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The Journal of Clinical Endocrinology and Metabolism logoLink to The Journal of Clinical Endocrinology and Metabolism
. 2009 Oct 16;94(12):4828–4834. doi: 10.1210/jc.2008-2665

The Stability of Metabolic Syndrome in Children and Adolescents

Jennifer K Gustafson 1,a, Lisa B Yanoff 1,a, Benjamin D Easter 1, Sheila M Brady 1, Margaret F Keil 1, Mary D Roberts 1, Nancy G Sebring 1, Joan C Han 1, Susan Z Yanovski 1, Van S Hubbard 1, Jack A Yanovski 1,a
PMCID: PMC2795665  PMID: 19837941

Abstract

Context: Some studies suggest the presence of metabolic syndrome before adulthood may identify those at high risk for later cardiovascular morbidity, but there are few data examining the reliability of pediatric metabolic syndrome.

Objective: To examine the short- and long-term stability of pediatric metabolic syndrome.

Design: Metabolic syndrome was defined as having at least three of the following: waist circumference, blood pressure, and fasting serum triglycerides in the 90th or higher percentile for age/sex; high-density lipoprotein-cholesterol 10th or lower percentile for age/sex; and fasting serum glucose of at least 100 mg/dl. Short-term metabolic syndrome stability (repeated measurements within 60 d) was assessed in obese youth ages 6–17 yr. Long-term metabolic syndrome stability (repeated measurements more than 1.5 yr apart) was studied in 146 obese and nonobese children age 6–12 yr at baseline.

Patients and Setting: Convenience samples of obese and nonobese youth ages 6–17 yr participating in research studies were collected at a clinical research hospital.

Results: Short-term metabolic syndrome stability (repeat measurements performed 19.7 ± 13.1 d apart) was assessed in 220 children. The diagnosis of metabolic syndrome was unstable in 31.6% of cases. At their short-term follow-up visit, incidence of metabolic syndrome among participants who did not have metabolic syndrome at baseline was 24%. In the long term (repeat measurements performed 5.6 ± 1.9 yr apart), the diagnosis of metabolic syndrome was unstable in 45.5% of cases.

Conclusions: Cutoff-point-based definitions for pediatric metabolic syndrome have substantial instability in the short and long term. The value of making a cutoff-point-based diagnosis of metabolic syndrome during childhood or adolescence remains in question.

Cut-point-based definitions for pediatric metabolic syndrome have substantial instability in the short- and long-term that may limit the value of making this diagnosis.

In adults, the clustering of metabolic abnormalities that includes elevated waist circumference, abnormal glucose homeostasis, hypertension, and dyslipidemia is termed the metabolic syndrome (1). Adult metabolic syndrome is associated with increased risk for cardiovascular disease (2,3) and type 2 diabetes mellitus (4) and was originally conceptualized as a diagnostic construct to help target those at greatest risk for developing these disorders (5). In adults, the diagnosis of metabolic syndrome has a stability of almost 75% over a 3-yr interval (6). Among adults and adolescents, metabolic syndrome is more prevalent in overweight and obese than in non-overweight samples (7,8). With the high rate of pediatric obesity (9), interest in the metabolic syndrome among youth has increased because of the possibility that the diagnosis of pediatric metabolic syndrome might identify those at increased risk for developing cardiovascular disease in adulthood. Indeed, it has been established that clustering of metabolic risk factors begins early in life (10,11), and analyses of some longitudinal studies have suggested that pediatric metabolic syndrome may confer increased risk for developing adult cardiovascular disease, type 2 diabetes, and metabolic syndrome (12,13,14,15).

Significant challenges, however, affect the utility of the diagnosis of metabolic syndrome for child and adolescent samples. First, there is no standard accepted definition of pediatric metabolic syndrome (16). Therefore, estimated prevalence rates of metabolic syndrome vary widely among studies, depending on the definition applied (17,18,19,20,21). Second, Goodman et al. (22) have found metabolic syndrome in adolescents to have relatively low long-term stability, when stability is defined as the prevalence of diagnoses of metabolic syndrome identified at baseline that is confirmed at follow-up testing. Among adolescents, Goodman et al. (22) reported that only half of diagnosed cases of metabolic syndrome are stable over a 3-yr follow-up period regardless of the definition of metabolic syndrome used. Low stability thus appears to limit the clinical utility of diagnosing adolescent metabolic syndrome in individual teens. Little, however, is known about the long-term stability of metabolic syndrome first diagnosed in younger children, and data regarding the short-term stability of pediatric or adolescent metabolic syndrome, have not, to our knowledge, been previously reported.

The primary purpose of the current study was to determine the stability of pediatric metabolic syndrome in the short and long term by studying two distinct cohorts: a short-term follow-up interval group, in which the variability in ascertainment of a diagnosis of metabolic syndrome could be examined, and a long-term follow-up interval group, in which the poor stability reported by Goodman et al. (22) in adolescents could be verified. We hypothesized that rates of instability for the diagnosis of metabolic syndrome would be sizable in both short- and long-term follow-up.

