children. 1 Many of the medical sequelae that are seen in the
setting of longstanding obesity, including cardiovascular
disease (CVD) and type 2 diabetes (T2DM), are associated
with the metabolic syndrome. 2 This cluster of clinical com-
ponents is comprised of insulin resistance, hypertension, low
ediatric obesity continues at critical levels and threat-
ens to shorten the life span of the current generation of
high-density lipoprotein (HDL), and hypertriglyceridemia,
fi ndings that appear to be linked by poorly understood un-
derlying processes related to infl ammation and visceral
adiposity. 3 Because of the association between childhood
metabolic syndrome and risk for sequelae in adulthood, 4
many have proposed using a diagnosis of metabolic syn-
drome in childhood as a trigger for early intervention to de-
crease body fat and increase exercise. 5 , 6
Objective: The aim of this study was to compare currently proposed sets of pediatric metabolic syndrome cri-
teria for the ability to predict elevations in “surrogate” factors that are associated with metabolic syndrome and
with future cardiovascular disease and type 2 diabetes mellitus. These surrogate factors were fasting insulin,
hemoglobin A1c (HbA1c), high-sensitivity C-reactive protein (hsCRP), and uric acid.
Methods: Waist circumference (WC), blood pressure, triglycerides, high-density lipoprotein cholesterol (HDL-C),
fasting glucose, fasting insulin, HbA1c, hsCRP, and uric acid measurements were obtained from 2,624 adolescent
(12–18 years old) participants of the 1999–2006 National Health and Nutrition Examination Surveys. We identi-
fi ed children with metabolic syndrome as defi ned by six commonly used sets of pediatric metabolic syndrome
criteria. We then defi ned elevations in the surrogate factors as values in the top 5% for the cohort and calculated
sensitivity, specifi city, positive predictive value (PPV), and negative predictive value (NPV) for each set of meta-
bolic syndrome criteria and for each surrogate factor.
Results: Current pediatric metabolic syndrome criteria exhibited variable sensitivity and specifi city for surrogate
predictions. Metabolic syndrome criteria had the highest sensitivity for predicting fasting insulin (40–70%), fol-
lowed by uric acid (31–54%), hsCRP (13–31%), and HbA1c (7–21%). The criteria of de Ferranti (which includes chil-
dren with WC >75 th percentile, compared to all other sets including children with WC >90 th percentile) exhibited
the highest sensitivity for predicting each of the surrogates, with only modest decrease in specifi city compared
to the other sets of criteria. However, the de Ferranti criteria also exhibited the lowest PPV values. Conversely, the
pediatric International Diabetes Federation criteria exhibited the lowest sensitivity and the highest specifi city.
Conclusions: Pediatric metabolic syndrome criteria exhibit moderate sensitivity for detecting elevations in sur-
rogate factors associated with metabolic syndrome and with risk for future disease. Inclusion of children with
more modestly elevated WC improved sensitivity.
METABOLIC SYNDROME AND RELATED DISORDERS
Volume 8, Number 4, 2010
© Mary Ann Liebert, Inc.
Pp. 343– 353
Departments of 1 Pediatrics and 2 Public Health Sciences, University of Virginia School of Medicine, Charlottesville, Virginia.
Ability Among Adolescents for the Metabolic Syndrome
to Predict Elevations in Factors Associated with Type 2 Diabetes
and Cardiovascular Disease: Data from the National Health
and Nutrition Examination Survey 1999–2006
Mark D. DeBoer , M.D., M.Sc., M.C.R. ,1 and Matthew J. Gurka , Ph.D. 1,2
DEBOER AND GURKA344
Diagnosis of metabolic syndrome
We defi ned metabolic syndrome using six previously
published sets of criteria ( Table 1 ). We excluded subjects
with known diabetes and pregnancy. We also excluded
children taking antidiabetic medication, growth hormone,
glucocorticoids, sex hormones, lipid-lowering agents, and
antihypertensives, given that each of these is known to
affect the metabolic parameters tested. We included only
children 12–18 years old in our data analyses, given that fast-
ing values for triglycerides and glucose were only obtained
in participants 12 years and older. We also excluded children
whose CRP was greater than 10 mg/L, as performed in pre-
vious analyses. 38
Statistical analysis was performed using SUDAAN (ver-
sion 10; Research Triangle Institute, Research Triangle Park,
NC), which accounts for the survey design when estimating
standard errors. Population-based estimates were obtained
by assigning sampling weights to each sampled child for
whom an interview was completed. We combined all data
sets for statistical analyses, thereby increasing our total
sample size and power. Pearson r correlation coeffi cients
were computed to assess the degree of linear association
between each component of metabolic syndrome and each
of the examined surrogates. Prevalence rates of the various
defi nitions of metaoblic syndrome were calculated by gender
and race/ethnicity (non-Hispanic white, non-Hispanic black,
and Mexican American) and compared via chi-squared
tests. Mean surrogate levels were also compared in a similar
fashion via t -tests (gender) and analysis of variance (race/
ethnicity). “High” levels of the surrogates (fasting insulin,
HbA1c, hsCRP, and uric acid) were determined to be the top
5% for each surrogate in the sample. The overall abilities of
each proposed set of metabolic syndrome criteria to predict
“high” levels of these surrogates were assessed by com-
puting sensitivity and specifi city. Sensitivity and specifi city
with respect to prediction of having one or more elevated
level of these three surrogates were also computed, with cor-
responding 95% confi dence intervals. Statistical signifi cance
was defi ned as a P value <0.05.
Table 3 shows the percentage of subjects with metabolic
syndrome by each of the six criteria. The proportion of ado-
lescents with metabolic syndrome ranged from 4.0% by the
IDF criteria to 15.1% by the de Ferranti criteria. Whereas there
was a tendency for metabolic syndrome to be more common
among males than females, this reached statistical signifi -
cance for only three of the six criteria. Non-Hispanic white
and Mexican-American adolescents had similar levels of
metabolic syndrome, whereas non-Hispanic blacks had lower
prevalence. There were no trends in metabolic syndrome di-
agnosis by NHANES cycle over the time period studied.
