Annals of Human Biology, March–April 2013; 40(2): 168–174
Copyright q Informa UK, Ltd.
ISSN 0301-4460 print/ISSN 1464-5033 online
DOI: 10.3109/03014460.2012.753109
RESEARCH PAPER
Distribution of obesity-related metabolic markers among 5– 15 year
old children from an urban area of Sri Lanka
V.P. Wickramasinghe1, C. Arambepola2, P. Bandara1, M. Abeysekera1, S. Kuruppu1, P. Dilshan1 &
B.S. Dissanayake1
Department of Paediatrics, and 2Department of Community Medicine, University of Colombo, Colombo, Sri Lanka
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1
INTRODUCTION
Background: Obesity-associated metabolic consequences are
commonly seen among young South Asians.
Objective: To assess the nutritional status, prevalence
of metabolic derangements and to identify the validity
of different obesity diagnostic criteria in the detection of
metabolic derangements among 5–15 year old school children
in the Colombo district of Sri Lanka.
Materials and procedures: After a 12-hour overnight fast, blood
was drawn for glucose, lipid profile and alanine amino
transferase (ALT) enzyme. Oral glucose tolerance test (OGTT)
was done with blood taken for random blood sugar 2 hours
after glucose load. Height, weight, waist circumference (WC)
and blood pressure were measured.
Results: Nine hundred and twenty children were studied (boys,
n ¼ 547). Thirty-two (3.5%) were obese according to IOTF
classification. Five (0.5%) and 57 (6.2%) children had systolic
and diastolic hypertension. Twelve (1.3%) and three (0.3%) had
impaired fasting glucose and 2-hour OGTT, respectively. One
hundred and thirty-nine (15.1%) had hypercholesterolemia and
36 (3.9%) hypertriglyceridaemia. Two hundred and fifteen
(23.3%) had low HDL. Fifteen (1.6%) had metabolic syndrome
according to IDF definition. Two hundred and eighty-three
(30.7%) had one metabolic derangement; 95 (10.3%) had two
metabolic derangements; and 16 (1.7%) had three or more
metabolic derangements. Sri Lankan BMI and WC obesity cut-offs
had a higher sensitivity in detecting metabolic abnormalities
than international cut-offs.
Conclusion: Metabolic derangements are prevalent in children
who were detected to be non-obese by anthropometric
measures, and clinicians should actively look and correct them.
New research is needed to study the long-term effects on health.
Obesity, the new form of malnutrition, is ever increasing. It
is not confined to the developed world but also seen in
economies in transition. Obesity-related morbidity may
affect any system of the body ranging from cardiovascular
disease (CVD), type 2 diabetes mellitus (T2DM), nonalcoholic fatty liver disease, skeletal abnormalities and
psychological conditions such as depression, bullying, low
companionship, less job opportunities, etc. (Ebbeling et al.
2002). Obesity-associated metabolic derangements are
related to the fat content of the body (WHO 2000). South
Asian populations are particularly at a higher risk of
developing these derangements at a younger age
(Whincup et al. 2002). Compared to children of white
European origin, South Asian children tend to accumulate
more fat in the abdominal region when they put on weight,
possibly contributed to by their high fat and carbohydrate
diet and genetic predisposition (Malina et al. 1995).
Obesity-related morbidity is mainly associated with
insulin resistance, which leads to ‘metabolic syndrome’
(Halpern et al. 2010). Abdominal obesity, impaired glucose
metabolism, atherogenic dyslipidaemia, pro-inflammatory
state (denoted by elevated C-reactive protein), prothrombotic state (denoted by elevated plasminogen activator
inhibitor-1 (PAI-1)) and hypertension are identified as the
six main components of insulin-resistant related metabolic
derangements seen in obese individuals (Grundy et al.
2004). Since identifying the components of metabolic
syndrome (MetS), attempts have been made to
develop diagnostic criteria for adults (Balkau and Charles
1999; WHO 1999; NCEP/ATP III 2001). International
Diabetes Federation (IDF) proposed a consensus diagnostic
criteria in 2006 (Alberti et al. 2006).
