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The purpose of this study was to determine the cut-off values for waist circumference (WC) and mid-upper arm circumference (MUAC) and to assess their use in screening for obesity in children. Anthropometric measurements of a total of 2621 boys and 2737 girls aged 6-17 years were analyzed. WC and MUAC values were compared with ROC analysis using body mass index (BMI) cut-off values of the International Obesity Task Force (IOTF) and using WC≥ 90th percentile.for MUAC. In both genders, except for boys and girls in the 6-year age group and post-pubertal boys, the differences between area under curve (AUC) values for WC and MUAC were not significant, indicating that both indices performed equally well in predicting obesity. Sensitivity was suboptimal through age groups 6-9 years in the boys and sensitivity was suboptimal at 6, 7,14 and 17 years both in boys and girls. We conclude that MUAC can be a useful parameter in screening obesity and body fat distribution in children and, can be applied in epidemiological studies and in clinical practice.
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J Clin Res Ped Endo 2010;2(4):144-150
DOI: 10.4274/jcrpe.v2i4.144
M. Mümtaz Maz›c›o¤lu1, Nihal Hatipo¤lu2, Ahmet Öztürk3, Betül Çiçek4, H. Bahri Üstünbafl1, Selim Kurto¤lu5
1Department of Family Medicine, School of Medicine, Erciyes University, Kayseri, Turkey
2Departman of Pediatric Endocrinology, Sisli Etfal Education and Research Hospital, Istanbul, Turkey
3Department of Biostatistics, School of Medicine, Erciyes University, Kayseri, Turkey
4Department of Nutrition and Dietetics, Atatürk Health School, Erciyes University, Kayseri, Turkey
5Department of Pediatric Endocrinology, School of Medicine, Erciyes University, Kayseri, Turkey
Address for Correspondence
Nihal Hatipoglu MD, Department of Pediatric Endocrinology, Sisli Etfal Education and Research Hospital, ‹stanbul, Turkey
Gsm: +90 536 323 03 02 Fax: +90 352 437 52 85 E-mail:
© Journal of Clinical Research in Pediatric Endocrinology, Published by Galenos Publishing.
Waist Circumference and Mid-Upper Arm
Circumference in Evaluation of Obesity in
Children Aged Between 6 and 17 Years
Original Article
The global trend of increasing childhood obesity is well
documented. Obesity in childhood has therefore become a
health issue of concern to health professionals throughout the
world as a leading factor for certain chronic diseases such as
hyperlipidaemia, hyperinsulinemia, hypertension, and early
atherosclerosis (1,2,3). Whether it persists or not in adulthood,
childhood obesity is substantially related with increased
morbidity and mortality (4). However, the detection of obesity
during childhood is more difficult than during adulthood due to
the developmental changes in children. Additionally, there is no
general consensus on the reliability, use, application of direct
and indirect anthropometric indices describing obesity in
children (5).
For diagnosis of obesity and for evaluation of current and
future metabolic risks, individual assessment with body mass
index (BMI) is essential, but additional anthropometric indices
are needed to describe accurately the body fat distribution.
Although precise methods to determine body fat content
and distribution exist, these methods are not practical for
epidemiologic studies. On the other hand, anthropometric
indices provide a valid tool to screen large groups (6).
Waist circumference (WC), skinfold thickness and
mid-upper arm circumference (MUAC) are the leading indirect
methods used to assess fat reserve and the application of these
anthropometric indices is recommended to screen the child and
adolescent population for obesity (7).
Objective: The purpose of this study was to determine the cut-off values for
waist circumference (WC) and mid-upper arm circumference (MUAC) and to
assess their use in screening for obesity in children.
Methods: Anthropometric measurements of a total of 2621 boys and 2737
girls aged 6-17 years were analyzed. WC and MUAC values were compared
with ROC analysis using body mass index (BMI) cut-off values of the
International Obesity Task Force (IOTF) and using WC90th percentile.for
Results: In both genders, except for boys and girls in the 6-year age group and
post-pubertal boys, the differences between area under curve (AUC) values for
WC and MUAC were not significant, indicating that both indices performed
equally well in predicting obesity. Sensitivity was suboptimal through age
groups 6-9 years in the boys and sensitivity was suboptimal at 6, 7,14 and 17
years both in boys and girls.
Conclusions: We conclude that MUAC can be a useful parameter in
screening obesity and body fat distribution in children and, can be applied in
epidemiological studies and in clinical practice.
KKeeyy wwoorrddss::
Mid-upper arm circumference, obesity, waist circumference
CCoonnfflliicctt ooff iinntteerreesstt::None declared
RReecceeiivveedd::28.10.2010 AAcccceepptteedd::09.11.2010
This is an open-access article distributed under the terms of the Creative Commons Attiribution License, which
permits unrestricted use, distribution and reprodiction in any medium, provided the original work is properly cited.
Maz›c›o¤lu MM et al.
Waist and Mid-Upper Arm Circumference
Table 1. Mean (95%CI) and median (minimum-maximum) values for WC, MUAC, BMI in male and female Turkish children and adolescents
Boys Age* n MUAC (cm) WC (cm) BMI (kg/m2) Girls Age* n MUAC (cm) WC (cm) BMI (kg/m2)
(years) χ(95% CI)χ(95% CI)χ(95% CI)(years) χ(95% CI)χ(95% CI)χ(95% CI) †
Med (min-max) Med (min-max) Med (min-max) Med (min-max) Med (min-max) Med (min-max)
6 124 17.1 (16.7-17.3) 53.7 (52.9-54.4) 16.6(16.3-16.9) 6 126 17.1 (16.7-17.4) 53.3 (0.4) 16.3 (0.2)
17.0 (13.9-21.0) 53.5 (46.0-65.0) 16.4 (12.6- 23.2) 17.1 (13.9-21.0) 53.0 (45.0-69.0) 16.1 (13.1-21.2)
7 208 17.4 (17.0-17.6) 54.5 (53.8-55.3) 16.8(16.6-17.1) 7 166 17.5 (17.1-17.7) 54.3 (53.5-55.0) 16.5 (16.3-16.8)
17.3 (14.0-23.7) 54.5 (41.0-71.0) 16.5 (13.0-24.3) 17.5 ()14.0-24.0) 54.0 (45.0-68.0) 16.4 (12.1-22.4)