Regarding levels of metabolic syndrome–associated
factors, Table 3 gives the mean and 95 th percentile values.
Although there was not a gender difference for levels of
insulin or hsCRP, among adolescent females versus males
However, our ability to diagnose metabolic syndrome dur-
ing childhood has been hampered by a lack of knowledge of
how the biological processes behind metabolic syndrome are
manifest in children to place them at greater risk for future
disease, leading to a lack of consensus regarding which of a
number of criteria to use to diagnose metabolic syndrome in
children 5 , 7–10 ( Table 1 ). Before such a consensus is established,
further investigation is warranted into how well the current
sets of pediatric metabolic syndrome criteria identify chil-
dren with evidence of insulin resistance and other biological
processes that contribute to metabolic syndrome.
In the absence of outcomes studies with suffi ciently long
follow up (i.e., children followed prospectively to identify
factors predictive for T2DM and CVD), we used a cross-
sectional database—the National Health and Nutrition
Evaluation Survey (NHANES)—to analyze six proposed
sets of pediatric metabolic syndrome criteria for their ability
to predict elevations in four factors that are associated with
metabolic syndrome: Fasting insulin, 11–14 hemoglobin A1c
(HbA1c), 15–17 high-sensitivity C-reactive protein (hsCRP), 18–25
and uric acid 9,26–28 ( Table 2 ). Fasting insulin, although not it-
self a criterion used to diagnose metabolic syndrome, is sig-
nifi cantly correlated with insulin resistance in children. 12
HbA1c is a glycosylation product of hemoglobin that rises
with average blood sugars. Low-grade infl ammation, as
assessed by levels of hsCRP, is associated with obesity and in-
sulin resistance in children, 24 , 25,29–31 and in adults is linked to
long-term risk for T2DM 23 , 32 and myocardial infarction. 18 , 33 , 34
High levels of uric acid are strongly associated with met-
abolic syndrome in children 9 and appear to play a role in
causing hypertension. 35–37
In the ranges of values studied here, these factors are
linked among adolescents to increased risk of carotid in-
tima media thickness, a marker for early atherosclerotic
disease, 11 , 20 , 21 , 28 and/or among adults to increased risk for the
development of future CVD and T2DM ( Table 2 ). As such,
we termed these factors “surrogate” markers of the bio-
logical processes linking metabolic syndrome with future
disease risk. Our ultimate goal is to identify children who
may benefi t most from lifestyle intervention. In the current
experiment, we essentially treated a diagnosis of metabolic
syndrome as a screening test for detecting elevations in
these surrogate factors. We set out to determine which of the
current pediatric metabolic syndrome criteria demonstrated
the best sensitivity with acceptable specifi city (≥80%) in pre-
dicting elevations in these surrogates.
Data were obtained from the NHANES, 1999–2006, a
complex, multistage probability sample of the U.S. popula-
tion. These annual surveys are conducted by the National
Center for Health Statistics (NCHS) of the Centers for
Disease Control (CDC) with data released every 2 years
(www.cdc.gov/nchs/nhanes.htm). The NCHS ethics review
board reviewed and approved the survey, and participants
were provided with informed consent prior to participation.
Waist circumference (WC), blood pressure, and laboratory
measures of triglycerides, HDL cholesterol (C), and glucose
were obtained using standardized protocols and calibrated
equipment. All laboratory values used for analyses were
obtained from participants asked to fast for 8 h prior to the
T able 1. P roposed C riteria for the D iagnosis of the M etabolic S yndrome in C hildren
Cook (2003) 7
SBP or DBP ≥90% for age, sex,
TG ≥110 mg/dL
HDL ≤ 40
DeFerranti (2004) 8
SBP or DBP ≥90% for age, sex,
TG ≥ 97 mg/dL
Males 15-19: <45
All others: < 50
Ford (2007) 9
SBP or DBP ≥90% for age, sex,
TG ≥110 mg/dL
HDL ≤ 40
(Zimmet 2007) 5
6-10 y.o.: WC ≥90%
TG ≥150 mg/dL
HDL < 40
10-16 y.o.: WC ≥90%
TG ≥150 mg/dL
Females < 50
Jolliffe (2007) (ATP III) 10 , b
12 y.o. boys: 94.2 cm 12 y.o. girls: 79.5 cm 20 y.o. boys: 101.8 cm
20 y.o. girls: 88 cm
12 y.o. boys: 121/76 mmHg 12 y.o. girls: 121/80 mmHg 20 y.o. boys: 130/85 mmHg 20 y.o. girls: 130/85 mmHg
12 y.o. boys: 127 12 y.o. girls: 142
20 y.o. boys: 150 20 y.o. girls: 150
12 y.o. boys: 43.7
12 y.o. girls: 39.8
20 y.o. boys: 48.3
20 y.o. girls: 50.3
Jolliffe (2007) (IDF) 10,a,b
12 y.o. boys: 85.1 cm
12 y.o. girls: 72.5 cm
20 y.o. boys: 94 cm 20 y.o. girls: 80 cm
Same as Joliffe ATP III
Same as Joliffe ATP III
Same as Joliffe ATP III
Same as Joliffe
a Elevated WC is a prerequisite and is not counted toward the number of components needed for diagnosis.
b Values of each component are adjusted gradually on a gender-specifi c basis between childhood levels (starting at 12 y.o.) and levels in ATP III or IDF (at 18 y.o.).
Abbreviations: HDL, High-density lipoprotein; WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglycerides; IDF, International Diabetes
Federation; ATP, Adult Treatment Panel III; y.o., years old.
DEBOER AND GURKA 346
International Diabetes Federation (IDF) criteria had the
lowest sensitivity and positive predictive value (PPV) and
the highest specifi city and negative predictive value (NPV)
( Table 5 ). Also for each surrogate, the set of criteria with
highest prevalence of metabolic syndrome diagnosis (de
Ferranti) had the highest sensitivity and NPV and the low-
est specifi city and PPV.
Regarding the prediction of an elevated insulin level,
sensitivities ranged from 40% to 70% with specifi cities of
88% to 98% and PPV values of 23% to 50% ( Fig. 1 , Table 5 ).
