overweight or obesity in Indonesians as they have higher sensitivity and low bias compared to other anthropometriccategories for obesity.
ACKNOWLEDGEMENTS
The current study was supported by the Directorate General ofHigher Education Republic of Indonesia, Universitas GadjahMada, Indonesia, and Queensland University of Technology,
Australia. We are grateful to the participants in the current study
and staff who assisted with data collection. For laboratory analyses, we would like to thank Ms Connie Wishart.
AUTHOR DISCLOSURES
None of the authors have any conflicts of interest.

655

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