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Prevalence and Determinants of Insulin Resistance Among U.S. Adolescents A population-based study

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We sought to examine the distribution of insulin and homeostasis model assessment of insulin resistance (HOMA-IR) and associations of HOMA-IR with sex, race/ethnicity, age, and weight status, as measured by BMI, among U.S. adolescents. Of 4,902 adolescents aged 12-19 years who participated in the National Health and Nutrition Examination Survey 1999-2002, analysis was performed for a nationally representative subsample of 1,802 adolescents without diabetes who had fasting laboratory measurements. The main outcome measure was HOMA-IR, calculated from fasting insulin and glucose and log transformed for multiple linear regression analyses. In adjusted regression models that included age and weight status, girls had higher HOMA-IR than boys and Mexican-American children had higher HOMA-IR levels than white children. There were no significant differences in adjusted HOMA-IR between black and white children. Obese children (BMI >/=95th percentile) had significantly higher levels of HOMA-IR compared with children of normal weight (BMI <85th percentile) in adjusted comparisons (mean HOMA-IR 4.93 [95% CI 4.56-5.35] vs. 2.30 [2.21-2.39], respectively). Weight status was by far the most important determinant of insulin resistance, accounting for 29.1% of the variance in HOMA-IR. The prevalence of insulin resistance in obese adolescents was 52.1% (95% CI 44.5-59.8). Obesity in U.S. adolescents represents the most important risk factor for insulin resistance, independent of sex, age, or race/ethnicity. The prevalence of insulin resistance in obese children foreshadows a worrisome trend for the burden of type 2 diabetes in the U.S.
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Prevalence and Determinants of Insulin
Resistance Among U.S. Adolescents
A population-based study
JOYCE M. LEE,
MD, MPH
1,2
MEGUMI J. OKUMURA,
MD
3
MATTHEW M. DAVIS,
MD, MAPP
2,4,5
WILLIAM H. HERMAN,
MD, MPH
5,6
JAMES G. GURNEY,
PHD
2
OBJECTIVE We sought to examine the distribution of insulin and homeostasis model
assessment of insulin resistance (HOMA-IR) and associations of HOMA-IR with sex, race/
ethnicity, age, and weight status, as measured by BMI, among U.S. adolescents.
RESEARCH DESIGN AND METHODS Of 4,902 adolescents aged 12–19 years who
participated in the National Health and Nutrition Examination Survey 1999 –2002, analysis was
performed for a nationally representative subsample of 1,802 adolescents without diabetes who
had fasting laboratory measurements. The main outcome measure was HOMA-IR, calculated
from fasting insulin and glucose and log transformed for multiple linear regression analyses.
RESULTS In adjusted regression models that included age and weight status, girls had
higher HOMA-IR than boys and Mexican-American children had higher HOMA-IR levels than
white children. There were no significant differences in adjusted HOMA-IR between black and
white children. Obese children (BMI 95th percentile) had significantly higher levels of
HOMA-IR compared with children of normal weight (BMI 85th percentile) in adjusted com-
parisons (mean HOMA-IR 4.93 [95% CI 4.56 –5.35] vs. 2.30 [2.21–2.39], respectively). Weight
status was by far the most important determinant of insulin resistance, accounting for 29.1% of
the variance in HOMA-IR. The prevalence of insulin resistance in obese adolescents was 52.1%
(95% CI 44.5–59.8).
CONCLUSIONS Obesity in U.S. adolescents represents the most important risk factor for
insulin resistance, independent of sex, age, or race/ethnicity. The prevalence of insulin resistance
in obese children foreshadows a worrisome trend for the burden of type 2 diabetes in the U.S.
Diabetes Care 29:2427–2432, 2006
T
he pathogenesis of type 2 diabetes in
children is hypothesized to be re-
lated to two principal factors: insu-
lin resistance and impaired insulin
secretion (1). Insulin resistance repre-
sents an insensitivity of the peripheral tis-
sues (e.g., muscle, liver, adipose tissue) to
the effects of insulin. To maintain glucose
homeostasis, pancreatic -cells compen-
sate for insulin resistance by augmenting
insulin secretion, leading to a state of
chronic hyperinsulinemia. However, the
progressive failure of the -cells to main-
tain adequate insulin secretion is believed
to result in the development of type 2 di-
abetes (2). Longitudinal studies in adults
have demonstrated that insulin resistance
is strongly predictive of the development
of type 2 diabetes (3). Furthermore, stud-
ies in obese children have shown that in-
sulin resistance is associated with
abnormalities in glucose metabolism,
such as impaired glucose tolerance or
type 2 diabetes (4). Therefore, there is in-
creasing recognition of the important role
of insulin resistance in the pathogenesis of
type 2 diabetes in children (1,2,5).
Given that insulin resistance repre-
sents an important risk factor for develop-
ment of type 2 diabetes, identification of
children with insulin resistance has been
proposed as a strategy for identifying
high-risk children for targeted diabetes
prevention interventions (6). The gold-
standard test for insulin resistance in-
cludes the hyperinsulinemic-euglycemic
clamp (7), and another accepted method
is the minimal-model analysis frequently
sampled intravenous glucose tolerance
test (FSIVGTT) (8). These tests are inva-
sive, labor intensive, and expensive,
which discourages their use in large pop-
ulation-based epidemiologic studies. A
simpler and more practical method to
measure insulin resistance, the homeosta-
sis model assessment of insulin resistance
(HOMA-IR), was therefore developed for
application in large epidemiologic studies
(9).
HOMA-IR is an estimate of insulin re-
sistance derived from fasting glucose and
insulin levels, with higher levels repre-
senting greater degrees of insulin resis-
tance. HOMA-IR has been validated as a
surrogate measure of insulin resistance in
nondiabetic children, with studies show-
ing correlations as high as 0.91 with
clamp or FSIVGTT measures (10–13).
Studies (14,15) in children have es-
tablished that increasing BMI is associated
with an increase in insulin resistance.