Results were similar when the 95 th percentile of the homeo-
stasis model of insulin resistance (HOMA-IR) was tested,
either for lean individuals or for the entire set (data not
shown). For predicting HbA1c, sensitivities ranged from 7%
to 21%, with specifi cities of 86% to 96% and PPV values of
12% to 15%. Regarding the prediction of an elevated hsCRP,
sensitivity rates for pediatric metabolic syndrome criteria
ranged from 13% to 31%, whereas specifi city rates ranged
from 86% to 97% and PPV values of 11% to 17%. For pre-
dicting an elevated uric acid, sensitivity ranged from 31%
to 54% with specifi cities of 87% to 98% and PPV values of
19% to 40%.
In considering the ability to predict an elevation in at
least one of the surrogates, sensitivity ranged from 16% to
35% with specifi cities of 90% to 99% and of PPV values of
47% to 80%, whereas the ability to predict elevations in at
least two of the surrogates had sensitivities 39% to 65%,
specifi cities of 87% to 97%, and PPV values of 14% to 31%
( Fig. 1 , Table 5 ).
there were lower mean levels of HbA1c (5.12% vs. 5.17%) and
uric acid (4.5 mg/dL vs. 5.7 mg/dL, P < 0.001). Regarding
racial/ethnic differences, insulin and hsCRP mean values
were lower among non-Hispanic white adolescents than ei-
ther Mexican Americans or non-Hispanic blacks. Conversely,
mean uric acid levels were lowest among non-Hispanic black
adolescents. There was a signifi cant trend toward increasing
HbA1c values with time ( Table 3 ).
Association with metabolic syndrome components
The metabolic syndrome–associated surrogate factors
in our analysis showed a high degree of linear association
with the components of metabolic syndrome ( Table 4 ). In
particular, fasting insulin, hsCRP, and uric acid were re-
lated to WC, systolic blood pressure (SBP), triglycerides, and
HDL. HbA1c was correlated with SBP; and fasting insulin,
HbA1c, and uric acid were correlated with fasting glucose.
Diastolic blood pressure (DBP) was negatively correlated
Sensitivity and specifi city
Figure 1 shows the sensitivity and specifi city for each
of the criteria in predicting elevations in the metabolic
syndrome–associated factors. In general, sensitivity and
specifi city for the prediction of metabolic syndrome–
related surrogate factors mirrored the rates of meta-
bolic syndrome diagnosis. For each of the surrogates, the
T able 2. M etabolic S yndrome A ssociations and P redictive V alue of F asting I nsulin , HbA1c, hsCRP, and U ric A cid
carotid IMT in
for future CAD
for future T2DM
Related to insulin resistance,
the sin qua non of
increased IMT 11
of coronary artery
calcifi cation over
a 2-year period,
RR 2.36 for 1%
adjusted for other
CHD factors 17
RR 6.0 (highest 10% vs.
lowest 10%, adjusted
for insulin sensitivity,
body fat, age, sex;
median follow up 7.1
HbA1cHigher in nondiabetic
children and adolescents
with metabolic syndrome;
independent predictor of
metabolic syndrome with
OR 3.25 15
Associated with insulin
resistance 24 , 25 and with
increasing number of
metabolic syndrome risk
factors 19 , 22
RR risk of metabolic
syndrome is 5.4 for
children in the highest
quartile vs. lowest
quartile of uric acid 9
RR 3–3.7 for future
diabetes for HbA1c
5.07–5.22 vs. <4.8 over
10.1 years 16
increased IMT 20 , 21
RR 1.49 (top 1/3 vs.
bottom 1/3; adjusted
for other CHD risk
factors; 12 years mean
follow up) 18
RR 1.46 (highest vs.
adjusted for renal
function; 6.2 years
mean follow up) 26
RR 4.2 (highest vs.
adjusted for T2DM
risk factors; 4 years
median follow up) 23
RR 1.17 per 1.0 mg/dL
increase in uric acid
years follow-up) 27
increased IMT 28
Abbreviations: HbA1c, Hemoglobin A1c; hsCRP, high-sensitivity C-reactive protein; IMT, intima media thickness; RR, relative risk;
OR, odds ratio.
T able 3. NHANES 1999–2006 C haracteristics : C hildren 12–18 Y ears O ld with D ata on A ll O utcomes of I nterest ( N = 2,624)
% with metabolic syndrome
Uric Acid (mg/dL)
P value a
P value b
P value b
a Chi-squared test comparing metabolic syndrome prevalences, t -test comparing outcomes of interest.
b Chi-squared test comparing metabolic syndrome prevalences, ANOVA comparing outcomes of interest (overall difference among the groups).
Abbreviations: NHANES, National Health and Nutrition Examination Survey; IDF, International Diabetes Federation; ATP, Adult Treatment Panel III; HbA1c, hemoglobin A1c; hsCRP,
high-sensitivity C-reactive protein; CI, confi dence interval; NHW, non-Hispanic white; MA, Mexican-American; NHB, non-Hispanic black.
DEBOER AND GURKA348
regarding metabolic syndrome, we are differentiating be-
tween somewhat arbitrary rules used to diagnose children
with metabolic syndrome (a series of cut-off values of the
involved components) and the presence in some children of
possible underlying processes—related to in part to genetics
and obesity—that may link these particular components of
metabolic syndrome. Given the differences in cut-off values
for the various pediatric metabolic syndrome criteria, some
sets of criteria may perform better at diagnosing children
We have demonstrated that currently proposed pediatric
metabolic syndrome criteria exhibit a variable sensitivity
(7% to 70%) for predicting elevations in four factors that are
associated with metabolic syndrome and that are themselves
independent predictors in adults of CVD and T2DM, sug-
gesting their involvement with the biological processes un-
derlying metabolic syndrome. In making these statements
IDFCook Ford Jolliffe
IDFCook Ford Jolliffe
De FerrantiIDF Cook Ford Jolliffe
De FerrantiIDF Cook Ford Jolliffe
IDFCook Ford Jolliffe
De Ferranti IDF Cook Ford Jolliffe
Sensitivity (%) &
Sensitivity (%) &
FIG. 1. Sensitivity and specifi city for predicting elevations in surrogate factors in adolescents. Sensitivity (dark grey bars)
and specifi city (light grey bars) are shown with confi dence intervals for each of six sets of pediatric metabolic syndrome cri-
teria regarding the ability of a diagnosis of metabolic syndrome in an adolescent to predict the presence of elevations in sur-
rogate factors that are associated with metabolic syndrome and with future risk for T2DM and CVD. Surrogate elevations
( A–D ) were defi ned as the top 5% of values for the entire adolescent cohort (age 12–18). Also shown is the ability of each set
of criteria to detect at least one ( E ) or at least two ( F ) elevated surrogate values. IDF, International Diabetes Federation; ATP,
Adult Treatment Panel III; HbA1c, hemoglobin A1c; hsCRP, high-sensitivity C-reactive protein. (*) P < 0.05 for sensitivity
compared to de Ferranti criteria.