Although the concept of metabolic syndrome was
initially described in adults, onsets of many of these
metabolic derangements are seen at young age with the
Keywords: Metabolic markers, Sri Lankan children, South Asian
population, fat mass, metabolic syndrome
Correspondence: Dr V. Pujitha Wickramasinghe, Department of Paediatrics, Faculty of Medicine, University of Colombo, Kynsey Road, Colombo,
Sri Lanka. Tel: þ 94 (011) 2688748 Ext 173; þ 94 (077) 7766595. Fax: þ 94 (011) 2691581. E-mail: pujithaw@yahoo.com
(Received 19 May 2012; revised 30 October 2012; accepted 12 November 2012)
168
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METABOLIC DERANGEMENTS IN SRI LANKAN CHILDREN
increase in the incidence of childhood obesity. The
prevalence of MetS among 7 – 17 obese Chinese children
and adolescents was 23.9% (Yu et al. 2012). Lean,
overweight and obese individuals suffering from at least
one metabolic abnormality were 40.3%, 65.6% and 90.6%,
respectively. About 62.9% of obese individuals suffered from
at least two metabolic abnormalities and 23.9% suffered
from at least three metabolic abnormalities. Individuals
suffering from four or more metabolic abnormalities were
either overweight or obese (Yu et al. 2012).
A Spanish study involving obese children and adolescents
recorded hypertension in 26.1%, hypertriglyceridemia
($ 150 mg/dL) in 16% and low HDL-C (, 40 mg/dL) in
11%. Prevalence of impaired fasting blood glucose levels
($ 100 mg/dL) was seen in 8%. The overall prevalence of
MetS in this population was 19.6% (Guijarro de Armas et al.
2012).
An Indian study showed that hypertension in normal
weight children was 10.1%, while in obese 18.3%. Systolic
and diastolic hypertension was 5.4% and 6.4%, respectively,
in normal children (Raj et al. 2007). Another study done in
India reported an overall MetS prevalence of 4.2%, with
more girls being affected among 12 – 17 year old adolescents
(Singh et al. 2007). The same study showed the prevalence of
hyperglycaemia to be 3.6%, hypertriglyceradaemia 18.8%,
low HDL-C 24.4% and elevated blood pressure 5.5%,
among the normal weight individuals. Among obese
individuals, hyperglycaemia was 28.3% and hypertriglyceradaemia was 40%. Low HDL was 61.7% and elevated blood
pressure was 31.6% (Singh et al. 2007).
A study of Argentinean adolescents showed that the mean
triglyceride level was 73 mg/dl and 90 mg/dl ( p , 0.001)
among normal and overweight individuals, respectively
(Musso et al. 2011). Similarly, HDL-C was 52 mg/dl and
47 mg/dl (p , 0.001) among normal and overweight
individuals, respectively. Similar differences were noted for
systolic blood pressure (108 mmHg vs 118 mmHg; p , 0.001)
and diastolic blood pressure (64 mmHg vs 70 mmHg;
p , 0.001) in favour of non-overweight individuals.
Metabolic abnormalities have been noted among
children and adolescents all over the world. With the rise
in childhood obesity, prevalence of MetS is expected to rise
and, therefore, requires a childhood definition for timely
detection of the condition. Although IDF fulfilled this
requirement, the recommendation is to use the criteria only
on 10 – 16 year old children and adult guidelines on children
above 16 years (Zimmet et al. 2007). As for children under
10 years, screening is required for metabolic derangements if
they have risk factors such as obesity but not to make a
diagnosis of MetS. Although ‘Metabolic Syndrome’ as a
disease entity has been challenged, adverse metabolic
profiles are seen among children either individually or in
combination. This study attempts to identify the nutritional
status, prevalence of metabolic derangements and to identify
the reliability of different obesity diagnostic criteria in the
detection of metabolic derangements among 5 –15 year old
school children in the Colombo district of Sri Lanka.
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169
MATERIALS AND METHODS
A cross-sectional descriptive study was carried out among
5 – 15-year-old apparently healthy Sri Lankan children
during April 2009 – April 2010. The Ethics Review
Committees of the Faculty of Medicine, University of
Colombo and Lady Ridgeway Hospital for Children
approved the study. A two-stage, probability proportionate
to size, cluster sampling technique was used to recruit a
minimum sample of 790 children from 15 schools in the
district of Colombo. The sample size was calculated to
ensure an expected proportion of children with obesity of
2%; level of precision of 0.01; confidence interval of 0.05;
and a non-repose rate of 5%. Stratified by age and gender,
one class from each grade of 1 – 10 was included as a cluster,
which was randomly selected from each school that
was selected according to probability proportionate to
size. Students with any illness or on any medication were
excluded, while the eligible students and their parents
were informed about the procedure and written consent
from parents and assent from children were obtained.