8 196 17.9 (17.6-18.2) 57.4 (56.5-58.4) 17.4 (17.1-17.7) 8 206 18.1 (17.8-18.4) 55.1 (54.2-55.9) 16.9 (16.6-17.2)
18.0 (14.0-25.0) 56.0 (45.0-84.0) 17.0 (13.3-26.4) 18.0 (14.0-26.0) 54.1 (42.0-77.0) 16.3 (13.2-26.1)
9 224 18.7 (18.4-19.0) 60.2 (59.1-61.2) 18.0 (17.6-18.4) 9 188 18.5 (18.1-18.8) 56.5 (55.7-57.3) 17.1 (16.7-17.4)
18.5 (14.8-27.0) 58.9 (46.0-88.0) 17.5 (13.5-28.6) 18.2 (14.9-25.0) 56.0 (45.0-76.0) 16.9 (12.1-26.3)
10 216 19.1 (18.7-19.4) 60.9 (59.9-61.9) 17.9 (17.5-18.2) 10 227 19.5 (19.2-19.8) 59.5 (58.6-60.4) 17.9 (17.6-18.3)
19.0 (15.0-27.5) 60.0 (48.0-86.0) 17.5 (13.6-27.1) 19.2 (15.8-28.1) 58.0 (47.0-81.5) 17.5 (13.0-27.9)
11 176 20.1 (19.7-20.5) 64.1 (62.8-65.3) 19.0 (18.519.4) 11 188 20.4 (20.0-20.7) 60.8 (59.9-61.7) 18.5 (18.1-18.9)
20.0 (16.0-29.2) 62.2 (49.0-94.4) 18.2 (13.7-33.9) 20.0 (16.2-28.0) 60.0 (48.5-82.1) 17.0 (14.1-25.8)
12 195 20.8 (20.3-21.2) 65.6 (64.6-66.7) 19.2 (18.8-19.6) 12 193 21.0 (20.6-21.4) 62.5 (61.5-63.5) 19.5 (19.1-20.0)
20.5 (16.1-29.3) 64.2 (47.0-90.2) 18.6 (14.4-29.0) 21.0 (16.3-29.3) 62.0 (50.0-89.0) 18.8 (13.4-33.3)
13 190 21.2 (20.7-21.5) 67.4 (66.2-68.5) 19.4 (19.0-19.8) 13 196 21.7 (21.3-22.0) 64.0 (63.1-64.9) 19.9 (19.5-20.3)
21.0 (16.4-30.3) 65.6 (52.0-97.0) 19.2 (14.4-30.3) 21.3 (13.5-34.3) 63.0 (49.0-94.0) 19.2 (15.1-31.7)
14 256 22.1 (21.6-22.4) 70.6 (69.5-71.7) 20.5 (20.2-20.8) 14 333 22.8 (22.4-23.0) 66.6 (65.9-67.3) 21.0 (20.6-21.3)
22.0 (16.7-32.0) 69.1 (52.0-99.0) 19.8 (14.7-32.5) 22.6 (17.8-34.0) 66.0 (53.5-88.0) 20.5 (13.9-33.6)
15 387 22.8 (22.4-23.1) 72.1 (71.3-72.9) 21.1 (20.8-21.4) 15 410 22.5 (22.3-22.8) 66.9 (66.3-67.6) 21.4 (21.1-21.7)
23.0 (17.0-35.0) 71.0 (56.0-104.5) 20.5 (15.4-31.2) 22.4 (10.0-32.0) 66.0 (54.0-97.0) 20.9 (15.2-35.0)
16 287 23.2 (22.7-23.5) 72.2 (71.1-73.1) 21.0 (20.6-21.4) 16 353 22.9 (22.5-23.1) 67.1 (66.4-67.7) 21.6 (21.3-22.0)
23.0 (17.7-35.0) 71.0 (54.0-102.0) 20.6 (13.7-35.2) 22.5 (18.0-31.5) 66.0 (55.0-88.5) 21.1 (16.0-32.2)
17 162 23.7 (23.0-24.0) 73.5 (72.3-74.8) 21.3 (20.9-21.8) 17 151 23.0 (22.5-23.3) 66.3 (65.3-67.3) 21.6 (21.1-22.1)
23.2 (10.5-35.0) 73.5 (56.0-103) 20.8 (16.1-30.2) 22.5 (19.0-31.5) 65.0 (52.0-90.0) 21.1 (15.9-32.9)
WC: waist circumference, MUAC: mid-upper arm circumference , BMI: body mass index
* Age indicates one-year age group, e.g. 6.0-6.99 years, etc.
Med (min-max): Median (minimum-maximum).
Mean (95% Confidence Interval); χ(95%CI)
This study aimed to evaluate 1) the role of WC and
MUAC, used in addition to BMI, in determining overweight and
obesity, 2) to establish cut-off values for defining overweight
and obesity with WC and MUAC (overweight and obesity are
defined according to BMI).
The data analyzed in this study were based on
measurements obtained in a study entitled “Determination
of Anthropometric Measures of Turkish Children and
Adolescents” (DAMTCA I) and conducted in the time
period from February to April 2006 (8). Children and adolescents
residing in Kayseri, Turkey constituted the study sample.
Kayseri province is a leading industrial and trade centre, which
has more than 1000 000 inhabitants and receives emigrants
from different parts of Turkey. Of a total of 5727 primary and
secondary school students included in the above-mentioned
study, data regarding WC, skinfold thickness and MUAC were
available in 2737 girls and 2621 boys, aged 6-17 years.
Methodology relating to sample selection, weight and height
measurements was given in the previous publication (8).
BMI (kg/m2) was calculated as weight (kg) divided by the
square of the height (m2). WC and MUAC were measured
to the nearest 0.1 cm with an anthropometric tape over light
clothing. WC was measured at the minimum circumference
between the iliac crest and the rib cage. MUAC measurements
were taken in centimeters with non-elastic tape to the nearest
0.1 mm on the upper left arm (halfway between the acromion
process and the olecranon process). The children/adolescent
stood relaxed with his/her side to the trained technician and the
arm hanging freely at the side; the tape was passed around the
arm at the level of the mid-point of the upper arm.
Results were presented as the mean 95% confidence
interval (95%CI), median, minimum-maximum (min-max)
for each age and gender. Simple linear regression analyses (R2)
Maz›c›o¤lu MM et al.
Waist and Mid-Upper Arm Circumference
Table 2. ROC curve analysis of WC and MUAC in male children and adolescents for overweight cut-off values
Age (years) Variables AUC-ROC (95%CI)Cut-off value P#Sensitivity Specificity
6 WC O.690 (0.601 - 0.770) 56.3* 0.930 42.3 83.7
MUAC 0.684 (0.595 - 0.765) 18.1* 50.0 88.9
7 WC 0.759 (0.695 - 0.815) 56.5** 0.228 64.4 79.1
MUAC 0.697 (0.630 - 0.759) 18.1** 57.8 79.8
8 WC 0.839 (0.780 - 0.888) 60.5** 0.247 66.0 94.0
MUAC 0.791(0.727 - 0.845) 18.9** 63.8 79.9
9 WC 0.903 (0.857 - 0.939) 61.5** 0.313 84.2 84.4
MUAC 0.873(0.822 - 0.913) 20.4** 68.4 97.6
10 WC 0.937 (0.896 - 0.965) 66.0** 0.953 85.3 92.9
MUAC 0.939 (0.898 - 0.967) 19.9** 97.1 76.9
11 WC 0.946(0.901 - 0.974) 66.7** 0.057 90.5 89.6
MUAC 0.891 (0.835 - 0.933) 21.9** 73.8 88.8
12 WC 0.944 (0.902 - 0.972) 68.6** 0.440 94.1 88.8
MUAC 0.919 (0.872 - 0.953) 21.9** 73.8 88.8
13 WC 0.965 (0.928 - 0.986) 72.5** 0.141 93.3 91.9
MUAC 0.919 (0.871 - 0.954) 22.6** 90.0 82.5
14 WC 0.924 (0.885 - 0.953) 71.9** 0.066 94.3 79.8
MUAC 0.874 (0.827 - 0.912) 22.8** 86.8 76.4
15 WC 0.912 (0.879 - 0.938) 76.7** 0.032 81.9 90.7
MUAC 0.852 (0.812 - 0.885) 24.9** 73.0 83.7
16 WC 0.952 (0.921 - 0.974) 73.8** 0.029 100.0 76.1
MUAC 0.884 (0.841 - 0.918) 24.2** 84.1 78.2
17 WC 0.967 (0.926 - 0.988) 78.3** 0.131 95.7 89.2
MUAC 0.898 (0.841 - 0.940) 25.7** 87.0 87.8
WC: waist circumference, MUAC: mid-upper arm circumference
† AUC-ROC (95% CI): area under ROC curve (95% CI)
# P: results of comparison of AUCs of WC, and MUAC for 6 age *(p<0.01)
# P: results of comparison of AUCs of WC, and MUAC for 7-17 ages **(p<0.001)
were computed to explore the relationships between BMI,
WC, and MUAC for each age.