T able 4. C orrelations Between M etabolic S yndrome C omponents and S urrogate M easures
of C ardiovascular D isease and T ype 2 D iabetes M ellitus
Pearson’s r (P value)
WC SBP DBP Triglycerides HDL Fasting glucose
Abbreviations: WC, waist circumference; SBP, systolic blood pressure; DBP, diastolic blood pressure; TG, triglycerides; HDL, high-
density lipoprotein; IDF, International Diabetes Federation; ATP, Adult Treatment Panel III; HbA1c, hemoglobin A1c; hsCRP, high-
sensitivity C-reactive protein.
T able 5. O verall P ositive P redictive V alue and N egative P redictive V alue (95% C onfidence I ntervals ) C omparisons
Set of Metabolic
High fasting insulin
(n = 156)
High HbA1c (>5.5)
(n = 305)
High hsCRP (>4.0)
(n = 170)
High uric acid (>7.3)
(n = 116)
One or more elevations
(n = 609)
Two or more
(n = 108)
Jolliffe (ATP III)
Abbreviations: HbA1c, hemoglobin A1c; hsCRP, high-sensitivity C-reactive protein; PPV, positive predictive value; NPV, negative predictive value; IDF, International Diabetes Federation;
ATP, Adult Treatment Panel III.
DEBOER AND GURKA 350
fasting insulin values, suggesting either that a fair number
of subjects have isolated insulin resistance without other
metabolic abnormalities or that our criteria are not identi-
fying all children with metabolic syndrome.
Uric acid is strongly associated with metabolic syndrome
and future risk of T2DM and CVD. 9 , 53 Although its role is not
known regarding the biological processes underlying meta-
bolic syndrome, elevated uric acid levels have been identifi ed
as a primary cause of hypertension. 37,54–60 Our analysis using
the 95 th percentile for uric acid (7.3 mg/dL, close to the most
commonly used cut off of 7.0 to identify risk associated with
uric acid 9 , 55 , 56 , 61 ) showed that current metabolic syndrome cri-
teria performed better at predicting uric acid elevations than
they did for any of the other surrogates besides insulin. This
ability to predict uric acid elevations occurred even though
overall uric acid was not as tightly linked to individual met-
abolic syndrome component as was insulin ( Table 4 ).
CRP is an acute-phase reactant that is produced in infl am-
matory processes but can also be elevated in children 24 , 25, 29–31
and adults 18 , 23,32–34 during low levels of infl ammation pro-
duced as a result of obesity and the metabolic syndrome.
Although hsCRP values in our sample were signifi cantly
correlated with the components of metabolic syndrome
( Table 4 ), the correlation with hsCRP was weaker than noted
for fasting insulin and uric acid. In addition, none of the
pediatric metabolic syndrome criteria exhibited impres-
sive sensitivity in predicting hsCRP elevations ( Fig. 1 , all
<31%), which is either indicative of a high level of non- met-
abolic syndrome–related elevations in hsCRP (such as due
to minor infections 62 ) or diffi culties in our current criteria
in identifying underlying infl ammation due to metabolic
syndrome or, more likely, a combination of these. Although
the hsCRP values in children and adolescents are not sig-
nifi cantly different from those seen in adults, 63 the weaker
association of hsCRP with metabolic syndrome components
may underscore a less precise role for prediction of future
disease using hsCRP among children than is observed in
adults. Future long-term studies are needed to better defi ne
the relationship of childhood hsCRP levels and risk for adult
The sensitivity for detecting mild elevations in HbA1c
was poorest for all of the surrogates, and HbA1c also had
the weakest correlation with metabolic syndrome compo-
nents, suggesting that mild elevations in HbA1c may not be
related to metabolic syndrome. 64 There was a trend toward
increasing HbA1c over time. Given that metabolic syndrome
itself was stable over this time period, the signifi cance of
this increasing trend is uncertain, although it may indi-
cate a subtle worsening of blood sugar values over the time
Overall, there was signifi cant variability in the ability
of these pediatric metabolic syndrome criteria to identify
individuals with elevations in the surrogate risk factors. In
particular, the criteria of de Ferranti et al. exhibited the best
sensitivity and the IDF criteria exhibited the poorest sensi-
tivity in predicting elevations in the factors. This was true
for each of the surrogates, as well as for the ability to pick up
at least one or two surrogates. The reason for this improved
sensitivity for the de Ferranti criteria likely lies in its inclu-
sion of adolescents with elevated WC (a surrogate for visceral
adiposity) from 75% to 90%, when each of the other sets of cri-
teria used 90% as a cut-off value. This inclusion of additional
who are affected by processes that drive the association of
insulin resistance, hypertension, and lipid abnormalities
and may be at higher risk for future disease.
To be fair, the biological processes underlying metabolic
syndrome have not been well delineated and are likely to
involve overlapping pathways with elements of infl amma-
tion, dysregulation of adipose-derived hormonal factors,
and changes in activity of insulin signaling. 3,39–42 The value
of metabolic syndrome has thus been questioned as an
entity that is more important than the sum of its parts. 43
Nevertheless, the tight associations linking the components
of metabolic syndrome 44 and their ability to predict adult
disease 4,45–48 make pediatric metabolic syndrome an in-
triguing way to identify children who would most benefi t
from early interventions to prevent future sequelae. The bet-
ter we can identify children with these future risks, the more
confi dent we will be in targeting these children for interven-
tions targeting weight loss and physical activity.