Assessment of nutritional status
Height was measured with a stadiometer to the last
completed 0.1 cm (Surgical and Medical products, Brisbane,
Australia) with occiput, back of chest, buttock and heel
touching the vertical plane and head kept in the horizontal
Frankfurt plane (Lohman 1989). Weight was measured to
the closest 100 grams using an electronic weighing scale,
wearing lightweight clothing (Soehnlew, Soehnle-Waagen
GmbH & Co, Germany). Waist circumference was measured
with the subject standing erect with abdomen relaxed, arms
at the sides of the body and feet together. The measurement
was taken with a non-stretchable tape, in the horizontal
plane, at the level of mid-point between the costal margin
and the iliac crest in the mid axillary line. BMI was
calculated (weight (kg)/height (m)2). Nutritional status of
children was assessed based on IOTF (Cole et al. 2000) cutoff values. Additionally, British WC centiles (McCarthy et al.
2001) and new Sri Lankan anthropometric cut-off values
(Wickramasinghe et al. 2011) based on BMI and WC were
used to assess the status of obesity.
Assessment of metabolic derangements
Blood pressure was measured in a seated position using a
mercury sphygmomanometer after a 10-minute rest period.
An appropriate cuff size was used depending on the size of
the child’s arm. The first and the fifth Korotkoff sounds were
used to represent the systolic and diastolic blood pressures,
respectively. If elevated blood pressure was noted, it was rechecked after a 30-minute rest (Task Force Report on High
Blood Pressure in Children and Adolescents 1996).
Blood was drawn after a 12-hour overnight fast, for
fasting blood glucose (FBS) lipid profile and Alanine amino
transferase enzyme (ALT) levels. Oral glucose tolerance test
(OGTT) was done after giving a drink of anhydrous glucose
1.75 g/kg per body weight to a maximum of 75 g and blood
was drawn 2 hours later for random blood sugar (RBS).
170
V. P. WICKRAMASINGHE ET AL.
Table I. Demographic and anthropometric characteristics of the study population according to age category and gender.
5 –10 years
Male
Male
Female
197
289
8.0 ^ 1.3
12.4 ^ 1.5
125.2 ^ 10.3
146.5 ^ 11.8
25.4 ^ 8.9*
36.4 ^ 11.3
15.8 ^ 3.5*
16.6 ^ 3.4
20.21 ^ 1.1
20.77 ^ 1.18
20.26 ^ 1.6*
—
20.64 ^ 1.8*
21.09 ^ 1.76
56.5 ^ 9.3*
61.8 ^ 10.0
* p , 0.05 when comparing each parameter between gender groups within each chronological age group.
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n
Age (years)
Height (cm)
Weight (kg)
BMI (kg/m2)
Height Z-score
Weight Z-score
BMI Z-score
Waist Circumference (cm)
. 10–15 years
Female
258
7.8 ^ 1.3
124.5 ^ 9.1
23.3 ^ 6.6
14.8 ^ 2.5
20.32 ^ 1.02
20.80 ^ 1.43
21.01 ^ 1.56
53.7 ^ 7.7
Biochemical analysis
Serum triglyceride was assessed using an enzymatic
colourimetric test with lipoprotein lipase cleavage of
triglycerides followed by oxidation to dihydroxyacetone
phosphate and hydrogen peroxide, and products were
assessed by colourimetry (Roche Diagnostics GMbH,
Germany). Serum cholesterol was assessed by enzymatic
colourimetric test with enzymatic cleavage with cholesterol
esterase and cholesterol oxidase and was assessed photometrically (Roche Diagnostics GMbH). Serum Lipoproteins
(HDL) were assessed using enzymatic spectrometry with
enzymatic analysis using cholesterol esterase, cholesterol
oxidase and peroxidase and quantitative assessment with
spectrophotometer (Roche Diagnostics GMbH). Blood
glucose (both FBS and RBS) was assessed using an enzymatic
spectrometric method using glucose oxidase and glucose
peroxidase enzymes and quantitative analysis using a
spectrometer (Roche Diagnostics GMbH). ALT-Liver
enzyme was quantitatively measured using enzymatic
spectrometric assay (Roche Diagnostics GMbH). All analysis
was done using a fully automated analyser (Hitachi 704,
Japan). LDL cholesterol was calculated using the total
cholesterol – (HDL þ TG/5) equation.
Definitions of metabolic derangements
Metabolic derangements were identified as: WC for
age . 90th centile of UK standards (McCarthy et al. 2001);
abnormal glucose homeostasis, if FBS . 100 mg/dl or
2-hour OGTT value . 140 mg/dl; HDL, , 40 mg/dl
(,1.03 mmol/L); triglyceride, . 150 mg/dl ($1.7 mmol/L);
and elevated blood pressure, . þ2 SD for age for both SBP or
DBP of UK standards (Jackson et al. 2007). This cut-off value
for SBP and DBP was chosen instead of the single cut-off value
given by IDF definition, as the latter value (130/85 mmHg) is
suitable only for the tallest 15 year old children and thus could
lead to an under-estimation of elevated blood pressure.
