Cardiovascular Diabetology
BioMed Central
Open Access
Original investigation
Utility of the modified ATP III defined metabolic syndrome and
severe obesity as predictors of insulin resistance in overweight
children and adolescents: a cross-sectional study
Sarita Dhuper*1, Hillel W Cohen2, Josephine Daniel1,
Padmasree Gumidyala1, Vipin Agarwalla1, Rosemarie St Victor1 and
Sunil Dhuper3
Address: 1Department of Pediatrics, Brookdale University Hospital & Medical Center, Brooklyn, NY, USA, 2Department of Epidemiology and
Population Health, Albert Einstein College of Medicine, Bronx, NY, USA and 3Department of Medicine, Coney Island Hospital, Brooklyn, New
York, USA
Email: Sarita Dhuper* - Dhupernbhn@aol.com; Hillel W Cohen - hicohen@aecom.yu.edu;
Josephine Daniel - josephinedaniel2000@yahoo.com; Padmasree Gumidyala - pgumidyala@yahoo.com;
Vipin Agarwalla - vagarwal@brookdale.edu; Rosemarie St Victor - rstvicto@brookdale.edu; Sunil Dhuper - sunil.dhuper@nychhc.org
* Corresponding author
Published: 14 February 2007
Cardiovascular Diabetology 2007, 6:4
doi:10.1186/1475-2840-6-4
Received: 20 December 2006
Accepted: 14 February 2007
This article is available from: http://www.cardiab.com/content/6/1/4
© 2007 Dhuper et al; licensee BioMed Central Ltd.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Background: The rising prevalence of obesity and metabolic syndrome (MetS) has received increased attention since both
place individuals at risk for Type II diabetes and cardiovascular disease. Insulin resistance (IR) has been implicated in the
pathogenesis of obesity and MetS in both children and adults and is a known independent cardiovascular risk factor.
However measures of IR are not routinely performed in children while MetS or severe obesity when present, are
considered as clinical markers for IR.
Objective: The study was undertaken to assess the utility of ATPIII defined metabolic syndrome (MetS) and severe obesity
as predictors of insulin resistance (IR) in a group of 576 overweight children and adolescents attending a pediatric obesity
clinic in Brooklyn.
Methods: Inclusion criteria were children ages 3–19, and body mass index > 95th percentile for age. MetS was defined
using ATP III criteria, modified for age. IR was defined as upper tertile of homeostasis model assessment (HOMA) within
3 age groups (3–8, n = 122; 9–11, n = 164; 12–19, n = 290). Sensitivity, specificity, positive predictive values and odds ratios
(OR) with 95% confidence intervals (CI) were calculated within age groups for predicting IR using MetS and severe obesity
respectively.
Results: MetS was present in 45%, 48% and 42% of the respective age groups and significantly predicted IR only in the
oldest group (OR = 2.0, 95% CI 1.2, 3.4; p = .006). Sensitivities were <55%; specificities <63% and positive predictive values
≤ 42% in all groups. Severe obesity was significantly associated with IR in both the 9–11 (p = .002) and 12–18 (p = .01)
groups but positive predictive values were nonetheless ≤ 51% for all groups.
Conclusion: The expression of IR in overweight children and adolescents is heterogeneous and MetS or severe obesity
may not be sufficiently sensitive and specific indicators of insulin resistance. In addition to screening for MetS in overweight
children markers for IR should be routinely performed. Further research is needed to establish threshold values of insulin
measures in overweight children who may be at greater associated risk of adverse outcomes whether or not MetS is
present.