An increasing number of studies are using long-term
cohort data to confi rm the utility of a childhood diagnosis
of metabolic syndrome in predicting future disease. 4,45–47
Unfortunately, as a tool for comparing current metabolic
syndrome criteria, these studies are frequently limited by a
lack of adequate information (including a lack of WC mea-
surements 4 ) collected at recruitment in childhood or by rela-
tively small numbers of subjects who have developed T2DM
or coronary artery disease (CAD) events. Because of these
limitations of long-term data, we chose to evaluate current
pediatric metabolic syndrome criteria by looking for eleva-
tions in four factors that are not directly a part of the diag-
nostic criteria of metabolic syndrome itself but are strongly
associated with metabolic syndrome. These factors were
fasting insulin, HbA1c, hsCRP, and uric acid ( Table 2 ). We
chose these factors because they are associated with meta-
bolic syndrome and linked to the long-term sequelae that
make metabolic syndrome a concerning condition; there-
fore, they may also be related to the underlying insulin re-
sistance and infl ammatory processes involved in metabolic
syndrome. This approach provides the means of comparing
current metabolic syndrome criteria for sensitivity in pre-
dicting elevations in factors that may identify processes un-
derlying metabolic syndrome.
Insulin resistance is the sin qua non of metabolic syn-
drome. We used fasting insulin values as our estimate of
insulin resistance, given their association with metabolic
syndrome and risk of later T2DM. 14 , 49 , 50 We chose fasting in-
sulin instead of the HOMA-IR (based on fasting insulin and
glucose 51 ) because each of the criteria that we evaluate use
fasting glucose values as part of the basis of the diagnosis of
metabolic syndrome. Prior studies have shown that fasting
insulin closely approximates HOMA-IR because of wide dif-
ferences in insulin variability (53-fold) compared to minor
differences in fasting glucose variability (1.8-fold). 52 When
we evaluated sensitivity and specifi city of the sample using
HOMA-IR as a surrogate instead of insulin, we obtained
nearly identical results.
Given the central nature of insulin resistance in metabolic
syndrome, it is notable that the current sets of criteria exhib-
ited better sensitivity for detecting elevated fasting insulin
(40% to 70%) than the other surrogate factors. Nevertheless,
current pediatric metabolic syndrome criteria missed detec-
tion of 30% to 60% of children that comprised the top 5% of
PEDIATRIC METABOLIC SYNDROME TO PREDICT SURROGATE FACTORS OF T2DM AND CVD
will be needed to defi ne how these sets of criteria perform
in the prediction of future disease risk, a more important
means to judge their adequacy.
This work was supported by National Institutes of Health
grant NIH HD060739-01 (to M.D.D.).
Author Disclosure Statement
No competing fi nancial interests exist.
1. Narayan KM , Boyle JP , Thompson TJ , Sorensen SW , Williamson
DF . Lifetime risk for diabetes mellitus in the United States .
JAMA 2003 ; 290 : 1884 – 1890 .
2. Reaven GM . Banting lecture 1988. Role of insulin resistance in
human disease . Diabetes 1988 ; 37 : 1595 – 1607 .
3. Haffner SM . The metabolic syndrome: Infl ammation,
diabetes mellitus, and cardiovascular disease . Am J Cardiol
2006 ; 97 : 3A – 11A .
4. Morrison JA , Friedman LA , Wang P , Glueck CJ . Metabolic
syndrome in childhood predicts adult metabolic syndrome
and type 2 diabetes mellitus 25 to 30 years later . J Pediatr
2008 ; 152 : 201 – 206 .
5. Zimmet P , Alberti G , Kaufman F , Tajima N , Silink M , Arslanian
S , Wong G , Bennett P , Shaw J , Caprio S , International Diabetes
Federation Task Force on Epidemiology and Prevention of
Diabetes. The metabolic syndrome in children and adolescents .
Lancet 2007 ; 369 : 2059 – 2061 .
6. Lieb DC , Snow RE , DeBoer MD . Socioeconomic factors in the
development of childhood obesity and diabetes . Clin Sports Med
2009 ; 28 : 349 – 378 .
7. Cook S , Weitzman M , Auinger P , Nguyen M , Dietz WH .
Prevalence of a metabolic syndrome phenotype in adoles-
cents: Findings from the third National Health and Nutrition
Examination Survey, 1988–1994 . Arch Pediatr Adolesc Med
2003 ; 157 : 821 – 827 .
8. de Ferranti SD , Gauvreau K , Ludwig DS , Neufeld EJ , Newburger
JW , Rifai N . Prevalence of the metabolic syndrome in American
adolescents: Findings from the Third National Health and
Nutrition Examination Survey . Circulation 2004 ; 110 : 2494 – 2497 .
9. Ford ES , Li C , Cook S , Choi HK . Serum concentrations of uric
acid and the metabolic syndrome among US children and ado-
lescents . Circulation 2007 ; 115 : 2526 – 2532 .
10. Jolliffe CJ , Janssen I . Development of age-specifi c adoles-
cent metabolic syndrome criteria that are linked to the Adult
Treatment Panel III and International Diabetes Federation cri-
teria . J Am Coll Cardiol 2007 ; 49 : 891 – 898 .
11. Beauloye V , Zech F , Tran HT , Clapuyt P , Maes M , Brichard SM .
Determinants of early atherosclerosis in obese children and
adolescents . J Clin Endocrinol Metab 2007 ; 92 : 3025 – 3032 .
12. Gungor N , Saad R , Janosky J , Arslanian S . Validation of sur-
rogate estimates of insulin sensitivity and insulin secretion in
children and adolescents . J Pediatr 2004 ; 144 : 47 – 55 .
13. Lee KK , Fortmann SP , Fair JM , Iribarren C , Rubin GD , Varady A ,
Go AS , Quertermous T , Hlatky MA . Insulin resistance indepen-
dently predicts the progression of coronary artery calcifi cation .
Am Heart J 2009 ; 157 : 939 – 945 .