Given the alarming rise in obesity rates
among youth in the U.S., there is great
concern that diabetes incidence will
shadow this trend. However, studies to
evaluate insulin resistance in population-
based samples of children are limited. We
sought to evaluate the distribution of
HOMA-IR in a diverse group of U.S. ado-
●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●
From the
1
Pediatric Endocrinology Unit, University of Michigan, Ann Arbor, Michigan; the
2
Child Health
Evaluation and Research (CHEAR) Unit, University of Michigan, Ann Arbor, Michigan; the
3
Division of
General Pediatrics, University of California, San Francisco, San Francisco, California; the
4
Gerald R. Ford
School of Public Policy, University of Michigan, Ann Arbor, Michigan; the
5
Department of Internal Medicine,
University of Michigan, Ann Arbor, Michigan; and the
6
Department of Epidemiology, University of Michi-
gan, Ann Arbor, Michigan.
Address correspondence and reprint requests to Joyce M. Lee, MD, MPH, 300 NIB, Room 6E05, Campus
Box 0456, Ann Arbor, MI 48109-0456. E-mail: joyclee@umich.edu.
Received for publication 31 March 2006 and accepted in revised form 16 August 2006.
Abbreviations: FSIVGTT, frequently sampled intravenous glucose tolerance test; HOMA-IR, homeostasis
model assessment of insulin resistance; NFG, normal fasting glucose; NHANES, National Health and Nu-
trition Examination Survey.
A table elsewhere in this issue shows conventional and Syste`me International (SI) units and conversion
factors for many substances.
DOI: 10.2337/dc06-0709
© 2006 by the American Diabetes Association.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby
marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
Epidemiology/Health Services/Psychosocial Research
ORIGINAL ARTICLE
DIABETES CARE, VOLUME 29, NUMBER 11, NOVEMBER 2006 2427
lescents who participated in the National
Health and Nutrition Examination Survey
(NHANES) from 1999 to 2002.
RESEARCH DESIGN AND
METHODS NHANES is a cross-
sectional, nationally representative exam-
ination survey of the U.S. civilian
noninstitutionalized population (16,17).
NHANES uses a stratified multistage
probability sampling design, oversam-
pling adolescents aged 12–19 years, non-
Hispanic blacks, and Mexican Americans
to provide reliable statistical estimates for
these subpopulations. Of the 4,902 ado-
lescents aged 12–19 years in the study, a
subsample had glucose and insulin levels
measured (n 2015). Adolescents who
had self-reported diabetes (n 10), were
pregnant (n 39), were using medica-
tions that would interfere with glucose
metabolism (antihypoglycemics, steroids,
or androgens) (n 29), and who did not
fast for at least8h(n 133) were ex-
cluded, which left 1,804 adolescents. Be-
cause two individuals had previously
undiagnosed diabetes (fasting glucose
126 mg/dl), 1,802 adolescents were
used for the analysis, including 222 with
impaired fasting glucose (100 glu-
cose 126 mg/dl) (10.7% of the
weighted population), and 1,580 with
normal fasting glucose (NFG; glucose
100 mg/dl) (89.3% of the weighted
population).
Because studies have shown that
HOMA-IR may serve as a valuable surro-
gate measure of insulin resistance in non-
diabetic children (12), we chose to
include adolescents with both NFG and
impaired fasting glucose for this analysis,
reporting an estimate of HOMA-IR reflec-
tive of the entire nondiabetic population.
We also performed analyses of only chil-
dren with NFG. Despite slightly lower
mean HOMA-IR levels, the relationship of
HOMA-IR with the demographic and
weight variables remained similar (data
available from the authors upon request).
Mexican-American adolescents with
fasting laboratory measurements had
lower mean BMI compared with those
without (23.8 vs. 24.5, respectively, P
0.04), and a lower proportion of black
females had fasting laboratory measure-
ments than those without (45.5 vs.
52.8%, respectively, P 0.004). Other-
wise, there were no significant differences
in age, sex, and BMI between groups for
the overall sample and among racial
groups.
Sociodemographic information was
obtained through household interviews
administered in English or Spanish. A
digital stadiometer was used to measure
height, and a digital weight scale was used
to measure weight; these instruments
were calibrated on a weekly and daily ba-
sis, respectively. For height measure-
ments, adolescents stood with the heels of
both feet together and feet flat on the
floor, and for weight measurements they
were undressed, wearing underwear and
paper gowns (18).
For determination of fasting status, a
detailed fasting questionnaire was admin-
istered (19). Fasting samples for insulin
and plasma glucose were obtained by ve-
nipuncture and analyzed at a central lab-
oratory at the University of Missouri-
Columbia. Samples were allowed to clot
at room temperature for 15–30 min, were
centrifuged, and the serum was frozen at
20° for insulin (20) and 70° for glu-
cose (21). Analysis of samples was per-
formed up to 1 week after the samples
were drawn. Insulin was measured using
the Pharmacia Insulin RIA kit (Pharmacia
Biotech, Uppsala, Sweden; interassay co-
efficient of variation [CV] 4.9%; intraas-
say CV 5.0%) with a cross-reactivity
with proinsulin of 40% (20). Plasma
glucose was measured using modified
hexokinase enzymatic methods (21).
Data analysis
HOMA-IR was calculated by dividing the
product of insulin (microunits per milli-
liter) and glucose (millimoles per liter) by
22.5 (22). BMI was converted to age- and
sex-standardized percentiles based on the
Centers for Disease Control and Preven-
tion 2000 growth charts, which are not
race specific (23). Adolescents were clas-
sified as normal weight if BMI was 85th
percentile, overweight if BMI was 85th
and 95th percentile, or obese if BMI was
95th percentile.