Acanthosis in children was diagnosed by visualization of dark
pigmented elevated skin around the neck and axilla.
Data analysis
Data were entered and analysed using the NCSS computer
package for windows. Sensitivity, specificity, positive
predictive value and efficiency of the anthropometry-based
cut-off values (diagnosed individual as obese) in detecting at
176
12.6 ^ 1.6
148.3 ^ 9.9
39.9 ^ 12.6*
17.9 ^ 4.3*
20.65 ^ 1.1
—
20.55 ^ 1.7*
64.8 ^ 10.8*
least one metabolic derangement, were evaluated using a
2 £ 2 table and the method is described elsewhere
(Wickramasinghe et al. 2005).
RESULTS
Nine hundred and thirty-two children were recruited, but
the data of 920 (boys, n ¼ 547) were used in the final
analysis. The sample was disaggregated according to age
(5 – 10, . 10 – 15 years) in line with IDF definition
categories. Table I shows the demographic characteristics
of the study population according to age group and sex.
When compared between sexes within each age category,
adiposity-related parameters (weight, BMI, WC) showed
statistically significant higher values in girls.
Table II shows nutritional status of the study population.
Thirty-two (3.5%) were obese according to IOTF classification. There were 193 (21%) with WC above the 90th
centile of UK standards. According to Sri Lankan standards,
33.9% and 21.9% of study population had an inappropriately high WC and BMI, respectively. About 48% of the
population was suffering from thinness according to IOTF
classification, while 9% was suffering from an extreme
degree of thinness.
Table III gives details of the distribution of adverse
metabolic profile according to age category and sex. Five
(0.5%) and 57 (6.2%) children had elevated systolic and
diastolic blood pressure, respectively. Twelve (1.3%) and
three (0.3%) had impaired fasting glucose and 2-hour
Table II. Nutritional status of the study population according to IOTF
classification and obesity based on other cut-off values.
5– 10 years
n
BMI - IOTF cut-off
Obese
Overweight
Normal
Thinness
Thinness 1
Thinness 2
Thinness 3
BMI - SL cut-off values
WC - UK standards
WC - SL cut-off values
.10 –15 years
Male
Female
Male
Female
258
197
289
176
8 (3%) 13 (7%)
6 (2%)
5 (3%)
16 (6%) 23 (12%) 25 (9%) 26 (15%)
96 (37%) 78 (40%) 105 (36%) 75 (43%)
75
37
26
39
41
58
(29%)
(14%)
(10%)
(15%)
(16%)
(23%)
38
27
18
50
55
67
(20%) 79 (27%)
(14%) 50 (17%)
(9%)
24 (8%)
(25%) 65 (23%)
(28%) 46 (16%)
(34%) 115 (40%)
29
25
16
48
51
72
(16%)
(14%)
(9%)
(27%)
(29%)
(41%)
Annals of Human Biology
METABOLIC DERANGEMENTS IN SRI LANKAN CHILDREN
171
Table III. Distribution of abnormal metabolic components in the study population by age category and sex.
5– 10 years
Male
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n
Systolic Blood Pressure
Diastolic Blood Pressure
Fasting Blood Sugar
2-h OGTT
Cholesterol
Triglyceride
HDL
LDL
Acanthosis
5
4
3
28
3
106
47
9
11– 15 years
Female
258
(1.9%)
(1.5%)
(1.1%)
0
(10.8%)
(1.1%)
(41.0%)
(18.2%)
(3.5%)
197
0
5 (2.5%)
1 (0.5%)
0
40 (20.3%)
7 (3.5%)
63 (32.0%)
48 (24.4%)
12 (6.1%)
OGTT, respectively. There were 139 (15.1%) with high
serum cholesterol, 36 (3.9%) with high serum triglyceride
and 215 (23.3%) with low HDL. Fifteen (1.6%) had
metabolic syndrome according to the modified IDF
definition and it was 22.1% among obese individuals
diagnosed based on 2007 WHO growth standards.
Table IV shows the frequency distribution of metabolic
derangements in the study population. Two hundred and
eighty-three (30.8%) had one abnormal metabolic parameter; 95 (10.3%) had two metabolic derangements; and
16 (1.7%) had three or more metabolic derangements. One
hundred and sixty-one (43%) girls and 233 (42.6%) boys
had at least one metabolic derangement. The distribution of
having at least one metabolic derangement was almost equal
in both gender groups. With advancing age, there was a
slight increase in the prevalence.