The WC90th percentile values for age and gender were
used to identify children and adolescents with abdominal
obesity in accordance with the International Obesity Task Force
(IOTF) cut-off values for overweight and obesity (9). The
performance and cut-offs of anthropometric indices were
determined by the receiver operating characteristic (ROC)
analysis (10). The ROC curves demonstrated the overall
discriminatory power of a diagnostic test: BMI, WC, and
MUAC. The better test has a curve skewed closer to the upper
left corner. The area under the ROC curve (AUC) is a measure
of the diagnostic power of a test. The perfect test will have an
AUC of 1.0, while an AUC value of 0.5 indicates that the test
performs no better than expected by chance. Sensitivity and
specificity of the anthropometric indices have been calculated
at all possible cut-off points to find the optimal cut-off value.
The optimal sensitivity and specificity were the values yielding
maximum sums from the ROC curves (Clinical significance of
‘cut-off’s were checked with the Youden index). Cut-off values
and AUCs of WC and MUAC were compared for each age and
gender. MedCalc software was used to test the significance of
the differences for the AUCs.
Agreement between these anthropometric indices
were assessed by Cohen’s κstatistic, with values of 0.00
to 0.20 indicating poor, 0.21 to 0.40 - fair, 0.41 to 0.60-
moderate, 0.61 to 0.80-good, and 0.81 to 1.00 - excellent
concordance (11).
The current study included 5358 subjects (2621 boys and
2737 girls). The mean and medians of WC and MUAC for each
age and gender are shown in Table 1. We determined the WC
cut-off values by relating WC and MUAC with BMI according to
Maz›c›o¤lu MM et al.
Waist and Mid-Upper Arm Circumference
Table 3. ROC curve analysis of WC and MUAC in female children and adolescents for overweight cut-off values
Age (years) Variables AUC-ROC (95%CI)Cut-off value P#Sensitivity Specificity
6 WC 0.744(0.658 - 0.817) 57.1* 0.135 38.7 94.7
MUAC 0.645 (0.555 - 0.728) 17.9** 67.7 75.8
7 WC 0.714 (0.639 - 0.781) 56.4** 0.898 60.0 84.0
MUAC 0.721 (0.646 - 0.787) 18.2** 60.0 83.2
8 WC 0.854 (0.798 - 0.899) 59.5** 0.817 76.3 93.5
MUAC 0.862 (0.808 - 0.906) 18.7** 80.8 75.0
9 WC 0.885 (0.831 - 0.927) 60.4** 0.900 76.7 85.4
MUAC 0.890 (0.837 - 0.931) 20.2** 76.7 90.5
10 WC 0.942 (0.903 - 0.969) 61.9** 0.067 93.8 85.5
MUAC 0.887 (0.838 - 0.925) 20.6** 81.3 86.0
11 WC 0.961 (0.923 - 0.984) 63.3** 0.131 92.1 88.7
MUAC 0.913 (0.864 - 0.949) 20.5** 94.7 68.7
12 WC 0.916 (0.868 - 0.951) 64.0** 0.679 92.9 84.1
MUAC 0.902 (0.852 - 0.940) 22.6** 73.8 89.4
13 WC 0.908 (0.859 - 0.945) 67.5** 0.369 82.4 84.6
MUAC 0.876 (0.821 - 0.919) 22.8** 85.3 82.7
14 WC 0.872 (0.832 - 0.906) 70.5** 0.900 68.2 90.6
MUAC 0.869 (0.828 - 0.903) 23.8** 84.5 76.4
15 WC 0.936 (0.908 - 0.958) 69.1** 0.267 92.2 83.8
MUAC 0.907 (0.875 - 0.934) 23.9** 89.1 81.5
16 WC 0.891 (0.854 - 0.922) 69.6** 0.250 85.3 81.2
MUAC 0.857 (0.816 - 0.892) 23.9** 82.0 78.1
17 WC 0.842 (0.774 - 0.897) 72.7** 0.141 57.1 96.2
MUAC 0.9131 (0.856 - 0.953) 24.5** 85.7 86.9
WC: waist circumference, MUAC: mid-upper arm circumference
† AUC-ROC (95% CI): area under ROC curve (95% CI)
# P: results of comparison of AUCs of WC, and MUAC
* (P<0.01), **(p<0.001)
the IOTF cut-off points. Since we could not find cut-off values
for MUAC in the relevant publications, we used WC90th
percentile as the cut-off value for the ROC analysis.
The AUC, cut-off value, sensitivity, and specificity for each
age and gender are shown in Tables 2-5. The AUC, both for WC
and MUAC, were statistically significant in both genders in the
age groups 6-17 years. The differences between AUCs for WC
and MUAC were not significant, indicating that both indices
performed equally well in predicting normal, overweight, and
obesity (except 15- and 16-year-old boys) in each gender in 6-17
years old children (Tables 2, 3).
The sensitivity of WC for 6-8 years old boys and the
sensitivity of MUAC for 6-8 and 15 years old boys were
estimated to be suboptimal for clinical use (Table 2).
The sensitivity of WC for 6,7,14,17 years old girls and
the sensitivity of MUAC for 6-7 years old girls were also found
suboptimal for clinical use (Table 3). The R2calculated
for R2showed that the values for BMI and WC were higher
than those for BMI and MUAC (Table 6).
The agreement between the two approaches (WC90th,
MUAC90th percentile) to define abdominal obesity
was moderate (κ=0.56, κ=0.50; p<0.001, respectively for
boys and girls).
Maz›c›o¤lu MM et al.