Means and SEs for insulin, glucose,
and HOMA-IR were calculated using
Stata 9.0 (Stata, College Station, TX),
which applies appropriate sampling
weights to adjust for the complex multi-
cluster sample design, oversampling, and
nonresponse. Separate sampling weights
were created to reflect the additional stage
of sampling and the additional nonre-
sponse for the subsample of adolescents
with fasting glucose and insulin. These
weights were used for the analysis, and
therefore each subsample is a nationally
representative sample (24). Taylor series
linearization was used for variance esti-
mation. Because of the right-skewed dis-
tribution of HOMA-IR, weighted linear
regression was performed on log-
transformed HOMA-IR, controlling for
age, sex, race, and weight status. Socio-
economic status, coded as a total family
income less than or greater than or equal
to $20,000, was evaluated. Because there
were no significant differences in log
HOMA-IR between groups in the unad-
justed comparisons and within the full
model, the socioeconomic status variable
was omitted in order to maintain the most
parsimonious model. Postregression
adjustment of the log-transformed
HOMA-IR was performed to convert log
estimates back to HOMA-IR values for the
demographic and weight groups (25).
Boot strapping was used to generate 95%
CIs for the adjusted HOMA-IR values.
Insulin resistance was defined based
on a number of different thresholds, in-
cluding HOMA-IR 4.39 (upper 2.5 per-
centile or 2 SD above mean HOMA-IR
for normal-weight adolescents with NFG
in this study), HOMA-IR 3.29 (upper
quartile of insulin resistance for all ado-
lescents) (26), HOMA-IR 3.16 (11),
and HOMA-IR 3.99 (27). We calcu-
lated the upper 2.5 percentile based on
non–log-transformed HOMA-IR for nor-
mal-weight subjects with NFG. Weighted
prevalence estimates of insulin resistance
were calculated for normal-weight, over-
weight, and obese children.
RESULTS In the study population,
12.9% of adolescents were overweight
and 16.3% were obese. Table 1 shows de-
mographic characteristics of the study
population and the distribution of weight
status among the various racial/ethnic
groups. There were no statistical differ-
ences in weight status by sex or age. How-
ever, a greater proportion of Mexican-
American and black adolescents were
overweight or obese compared with non-
Hispanic white adolescents.
The distribution of fasting glucose
within the population was narrow, with a
mean value of 91.4 mg/dl (95% CI 91.0
91.9). The correlation between HOMA-IR
and insulin levels was high (r 0.88).
Table 2 shows unadjusted insulin and
HOMA-IR levels and adjusted HOMA-IR
levels stratified by sex, race, weight status,
and age. For unadjusted comparisons,
there were no significant differences in in-
sulin or HOMA-IR by sex, but after ad-
justment for race, age, and weight status,
female subjects had significantly higher
mean HOMA-IR than male subjects (P
0.02). Unadjusted insulin and HOMA-IR
Insulin resistance among U.S. adolescents
2428 DIABETES CARE, VOLUME 29, NUMBER 11, NOVEMBER 2006
values were higher in black and Mexican-
American adolescents compared with
white adolescents. After adjustment for
age, sex, and weight status, Mexican-
American adolescents had higher mean
HOMA-IR levels than white adolescents
(P 0.001), but no significant differences
in HOMA-IR were found between black
and white adolescents (P 0.19).
Unadjusted and adjusted mean
HOMA-IR values for obese adolescents
were 5.18 (95% CI 4.74 –5.63) and 4.93
(4.56–5.35), respectively. After adjust-
ment for sex, age, and race, overweight
(P 0.001) and obese (P 0.001) ado-
lescents had higher HOMA-IR levels com-
pared with adolescents of normal weight
(Table 2).
Table 2 shows unadjusted and ad-
justed HOMA-IR levels by age for the
overall study population. Adolescents
aged 14 years had higher HOMA-IR levels
than those aged 12 years for unadjusted
and adjusted values. Table 3 shows mean
HOMA-IR values for each age stratified by
sex and weight status. HOMA-IR values
appear to peak at age 14 years in boys and
age 13 years in girls. Among normal-
weight and overweight adolescents,
HOMA-IR values did not differ substan-
tially between ages; however, there was an
apparent peak at age 14 years among
obese adolescents.
In a linear regression model predict-
ing log HOMA-IR, weight status alone ac-
counted for 29.1% of the variance. The
addition of covariates of age, sex, and
race/ethnicity only resulted in a slight in-
crease in the explained variance to 31.2%.
Figure 1 shows the prevalence of in-
sulin resistance stratified by weight status
based on different thresholds for defining
insulin resistance. Regardless of the defi-
nition used, the prevalence of insulin re-
sistance was substantially higher in obese
children compared with normal-weight
children. Using a definition of insulin re-
sistance of HOMA-IR 4.39, the preva-
lence of insulin resistance in obese
children was 52.1% (95% CI 44.5–59.8).
CONCLUSIONS We present the
first study to report the distribution of
HOMA-IR, a validated measure of insulin
resistance, for a population-based, ra-
cially diverse, nationally representative
sample of U.S. adolescents, finding that
HOMA-IR is markedly higher in obese
compared with normal-weight adoles-
cents. Although previous studies have
demonstrated that insulin resistance in
children/adolescents is influenced by a
Table 1—Characteristics of the study population
Characteristic
Overall
population
BMI 85th
percentile
BMI 85th and
95th percentile
BMI 95th
percentile
P value
across weight
status
Sex 0.33
Male 939 (52.0) 605 (68.0) 143 (14.6) 184 (17.4)
Female 863 (48.0) 564 (73.0) 136 (11.5) 156 (15.5)
Race 0.03
Non-Hispanic
white
490 (63.1) 357 (73.6) 57 (11.4) 72 (15.0)
Black 510 (14.0) 304 (59.2) 95 (19.0) 109 (21.9)
Mexican
American
672 (10.6) 422 (63.7) 106 (15.9) 138 (20.4)
Age (years) 0.32
12 237 (12.9) 152 (72.0) 37 (12.3) 47 (15.6)
13 258 (13.2) 149 (62.1) 51 (18.8) 58 (19.1)
14 219 (11.8) 137 (73.8) 32 (9.4) 49 (16.7)
15 196 (12.1) 128 (69.0) 34 (16.5) 33 (14.5)
16 228 (12.4) 150 (69.3) 31 (13.1) 45 (17.6)
17 244 (14.3) 171 (77.5) 31 (9.3) 39 (13.2)
18 208 (10.9) 141 (73.8) 35 (13.7) 31 (12.5)
19 212 (12.5) 141 (65.5) 28 (11.9) 40 (22.6)
Data are n (weighted %).