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Background
Insulin resistance (IR) and/or compensatory hyperinsulinemia are usually associated with obesity and are risk
factors for cardiovascular disease (CVD) and type 2 diabetes in both adults and children [1-8]. Furthermore, it is
now clear that polycystic ovary syndrome [9] nonalcoholic liver disease [10] sleep disordered breathing [11],
renal disease, systemic inflammation, [12,13] asthma [14]
and several types of cancer [15] are also associated with
IR. Given the increasing prevalence of obesity, type 2 diabetes [16] and metabolic syndrome (MetS) in children[17], and the known relationship of IR as a precursor
to these clinical syndromes [1-3], identifying IR in children may be of substantial clinical importance. Although
MetS and IR undoubtedly overlap, they are not the same
[18,19]. The NCEP ATP III. (National Cholesterol Education Program, Adult treatment panel III) definition of
MetS [20] does not include a direct measure of insulin and
it is likely that IR confers CVD or other disease risk distinct
from that conferred by MetS itself [21-25]. Recent longitudinal studies in children have shown that cardiovascular
risk factors like blood pressure and triglycerides decreased
with any degree of decrease in HOMA, independent of
changes in weight status, supporting the hypothesis that
insulin resistance is a key abnormality contributing to
these cardiovascular risk factors [26]. In light of the substantial overlap of MetS and IR, the question arises
whether a direct measure of insulin or glucose tolerance is
needed when MetS is more easily assessed and could be
used as a marker for IR. Recently, however, the clinical
utility of MetS to serve as a marker of IR has been questioned in adults [27,28]. We have undertaken the current
study to examine this question in a population of overweight children.
Methods
Data were collected on all overweight children and adolescents(3–19 years) with BMI > 95th percentile for age,
(BMIZ range 1.2, 6.3) who attended a pediatric obesity
program at an inner city University Hospital, in Brooklyn,
New York from January1, 2002 through December 31,
2005. Five hundred and seventy six (576) patients with
complete data for metabolic risk factors constituted the
study sample. The study was approved by the Internal
Review Board and informed consent to participate in the
community based obesity program and to be included in
the database was obtained from the parent or guardian
and assent from the children when first enrolled. Each
individual was assessed for the presence of MetS. Fasting
plasma concentrations of glucose, insulin, triglycerides,
and HDL cholesterol were assessed using standard laboratory methods. Weight and height were measured to the
nearest 0.1 kg and 0.5 cm respectively. BMI was calculated
as weight in kilograms divided by height in meters
squared and converted to BMI-Z score standard units
http://www.cardiab.com/content/6/1/4
according to CDC age-sex tables [29]. Waist circumference
was measured at the midpoint between the lateral iliac
crest and the lowest rib in cm during expiration and
defined as abnormal if > 90th percentile for age, sex and
race [30]. Blood pressure was measured with an appropriate size cuff with a standardized automated dynamapp in
the right arm in the sitting position and the average of
three readings was recorded for analysis. Elevated systolic
or diastolic blood pressure was defined as values above
the 90th percentile for age and gender [31]. Triglycerides
were characterized as elevated if ≥ 90th percentile for age
and sex based on national standards [32]. HDL ≤ 40 mg/
dL were considered abnormal [32]. Fasting glucose ≥ 100
mg/dL was considered elevated based on the revised
American Diabetes Association criterion [33]. MetS was
defined consistent with Cook et al.[17] as age-modified
ATPIII criteria with abnormal values for at least 3 of the 5
criteria: systolic or diastolic blood pressure, fasting glucose, HDL, waist circumference and triglycerides.
Since IR is affected by age and pubertal status, and tanner
stages were not recorded on all patients in the database,
the population was further divided into 3 age groups (3–
8, 9–11, and 12–19) reflecting prepubertal, pubertal and
post pubertal stages and also to assess the age related associations of MetS and IR. Currently there are no accepted
values to define insulin resistance in normal and overweight children, so we used the upper tertile of homeostasis model assessment (HOMA: fasting serum insulin
(µunits/ml) × fasting plasma glucose (mmol/l)/22.5)
within age group to denote higher IR consistent with previous work in both adults [34] and children[35]. Analyses
were repeated using upper quartile HOMA for IR to check
if the results were threshold dependent.
Statistical analysis
Sensitivity, specificity, and positive predictive value of
using MetS to identify higher IR were computed. In addition, similar to Weiss et al[35] we divided the patients into
severe obesity (BMI Z score ≥ 2.5) and moderate obesity
(BMI Z Score < 2.5) and analyzed the sensitivity, specificity, positive and negative predictive value of severe obesity to identify higher IR in these age groups. The
associations of the five components of MetS as categorical
variables and associations with higher IR within the three
age groups were assessed with chi-square. Associations of
continuous MetS variables within age group were assessed
with analysis of variance. Associations of these with continuous HOMA and with higher IR (1 = higher, 0 = lower)
were assessed with non-parametric Spearman's correlation coefficient. Binary logistic regression models predicting higher IR with MetS and BMIZ separately while
adjusting for sex, age and race were also constructed. All
analyses were performed within age group using SPSS for
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Windows (version 13). A two-tailed alpha of .05 was used
to denote statistical significance.