14. Weyer C , Hanson RL , Tataranni PA , Bogardus C , Pratley RE . A
high fasting plasma insulin concentration predicts type 2 diabetes
independent of insulin resistance: Evidence for a pathogenic role
of relative hyperinsulinemia . Diabetes 2000 ; 49 : 2094 – 2101 .
15. Lopez-Capape M , Alonso M , Colino E , Mustieles C , Corbaton
J , Barrio R . Frequency of the metabolic syndrome in obese
Spanish pediatric population . Eur J Endocrinol 2006 ; 155 : 313 – 319 .
individuals with milder degrees of elevated WC resulted in
more overall diagnoses of metabolic syndrome, as well as
improved sensitivity and decreased specifi city in prediction
of surrogates for underlying processes related to metabolic
syndrome. This happened despite the de Ferranti criteria
having the most stringent cut off for fasting glucose, 110 mg/
dL, which was based on a previous recommendation by the
American Diabetes Association (ADA). 65 It is not surprising
that this inclusion of further individuals with elevated WC
improved the sensitivity of predicting these elevations, be-
cause each of the surrogates, with the exception of HbA1c,
was positively correlated with WC ( Table 4 ). WC is itself a
surrogate for visceral adipose tissue, which is felt to drive
many of the processes underlying metabolic syndrome. 66
The positive predictive values were highest for the IDF cri-
teria and lowest for the de Ferranti criteria, which is largely
indicative of the relative amounts of metabolic syndrome di-
agnoses yielded by each set of criteria. The sets of criteria that
yielded more metabolic syndrome diagnoses overall were
more likely to include individuals with surrogate elevations,
but were even more likely to pick up additional metabolic
syndrome (+) individuals without surrogate elevations, di-
luting the specifi city of a metabolic syndrome diagnosis for
detecting these elevations. Used as a test to detect individu-
als with elevations in these factors associated with increased
risk in adults, some sets of criteria have more false positives
(e.g., the de Ferranti criteria) for these surrogates whereas
others produce fewer false negatives (e.g., the IDF critieria).
In considering the utility of a test to appropriately trigger a
given work up or intervention, it is important to take into
account whether the population would be better served by
including a higher number of false positives for work up and
intervention (and thus incur increased costs and anxiety) or
to inappropriately exclude false negatives (and thus fail to
intervene on certain individuals at higher risk). Given the in-
tervention that we would propose to be triggered (i.e., effec-
tive lifestyle intervention) is labor-intensive for health-care
practitioners, but is likely to provide cardiovascular benefi ts
to most overweight adolescents, we would submit that it is
better to have a higher level of false positives. 67 This is partic-
ularly true because these surrogates are not the only mark-
ers of underlying disease risk but merely are suggestive of
the presence of biological processes that drive the associa-
tions of metabolic syndrome.
In conclusion, current pediatric metabolic syndrome
criteria exhibit a range of sensitivity, specifi city, PPV and
NPV values in the prediction of surrogates for the under-
lying processes behind metabolic syndrome. In particular,
the currently proposed sets of metabolic syndrome criteria
exhibited high NPV, excluding children who did not have
elevated levels of these surrogates. The sets of criteria did
not perform as well with respect to PPV, meaning that a
large proportion of adolescents diagnosed with metabolic
syndrome did not have signifi cant elevations in the fac-
tors we tested for, potentially indicating that they exhib-
ited a less-advanced condition of metabolic syndrome or
otherwise revealing constraints of this means of analysis.
Should a diagnosis of metabolic syndrome be used to trig-
ger interventions for individuals at elevated risk for future
disease, missing as few as possible, the de Ferranti criteria
demonstrated the best sensitivity with adequate specifi city
(all >85%). Further experiments using longitudinal data sets
DEBOER AND GURKA352
33. Danesh J , Collins R , Appleby P , Peto R . Association of fi brin-
ogen, C-reactive protein, albumin, or leukocyte count with
coronary heart disease: Meta-analyses of prospective studies .
JAMA 1998 ; 279 : 1477 – 1482 .
34. Ridker PM , Rifai N , Rose L , Buring JE , Cook NR . Comparison of
C-reactive protein and low-density lipoprotein cholesterol lev-
els in the prediction of fi rst cardiovascular events . N Engl J Med
2002 ; 347 : 1557 – 1565 .
35. Feig DI , Johnson RJ . Hyperuricemia in childhood primary hy-
pertension . Hypertension 2003 ; 42 : 247 – 252 .
36. Feig DI , Rodriguez-Iturbe B , Nakagawa T , Johnson RJ . Nephron
number, uric acid, and renal microvascular disease in the path-
ogenesis of essential hypertension . Hypertension 2006 ; 48 : 25 – 26 .
37. Siu YP , Leung KT , Tong MK , Kwan TH . Use of allopurinol in
slowing the progression of renal disease through its ability to
lower serum uric acid level . Am J Kidney Dis 2006 ; 47 : 51 – 59 .
38. Ford ES , Giles WH , Myers GL , Rifai N , Ridker PM , Mannino
DM . C-reactive protein concentration distribution among US
children and young adults: Findings from the National Health
and Nutrition Examination Survey, 1999–2000 . Clin Chem
2003 ; 49 : 1353 – 1357 .
39. Lago F , Gomez R , Gomez-Reino JJ , Dieguez C , Gualillo O .
Adipokines as novel modulators of lipid metabolism . Trends
Biochem Sci 2009 ; 34 : 500 – 510 .
40. Guilherme A , Virbasius JV , Puri V , Czech MP . Adipocyte dys-
functions linking obesity to insulin resistance and type 2
diabetes . Nat Rev Mol Cell Biol 2008 ; 9 : 367 – 377 .
41. Kahn BB , Flier JS . Obesity and insulin resistance . J Clin Invest
2000 ; 106 : 473 – 481 .
42. DeBoer MD . Underdiagnosis of the metabolic syndrome in non-
hispanic black adolescents: A call for ethnic-specifi c criteria.
Curr Cardiovas Risk Rep 2010 ; 4:302–310 .
43. Kahn R . Metabolic syndrome: Is it a syndrome? Does it matter?
Circulation 2007 ; 115 : 1806 – 1810 ; discussion 1811.