Table 2—Mean HOMA-IR values by demographic characteristics
Unadjusted insulin
(95% CI) (U/ml)
Unadjusted
HOMA-IR
(95% CI)
Adjusted
HOMA-IR
(95% CI)
Overall 12.57 (12.00–13.15) 2.87 (2.73–3.01)
Sex *
Male 12.15 (11.37–12.93) 2.84 (2.64–3.03) 2.74 (2.61–2.89)
Female 13.03 (12.23–13.84) 2.91 (2.71–3.10) 2.97 (2.82–3.11)†
Race
White 11.88 (11.09–12.67) 2.72 (2.53–2.91) 2.76 (2.63–2.91)
Black 14.19 (13.37–15.01)§ 3.17 (2.96–3.37) 2.90 (2.76–3.05)
Mexican American 14.04 (13.07–15.01)¶ 3.27 (3.04–3.50)¶ 3.08 (2.94–3.23)§
Weight status #
BMI 85th
percentile
10.09 (9.59–10.58) 2.28 (2.16–2.39) 2.30 (2.21–2.39)
BMI 85th and
95th percentile
13.56 (12.69–14.42)¶ 3.09 (2.86–3.32)¶ 3.16 (2.95–3.40)¶
BMI 95th
percentile
22.28 (20.44–24.13)¶ 5.18 (4.74–5.63)¶ 4.93 (4.56–5.35)¶
Age (years) **
12 12.30 (11.00–13.60) 2.84 (2.52–3.16) 2.78 (2.53–3.05)
13 12.94 (11.55–14.34) 2.94 (2.63–3.25) 2.82 (2.57–3.08)
14 14.81 (12.75–16.88)† 3.43 (2.92–3.94)†† 3.27 (2.97–3.61)‡‡
15 12.20 (10.72–13.69) 2.80 (2.44–3.16) 2.87 (2.63–3.15)
16 11.90 (10.93–12.87) 2.69 (2.45–2.92) 2.73 (2.55–2.95)
17 11.84 (10.65–13.04) 2.66 (2.38–2.94) 2.76 (2.57–2.97)
18 11.79 (10.53–13.04) 2.67 (2.37–2.98) 2.77 (2.54–3.01)
19 12.89 (11.74–14.05) 2.96 (2.67–3.24) 2.81 (2.58–3.08)
For unadjusted and adjusted comparisons, reference groups are male, white, normal weight, and children
aged 12 years. *Adjusted for race, weight status, and age. ‡Adjusted for sex, weight status, and age. #Adjusted
for sex, race, and age. **Adjusted for sex, race, and weight status. P 0.02; §P 0.001; P 0.005; P
0.001; ††P 0.04; ‡‡P 0.002.
Lee and Associates
DIABETES CARE, VOLUME 29, NUMBER 11, NOVEMBER 2006 2429
number of factors, including sex, race, de-
gree of adiposity, and pubertal stage (1),
we found that no other factor considered,
i.e., age, sex, or race/ethnicity, was nearly
as influential on HOMA-IR status as
weight status, specifically obesity. Our re-
sults are consistent with previous studies
that demonstrated that obesity is one of
the most important risk factors for insulin
resistance (28,29).
Girls and children from minority
groups have been shown to have a higher
propensity toward insulin resistance (30
33). In our study, girls had a higher mean
HOMA-IR than boys after accounting for
weight, age, and race/ethnicity. Similarly,
after adjustment for sex, age, and weight
status, we found higher mean HOMA-IR
values for Mexican-American adoles-
cents, but we did not find significant dif-
ferences in HOMA-IR between black and
white adolescents. This lack of a differ-
ence between black and white children is
in contrast to the results of previous stud-
ies (31,32) using clamps or FSIVGTT.
One study (12) compared measures of in-
sulin resistance by clamp and fasting
methods in a small sample of black and
white prepubertal nonobese children
(n 44). Although statistical differences
in insulin resistance between black and
white children were detected using clamp
measures, differences in HOMA-IR only
approached statistical significance (P
0.095). It was postulated that this was due
to the small sample size using a less sen-
sitive measure of insulin resistance (12).
Despite the large sample size in our study,
we did not find differences in HOMA-IR
levels between black and white children.
We speculate that the lack of racial differ-
ences noted in our study may be due to
the fact that HOMA-IR is measured in the
fasting state, whereas clamp studies eval-
uate the insulin-stimulated state.
We were unable to assess the impact
of puberty on HOMA-IR levels. Puberty is
associated with temporary increases in in-
sulin resistance (30,34,35) with a peak
reduction in insulin sensitivity of 25–30%
by Tanner stage 3 and complete recovery
by pubertal completion (36). We did ex-
amine HOMA-IR levels for each year of
adolescence (12–19 years), a period dur-
ing which most children would experi-
ence puberty. The earlier peak in
HOMA-IR levels that we saw in girls com-
pared with boys may reflect the effect of
puberty on insulin resistance, as girls ex-
perience puberty at an earlier age than
boys. Prevailing wisdom would suggest
that HOMA-IR values performed during
puberty are difficult to interpret because
of the effects of puberty on insulin resis-
tance. We saw substantial variability by
age in HOMA-IR for obese children but
not for normal- or overweight children.
Therefore, we speculate that HOMA-IR val-
ues during puberty may still accurately re-
flect the degree of insulin resistance, at least
for normal- and overweight children.