Results
Of the 576 participants, 122 were ages 3–8, 164 were ages
9–11 and 290 were 12–19. Almost all were African American (81.4 %) or Hispanic (16.0%). Table 1 describes sample characteristics and metabolic risk factors according to
age groups. These groups did not differ significantly by
race/ethnicity or sex. Each of the MetS factors as continuous variables increased significantly with age (p < .01 for
linear trend for all). Application of the modified ATP III
criteria to these groups identified MetS in 45.1 % of the
younger children, 48.8% of the 9–11 year olds and 42.4%
of the adolescents. Although the prevalence of MetS was
similar in these age groups, the distribution of MetS components all differed significantly.
Defining higher IR by upper age-specific tertiles of
HOMA, threshold values of 2.31 for ages 3–8, 4.29 for
those 9–11 and 4.26 for the 12–19 year olds were
obtained. Upper tertiles of fasting insulin for these age
groups were 11.8, 21.1 and 20.5 (units) respectively. Adolescents with MetS (Table 2) were twice as likely as those
without to have higher IR (odds ratio (OR) 2.0, 95% CI
1.2, 3.4, p <.01) and MetS as a predictor of higher IR had
a sensitivity of 54%, a specificity of 63% and a positive
predictive value of 42%. For the two younger age groups,
the odds ratios of MetS as a predictor of higher IR were
lower, (ages 3–8: OR 1.3, 95% CI 0.6, 2.7, p = 0.70; ages
9–11: OR: 1.4, 95% CI 0.7, 2.7: p = 0.41). The sensitivities, specificities and positive predictive values for these
age groups were comparable to the adolescents. Severe
obesity was significantly associated with IR in both the 9–
11 (p = .002) and 12–18 (p = .01) groups but positive predictive values were nonetheless ≤ 51% for all
groups.(Table 2)
Examining male and female subgroups separately gave
results similar to the group as a whole.
Table 3 shows the associations of the individual criteria of
MetS with higher IR. In all age groups, as expected, elevated glucose was significantly associated with higher IR
and this was the only factor associated with IR in the
youngest group. In the 9–11 group, only WC among the
other risk factors for MetS had a borderline significant
association with higher IR. Among the adolescents, both
elevated triglycerides and WC showed an association with
higher IR, and lower HDL and elevated blood pressure
showed trends towards associations but these were not
statistically significant.
Table 1: Demographic and Metabolic Syndrome Characteristics by Age Group
Characteristic*
Female (%)
Ethnicity (%)
African American
Hispanic
Other
Body Mass Index (BMI) (kg/m2)
Age-sex standardized Body Mass Index
(BMIZ) (standardized units)
Glucose ≥ 100 mg/dL (%)
Elevated Waist Circumference† (%)
High Density Lipoprotein <40 mg/dL (%)
Elevated Triglycerides† (%)
Hypertension† (%)
Metabolic syndrome‡ (%)
Glucose (mg/dL)
Waist Circumference (cm)
High Density Lipoprotein (mg/dL)
Triglycerides (mg/dL)
Blood pressure (mmHg)
Systolic
Diastolic
Ages 3–8
n = 122
Age 9–11
n = 164
Ages 12–19
n = 290
Total
n = 576
p value
55.7
53.0
53.1
53.6
.87
.92
79.5
17.2
3.3
26.8 ± 4.7
2.8 ± 0.71
81.7
15.2
3.0
31.0 ± 5.5
2.4 ± 0.42
82.1
15.9
2.1
37.1 ± 7.2
2.4 ± 0.36
81.4
16.0
2.6
33.2 ± 7.6
2.5 ± 0.50
<.001
<.001
2.5
94.3
39.3
34.4
64.8
45.1
80 ± 10.5
80 ± 11.4
46 ± 11.5
83 ± 33.9
4.3
95.1
45.7
47.0
53.7
48.8
83 ± 11.1
95 ± 12.5
43 ± 10.5
95 ± 41.4
3.8
85.9
50.0
29.0
59.0
42.4
85 ± 20.3
108 ± 15.7
41 ± 9.4
98 ± 54.3
3.6
90.3
46.5
35.2
58.7
44.6
83 ± 16.4
98 ± 17.7
43 ± 10.3
94 ± 47.4
.71
.001
.14
.001
.17
.49
.02
<.001
<.001
.01
114 ± 10.7
65 ± 8.2
118 ± 9.4
69 ± 8.2
125 ± 10.9
73 ± 8.9
121 ± 11.4
70 ± 9.2
<.001
<.001
* Values are presented as % for categorical variables and mean ± standard deviation for continuous variables. P values for comparison by age
assessed by chi-square for categorical variables and analysis of variance for continuous variables. All continuous variables had p < .01 for linear
trend.