Table V gives the distribution of metabolic derangements
in obese children, where obesity was diagnosed using
different BMI (based on IOTF and Sri Lankan) and WC
(based on UK and Sri Lankan) cut-off values. Sri Lankan
BMI and WC cut-offs were able to detect many more cases
with metabolic abnormalities than the WC of UK standards
and IOTF cut-offs. However, Sri Lankan cut-offs have high
false positive rates. There were many with abnormal
metabolic profiles with a normal BMI or WC according to
defined international cut-off values.
Table VI shows the results of validation of each
anthropometric cut-off value used in the diagnosis of
obesity in detecting at least one metabolic derangement in
Sri Lankan children. In both boys and girls, the BMI-based
IOTF obesity cut-off had a very low sensitivity, ranging from
6.0% in boys and 10.5% in girls, detecting at least a single
metabolic derangement. However, it had 100% specificity.
33
4
1
32
12
111
42
23
Male
Female
289
0
(11.4%)
(1.4%)
(0.3%)
(11.1%)
(4.2%)
(38.4%)
(14.5%)
(7.9%)
176
0
15 (8.5%)
4 (2.3%)
2 (1.1%)
39 (22.2%)
14 (7.9%)
62 (35.2%)
39 (22.2%)
21 (11.9%)
Sensitivity was improved when the IOTF cut-off was
lowered to overweight level. Sri Lankan-based BMI cut-off
values improved the sensitivity (37.5% in boys and 54% in
girls) with a satisfactory level of specificity (. 94.0%).
British WC cut-off had improved sensitivity as well as
specificity compared to BMI-based IOTF cut-off values. The
Sri Lankan-based WC cut-off values had higher sensitivity,
better than all tested obesity diagnostic tools, but the
specificity was the lowest. Positive predictive value was
lowest compared to others, but efficiency was equal to other
methods.
DISCUSSION
This study clearly shows that adverse metabolic profiles are
prevalent in children of South Asian origin, even at a
younger age range of 5– 10 years. The prevalence of
metabolic derangements is high among obese individuals.
The concept of MetS in children and adolescents is still a
matter of discussion, mainly because data on this age
group are scarce. Although there is no consensus regarding
the diagnosis of MetS in children and adolescents, it is
evident that each component of the syndrome must be
identified as early as possible in order to prevent definitive
lesions. The challenge is therefore to decide on suitable
diagnostic criteria and adopt suitable cut-off values to
diagnose such metabolic derangements early in the course of
evolution (Halpern et al., 2010).
Chances of developing metabolic derangements and
severity of the metabolic consequences depend on how early
the cardiovascular risks develop and the duration of
exposure to the adverse metabolic environment. The age
at which these may occur appears to be related to the
Table IV. Frequency distribution of the metabolic derangements in the study population according to each age category and sex.
No of abnormal
metabolic components
0
1
2
3
4
Total no with metabolic
derangement
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Total population
5 – 10 year age group
.10– 15 year age group
Male (547)
Female (373)
Male (258)
Female (197)
Male (289)
314 (57.4%)
174 (31.8%)
52 (9.5%)
6 (1.1%)
1 (09.2%)
233 (42.6%)
212
109
43
7
2
161
154 (59.6%)
86 (33.3%)
15 (5.9%)
2 (0.8%)
1 (0.4%)
104 (40.4%)
118 (59.9%)
53 (26.9%)
23 (11.7%)
3 (1.5%)
0
79 (40.1%)
160
88
37
4
(56.8%)
(29.3%)
(11.5%)
(1.9%)
(0.5%)
(43.2%)
(55.4%)
(30.4%)
(12.8%)
(1.4%)
0
129 (44.6%)
Female (176)
94 (53.4%)
56 (31.8%)
20 (11.4%)
4 (2.3%)
2 (1.1%)
82 (46.6%)
172
V. P. WICKRAMASINGHE ET AL.
Table V. Distribution of the frequency of abnormal metabolic components in obese children diagnosed by different anthropometric methods.
Obesity detected by different methods
No of abnormal
metabolic components
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0
1
2
3
4
Total no with metabolic
derangement
BMI (Sri Lankan)
cut-off
Waist Circumference
(Sri Lanka) cut-off
Waist Circumference
(UK) cut-off
Male (104)
Female (98)
Male (173)
Female (139)
17
42
38
6
1
87
11
48
33
4
2
87
52 (22.7%)
71 (30.5%)
43 (18.5%)
6 (2.5%)
1 (0.4%)
121 (69.9%)
22
69
40
6
2
117
(7 .3%)
(18.0%)
(16.3%)
(2.5%)
(0.4%)
(83.6%)
(6.8%)
(29.8%)
(20.5%)
(2.5%)
(1.2%)
(88.8%)
presence and aggregation of cardiovascular risk factors in
the course of life (Halpern et al., 2010). A study done in
Brazil involving 720 school-age children of 14 –19 years
found that 8.3% had higher capillary glucose levels and most
of them were females (Halpern et al., 2010). Among the
10 – 15 year age group, it was 1.7%.