Currently, there is no strict definition
for insulin resistance in children or ado-
lescents (37). We present prevalence esti-
mates of insulin resistance based on
different definitions (11,27). Regardless
of the defined threshold, a significantly
higher proportion of overweight and
obese children had insulin resistance. A
Figure 1—Prevalence of insulin resistance according to various HOMA-IR cutoffs. *HOMA-IR
threshold based on receiver-operator curve analysis (11). †HOMA-IR threshold defined by the
upper quartile of insulin resistance for all adolescents in this study. ‡HOMA-IR threshold based on
adult studies (27). §HOMA-IR threshold defined by the upper 2.5 percentile based on non–log-
transformed HOMA-IR for normal-weight adolescents with NFG in this study. E, normal weight;
f, overweight; F, obese.
Table 3—Unadjusted HOMA-IR (95% CI) stratified by sex and weight status
Age (years) Male Female BMI 85th percentile
BMI 85th
and
95th percentile BMI 95th percentile
12 2.45 (2.12–2.78) 3.21 (2.73–3.69) 2.31 (2.04–2.58) 2.79 (2.29–3.28) 5.33 (3.86–6.81)
13 2.70 (2.28–3.12) 3.23 (2.77–3.70) 2.31 (1.95–2.66) 3.23 (2.79–3.67) 4.72 (4.18–5.26)
14 3.76 (2.80–4.73) 3.12 (2.62–3.63) 2.45 (2.22–2.68) 2.81 (2.37–3.25) 8.10 (6.25–9.94)
15 2.98 (2.44–3.52) 2.59 (2.13–3.04) 2.22 (2.02–2.43) 3.38 (2.66–4.11) 4.93 (3.58–6.29)
16 2.57 (2.26–2.89) 2.80 (2.53–3.08) 2.25 (2.10–2.39) 2.89 (2.34–3.44) 4.23 (3.23–5.24)
17 2.66 (2.38–2.94) 2.66 (2.15–3.16) 2.20 (2.02–2.38) 2.85 (2.44–3.25) 5.22 (3.77–6.67)
18 2.64 (2.13–3.14) 2.72 (2.49–2.96) 2.19 (1.95–2.44) 2.88 (2.44–3.32) 5.26 (4.12–6.39)
19 3.04 (2.59–3.48) 2.86 (2.50–3.22) 2.32 (1.92–2.71) 3.66 (3.09–4.24) 4.29 (3.71–4.87)
Insulin resistance among U.S. adolescents
2430 DIABETES CARE, VOLUME 29, NUMBER 11, NOVEMBER 2006
HOMA-IR threshold of 2 SD above the
mean for normal-weight adolescents with
NFG (4.39) may represent a reasonable
definition of insulin resistance, as it was
derived through a standard method for
determining abnormal values within a
population, and as a conservative esti-
mate it results in the lowest proportion of
normal-weight children classified as hav-
ing insulin resistance.
Given that insulin resistance is
thought to represent an initial step in the
pathogenesis of type 2 diabetes (1,12), the
high prevalence estimates of insulin resis-
tance in obese children foreshadows a
concerning trend for the future burden of
type 2 diabetes in U.S. children.
Limitations
Previous studies have demonstrated high
correlations of HOMA-IR with more pre-
cise measures of insulin resistance de-
rived from clamp studies or FSIVGTT
(10,12). We acknowledge, however, that
HOMA-IR is not as sensitive a method for
determining insulin resistance compared
with these other methods. Therefore, we
may have somewhat underestimated the
prevalence of insulin resistance in our
study population. Furthermore,
HOMA-IR is measured in the fasting state,
whereas clamp studies evaluate insulin
resistance in an insulin-stimulated state.
BMI is a surrogate measure of adipos-
ity, which correlates with fat-free mass
and total body fat (38). We did not ac-
count for differences in body fat distribu-
tion, which may differ among races and
impact insulin sensitivity (39). Although
studies have demonstrated that waist cir-
cumference is associated with abdominal
fat and insulin resistance in children (40),
we did not include waist circumference in
this analysis as there are no defined stan-
dard categories of risk for waist circum-
ference in children as with BMI (41).
We created a definition of insulin re-
sistance based on normal-weight children
with NFG; however, glucose tolerance
tests were not performed. Therefore, it is
possible that some children in the nor-
mal-weight group may have had impaired
glucose tolerance, which would limit the
definition of “normal” in these patients.
Finally, NHANES used a single uni-
form assay on the subjects’ specimens, al-
lowing for comparison of HOMA-IR
levels among a diverse representative
population of U.S. children. However,
there is concern regarding uncertain com-
parability of insulin levels among various
laboratories (42).
In this population-based sample of
adolescents, after accounting for race, sex,
and age, weight status was the most im-
portant determinant of insulin resistance
as measured by HOMA-IR. A majority of
obese adolescents in the U.S. have insulin
resistance. Strategies for weight reduction
and prevention of obesity in children are
likely necessary to prevent the future de-
velopment of type 2 diabetes among chil-
dren and young adults in the U.S.
Acknowledgments J.M.L. was supported
by a T32HD 07534-05 National Institutes of
Health/National Institute of Child Health and
Human Development Pediatric Health Ser-
vices Research Training Grant.
This work was presented as an abstract at
the American Diabetes Association’s Scientific
Sessions in June 2006.
We thank Ram Menon for his thoughtful
contributions to the manuscript.
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2432 DIABETES CARE, VOLUME 29, NUMBER 11, NOVEMBER 2006
... Using these criteria, approximately one-quarter of US children and adolescents are eligible for diabetes screening, of which only 4% have elevation of hemoglobin A1c greater than or equal to 5.7% [5]. However, nearly half of obese adolescents have elevation of homeostatic model assessment of insulin resistance (HOMA-IR) indicating insulin resistance (IR), an established risk factor for metabolic syndrome, type 2 diabetes mellitus (T2D), and future cardiovascular disease [6]. Therefore, reliance on hemoglobin A1c leads to under-detection of IR. ...