† ≥ 90th percentile for age and sex.
‡ By modified ATP criteria: abnormal values for 3 or more of waist circumference, high density lipoprotein, triglycerides, hypertension and glucose.
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Table 2: Using Metabolic Syndrome or Severe Obesity to Predict Higher Insulin Resistance*
Predictor
Metabolic
Syndrome
Ages 3–8
9–11
12–19
Severe Obesity
Ages 3–8
9–11
12–19
n†
OR (95% CI)
p
Sensitivity‡
Specificity
PPV
55
79
123
1.3 (0.6, 2.7)
1.4 (0.7, 2.7)
2.0 (1.2, 3.4)
.70
.41
.006
49%
54%
54%
57%
55%
63%
36%
37%
42%
80
65
116
0.9 (0.4, 1.9)
3.0 (1.5, 5.9)
2.0 (1.2, 3.3)
.88
.002
.01
63%
57%
51%
33%
69%
66%
33%
48%
51%
*Metabolic syndrome (MetS) by modified ATP III criteria; higher insulin resistance: upper tertile of homeostatis model (HOMA); severe obesity:
BMI Z score ≥ 2.5.
† n = number in age group with MetS or severe obesity respectively; OR (95%CI): Odds ratio and 95% confidence interval.
‡ Sensitivity: % of those with higher insulin resistance who have predictor. Specificity: % of those without predictor who do not have higher insulin
resistance. Positive predictive value (PPV): % of those with predictor who have higher insulin resistance.
Table 3: Metabolic Syndrome criteria and Insulin Resistance
Age 3–8
Higher IR
Age 9–11
Higher IR
Age 12–19
Higher IR
MetS Criterion*
Yes
n = 41
No
n = 81
Yes
n = 54
No
n = 110
Yes
n = 96
No
n = 194
Waist
Circumference %
Blood Pressure %
Glucose %
Triglycerides %
HDL %
95
94 (p > .99)
100.
93 (p = .05)
94
82 (p = .01)
66
7
37
44
64 (p > .99)
0 (p = .04)
33 (p = .88)
37 (p = .59)
56
11
48
48
53 (p = .86)
1 (p = .01)
46 (p = .96)
45 (p = .79)
65
9
41
56
56 (p = .21)
1 (p < .01)
23 (p < .01)
47 (p = .17)
Age 3–8
None
Any one
Any two
Any three
Any four
All five
Age 9–11
Higher IR
Higher IR†
Number of
Components‡
Age 12–19
Higher IR
n
% (n)
n
% (n)
n
% (n)
3
21
43
40
15
0
0 (0)
29 (6)
35 (15)
35 (14)
40 (6)
-
1
32
52
51
26
2
0 (0)
22 (7)
35 (18)
37 (19)
31 (8)
100 (2)
12
67
88
80
38
5
8 (1)
24 (16)
31 (27)
35 (28)
53 (20)
80 (4)
* Metabolic syndrome (MetS) by modified ATP III criteria: any 3 or more of waist circumference, blood pressure and triglycerides ≥ 90th percentile,
HDL ≤ 40 mg/dL, Glucose ≥ 100 mg/dl. Higher insulin resistance (IR): upper tertile of HOMA. Values presented as %.
† % of the number (n) within that age group who had insulin resistance (IR), defined as upper tertile of HOMA.