44. Li C , Ford ES . Is there a single underlying factor for the meta-
bolic syndrome in adolescents? A confi rmatory factor analysis .
Diabetes Care 2007 ; 30 : 1556 – 1561 .
45. Burns TL , Letuchy EM , Paolos R , Witt J. , Childhood predic-
tors of the metabolic syndrome in middle-aged adults: The
Muscatine Study . J Pediatr 2009 ; 155 : e17 – e126 .
46. Franks PW , Hanson RL , Knowler WC , Moffett C , Enos G , Infante
AM , Krakoff J , Looker HC . Childhood predictors of young-on-
set type 2 diabetes . Diabetes 2007 ; 56 : 2964 – 2972 .
47. Huang TT , Nansel TR , Belsheim AR , Morrison JA . Sensitivity,
specifi city, and predictive values of pediatric metabolic syn-
drome components in relation to adult metabolic syndrome:
The Princeton LRC follow-up study . J Pediatr 2008 ; 152 : 185 – 190 .
48. Wannamethee SG , Shaper AG , Lennon L , Morris RW . Metabolic
syndrome vs Framingham Risk Score for prediction of coro-
nary heart disease, stroke, and type 2 diabetes mellitus . Arch
Intern Med 2005 ; 165 : 2644 – 2650 .
49. Hanley AJ , Williams K , Gonzalez C , D’Agostino RB Jr ,
Wagenknecht LE , Stern MP , Haffner SM , San Antonio Heart
Study, Mexico City Diabetes Study, Insulin Resistance
Atherosclerosis Study . Prediction of type 2 diabetes using
simple measures of insulin resistance: Combined results from
the San Antonio Heart Study, the Mexico City Diabetes Study,
and the Insulin Resistance Atherosclerosis Study . Diabetes
2003 ; 52 : 463 – 469 .
50. Hanson RL , Pratley RE , Bogardus C , et al. Evaluation of simple
indices of insulin sensitivity and insulin secretion for use in
epidemiologic studies . Am J Epidemiol 2000 ; 151 : 190 – 198 .
51. Matthews DR , Hosker JP , Rudenski AS , Naylor BA , Treacher DF ,
Turner RC . Homeostasis model assessment: insulin resistance
and beta-cell function from fasting plasma glucose and insulin
concentrations in man . Diabetologia 1985 ; 28 : 412 – 419 .
52. Schwartz B , Jacobs DR , Jr. , Moran A , Steinberger J , Hong CP ,
Sinaiko AR . Measurement of insulin sensitivity in children:
16. Pradhan AD , Rifai N , Buring JE , Ridker PM . Hemoglobin A1c
predicts diabetes but not cardiovascular disease in nondiabetic
women . Am J Med 2007 ; 120 : 720 – 727 .
17. Selvin E , Coresh J , Golden SH , Brancati FL , Folsom AR , Steffes
MW . Glycemic control and coronary heart disease risk in per-
sons with and without diabetes: The atherosclerosis risk in
communities study . Arch Intern Med 2005 ; 165 : 1910 – 1916 .
18. Danesh J , Wheeler JG , Hirschfi eld GM , Eda S , Eiriksdottir G ,
Rumley A , Lowe GD , Pepys MB , Gudnason V . C-reactive protein
and other circulating markers of infl ammation in the predic-
tion of coronary heart disease . N Engl J Med 2004 ; 350 : 1387 – 1397 .
19. de Ferranti SD , Gauvreau K , Ludwig DS , Newburger JW , Rifai
N . Infl ammation and changes in metabolic syndrome abnor-
malities in US adolescents: Findings from the 1988–1994 and
1999–2000 National Health and Nutrition Examination Surveys .
Clin Chem 2006 ; 52 : 1325 – 1330 .
20. Giannini C , de Giorgis T , Scarinci A , Ciampani M , Marcovecchio
ML , Chiarelli F , Mohn A . Obese related effects of infl amma-
tory markers and insulin resistance on increased carotid in-
tima media thickness in pre-pubertal children . Atherosclerosis
2008 ; 197 : 448 – 456 .
21. Jarvisalo MJ , Harmoinen A , Hakanen M , Paakkunainen
U , Vlikari J , Hartiala J , Lehtimaki T , Simell O , Raitakar OT .
Elevated serum C-reactive protein levels and early arte-
rial changes in healthy children . Arterioscler Thromb Vasc Biol
2002 ; 22 : 1323 – 1328 .
22. Patel DA , Srinivasan SR , Xu JH , Li S , Chen W , Berenson GS .
Distribution and metabolic syndrome correlates of plasma
C-reactive protein in biracial (black-white) younger adults: The
Bogalusa Heart Study . Metabolism 2006 ; 55 : 699 – 705 .
23. Pradhan AD , Manson JE , Rifai N , Buring JE , Ridker PM .
C-reactive protein, interleukin 6, and risk of developing type 2
diabetes mellitus . JAMA 2001 ; 286 : 327 – 334 .
24. Wu DM , Chu NF , Shen MH , Wang SC . Obesity, plasma
high sensitivity C-reactive protein levels and insulin resis-
tance status among school children in Taiwan . Clin Biochem
2006 ; 39 : 810 – 815 .
25. Yoshida T , Kaneshi T , Shimabukuro T , Sunagawa M , Ohta T .
Serum C-reactive protein and its relation to cardiovascular
risk factors and adipocytokines in Japanese children . J Clin
Endocrinol Metab 2006 ; 91 : 2133 – 2137 .
26. Brodov Y , Chouraqui P , Goldenberg I , Boyko V , Mandelzweig L ,
Behar S . Serum uric acid for risk stratifi cation of patients with
coronary artery disease . Cardiology 2009 ; 114 : 300 – 305 .
27. Kodama S , Saito K , Yachi Y , Asumi M , Sugawara A , Totsuka K ,
Saito A , Sone H . Association between serum uric acid and de-
velopment of type 2 diabetes . Diabetes Care . 2009 ; 32 : 1737 – 1742 .
28. Meyer AA , Kundt G , Steiner M , Schuff-Werner P , Kienast W .
Impaired fl ow-mediated vasodilation, carotid artery intima-
media thickening, and elevated endothelial plasma markers
in obese children: The impact of cardiovascular risk factors .