... IR was measured by HOMA-IR, which was calculated by the formula HOMA-IR=(fasting glucose in mg/dL*fasting insulin in mIU/ L)/405. HOMA-IR of 5.0 or greater was considered indicative of IR based on results from previous population studies in this age group [6]. Data were collected on patients' lipid panels including triglycerides (TG), total cholesterol, high density lipoproteins (HDL-C), non-HDL-C, low-density lipoproteins (LDL-C), and TG/HDL-C ratio. ...
... Based on HOMA-IR measurement, IR was a common finding in this group of children and adolescents with dyslipidemia. Although there is no HOMA-IR level which is universally agreed to define IR, NHANES data of US adolescents showed a mean HOMA-IR of 2.3 in children of normal weight and 4.9 in obese children [6]. Using a HOMA-IR level greater than or equal to 5.0 to define IR for our population, 43.7% demonstrated IR. ...
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Background Childhood obesity and associated comorbidities, including insulin resistance, are increasing in the United States. Our objectives were to (1) determine the prevalence of insulin resistance in children seen in dyslipidemia clinic and (2) evaluate which aspects of the lipid profile correlate with insulin resistance. Methods Children and adolescents seen in a specialized pediatric dyslipidemia clinic without secondary diagnoses known to alter the lipid panel were included. Simultaneous fasting lipid panel, insulin, and glucose levels were available in 572 children (50.5% male). Results Mean patient age was 15.0±3.6 years with the majority being over 10 years of age (92.5%). Mean BMI was 29.8±8.1 kg/m ² and BMI standard deviation score was 1.80±0.9. Mean HOMA-IR was 6.2±5.7 with a range of 0.4–49.3, and interquartile range of 2.7–7.6. Triglyceride level had a positive correlation with HOMA-IR (p<0.001). HDL-C negatively correlated with HOMA-IR even controlling for triglyceride level by multivariate analysis (p=0.001) and HDL-C <30 mg/dL predicted IR with 41.5% PPV. Conclusions In children and adolescents with dyslipidemia, insulin resistance is common and significantly correlates with reduced HDL-C levels. Non-fasting samples are easier to obtain in children and low HDL-C, which is minimally affected on non-fasting samples, could be an easily obtained indicator of IR. Increasing detection of insulin resistance in children with dyslipidemia may provide greater opportunities for lifestyle interventions and possible pharmacotherapy to modify cardiovascular risk.
... However, there is no consensus on the threshold value to define insulin resistance for either surrogate estimate of insulin sensitivity. Different cut-offs ranging from the 2.5 th -to-25 th percentile of distribution are employed [20,21]. To estimate insulin sensitivity, we used QUICKI, as in contrast to HOMA-IR, it returns normally distributed data. ...
... To estimate insulin sensitivity, we used QUICKI, as in contrast to HOMA-IR, it returns normally distributed data. We set the cut-off arbitrarily to the 15 th percentile, an approximate mean between the employed ranges [20,21]. However, we cannot determine whether the selected value is more or less optimal than other used ones. ...
... As expected, and in line with other studies in adolescents [21][22][23][24], we observed a higher prevalence of insulin resistance in overweight/ obese subjects compared with lean ones; about 4-fold in males and 3-fold in females. Comparison of prevalence reported in different studies is difficult because of the use of different categorizations of both, insulin resistance and overweight/obesity. ...
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Aim We investigated whether lean insulin-resistant individuals manifest increased cardiometabolic risk. Methods 2,341 (51.8% females) healthy 16–23-year-old subjects were categorized as lean or overweight/obese; and insulin-sensitive or insulin-resistant, and compared. Results n both sexes, lean insulin-sensitive and insulin-resistant subjects displayed similar measures of obesity (e.g., males, waist-to-height ratio: lean insulin-sensitive: 0.42±0.03, lean insulin-resistant: 0.43±0.03, overweight/obese insulin-sensitive: 0.49±0.05, overweight/obese insulin-resistant: 0.53±0.06). Lean insulin-sensitive individuals were more insulin-sensitive compared with their overweight/obese peers; insulin-resistant groups presented similar insulin-sensitivity (males, the Quantitative insulin-sensitivity check index (QUICKI): lean insulin-sensitive: 0.354±0.022, lean insulin-resistant: 0.304±0.013, overweight/obese insulin-sensitive: 0.343±0.019, overweight/obese insulin-resistant: 0.299±0.015). The two-factor analysis of variance indicated an independent effect of insulin sensitivity, overweight/obesity, and their interaction on the continuous metabolic syndrome score (p<0.001, all; males, lean insulin-sensitive: 1.87±0.35, lean insulin-resistant: 2.14±0.42, overweight/obese insulin-sensitive: 2.15±0.40, overweight/obese insulin-resistant: 2.75±0.69). C-reactive protein, leukocyte count, and glomerular filtration rate in both sexes; uric acid, asymmetric dimethyl-arginine, and soluble vascular adhesion protein-1 in males; and soluble receptor for advanced glycation end-products in females were independently associated with insulin resistance. Among phenotypes associated with low QUICKI, the distribution of insulin-resistant individuals was random. Conclusion Later clinical consequences of insulin resistance in lean subjects remain to be elucidated in longitudinal studies.
... It is also inappropriate to apply their cutoff values directly in other races. Consequently, cutoff values for the diagnosis of IR have been defined based on the HOMA-IR distribution of a healthy reference population or the study population itself (19,21,(33)(34)(35)(36)(37). The cutoff values of the HOMA-IR used for defining IR in adolescents varies widely according to ethnicity, age, gender, and screening methods, and ranges from 2.4 to 5.22 (19,34,36,39). ...
... This finding was similar to that of previous population-based studies (36,37). Several studies have reported widely varying prevalence rates of IR among adolescents with obesity in different countries, such as the United States (52.1%) (33) and Brazil (29.1%) (42). Similar to other studies, there were no obvious differences based on sex in the frequency of IR (35,40,41). ...