‡Test of association (Spearman's rho) of number of categories with higher IR within age group: age 3–8 (p = .34), age 9–11 (p = .18), age 12–19 (p <
.01).
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Although there was a cumulative effect on the prevalence
of higher IR with the presence of increasing number of
components of MetS in all age groups, this trend was statistically significant only among the adolescents. Among
the patients with less than 3 metabolic risk factors (non
MetS), there was a prevalence of higher IR in 31% of the
3–8 yr old, 29% in the 9–11 yr old and 20% in those 12–
19 yr old.
Table 4 shows the correlations of HOMA as a continuous
variable with the individual MetS criteria and BMIZ scores
also as continuous variables. As expected, glucose correlated significantly with HOMA for all age groups. Among
the youngest group, waist circumference, and diastolic BP
were also significantly associated with HOMA. Waist circumference and BMI Z-score were significantly associated
with HOMA in the 9–11 age group. Among the adolescents all of the criteria were significantly associated with
HOMA except for diastolic BP which had a borderline significant association.
In binary logistic regression models adjusting for sex, age
and race, we found MetS and BMIZ were not significant
predictors of higher IR (p = .55 and p = .24 respectively)
for 3–8 year olds. For 9–11 year olds, MetS showed a nonsignificant trend (p = .13) while BMIZ was a significant
predictor (p < .01). Both MetS and BMIZ were significantly associated with higher IR for 12–19 year olds (p <
.01 for each). In sensitivity analyses, using upper quartile
of HOMA instead of upper tertile to indicate higher insulin resistance gave similar results.
Discussion
The principle findings in our study were that the ATP III
definition of MetS, as modified for the pediatric populations, and severe obesity are but modest predictors of IR,
defined here as upper tertile of HOMA in this sample of
overweight, children and adolescents. We found that
although the prevalence of MetS was high and relatively
similar in the three age groups, a statistically significant
association with IR was evident only in adolescence. As
described in table 2 using MetS would miss 51%, 46%
and 46% of those with IR for ages 3–8, 9–11 and 12–19
respectively and incorrectly label 43%, 45%, and 37%
respectively as IR despite HOMA being in the lower two
tertiles for age. Interestingly, either severe obesity (BMIZ ≥
2.5) or BMIZ as a continuous variable was at least as good
a predictor of higher IR as MetS in bivariate and multivariate analysis. Like MetS, severe obesity however, also had
relatively low sensitivity and specificity as a predictor of
higher IR in all age groups, and by itself would also lead
to substantial numbers of false negatives and false positives for predicting IR. This goes to show that overweight
children with IR defined as HOMA levels in the upper tertile may not be identified using the MetS criteria or severe
obesity alone. Among individual MetS components, all
were significantly associated with increasing HOMA in the
12–19 age group, whereas in the younger age groups only
glucose and waist circumference showed statistically significant association, suggesting that the associations
between IR, obesity and CVD risk factors likely increase
with age.
Although MetS is believed to be closely related to IR, and
both are independently associated with serious adverse
outcomes among adults[34], recent studies in adults have
shown that the widely accepted ATPIII criteria for MetS
have low sensitivity for identifying adults with IR [36,37].
Several studies suggest that IR in children may also indicate increased and perhaps independent risk of subsequent CVD [25,38-40]. These studies showing the
importance of IR did not suggest the MetS had to be
present for additional CVD risk nor did they assess, to
what extent MetS, a widely used clinical marker for IR and
disease risk in overweight children, could be used to correctly identify children and adolescents with IR. As shown
in this study using the modified ATP III criteria may fail to
identify the potential increase in metabolic and cardiovascular disease risk in this younger population with IR but
without MetS, at an age when risk factor intervention
would be most beneficial.
Currently there are no widely accepted values to define IR
in either normal or overweight children. Hence, markers
Table 4: Correlations of continuous HOMA with components of Metabolic Syndrome and BMIZ for the 3 age groups.