Waist and Mid-Upper Arm Circumference
Table 4. ROC curve analysis to determine cut-off MUAC values for WC90th percentile in male children and adolescents
Age (years) AUC-ROC (95%CI)Cut-off value # Sensitivity Specificity
6 0.755 (0.669 - 0.828) 18.2* 66.8 82.1
7 0.744 (0.679 - 0.802) 19.8** 52.6 93.6
8 0.862 (0.806- 0.907) 19.4** 70.0 86.9
9 0.926 (0.884 - 0.957) 20.9** 87.0 91.5
10 0.930 (0.888- 0.960) 21.2** 83.3 88.9
11 0.939 (0.892 - 0.969) 22.0** 88.2 86.8
12 0.912 (0.863 - 0.948) 21.7** 89.5 77.8
13 0.972 (0.937 - 0.990) 23.5** 100.0 88.3
14 0.954 (0.921 - 0.976) 24.3** 92.6 86.0
15 0.910 (0.877 - 0.937) 25.3** 81.4 89.2
16 0.928 (0.892 - 0.955) 26.0** 82.1 90.7
17 0.859 (0.795 - 0.908) 26.5** 72.2 92.4
WC: waist circumference, MUAC: mid-upper arm circumference
† AUC-ROC (95% CI): area under ROC curve (95% CI)
# AUCs of MUAC: Statistically significant (p<0.01)*, (p<0.001) **
Table 5. ROC curve analysis to determine cut-off values of MUAC for WC90th percentile in female children and adolescents
Age (years) AUC-ROC (95%CI)Cut-off value # Sensitivity Specificity
6 0.840 (0.764 - 0.899) 18.0** 76.9 81.4
7 0.883 (0.824 - 0.928) 17.9** 100.0 63.3
8 0.946 (0.905 - 0.972) 20.1** 93.0 11.5
9 0.909 (0.858 - 0.946) 20.3** 88.2 87.1
10 0.893 (0.845 - 0.930) 22.3** 80.0 92.3
11 0.894 (0.841 - 0.934) 22.9** 72.2 89.4
12 0.905 (0.855 - 0.942) 21.9** 100.0 67.8
13 0.930 (0.884 - 0.961) 23.0** 95.0 85.2
14 0.927 (0.894 - 0.953) 24.1** 93.9 81.0
15 0.919 (0.889 - 0.944) 23.9** 95.1 77.8
16 0.926 (0.894 - 0.951) 44.3** 94.3 83.0
17 0.887 (0.825 - 0.932) 25.7** 69.2 90.6
WC: waist circumference, MUAC: mid-upper arm circumference
† AUC-ROC (95% CI), area under ROC curve (95% CI)
# AUCs of MUAC Statistically significant (p<0.001)**
To the best of our knowledge this is the first and
comprehensive study discussing the use of different
anthropometric indices in evaluation of obesity in 6- to 17-year-
old children.
Increasing obesity prevalence among children and
adolescents is one of the leading public health problems
globally. Simple and practical methods are needed in screening
obesity. BMI is accepted as an index of body fat reserve, but
for the same BMI, body fat reserve may be different between
individuals. Another major drawback concerning BMI is that its
measurement gives no indication of body fat distribution. It has
been known for some time that a central distribution of body
fat, particularly an excess accumulation of fat intraabdominally
rather than a more peripheral distribution, carries a higher
risk for obesity-related comorbidities. Hence, WC is proposed
to describe body fat distribution as an index additional
to BMI. Laboratory-based methods (e.g. dual-energy X-ray
absorptiometry, underwater weighing) are also used to assess
body fat in children, but these methods are expensive and
usually limited to small-scale studies (12,13).
In this study, we measured MUAC in addition to WC
to describe obesity defined by BMI. We consider that each
country must determine their own cut-off values for WC, BMI,
and MUAC. While the use of BMI as a surrogate for fat excess
among children raises debates, WC is increasingly recognized
as a useful index reflective of both fat excess and risk of
diseases (14). Some anthropometric indices like WC and
MUAC, which are used to determine adiposity, show a good
level of correlation with corporal mass (15).
Early identification and treatment of children with central
adiposity is crucial to detect the risks for future metabolic
complications. WC is considered as the best
indicator of abdominal obesity, but in circumstances where
WC measurement is not feasible (skeletal deformities,
intraabdominal disorders or change in abdominal circumference
related with respiratory movements), measurement of
MUAC may be an alternative and reliable index. Thus,
anthropometric ndicators such as BMI, WC and MUAC can be
used as screening tools for obesity in children and adolescents
(16,17). However, systematic monitoring of WC and MUAC is
not a commonly performed method in pediatric studies in many
countries and internationally accepted cut-off values are also
not yet established.
WC rather than BMI is recommended as an index of
obesity-related health risks in adults (18,19). WC is a highly
sensitive and specific measure of truncal adiposity and a strong
predictor of visceral adiposity also in the pediatric population.
Furthermore, WC shows a relationship with the metabolic
consequences of obesity, including negative lipid profile,
increased blood pressure, and insulin resistance in children and
adolescents (20). In adults, specific WC cut-off values are
reported from different countries for screening metabolic
syndrome, cardiovascular diseases, type 2 diabetes and
hypertension, but studies describing specific WC cut-off values
in children are scarce (21,22,23).
MUAC is proposed as another important indicator of
obesity, and is also reported to closely reflect body fat tissue
(24). Analyzing the NHANES data, Gortmaker and Dietz
reported that while obesity prevalence was increased by 40%
Maz›c›o¤lu MM et al.
Waist and Mid-Upper Arm Circumference
Table 6. Simple linear regression coefficients (R2) between WC, MUAC, and BMI in male and female children and adolescents
Boys Girls
6 0.20 0.18 0.09 6 0.18 0.19 0.18
7 0.17 0.27 0.16 7 0.34 0.26 0.26
8 0.40 0.49 0.31 8 0.54 0.52 0.51
9 0.53 0.69 0.54 9 0.57 0.54 0.52
10 0.53 0.67 0.60 10 0.55 0.66 0.56
11 0.66 0.64 0.52 11 0.49 0.50 0.55
12 0.59 0.66 0.61 12 0.50 0.65 0.59
13 0.54 0.69 0.50 13 0.59 0.62 0.52
14 0.63 0.64 0.49 14 0.46 0.55 0.52
15 0.44 0.66 0.38 15 0.45 0.55 0.56
16 0.49 0.67 0.47 16 0.51 0.57 0.57
17 0.38 0.65 0.48 17 0.42 0.45 0.57
WC: waist circumference, MUAC: mid-upper arm circumference , BMI: body mass index
All correlation coefficients were statistically significant (p<0.001)
in a 20-year period, BMI values remained relatively constant
(25). This finding indicates that the proportion of body fat and
lean body mass have changed longitudinally.
Additionally, energy intake, growth, and fat storage
characteristics of children may also lead to discordance in
assessing overweight and obesity. Arm anthropometry appears
as a popular, cheap and non-invasive method. Especially in
epidemiologic studies, MUAC is a practical tool and can be
measured easily almost in any situation. The primary limitation
may be the absence of studies to determine the validity of this
method (25).
Finally, we believe that the primary contribution of this
present study was the finding that both WC and MUAC can be
substituted for one another as an additional evaluation tool
next to BMI in detecting overweight and obese children and
adolescents. It was found that in boys, clinically significant WC
cut-off values could be obtained at ages 9 to 17, while the
optimal ages to obtain MUAC cut-off values were 9-14 and
16-17 years (Table 2). Optimal ages to get clinically significant
WC and MUAC cut-offs in the girls were 8-13 years for WC and
15-16 years for MUAC.
In conclusion, our data revealed that both WC and
MUAC show a good correlation with BMI and that these two
parameters have the characteristic of indirectly defining the
composition (lean and fat tissue content) of the body rather
than providing information on total mass. We believe that the
present study contributes to providing cut-off values for two
practical tools, which can be used to determine body fat
reserve. Additionally, these two indices may also be used in
epidemiologic studies to assess cardiovascular and metabolic
risk in overweight and obese children.
The authors would like to thank the students of Kayseri
Health Institute for their valuable help in data collection and the
Erciyes University School of Medicine for their support.
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Maz›c›o¤lu MM et al.
Waist and Mid-Upper Arm Circumference
[Full Text] / [PDF]
... Therefore, different studies utilized different anthropometric indices, i.e., waist circumference (WC), waistto-height ratio (WHtR), and neck circumference (NC) as an index to determine regional [10][11][12][13]. Few investigators in recent years also evaluated the diagnostic performance of midupper arm circumference (MUAC) and arm-to-height ratio (AHtR) and proposed that both of these indicators are simple, inexpensive, and accurate measures for obesity screening in children [14][15][16][17]. ...