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... The following diagnostic criteria and definitions were applied in this study: Elevated total cholesterol (TC): TC ≥ 5.18 mmol/L [31]; elevated low high-density lipoprotein (LDL): LDL ≥ 3.36 mmol/L [31]; ...
... The following diagnostic criteria and definitions were applied in this study: Elevated total cholesterol (TC): TC ≥ 5.18 mmol/L [31]; elevated low high-density lipoprotein (LDL): LDL ≥ 3.36 mmol/L [31]; ...
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... (1) central obesity: WC ≥ age-and gender-specific 90th percentile [20]; (2) elevated TG: TG ≥ 1.24 mmol/L; (3) low HDL: HDL ≤ 1.03 mmol/L; (4) elevated blood pressure: SBP and/or DBP ≥ 90th percentile for gender, age and height [21]; (5) elevated FBG: glucose ≥ 6.1 mmol/L. Elevated TC: TC ≥ 5.18 mmol/L [22]; elevated LDL: LDL ≥ 3.36 mmol/L [22]. Hyperuricemia: serum uric acid (SUA) ≥ 357µmol/L [23]. ...
... (1) central obesity: WC ≥ age-and gender-specific 90th percentile [20]; (2) elevated TG: TG ≥ 1.24 mmol/L; (3) low HDL: HDL ≤ 1.03 mmol/L; (4) elevated blood pressure: SBP and/or DBP ≥ 90th percentile for gender, age and height [21]; (5) elevated FBG: glucose ≥ 6.1 mmol/L. Elevated TC: TC ≥ 5.18 mmol/L [22]; elevated LDL: LDL ≥ 3.36 mmol/L [22]. Hyperuricemia: serum uric acid (SUA) ≥ 357µmol/L [23]. ...
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Vitamin A, a fat-soluble essential vitamin, is implicated in a large range of physiological processes. Up to now, the associations between vitamin A and metabolic syndrome (MetS) or other metabolic risk factors are controversial in children and adolescents. Thus, we aimed to dig into the relationship of vitamin A with MetS and many other metabolic risk factors. This was a cross-sectional study derived from the China National Nutrition and Health Surveillance of Children and Lactating Mothers. A total of 3025 school-aged (7–17 years) children and adolescents were selected by applying multistage stratified cluster random sampling methods in the Jiangsu Province of eastern China. Through enquiry survey, anthropometric measurement and laboratory examination, relevant information and blood biochemical indexes of the participants were collected in this study. MetS was identified according to the modified criteria of the National Cholesterol Education Program–Adult Treatment Panel III (NCEP-ATP III). Multivariate logistic analysis and the generalized additive model (GAM) were used to analyze the relationship between vitamin A and various metabolic risk factors. The overweight, obesity and MetS prevalence of children and adolescents in this study was 14.0%, 11.9% and 5.1%, respectively. The risk of prevalent MetS, general obesity, high low-density lipoprotein (LDL), high total cholesterol (TC) and hyperuricemia increased with vitamin A in a dose-dependent way. Logistic regression analysis showed that serum vitamin A Z scores were positively associated with MetS and central obesity, elevated blood pressure (BP) and elevated triglyceride (TG). Sex stratification analysis showed that both in male and female participants, the risk of prevalent MetS, general obesity, high LDL, high TC and hyperuricemia still increased with vitamin A levels. MetS was at a high prevalence level in children and adolescents in Jiangsu that were 7–17 years old. Vitamin A was positively associated with obesity, MetS, dyslipidemia and hyperuricemia. More public health measures and new visions should focus on the effects of retinol on children and adolescents.
... A third advancement was focusing on Latino youth who are at risk for T2D due to elevated adiposity [61]. Youth with obesity may be especially susceptible to neighborhood characteristics [62] given how neighborhoods can structure the food environment, as well as opportunities for physical activity. ...
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Type 2 Diabetes (T2D) has reached epidemic levels among the pediatric population. Furthermore, disparities in T2D among youth are distributed in a manner that reflects the social inequality between population sub-groups. Here, we investigated the neighborhood determinants of T2D risk among a sample of Latino adolescents with obesity residing in Phoenix, Arizona (n = 133). In doing so we linked together four separate contextual data sources: the American Community Survey, the United States Department of Agriculture Food Access Research Atlas, the Arizona Healthy Community Map, and the National Neighborhood Data Archive to systematically analyze how and which neighborhood characteristics were associated with T2D risk factors as measured by fasting and 2-h glucose following a 75 g oral glucose tolerance test. Using linear regression models with and without individual/household covariates, we investigated how twenty-two housing and transportation sociodemographic and built and food environment characteristics were independently and jointly associated with T2D risk. The main finding from these analyses was the strong association between the density of fast food restaurants and 2-h glucose values (b = 2.42, p < 0.01). This association was independent of individual, household, and other neighborhood characteristics. Our results contribute to an increasingly robust literature demonstrating the deleterious influence of the neighborhood food environment, especially fast food, for T2D risk among Latino youth.
... This may be driven by a higher prevalence of obesity-related comorbidities for which metformin is often prescribed in Hispanics/Latinos compared to non-Hispanic Whites. [27][28][29][30] In our study, metformin was prescribed nearly twice as often, and the prevalence of diabetes/insulin resistance (an indication for metformin use) was more than double, in Hispanic/Latino compared to non-Hispanic White youth. Therefore, it may have been that metformin was prescribed more often in Hispanics/Latinos not primarily for weight loss, but rather for other indications more prevalent in this population. ...