Pediatric 2006 ; 117 : 1560 – 1567 .
29. Ford ES , Ajani UA , Mokdad AH . The metabolic syndrome and
concentrations of C-reactive protein among U.S. youth . Diabetes
Care 2005 ; 28 : 878 – 881 .
30. Ford ES , Galuska DA , Gillespie C , Will JC , Giles WH , Dietz WH .
C-reactive protein and body mass index in children: Findings
from the Third National Health and Nutrition Examination
Survey, 1988–1994 . J Pediatr 2001 ; 138 : 486 – 492 .
31. Visser M , Bouter LM , McQuillan GM , Wener MH , Harris TB .
Low-grade systemic infl ammation in overweight children .
Pediatrics 2001 ; 107 : E13 .
32. Freeman DJ , Norrie J , Caslake MJ , Gaw A , Ford I , Lowe GD ,
O’Reilly DS , Packard CJ , Sattar N , West of Scotland Coronary
Prevention Study . C-reactive protein is an independent
predictor of risk for the development of diabetes in the
West of Scotland Coronary Prevention Study . Diabetes
2002 ; 51 : 1596 – 1600 .
PEDIATRIC METABOLIC SYNDROME TO PREDICT SURROGATE FACTORS OF T2DM AND CVD
bolic syndrome in Japanese workingmen . J Cardiometab Syndr
2007 ; 2 : 158 – 162 .
62. Melbye H , Hvidsten D , Holm A , Nordbo SA , Brox J . The course
of C-reactive protein response in untreated upper respiratory
tract infection . Br J Gen Pract 2004 ; 54 : 653 – 658 .
63. Ford ES , Giles WH , Mokdad AH , Myers GL . Distribution and
correlates of C-reactive protein concentrations among adult US
women . Clin Chem 2004 ; 50 : 574 – 581 .
64. Jansen H , Wijga AH , Smit HA , Scholtens S , Kerkhof M ,
Koppelman GH , de Jongste JC , Stolk RP . HbA(1c) levels in non-
diabetic Dutch children aged 8–9 years: The PIAMA birth co-
hort study . Diabet Med 2009 ; 26 : 122 – 127 .
65. Genuth S , Alberti KG , Bennett P , Buse J , Defronzo R , Kahn
R , Kitzmiller J , Knowler WC , Lebovitz V , Lernmark A ,
Nathan D , Palmer J , Rizza R , Saudek C , Shaw J , Steffes M , Stern
M , Tuomilehto J , Zimmet P , Expert Committee on the Diagnosis
and Classifi cation of Diabetes Mellitus. Follow-up report
on the diagnosis of diabetes mellitus . Diabetes Care
2003 ; 26 : 3160 – 3167 .
66. de Ferranti S , Mozaffarian D . The perfect storm: Obesity, ad-
ipocyte dysfunction, and metabolic consequences . Clin Chem
2008 ; 54 : 945 – 955 .
67. Walker SE, Gurka MJ, Oliver MN, Johns DW, DeBoer MD.
Racial/ethnic discrepancies in the metabolic syndrome
begin in childhood and persist after adjustment for environ-
mental factors. Nutr Metab Cardiovasc Dis 2010; doi:10.1016/
Comparison between the euglycemic-hyperinsulinemic clamp
and surrogate measures . Diabetes Care 2008 ; 31 : 783 – 788 .
53. Ebrahimpour P , Fakhrzadeh H , Heshmat R , Bandarian F ,
Larijani B . Serum uric acid levels and risk of metabolic syn-
drome in healthy adults . Endocr Pract 2008 ; 14 : 298 – 304 .
54. Alper AB , Jr. , Chen W , Yau L , Srinivasan SR , Berenson GS ,
Hamm LL . Childhood uric acid predicts adult blood pressure:
The Bogalusa Heart Study . Hypertension 2005 ; 45 : 34 – 38 .
55. Mellen PB , Bleyer AJ , Erlinger TP , Evans GW , Nieto FJ ,
Wagenknecht LE , Wofford MR , Herrington DM . Serum uric
acid predicts incident hypertension in a biethnic cohort:
The atherosclerosis risk in communities study . Hypertension
2006 ; 48 : 1037 – 1042 .
56. Perlstein TS , Gumieniak O , Williams GH , Sparrow D , Vokonas
PS , Gaziano M , Weiss ST , Litonjua AA . Uric acid and the de-
velopment of hypertension: The normative aging study .
Hypertension 2006 ; 48 : 1031 – 1036 .
57. Sundstrom J , Sullivan L , D’Agostino RB , Levy D , Kannel WB ,
Vasan RS . Relations of serum uric acid to longitudinal blood
pressure tracking and hypertension incidence . Hypertension
2005 ; 45 : 28 – 33 .
58. Khosla UM , Zharikov S , Finch JL , Nakagawa T , Roncal C , Mu W ,
Krotova K , Block ER , Prabhakar S , Johnson RJ . Hyperuricemia
induces endothelial dysfunction . Kidney Int 2005 ; 67 : 1739 – 1742 .
59. Mazzali M , Hughes J , Kim YG , Jefferson JA , Kang DH , Gordon
KL , Lan HY , Kivlighn S , Johnson RJ . Elevated uric acid increases
blood pressure in the rat by a novel crystal-independent mecha-
nism . Hypertension 2001 ; 38 : 1101 – 1106 .
60. Mazzali M , Kanellis J , Han L , Feng L , Xia YY , Chen Q , Kang
DH , Gordon KL , Watanabe S , Nakagawa T , Lan HY , Johnson
RJ . Hyperuricemia induces a primary renal arteriolopathy in
rats by a blood pressure-independent mechanism . Am J Physiol
Renal Physiol 2002 ; 282 : F991 – F997 .
61. Kawada T , Otsuka T , Katsumata M , Suzuki H . Serum uric
acid is signifi cantly related to the components of the meta-
Address correspondence to:
Mark D. DeBoer, M.D., M.Sc., M.C.R.
P.O. Box 800386
Charlottesville, VA 22908
E-mail : firstname.lastname@example.org
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