In a group of 10–18 year old Mexican children, the
prevalence of MetS varied between 3.8–7.8% based on
different types of definitions. However, the authors did not use
the new IDF definition. The prevalence of MetS was 26.1% in
obese children and at least one abnormal biochemical test was
seen among 20.6% of lean individuals (Rodriguez-Moran et al.
2004). This prevalence was quite similar to the values seen by
De Silva et al. (2004) among Sri Lankan children in 2006.
However, they too used diagnostic criteria different to IDF.
Among 7–14 year old Chinese children, prevalence of
metabolic syndrome was 6.6% in the general public and
33.1% among obese children, 2.3% among the lean population
(Liu et al., 2010). Both Mexican and Chinese values were more
than double compared to our data.
Another large cross-sectional study involving 7 – 11 year
old Chinese children from six cities had an overall MetS
prevalence of 0.8% and 6.6% among the normal population
and among obese children, respectively, based on the IDF
definition. Compared to our data the prevalence was low,
perhaps due to the low sensitivity of the IDF definition
(Xu et al. 2012). In the same study the prevalence of
abdominal obesity, high triglycerides level, low HDL-C level,
elevated blood pressure and impaired glucose metabolism
among children aged 10 – 11 years was 15.1%, 5.0%, 5.8%,
(13.7%)
(42.8%)
(24.8%)
(3.7%)
(1.2%)
(84.2%)
IOTF
Male (87)
Female (106)
Male (14)
Female (18)
0
(18.5%)
(15.9%)
(2.5%)
(0.4%)
(100%)
0
60 (37.3%)
38 (23.6%)
6 (3.7%)
2 (1.2%)
106 (100%)
0
7 (3.0%)
5 (2.1%)
2 (0.8%)
0
14 (100%)
1 (0.6%)
6 (3.7%)
9 (5.5%)
2 (1.2%)
0
18 (100%)
43
37
6
1
87
2.6% and 2.7%, respectively. Among our 10 – 15 year old
children, the prevalence of the same parameters in the same
order was 21.0%, 5.6%, 37.2%, 10.3% and 2.3%. The
prevalence of most of our parameters was high, possibly due
to the older age group in our study. The proportion of
children with at least one, two or three items of metabolic
abnormalities in this Chinese group of children were 25.0%,
5.4% and 0.9%, respectively (Xu et al. 2012) and, among our
children, one, two and three or more metabolic abnormalities were present in 30.7%, 10.3% and 1.7%, respectively.
Present IDF cut-off values have single cut-off values for
each metabolic component (except for WC) across a wide
age range. They are not realistic, as children are a growing
population and these biological parameters change depending on the age and sex of the individual. Use of a single cutoff value for each metabolic parameter will lower the
sensitivity, as norms would depend on the age and sex of the
children. Therefore, age- and sex-specific cut-off values
should be used for the diagnosis of metabolic parameters in
order to improve diagnosis and for early detection of the
related complications. Elevated fasting blood sugar
(impaired or overt diabetes level) is of low prevalence in
this group of children and it is a late manifestation of
metabolic complications. However, prevalence of other
metabolic complications is high, denoting that early insulin
resistance may have set in. Insulin resistance is the precursor
to the development of abnormal glucose homeostasis and
use of insulin resistance would be a better tool for early
detection of metabolic derangements in children, perhaps
better than assessing individual metabolic parameters and
Table VI. Validity of obesity diagnostic methods in detecting at least one metabolic abnormality in the study population.
Male
Se
Sp
Pv
Ef
Female
Se
Sp
Pv
Ef
IOTF (obesity)
IOTF (overweight
& obesity)
BMI Sri Lankan
cut-off
WC British
cut-off
WC Sri Lankan
cut-off
6.0%
100%
100%
60.0%
23.3%
99.7%
98.2%
67.1%
37.3%
94.6%
83.6%
70.2%
37.3%
100%
100%
73.3%
52.0%
83.4%
70.0%
70.0%
10.5%
99.5%
94.4%
61.1%
40.0%
98.6%
95.5%
73.2%
54.0%
94.8%
88.8%
77.5%
66.2%
100%
100%
85.4%
73.0%
89.6%
80.6%
82.5%
Se, Sensitivity; Sp, Specificity; Pv, Positive Predictive value; Ef, Efficiency.
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METABOLIC DERANGEMENTS IN SRI LANKAN CHILDREN
especially blood glucose levels. There is evidence even in
adults that fasting blood glucose itself does not diagnose
many with IGT, especially of different ethnic groups (Anand
et al. 2003).