... A systematic review and meta-analysis by Alves et al. [26] indicated that BMI, WC, and WHtR had high discriminatory power (AUC > 0:897) to identify body fat in children and adolescents. In recent years, few reports also utilized some other anthropometric indices (i.e., MUAC, AHtR, and NC) as an index to determine regional adiposity [10,[14][15][16][17]. ...
... We also found that the ability of WC, MUAC, and NC to detect childhood obesity was "highly accurate" (AUC > 0:65) that was consistent with recent findings [10,14,15]. However, the diagnostic accuracy of NC was lower than the WC and MUAC. ...
Full-text available
In the clinical settings, different anthropometric indicators like neck circumference (NC), waist circumference (WC), midupper arm circumference (MUAC), waist-to-height ratio (WHtR), and arm-to-height ratio (AHtR) have been suggested for evaluating overweight and obesity in children. The comparative ability of these indicators in Pakistan is yet unknown. This study is aimed at examining the validity of different anthropometric indicators of overweight and obesity simultaneously and at determining their superlative cut-off values that would correctly detect overweight and obesity in children. For this purpose, the dataset of anthropometric measurements height, weight, WC, MUAC, and NC of 5,964 Pakistani children, aged 5-12 years collected in a cross-sectional multiethnic anthropometric survey (MEAS), was used. Receiver operating characteristic (ROC) curve analysis was performed to assess the validity of different anthropometric indicators. The most sensitive and specific cut-off points, positive and negative predictive values of each indicator were also calculated. The results of the ROC curve indicated that all the studied indicators had a good performance but the indicators AHtR and WHtR had the highest value of the area under the curve (AUC) for the screening of children with overweight and obesity (AUC>0.80). In the overall sample, AHtR, WHtR, MUAC, WC, and NC cut-off points indicative of overweight, in both boys and girls, were 0.14, 0.46, 18.41 cm, 62.86 cm, and 26.36 cm and 0.14, 0.47, 18.16 cm, 64.39 cm, and 26.54 cm, respectively; the corresponding values for obesity were 0.14, 0.47, 18.67 cm, 62.10 cm, and 26.36 cm and 0.14, 0.48, 20.19 cm, 64.39 cm, and 25.27 cm. We concluded that the sex-specific cut-off points for AHtR, WHtR, MUAC, WC, and NC can be used to diagnose overweight and obesity in Pakistani children.
... All of the studies were cross-sectional and were published from 2013 to 2021. They were conducted in twenty-one countries including Brazil (36) , China (37) , Netherlands (38) , Ethiopia (20) , India (39)(40)(41) , Indonesia (42) , Nigeria (43,44) , Pakistan (25,45) , Seri Lanka (24) , South Africa (46,47) , Thailand (48) , Turkey (49,50) , Trinidad and Tobago (51) and twelve countries (17) (Australia, Brazil, Canada, China, Colombia, Finland, India, Kenya, Portugal, South Africa, the UK and the USA) (n 1). The number of participants varied substantially between studies (range from 211 to 31 471), with a pooled population of 54 381 children and adolescents. ...
... Studies used different reference methods: three studies used bioelectrical impendency (18,44) ; two used waist circumferences (49,52) ; the rest used BMI. Most studies used an 85th percentile (Z score > 1 þ SD) cut-off of BMI growth curves for overweight and a 95th percentile cut-off (Z score > 2 þ SD) of BMI curve for obesity. ...
Full-text available
Objective This study aimed to synthesize the existing evidence on the performance of Mid-Upper Arm Circumference (MUAC) to identify children and adolescents with overweight and obesity. Design Systematic review and meta-analysis. Setting We searched PubMed, EMBASE, SCOPUS, Cochrane Library, Web of Science, CINAHL, and Google scholar databases from their inception to December 10, 2021, for relevant studies. There were no restrictions regarding the language of publication. Studies reporting measures for the diagnostic performance of MUAC compared to a reference standard for diagnosing overweight and obesity in children and adolescents aged 2 to 19 years were included. Participants A total of 54,381 children and adolescents from twenty-one studies were reviewed; 10 studies contributed to meta-analyses. Results In Boys, MUAC showed a pooled Area Under the Curve (AUC) of 0.92 (95% CI 0.89 - 0.94), sensitivity of 84.4 (95% CI 84.6-.90.8), and a specificity of 86.0 (95% CI 79.2-90.8), when compared against BMI z-score, defined overweight and obesity. As for girls, MUAC showed a pooled AUC of 0.93 (95% CI 0.90 - 0.95) sensitivity of 86.4 (95% CI 79.8- 91.0), specificity of 86.6 (95% CI 82.2-90.1) when compared against overweight and obesity defined using BMI z-scores. Conclusion In comparison with BMI, MUAC has an excellent performance to identify overweight and obesity in children and adolescents. However, no sufficient evidence on the performance of MUAC compared to gold standard measures of adiposity. Future research should compare performance of MUAC to the “golden standard” measure of excess adiposity.
... ROC curves show the overall discriminatory power of a diagnostic test. The perfect test has an AUC of 1.0 [34]. As a result of the study, AUC's value close to 1 shows that fractal analysis can be used as a reliable diagnostic test. ...
Full-text available
Objectives Fractal analysis is a mathematical method used for the calculation of bone trabeculation and lacunarity. This study aims to evaluate the relationship between resonance frequency analysis (RFA) and fractal dimension (FD) of peri-implant bone to determine the preload stability of implants. Materials and methods In this study, the results of the fractal analysis calculated from the resonance frequency analysis results taken in the 3rd month of the patients who underwent 2-stage implant by the same doctor and the radiographs taken in the same session were evaluated. A hundred implants in 20 patients were applied in this study. The implant stability quotient (ISQ) values of the implants and fractal dimension values of the peri-implant bone were calculated. Results The findings showed that the ISQ1 (p = 0.008), ISQ2 (p = 0.038), ROI2 (p = 0.013), and ROI3 (p < 0.001) values were statistically significantly higher in men than women. The ISQ1 (p = 0.003), ISQ2 (p = 0.013), ROI1 (p = 0.011), and ROI3 (p < 0.001) of the mandible were statistically higher than the maxilla. The fractal dimension cut-off value to assess prosthetic loading was found 1.198. Conclusion Fractal analysis is a non-invasive method that can be used in conjunction with clinical examination in the prosthetic loading decision of implants. It is a valuable parameter that can be used without the need for an extra device when it is necessary to reduce the clinical study time. Clinical relevance Calculating the fractal dimension of the peri-implant bone is a practical, economical, and applicable method for clinicians. FD calculated from panoramic radiographs used for diagnosis in routine treatments in clinics where access to the necessary devices for ISQ measurement is not available will contribute to clinical practice.
... 20 21 However, it may be challenging to take accurate measurements of individuals with skeletal deformities, intra-abdominal disorders or those with changes in abdominal circumference associated with respiratory movements. 22 Moreover, a lack of consensus regarding the acceptable body location to measure WC poses the problem. 23 Thus, the MUAC measurement is much easier to handle and suitable for large-scale population-based surveys in low-income countries. ...