Criterion*
Waist Circ
Systolic BP
Diastolic BP
Glucose
Triglycerides
HDL
BMIZ
Age 3–8
n = 92
Age 9–11
n = 123
Age 12–19
n = 219
.48 (p < .01)
.09 (p = .32)
.20 (p = .03)
.37 (p < .01)
.08 (p = .37)
-.09 (p = .33)
-.05 (p = .60)
.17 (p = .03)
.09 (p = .26)
-.01 (p = .87)
.43 (p < .01)
.13 (p = .11)
-.05 (p = .54)
.26 (p < .01)
.30 (p < .01)
.15 (p = .01)
.11 (p = .07)
.38 (p < .01)
.28 (p < .01)
-.19 (p < .01)
.27 (p < .01)
*Values are Spearman's rho correlation coefficient of component with continuous measure of HOMA and (p value tests for probability of rho = 0).
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of IR such as HOMA as described by Matthews and coworkers [41] have been used as a surrogate measure of IR in
epidemiological studies to identify associated risk factors.
HOMA as a measure of IR correlates with euglycemic
clamp measures in men and women, younger and older
adults, and obese and nonobese individuals [42-44]. It
has recently been validated to correlate with insulin sensitivity indices obtained from the minimal model frequently sampled intravenous glucose tolerance test
(FSIVGTT) in overweight prepubertal and pubertal children and adolescents [45]. Since no standard value for
fasting insulin or HOMA has been validated as a predictor
of CVD or diabetes, and these measures are known to vary
by ethnicity and method of assay, most studies have used
upper tertiles or quartiles for HOMA to identify insulin
resistant individuals in the population being studied[34].
Higher IR thus defined has been found to be associated
with significantly greater risk to develop type 2 diabetes,
hypertension, CVD, and cancer [46-48] and similarly we
chose to use this definition for our study. In this study the
younger age group 3–8 years old had lower upper tertile
HOMA values compared to the older children and adolescents, even though all groups had a similar prevalence of
MetS by the modified ATPIII criteria. This may indicate
that the clustering of metabolic risk factors in the younger
children is more related to adiposity than IR and that the
association with IR progresses over time.
ter of many obesity related risk factors with different
underlying mechanisms. A recent review by Jones K [51]of
metabolic syndrome in children raised similar questions
as to the clinical value of recognizing MetS in children vs
identifying the individual risk factors associated with
obesity including those although not components of the
MetS such as IR, LDL cholesterol and inflammatory markers, are nevertheless associated with CVD risk.
Several reasons may account for the relatively low MetS
sensitivity and specificity for identifying IR in this pediatric population. One is that in the ATP III definition all criteria are given equal importance in defining the syndrome
such that impaired fasting glucose(IFG), the factor most
closely associated with IR, need not be one of the ≥ 3 criteria for an individual to have MetS. If on the other hand
IFG was a required criterion, the specificity for such a definition of MetS would be greater, while the sensitivity and
positive predictive value would be far lower since very few
(<4%) of the overweight children and adolescents in this
sample had (IFG) ≥ 100 mg/dL. One reason for the relatively low proportion with elevated glucose may be that
insulin resistance is generally associated with increased
levels of insulin production. The ability of an insulin
insensitive individual's beta cells to compensate for IR by
producing extra quantities of insulin in response to a glucose load may decrease with age[49] and elevated glucose
is a relatively late feature in the natural history of progression to type II diabetes.
Our study has several limitations. The sample was drawn
from participants in an obesity clinic situated with a predominantly African American and Hispanic catchments
area. Thus our findings can only be generalized to overweight non-white youth. Our findings are nonetheless relevant given the higher prevalence of MetS found in this
population compared to other studies [35] in patients of
similar ethnic background, as well as higher rate of clinical complications of type II diabetes and CVD seen in
adults of similar ethnic backgrounds.
Second unlike the WHO definition for MetS [50], the clustering of risk factors by ATP III definition may have multiple etiologies other than insulin resistance such as obesity,
physical inactivity, and genetics[20]. Currently there is
still no consensus on the diagnosis criteria of MetS in children and adults and whether MetS is one disease or a clus-
Other reasons for the low sensitivity of Mets criteria to
identify IR are that, the deleterious effects of the prolonged presence of elevated insulin levels may take time
to manifest themselves with regard to increase in triglycerides, blood pressure as well as circulating glucose levels as
shown in this study where the associations of IR and metabolic risk factors were evident in the adolescents but not
in the younger age groups. Alternately, as shown in a
recent study by Lambert et al[52] for the pediatric population, some of these risk factors may be more reflective of
adiposity than insulin resistance itself, leading to the
modest specificity as well as sensitivity of MetS for IR.