The anthropometric cut-off values used at present
(internationally available) have a low sensitivity and,
therefore, are not suitable for screening of individuals for
metabolic derangements in this population. This could be
due to two reasons; first, these cut-off values are developed
on populations other than of South Asian origin and,
second, they have used a population distribution of a
parameter rather than a biological end point. Although
specificity was low, Sri Lankan-based anthropometric cutoff values had a high sensitivity. It would be prudent for a
screening tool to ‘over diagnose’ a condition in a screening
programme than ‘under diagnose’, as the latter would do
more harm than good. Even a premature alert would be a
safe practice, as it would encourage children and parents to
take necessary behaviour modification measures, thus
leading to a healthy lifestyle. BMI-based cut-off values
available in the published literature are not sensitive in
detecting overweight and obesity among Sri Lankan
children (Wickramasinghe et al. 2009). Therefore, anthropometric cut-off values should be developed, taking ethnic
origin and biological/metabolic abnormalities into consideration. Sri Lankan cut-off values were based on fat
content of the body that is associated with metabolic
complications and could be considered as a more
biologically relevant cut-off value.
Prevalence of obesity-related metabolic derangements
among Sri Lankan children is seen from a young age, as is
the case of many children from other parts of the world. This
highlights the fact that potential risk factors for cardiovascular diseases tend to cluster from a younger age and are
strongly associated with obesity. Our observations suggest
that the development of the metabolic cardiovascular
syndrome has its origin in childhood. The prevalence of
such abnormal metabolic profiles depends on the type of
cut-off values used. Therefore, consensus needs to be
reached on appropriate cut-off values for timely detection of
metabolic abnormalities in children. Current anthropometry-based screening tools are not sensitive in early detection
of cardiovascular risk in children and, therefore, suitable
screening tools with high sensitivity need to be developed
for early detection and treatment of such metabolic
derangements among this highly vulnerable South Asian
population.
ACKNOWLEDGEMENTS
We are grateful to all children and their parents for
participating in this study. Ms Amara S. de S. Wijerathna
and Ms A. U. A. Gunawardhana of the Reproductive Biology
Laboratory, Department of Obstetrics and Gynaecology and
Mr S. D. D. Dissanayake of the Department of Paediatrics,
University of Colombo in analysing the blood samples. This
study was carried out through an educational grant from
Anchor Institute, to University of Colombo.
q Informa UK, Ltd.
173
Declaration of interest: The authors report no conflicts of
interest. The authors alone are responsible for the content
and writing of the paper.
REFERENCES
Alberti KGMM, Zimmet P, Shaw J. 2006. Metabolic syndrome a new
world wide definition. A concensus statement from the international diabetes federation. Diabet Med 23:469 – 480.
Anand SS, Razak F, Vuksan V, Gerstein HC, Malmberg K, Yi Q, Teo KK,
Yusuf S. 2003. Diagnostic strategies to detect glucose intolerance in a
multiethnic population. Diabetes Care 26:290– 296.
Balkau B, Charles MA. 1999. Comment on the provisional report from
the WHO consultation. European Group for the Study of Insulin
Resistance (EGIR). Diabet Med 16:442– 443.
Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. 2000. Establishing a
standard definition for child overweight and obesity world wide:
International survey. BMJ 320:1 – 6.
De Silva KSH, Wickramasinghe VP, Goonerathne INA. 2004. Metabolic
consequences of childhood obesity – a preliminary report. Ceylon
Med J 51:105 – 109.
Ebbeling CB, Pawlak DB, Ludwig DS. 2002. Childhood obesity: publichealth crisis, common sense cure. Lancet 360:473 – 482.
Guijarro de Armas MA, Monereo Megı́as S, Merino Viveros M, Iglesias
Bolaños P, Vega Piñero B. 2012. Prevalence of metabolic syndrome
in a population of obese children and adolescents. Endocrinol Nutr
59:155– 159.
Grundy SM, Brewer HB, Cleeman JI, Smith SC, Jr, Lenfant C. 2004.
Definition of Metabolic Syndrome. Report of the national heart lung
and blood institute/ American heart association conference on
scientific issues related to definition. Circulation 109:433 –438.
Halpern A, Mancini MC, Magalhães ME, Fisberg M, Radominski R,
Bertolami MC, Bertolami A, de Melo ME, Zanella MT, Queiroz MS,
Nery M. 2010. Metabolic syndrome, dyslipidemia, hypertension and
type 2 diabetes in youth: from diagnosis to treatment. Diabetol
Metab Syndr 18:2 –55.