Full-text available
Objectives The present study evaluates body circumferences as a nutrition screening tool for women of reproductive age with children less than 5 years of age to improve the detection of overweight and obesity in a community setting. Design This study draws data from a community-based cross-sectional study conducted between July–August 2017 and January–February 2018 to account for seasonality in Addis Ababa, Ethiopia. Setting One hundred and sixteen districts were included in Addis Ababa, Ethiopia. Participants A total of 4914 women of reproductive age with children less than 5 years of age were participated in this study. Primary and secondary outcome measures Primary outcome measures included anthropometric indices. There were no secondary outcomes. Results The optimal cut-off points to identify overweight women of reproductive age were >87.5 cm for waist circumference (WC), >31.7 cm for neck circumference (NC) and >28.0 cm for mid-upper arm circumference (MUAC) based on the highest corresponding Youden index. The area under the receiver operating characteristics curve was 0.92 (95% CI: 0.91 to 0.93) for WC, 0.83 (95% CI: 0.82 to 0.84) for NC and 0.91 (95% CI: 0.89 to 0.92) for MUAC. Conclusions Our result shows that WC and MUAC are alternative tools to body mass index. Both WC and MUAC are effective in identifying overweight women. We recommend using MUAC in large-scale population-based assessments to identify overweight and obesity in low-income settings as it is logistically simpler and operationally feasible.
... In the present study, NC was shown to have the lowest sensitivity and thus was a poor indicator for screening obesity in adolescents and showed cut-off values ranging between 30.6 and 31.85 cm, slightly higher in males. The cut-off scores, however, fall within the range of other previous studies in children, as reported by Teheri et al. [52] MUAC has been reported as a possible tool for screening obesity [53] in children. In the present study, MUAC was not a good indicator for obesity. ...
Full-text available
The assessment of obesity in sub-Saharan Africa relies on cut-offs established from western populations. This study assessed anthropometric indices to determine optimal cut-off values for obesity screening in the South African adolescent population. A cross-sectional study involving 1144 (796 females and 348 males) adolescents aged 11–17 years from the Eastern Cape Province of South African was conducted. Anthropometric parameters were measured. Receiver operating characteristic (ROC) analysis was performed to assess the sensitivity and specificity of obesity screening tools and establish cut-offs. The optimal cut-offs for obesity in the cohort using waist-to-height ratio (WHtR) as reference were: neck circumference (NC) = 30.6 cm, mid-upper arm circumference (MUAC) = 25.9 cm, waist circumference (WC) = 75.1 cm, hip circumference (HC) = 92.15 cm and body mass index percentile (pBMI) = p85.2th. The new pBMI cut-off value at p85.2th improved the sensitivity of the test by approximately 30% compared to the CDC recommended BMI percentile (pBMIr) of p95.0th. When pBMI was used as reference, the optimal cut-offs in the cohort were: WHtR = 0.481, NC = 30.95 cm, MUAC = 27.95 cm, WC = 76.1 cm and HC = 95.75 cm. The WHtR optimal cut-off of 0.481 was close to the recommended cut-off value of 0.5. The predicted prevalence of obesity obtained using cut-offs from ROC analysis was higher than those from recommended references. All cut-off values for the various anthropometric measures generally increased with age for all percentile ranges. This study reveals a lower pBMI cut-off value, different from the CDC recommended cut-off, for screening obesity in a South African adolescent population. The study has established that the optimal pBMI cut-off for obesity screening may be ethnic-specific.
... In this cross-sectional prospective cohort study, we included 56 obese adolescents (age range: 11 -16 years) referred to the pediatric outpatient clinic (Istanbul Okmeydani training and research hospital, Turkey) between June and August 2020. As described by Mazıcıoglu et al., patients with a BMI ≥ 95th percentile, equivalent to a standard deviation score (SDS) of > 2, were considered to be obese based on their age and gender (20). Adolescents with grade 2-3 hepatosteatosis on upper abdominal ultrasonography (US) were considered to have NAFLD and were included in the NAFLD obese group (n = 28). ...
Background: Omentin-1 is an adipocytokine secreted from visceral adipose tissue that is thought to increase insulin sensitivity. Non-alcoholic fatty liver disease (NAFLD) is a comparatively extensive problem in obese adolescents. Decreased omentin-1 levels have been reported in obese patients, but the relationship between NAFLD and omentin-1 is contradictory. Objectives: We aimed to evaluate the omentin-1 levels in the sera of obese adolescents with and without NAFLD and compare them with each other. Methods: In this study, a total of 88 adolescents (56 obese and 32 normal-weight) were enrolled. Abdominal ultrasonography (US) identified 28 obese adolescents with grade 2-3 hepatosteatosis constituting the NAFLD group and 28 without hepatosteatosis on US constituting the non-NAFLD group. The control group included 32 age- and gender-matched cases without hepatosteatosis and with normal percentile body mass index (BMI). Serum omentin-1 levels were evaluated and compared. Results: The mean age of the research group was 12.72 ± 1.91 years. Unsurprisingly, BMI, glycated hemoglobin (HbA1c), liver transaminases (AST, ALT), total cholesterol, triglyceride, low-density lipoprotein cholesterol (LDL), homeostatic model assessment for insulin resistance (HOMA-IR), and insulin rates were noticeably elevated in obese adolescents compared to controls (P < 0.05). However, omentin-1 and high-density lipoprotein cholesterol (HDL) levels were remarkably lower in the obese group (P < 0.05). No significant difference was found between the NAFLD and non-NAFLD groups regarding omentin-1, HbA1c, glucose, urea, creatinine, AST, C-reactive protein (CRP), total cholesterol, triglyceride, HDL, LDL, thyroid stimulating hormone, 25-hydroxyvitamin D3, HOMA-IR, and insulin. The BMI and ALT grades of the non-NAFLD group were notably lower than the NAFLD group (P < 0.05). While there was no significant difference between omentin-1 and other parameters in obese adolescents without NAFLD (P > 0.05), we found a significant difference between omentin-1 and BMI, AST, ALT, HOMA-IR, and insulin values in obese adolescents with NAFLD (P < 0.05). Conclusions: Omentin-1 levels were decreased in obese adolescents regardless of the presence of NAFLD. However, in obese patients with NAFLD, there was a significant difference between omentin-1 and several markers of obesity and insulin resistance.
... [5,6] In addition, the possibility of use of MUAC and its performance in identifying obesity had been shown in 5-14-years of age group children and adolescents. [7][8][9][10] However, there is a paucity of research on the potential role of MUAC among adolescents, 10-19-year age group. With this background, the present study was conducted to assess the diagnostic performance of MUAC in identifying overweight and obese adolescents and estimating the cut-off values for MUAC among adolescents (age-specific, early adolescents: 10-14 years, and late adolescents: 15-19 years), separately for males and females compared with BAZ as the gold standard. ...