These observations of Lambert et al [52] and results of our
study emphasize the fact that obesity in children represents heterogeneous metabolic and phenotypical expressions of insulin resistance and individuals with the same
degree of obesity could have varying degrees of insulin
resistance and metabolic aberrations
In this study, we followed the example of others [46,47],
in using upper tertile of HOMA as the measure of IR. It is
possible that upper tertile may not be the appropriate
threshold for IR in these overweight children. However,
without data linking particular levels of HOMA with clinical outcomes, a specific clinical threshold is not yet available. In a study by Perichart- Perera et al, [53] IR defined
by a HOMA value of 3.1 was present in 90% of obese Mexican children 9–12 years of age. It should be noted that
the upper tertile values of HOMA in the overweight youth
in our study are higher than this value and also higher
then the upper tertile value in the general population
observed by NHANES for children ≥ 12 years old which
was 3.4 [54] and is thus more likely to indicate a potentially problematic level and improve the specificity of the
analysis. In the study by Bueno G et al[55] mean HOMA
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levels were 5.6 for males and 5.5 for females diagnosed
with MetS using similar Cook criteria in 10 year old Spanish children indicating significant IR even though their
prevalence of MetS was lower compared to our study for
the 9–11 age group. However when we used an even
higher level threshold (upper quartile of HOMA) in sensitivity analyses, results were similar. This may reflect different degrees of insulin sensitivities in different ethnic
groups.
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Abbreviations
National Cholesterol Education Program (NCEP)
Cardiovascular Disease(CVD)
Adult Treatment Panel (ATP)
Metabolic Syndrome (MetS)
Insulin Resistance (IR)
It is also possible that the modified ATP III criteria for
MetS that we used in this study may not be the most
meaningful with regard to long-term clinical outcomes.
The individual components and the specific cutoffs for
children are not based on any prospective studies but have
been defined by a consensus statement of an expert panel
for adults. Since there is no universally accepted definition
of MetS for children and the thresholds for adult ATP III
criteria cannot be applied to a pediatric population given
there are changes in the metabolic parameters as a function of age, we used modified ATP III adult criteria for
children similar to that used by Cook et all which has
been widely quoted and applied in different populations[17]. It is possible that using other MetS criteria such
as proposed by De Ferranti et all[56] the prevalence of
MetS in our study would be much higher as shown by others [55] and thus may increase the sensitivity of MetS to
predict IR. We did not however test this definition as we
have been routinely using the Cook definition in our
clinic
In conclusion, the growing epidemic of childhood obesity, early onset of type II diabetes and clustering of cardiovascular risk factors has raised great concern. To date, this
concern has led to increasing attention to the presence of
MetS in children and adolescents. Even if MetS is an
important marker or risk factor for CVD and type II diabetes in itself in a pediatric population, IR unaccompanied
by MetS is also important.
The major benefit of defining MetS in children is to draw
attention to risk factor clustering associated with obesity
and insulin resistance. While the convergence of IR and
MetS becomes evident over time these data suggest that in
addition to screening for Mets, surrogate markers of insulin resistance such as fasting insulin, HOMA or if
resources allow, glucose tolerance test, could be a useful
addition to routine evaluations of overweight children in
order to alert clinicians to potential increased risk in those
with IR even without MetS. Further studies are needed to
assess clinically relevant threshold values of insulin measures in overweight children and adolescents of different
ethnicities that can predict the development of CVD, diabetes and other clinical syndromes in patients both with
and without the metabolic syndrome.
Homeostasis model assessment (HOMA)
Center for disease control (CDC)
Impaired Fasting glucose (IFG)
World Health Organization(WHO)
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
All authors made a significant contribution in the conception, design and manuscript preparation.
Acknowledgements
This study was supported in part by the New York State Department of
Health (Enhanced services for children and youth; Grant Contract no: C019043). We wish to thank Dr. Steven Cook for his valuable comments and
review of the manuscript.
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