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Background Race/ethnicity and low English proficiency healthcare disparities are well established in the United States. We sought to determine if there are race/ethnicity differences in anti-obesity medication (AOM) prescription rates among youth with severe obesity treated in a pediatric weight management clinic and if, among youth from non-primary English speaking families, there are differences in prescriptions between those using interpreters during visits versus not. Methods We reviewed electronic health records of 2- to 18-year-olds with severe obesity seen from 2012 to 2021. Race/ethnicity was self-report, and AOMs included topiramate, stimulants (e.g. phentermine, lisdexamfetamine), naltrexone (±bupropion), glucagon-like peptide-1 agonists, and orlistat. We used general linear regression models with log-link to compare incidence rate ratios (IRRs) within the first 1 and 3 years of being followed, controlling for age, percent of the 95th BMI percentile (%BMIp95), number of obesity-related comorbidities (e.g. insulin resistance, hypertension), median household income, and interpreter use. We repeated similar analyses among youth from non-primary English speaking families, comparing those using interpreters versus not. Results 1,725 youth (mean age 11.5 years; %BMIp95 142%; 53% non-Hispanic White, 20% Hispanic/Latino, 16% non-Hispanic black; 6% used interpreters) were seen, of which 15% were prescribed AOMs within 1 year. The IRR for prescriptions was lower among Hispanic/Latino compared to non-Hispanic White youth at one (IRR 0.70; CI: 0.49–1.00; p = 0.047) but not 3 years. No other statistically significant differences by race/ethnicity were found. Among non-primary English speaking families, the IRR for prescriptions was higher at 1 year (IRR 2.49; CI: 1.32–4.70; p = 0.005) in those using interpreters versus not. Conclusions Among youth seen in a pediatric weight management clinic, AOM prescription incidence rates were lower in Hispanics/Latinos compared to non-Hispanic Whites. Interpreter use was associated with higher prescription incidence rates among non-primary English speakers. Interventions to achieve equity in AOM prescriptions may help mitigate disparities in pediatric obesity.
... This can be seen in a 2006 population-based study on non-diabetic US adolescents (12-19 years old), which reported that about 13% of them were insulin resistant. 120 Postprandial hypoglycemia in insulin resistant subjects is associated with impaired first phase glucose-stimulated insulin response and a compensatory increased late insulin response. 4,50,156 Half a century ago, it was hypothesized that an increase in insulin sensitivity might also underlie postprandial hypoglycaemia, 131 and this was later demonstrated with the hyperinsulinemic normoglycemic glucose clamp technique. ...
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Full-text available
Increasing evidence suggests that migraine may be the result of an impaired brain glucose metabolism. Several studies have reported brain mitochondrial dysfunction, impaired brain glucose metabolism and gray matter volume reduction in specific brain areas of migraineurs. Furthermore, peripheral insulin resistance, a condition demonstrated in several studies, may extend to the brain, leading to brain insulin resistance. This condition has been proven to downregulate insulin receptors, both in astrocytes and neurons, triggering a reduction in glucose uptake and glycogen synthesis, mainly during high metabolic demand. This scoping review examines the clinical, epidemiologic and pathophysiologic data supporting the hypothesis that abnormalities in brain glucose metabolism may generate a mismatch between the brain's energy reserve and metabolic expenditure, triggering migraine attacks. Moreover, alteration in glucose homeostasis could generate a chronic brain energy deficit promoting migraine chronification. Lastly, insulin resistance may link migraine with its comorbidities, like obesity, depression, cognitive impairment and cerebrovascular diseases. Perspective: Although additional experimental studies are needed to support this novel “neuroenergetic” hypothesis, brain insulin resistance in migraineurs may unravel the pathophysiological mechanisms of the disease, explaining the migraine chronification and connecting migraine with comorbidities. Therefore, this hypothesis could elucidate novel potential approaches for migraine treatment.
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Syndrome X, or the syndrome of insulin resistance, is a cluster of related metabolic abnormalities of hyperinsulinemia, glucose intolerance, increased very low density lipoprotein (VLDL), decreased high density lipoprotein (HDL), and hypertension in nonobese adults and plays an important role in the genesis of cardiovascular disease. The aim of the present study was to examine the relationships among insulin sensitivity, plasma lipid levels, and body composition in the pediatric age group to determine whether these associations are present in childhood. Twenty healthy Caucasian Tanner stage I (TI) children (age, 10.7 +/- 0.3 yr; body mass index, 18.9 +/- 0.8 kg/m2) and 22 pubertal Tanner stage II-IV (TII-IV) adolescents (age, 14.0 +/- 0.3 yr; body mass index, 20.0 +/- 0.4 kg/m2) were studied. In vivo insulin-mediated glucose disposal (Rd) was evaluated during a 40 mu/m2. min hyperinsulinemic-euglycemic clamp. Body composition was assessed isotopically by the H218O dilution principle. Fasting blood was obtained for cholesterol, triglyceride (TG), VLDL, low density lipoprotein (LDL), and HDL determinations. In both groups, the strongest correlation of Rd was with percent body fat (%BF) (TI: r = -0.82; P < 0.001; TII-IV: r = -0.73; P < 0.001). In addition, in TI, Rd was correlated with TG (r = 0.64; P = 0.001), VLDL (r = 0.64; P = 0.001), and diastolic blood pressure (r = -0.50; P = 0.01). There were no such correlations in TII-IV. In TI, % BF correlated positively with LDL and negatively with TG and VLDL. In TII-IV, % BF correlated positively with cholesterol and LDL. After correcting for %BF, partial correlation analysis revealed no relationship between Rd and lipid levels in either group. This suggests that the relationship of insulin sensitivity to lipid levels was secondary to the effect of body composition on lipid levels. However, regardless of body composition, the basal insulin level was correlated with TG (r = 0.38; P = 0.04) and VLDL (r = 0.40; P = 0.04) in TII-IV subjects. We conclude that 1) the primary correlate of insulin sensitivity is %BF in both prepubertal and pubertal subjects, with no relationship to plasma lipids; 2) in prepubertal children, diastolic blood pressure is negatively correlated with insulin sensitivity and positively with insulin levels, independent of adiposity; and 3) after the onset of puberty, basal insulin levels are positively correlated with VLDL and TG regardless of the degree of adiposity. This observation could be a very early manifestation of the genesis of syndrome X in childhood.