Background: Overweight and obesity during adolescence is an important public health problem. However, little is known about the age-and sex-specific mid-upper arm circumference (MUAC) cut-offs for identifying overweight and obese adolescents. Objectives: The present study was planned to assess diagnostic performance of MUAC in identifying overweight and obese adolescents and estimating age specific MUAC cut-offs, separately for males and females, taking body mass index for age Z-score (BAZ) as the gold standard. Methods: The present study is secondary data analysis using Comprehensive National Nutrition Survey, India, on 31,471 adolescents. The, area under curve receiver operating characteristic curve (AUC), and Youden Index were used to estimate MUAC cut-offs for overweight (BAZ > +1) and obesity (BAZ > +2). Results: The MUAC cut-offs to identify overweight were: For 10-14 years- 22.9/23.4 cm, for 15-19 years - 27.0/25.6 cm for males and females, respectively; and for obesity were: For 10-14 years - 24.5/25.1, for 15-19 years - 28.5/28.0 cm for males and females, respectively. For overweight, among males, the age-specific cut-off ranged between 21.2 cm (10 years) and 29.8 (19 years), and for females ranged between 21.2 cm (10 years) and 26.7 cm (19 years). For obesity, it ranged between 22.4 cm (10 years) and 31.1 cm (18 years) for males, and 23.9 cm (10 years) to 26.9 cm (19 years) for females. For obesity, AUC ranged between 0.81 and 0.92, indicating good to excellent diagnostic accuracy. Conclusion: Age- and sex-specific MUAC cut-offs could be considered as a screening tool for identifying overweight and obese adolescents.
This cross-sectional study aimed at comparing the cardiometabolic (CM) health of children and adolescents and identifying factors associated with CM complications shortly after cancer treatment. Cancer-related characteristics, blood pressure (BP), anthropometry, and biochemical parameters were collected in 80 patients (56.3% female, mean age: 11.8 years; range: 4.5 - 21.0) a mean of 1.4 years following therapy completion. Compared to children, adolescents had higher mean z-score of insulin (-0.47 vs. 0.20; P = 0.01), HOMA-IR (-0.40 vs. 0.25; P = 0.02), waist-to-height ratio (0.36 vs. 0.84; P = 0.01), subscapular skinfold thickness (-0.19 vs. 0.47; P = 0.02), total body fat (-1.43 vs. 0.26; P < 0.01), and lower mean z-score of HDL-C (0.07 vs. -0.53; P < 0.01). Adolescents were more likely to have high BP (42% vs. 15%; P < 0.01), dyslipidemia (64% vs. 15%; P < 0.001), and cumulating ≥ 2 CM complications (42% vs. 2%; P < 0.001) than children. Adiposity indices (z-scores) were associated with high BP [odds ratio (OR) ranging from 2.11 to 4.09] and dyslipidemia (OR ranging from 2.06 to 4.34). These results suggest that adolescents have a worse CM profile than children shortly after therapy and that adiposity parameters are associated with CM complications, highliting the importance to develop intervention strategies targeting this population.
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Amaç: Güncel literatürde, çocuklarda obezitenin belirlenmesinde farklı ölçüm yöntemlerini ve farklı istatistiksel analizleri içeren çok sayıda çalışma mevcuttur. Bu çalışmanın amacı, çocuklarda obeziteyi ölçmek için kullanılan antropometrik ölçüm yöntemlerini derlemektir. Yöntem: Çocuklarda obeziteyi ölçmek için kullanılan antropometrik ölçüm yöntemlerini araştıran çalışmalar üzerine sistematik bir derleme yapıldı. Pubmed/Medline ve Google Scholar veritabanları tarandı. Çalışmaların metodolojik kalitesi, Modifiye Downs and Black kontrol listesi kullanılarak incelenmiştir. Ardından önemli bulgular sentezlenmiştir. Bulgular: 2006-2020 yılları arasında yayınlanan 24 çalışma derlemeye dahil edildi. Çalışmaların örneklem büyüklükleri 30 ile 23043 katılımcı arasında değişmekteydi. Katılımcıların yaş aralığı 2-18 yıldı. Çalışmaların %87,5'inde (n=21) ölçüm yöntemi olarak Vücut Kitle İndeksi (VKİ) kullanılmış ve VKİ ölçüm performansı diğer antropometrik ölçüm yöntemleriyle karşılaştırılmıştır. Bel çevresi (n=16), bel-kalça oranı (n=13) ve kol çevresi ölçümleri (n=8) en sık kullanılan yöntemler olmuştur. Sonuç: Çocuk popülasyonunun obezite ve aşırı kiloluluk durumunu değerlendirmek için kullanılan antropometrik ölçümlerin karşılaştırılmasında VKİ skoru altın standart olarak görülmektedir. Bel çevresi ve bel-kalça oranı, Çift X-ışını Absorptiometri (DEXA) ve Hava Deplasmanlı Pletismografi (ADP) gibi daha doğru tekniklerin mümkün olmadığı çocuklarda obezite ve fazla kilonun ölçülmesinde en yaygın ve en etkin araçlardır. ABSTRACT Objective: In the current literature, there are many studies that include different measurement methods and different statistical analyzes in determining obesity in children. The aim of this study is to review the anthropometric measurement methods used to measure obesity in children. Method: A systematic review was completed for studies of anthropometric measurement methods used to measure obesity in children. The databases Pubmed/Medline and Google Scholar were searched. Methodological quality of studies was examined using the modified Downs and Black checklist. Subsequently, important findings were synthesized. Results: Twenty four studies published between the years 2006-2020 were included in the review. Sample sizes varied between 30 and 23043 participants. The age range of the participants varied between 2-18 years. In 87.5% of the studies (n=21), Body Mass Index (BMI) was used as the measurement method and the performance of BMI was compared with other anthropometric measurement methods. Waist circumference (n=16), waist-hip ratio (n=13) and arm circumference measurements (n=8) are the most common used methods. Conclusion: BMI score is seen as the gold standard in comparison of anthropometric measurements used to evaluate the obesity and overweight status of the child population. Waist circumference and waist-hip ratio are the most commonly used and effective tools for measuring obesity and overweight in children when more accurate techniques such as Dual X-ray Absorptiometry (DEXA) and Air-Displacement Plethysmography (ADP) are unfeasible.
Introduction: Obesity is a gradually more important multifactorial disease in both children and adults. Obese children and adolescents are at higher risk of becoming obese in adulthood, which is associated with an increased risk of mortality and morbidity. There is subclinical systemic inflammation in obesity. The study aimed to evaluate the hematological parameters as an indicator of inflammation in obese adolescents and to show the relationship of monocyte/HDL-cholesterol ratio, having a lipid component, with other inflammatory hematological parameters. Materials and methods: We retrospectively reviewed the medical files of 60 patients, 30 obese and 30 healthy controls, aged between 11 and 16 years, who applied to the pediatric outpatient clinic. Laboratory tests, hematological parameters, gender, age, and BMI were compared between the groups. Correlations between monocyte/HDL-cholesterol ratio and other laboratory parameters in the obese group were examined. Results: BMI, Alanine aminotransferase (ALT), C-reactive protein (CRP), triglyceride, insulin, and HOMA-IR levels of the obese adolescent group were statistically significantly higher than the control group (p<0.05). There was no statistically significant difference between the obese and control groups in terms of inflammatory hematological ratios (NLR, PLR, MLR, and monocyte/HDL-cholesterol ratio) (p>0.05). There was no statistically significant relationship between monocyte/HDL-cholesterol and other inflammatory hematological rates (p>0.05). There was a positive, moderate (48.6%), and statistically significant relationship between monocyte/HDL-cholesterol and MLR (p<0.05). Conclusions: In our study, the NLR, PLR, MLR, and monocyte/HDL-cholesterol values of the obese adolescent group were similar to the control group. There was correlation between monocyte/HDL-cholesterol and monocyte/lymphocyte values. There was no correlation between other rates. Our data do not support the utility of inflammatory hematological rates as a biomarker in adolescent obesity. However, we believe that our study can shed light on other studies to be conducted.