Jackson LV, Thalange NKS, Cole TJ. 2007. Blood pressure centiles for
Great Britain. Arch Dis Child 92:298 – 303.
Liu W, Lin R, Liu A, Du L, Chen Q. 2010. Prevalence and association
between obesity and metabolic syndrome among Chinese
elementary school children: a school-based survey. BMC Public
Health 10:780.
Lohman TG. 1989. Assessment of body composition in children.
Pediatr Exer Sci 1:19– 30.
Malina RM, Huang YC, Brown KH. 1995. Subcutaneous adipose tissue
distribution in adolescent girls of four ethnic groups. Int J Obes
Relat Metab Dis 19:793– 797.
McCarthy HD, Jarrett KV, Crawley HF. 2001. The development of waist
circumference percentiles in British children aged 5.0-16.9yrs. Eur J
Clin Nutr 55:902– 907.
Musso C, Graffigna M, Soutelo J, Honfi M, Ledesma L, Miksztowicz V,
Pazos M, Migliano M, Schreier LE, Berg GA. 2011. Cardiometabolic
risk factors as apolipoprotein B, triglyceride/HDL-cholesterol ratio
and C-reactive protein, in adolescents with and without obesity:
Cross-sectional study in middle class suburban children. Pediatr
Diabetes 12:229– 234.
National Cholesterol Education Programme/Adult Treatment Panel III
(NCEP/ATP III) 2001. Executive summary of Third Report of the
Expert Panel on Detection Evaluation and Treatment of high blood
cholesterol in adults. JAMA 285:2486 – 2497.
Raj M, Sundaram KR, Paul M, Deepa AS, Kumar RK. 2007. Obesity in
Indian children: time trends and relationship with hypertension.
Nat Med J India 77:S6 – S8.
Rodriguez-Moran M, Salazar-Vazquez B, Violante R,
Guerrero-Romero F. 2004. Metabolic syndrome among children
and adolescents aged 10– 18 Years. Diabetes Care 27:2516 – 2517.
174
V. P. WICKRAMASINGHE ET AL.
obesity in Australian white Caucasian and Australian Sri Lankan
children. Ann Hum Biol 32:60 – 72.
Wickramasinghe VP, Lamabadusuriya SP, Cleghorn GJ, Davies PS.
2009. Validity of currently used cutoff values of body mass index as a
measure of obesity in Sri Lankan children. Ceylon Med J 54:
114– 119.
Wickramasinghe VP, Lamabadusuriya SP, Cleghorn GJ, Davies PS.
2011. Defining anthropometric cut-off levels related to metabolic
risk in a group of Sri Lankan children. Ann Hum Biol 38:537 –543.
Xu H, Li Y, Liu A, Zhang Q, Hu X, Fang H, Li T, Guo H, Li Y, Xu G,
Ma J, Du L, Ma G. 2012. Prevalence of the metabolic syndrome
among children from six cities of China. BMC Public Health 12:13.
Yu D, Zhao L, Ma G, Piao J, Zhang J, Hu X, Fu P. 2012. Prevalence of
metabolic syndrome among 7-17 year-old overweight and obese
children and adolescents. Wei Sheng Yan Jiu 41:410 –413.
IDF consensus Group Zimmet P, Alberti KGMM, Kaufman F, Tajima N,
Silnk M, Arslanian S, Wong G, Bennett P, Shaw J, Caprio S. 2007.
The metabolic syndrome in children and adolescents – an IDF
consensus report. Pediatr Diabetes 8:299 – 306.
Ann Hum Biol Downloaded from informahealthcare.com by Lakehead University on 11/03/14
For personal use only.
Singh R, Bhansali A, Sialy R, Aggarwal A. 2007. Prevalence of metabolic
syndrome in adolescents from a north Indian population. Diabet
Med 24:195 –199.
Task Force Report on High Blood Pressure in Children and Adolescents
1987. Update in the 1987.A Working Group Report from the
National High Blood Pressure Education Program. Pediatrics 98:
649– 658.
Whincup RH, Gilg JA, Papacosta O, Seymour C, Miller GJ, Alberti KG,
Cook DG. 2002. Early evidence of ethnic difference in cardiovascular risk: cross sectional comparison of British South Asian and
white children. BMJ 324:1 –6.
WHO. 1999. Definition, diagnosis and classification of diabetes
mellitus and its complications. Report of a WHO consultation.
Geneva: WHO.
WHO. 2000. Obesity; Preventing and managing the global epidemic.
WHO Technical Report Series 894. Geneva: WHO.
Wickramasinghe VP, Cleghorn GJ, Edmiston KA, Murphy AJ,
Abbott RA, Davies PS. 2005. Validity of BMI as a measure of
Annals of Human Biology