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INTRODUCTION: Adolescence is a decisive period in human life in which important body composition changes occur. Increase of total body mass and its relative distribution are mainly related to gender and pubertal development.OBJECTIVE: This review explores the specific measurements that may be used in this age group to assess excess body fat and to define obesity and overweight.RESULTS: Identification of subjects at risk for adiposity requires simple anthropometric cutoffs for the screening of overweight and obesity. In this context, BMI criterion is the most frequently used but, in spite of its high sensitivity and specificity, an important number of adolescents classified as overweight or obese do not have really high adiposity (32.1% of females and 42% of males). Excess total body fat and intra-abdominal visceral fat are related to metabolic abnormalities that increase the risk of cardiovascular diseases. Waist circumference seems to be the best simple anthropometric predictor for the screening of the metabolic syndrome in children and adolescents.CONCLUSIONS: Early identification of adolescents at risk for adiposity and its related metabolic complications requires reliable, simple and specific measures of excess body fat for this age group.Keywords: body composition, adolescents, metabolic syndrome
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One of the mostly used and preferred method in following the growth of children is to plot weight and height values of the children on standard percentile charts. It is essential for each country to use its own populations' updated percentile curves. However, data on the growth of children living in different regions are also needed for comparison with the national standards. This study was conducted in Kayseri with a trained team in order to obtain anthropometrical measurements in children and adolescents. Weight and height measurements from 5727 (2785 boys, 2942 girls) healthy school children (aged between 6 to 18 years) from all socioeconomic levels were randomly selected. Smoothed percentile curves were produced by LMS method. Smoothed percentile curves including the percentile values for 3rd, 5th, 10th, 25th, 50th, 75th, 85th, 90th, 95th and 97th and standard deviation scores were calculated for boys and girls. The 3rd, 50th and 97th centiles of weight and height of these children were compared with the respective values of the established national standards obtained from Istanbul children. This study presents data and smoothed percentile curves for weight and height measurement of healthy central Anatolia children aged 6 to 18 years. Nationwide studies are needed to bring out the regional differences in our country.
Overweight and obesity represent a rapidly growing threat to the health of populations in an increasing number of countries. Indeed they are now so common that they are replacing more traditional problems such as undernutrition and infectious diseases as the most significant causes of ill-health. Obesity comorbidities include coronary heart disease, hypertension and stroke, certain types of cancer, non-insulin-dependent diabetes mellitus, gallbladder disease, dyslipidaemia, osteoarthritis and gout, and pulmonary diseases, including sleep apnoea. In addition, the obese suffer from social bias, prejudice and discrimination, on the part not only of the general public but also of health professionals, and this may make them reluctant to seek medical assistance. WHO therefore convened a Consultation on obesity to review current epidemiological information, contributing factors and associated consequences, and this report presents its conclusions and recommendations. In particular, the Consultation considered the system for classifying overweight and obesity based on the body mass index, and concluded that a coherent system is now available and should be adopted internationally. The Consultation also concluded that the fundamental causes of the obesity epidemic are sedentary lifestyles and high-fat energy-dense diets, both resulting from the profound changes taking place in society and the behavioural patterns of communities as a consequence of increased urbanization and industrialization and the disappearance of traditional lifestyles. A reduction in fat intake to around 20-25% of energy is necessary to minimize energy imbalance and weight gain in sedentary individuals. While there is strong evidence that certain genes have an influence on body mass and body fat, most do not qualify as necessary genes, i.e. genes that cause obesity whenever two copies of the defective allele are present; it is likely to be many years before the results of genetic research can be applied to the problem. Methods for the treatment of obesity are described, including dietary management, physical activity and exercise, and antiobesity drugs, with gastrointestinal surgery being reserved for extreme cases.
Background In adults the metabolic syndrome imposes a substantial risk for type 2 diabetes mellitus and premature coronary heart disease. Even so, no national estimate is currently available of the prevalence of this syndrome in adolescents.Objective To estimate the prevalence and distribution of a metabolic syndrome among adolescents in the United States.Design and Setting Analyses of cross-sectional data obtained from the Third National Health and Nutrition Examination Survey (1988-1994), which was administered to a representative sample of the noninstitutionalized civilian population of the United States.Participants Male and female respondents aged 12 to 19 years (n = 2430).Main Outcome Measures The prevalence and distribution of a metabolic syndrome among US adolescents, using the National Cholesterol Education Program (Adult Treatment Panel III) definition modified for age.Results The overall prevalence of the metabolic syndrome among adolescents aged 12 to 19 years was 4.2%; 6.1% of males and 2.1% of females were affected (P= .01). The syndrome was present in 28.7% of overweight adolescents (body mass index [BMI], ≥95th percentile) compared with 6.8% of at-risk adolescents (BMI, 85th to <95th percentile) and 0.1% of those with a BMI below the 85th percentile (P<.001). Based on population-weighted estimates, approximately 910 000 US adolescents have the metabolic syndrome.Conclusions Perhaps 4% of adolescents and nearly 30% of overweight adolescents in the United States meet these criteria for a metabolic syndrome, a constellation of metabolic derangements associated with obesity. These findings may have significant implications for both public health and clinical interventions directed at this high-risk group of mostly overweight young people.
Objective To develop an internationally acceptable definition of child overweight and obesity, specifying the measurement, the reference population, and the age and sex specific cut off points. Design International survey of six large nationally representative cross sectional growth studies. Setting Brazil, Great Britain, Hong Kong, the Netherlands, Singapore, and the United States. Subjects 97 876 males and 94 851 females from birth to 25 years of age. Main outcome measure Body mass index (weight/height 2 ). Results For each of the surveys, centile curves were drawn that at age 18 years passed through the widely used cut off points of 25 and 30 kg/m 2 for adult overweight and obesity. The resulting curves were averaged to provide age and sex specific cut off points from 2›18 years. Conclusions The proposed cut off points, which are less arbitrary and more internationally based than current alternatives, should help to provide internationally comparable prevalence rates of overweight and obesity in children.
The limitations of diagnostic "accuracy" as a measure of decision performance require introduction of the concepts of the "sensitivity" and "specificity" of a diagnostic test. These measures and the related indices, "true positive fraction" and "false positive fraction," are more meaningful than "accuracy," yet do not provide a unique description of diagnostic performance because they depend on the arbitrary selection of a decision threshold. The receiver operating characteristic (ROC) curve is shown to be a simple yet complete empirical description of this decision threshold effect, indicating all possible combinations of the relative frequencies of the various kinds of correct and incorrect decisions. Practical experimental techniques for measuring ROC curves are described, and the issues of case selection and curve-fitting are discussed briefly. Possible generalizations of conventional ROC analysis to account for decision performance in complex diagnostic tasks are indicated. ROC analysis is shown to be related in a direct and natural way to cost/benefit analysis of diagnostic decision making. The concepts of "average diagnostic cost" and "average net benefit" are developed and used to identify the optimal compromise among various kinds of diagnostic error. Finally, the way in which ROC analysis can be employed to optimize diagnostic strategies is suggested.
This paper presents a general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies. The procedure essentially involves the construction of functions of the observed proportions which are directed at the extent to which the observers agree among themselves and the construction of test statistics for hypotheses involving these functions. Tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interobserver agreement are developed as generalized kappa-type statistics. These procedures are illustrated with a clinical diagnosis example from the epidemiological literature.