Subjects and Methods

Subjects and clinical protocol

Subjects expected to have high prevalence of metabolic abnormalities were recruited from two patient groups. To study the short-term stability of metabolic syndrome, we took advantage of a convenience sample of obese girls and boys ages 6–17 yr who were recruited for weight loss intervention studies (23,24). Data were obtained from two visits that occurred within 60 d of each other; both visits occurred before any pharmaceutical or behavioral intervention was implemented. Eligibility requirements for these protocols have been previously described but included hyperinsulinemia for participants 6–11 yr old and, for those 12–18 yr old, at least one comorbid condition including hyperinsulinemia, dyslipidemia, sleep apnea, elevated liver function tests, dysglycemia, or hypertension (23,24). To study the long-term stability of metabolic syndrome in a childhood cohort, a second convenience sample of 216 girls and boys ages 6–12 yr was used who had been recruited between 1994 and 2007 by mailings sent to Washington, DC, metropolitan area schools, by advertisements in local newspapers, and from referrals by local physicians for participation in nonintervention studies (25). Recruitment efforts for the long-term cohort specifically targeted individuals who were at risk for adult obesity because of their own obesity [body mass index (BMI) for age and sex ≥95th percentile according to Centers for Disease Control growth charts (26)] or their parents’ overweight (at least one parent with BMI ≥25 kg/m2), but subjects were not required to demonstrate any obesity-related comorbid conditions. Long-term follow-up data are reported from the 146 children age 6–12 yr who underwent two visits that occurred more than 1.5 yr apart. Exclusion criteria for both short- and long-term groups included significant renal, hepatic, endocrinological, or pulmonary (with the exception of asthma) disorders, as determined by history and physical examination, the use of medications (or behavioral programs) expected to affect body weight or insulin sensitivity, and significant weight change during the previous 3 months. The clinical protocols were approved by the Institutional Review Board of the Eunice Kenney Shriver National Institute of Child Health and Human Development. Each subject gave written assent and a parent gave written consent for participation in the studies. Subjects were compensated for their time and inconvenience.

Measurements of weight, height, and waist circumference were obtained with the use of standardized techniques (27). Weight was measured to the nearest 0.1 kg by using a calibrated digital scale (Scale-Tronix, Wheaton, IL). Height was measured in triplicate to the nearest 1 mm by using a stadiometer calibrated before each set of height measurements (Holtain Ltd., Crymych, Wales, United Kingdom). Waist circumference was measured at least twice with a flexible nonelastic tape measure just above the lateral border of the iliac crest, and the average value was used. Because of the design of the intervention studies, waist circumference was measured only once in the short-term cohort (at the follow-up visit); therefore, the same waist circumference value was used for the diagnosis of metabolic syndrome at both baseline and follow-up visits for the subjects who participated in the short-term study. We excluded subjects from the short-term cohort who gained or lost more than 3% of their baseline body weight to limit the possibility that waist circumference might differ greatly at short-term follow-up from baseline. Using weight and height measurements, BMI was calculated as (weight in kilograms)/(height in meters)2. Age- and sex-based percentiles (26) were then used to categorize subjects as nonobese (<95th percentile) or obese (≥95th percentile). Blood pressure (Dynamap; GE Heathcare, Piscataway, NJ) was measured at the right brachial artery while subjects were seated. Venous blood sampling was performed in all subjects for serum glucose and lipids after an overnight fast. Each participant was asked at least twice about any eating or drinking before blood was drawn. A fasted state was encouraged to be reported truthfully by providing compensation for the visit regardless of whether the subject reported having fasted. Subjects consumed their habitual diet (invariably reported as ≥35% energy from carbohydrates) during the week before they were studied. Glucose, total cholesterol, and triglycerides were measured on a Hitachi 917 analyzer using reagents from Roche Diagnostics (Indianapolis, IN). Direct determinations of high-density lipoprotein cholesterol (HDL-C) were performed on a Cobas FARA analyzer (Roche Diagnostics, Indianapolis, IN) using reagents from Sigma Chemical (St. Louis, MO).

For girls, pubertal breast stage was assigned through physical examination by a pediatric endocrinologist or trained pediatric nurse practitioner to one of the five standards of Tanner (28). Testicular volume (in milliliters) for boys was also assessed using an orchidometer.

Definition of metabolic syndrome

To examine differences in prevalence rates of metabolic syndrome depending on the definition used, we applied an age- and sex-specific percentile-based cutoff point definition of metabolic syndrome (Table 1) based upon definitions used in previous papers on pediatric metabolic syndrome (29,30,31,32). An analysis using more inclusive cutoff points (33,34) was also carried out and yielded essentially similar results (data not shown). The definition used values of at least 90th percentile for waist circumference (35), systolic/diastolic blood pressure (36), and triglycerides (37) and no higher than 10th percentile for HDL-C (37). Age- and sex-specific Z-scores for each of these variables were also calculated. As has been employed in previous papers on pediatric metabolic syndrome, a fasting glucose value of at least 100 mg/dl was used to indicate impaired fasting glucose. Metabolic syndrome was considered present when a participant had any three of these risk factors.

Table 1.

Cutoff points used for individual components of pediatric metabolic syndrome

Component Cutoff point
Waist circumference ≥90th percentile
Systolic or diastolic blood pressure ≥90th percentile
Fasting serum triglycerides ≥90th percentile
HDL-C ≤10th percentile
Fasting serum glucose ≥100 mg/dl
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Metabolic syndrome was defined as the co-occurrence of criterion values for three or more components. Data reported from the U.S. Centers for Disease Control were used to define percentiles for age and sex for waist circumference (35) and triglycerides and HDL-C (37). Blood pressure percentiles were defined using age, sex, and height (36). 

Statistical analyses

Following the approach of Goodman et al. (22), we defined instability as the percentage of baseline metabolic syndrome-positive youth who were metabolic syndrome negative at follow-up and incidence as the proportion of new cases from those who had been metabolic syndrome negative at baseline. Instability and incidence of metabolic syndrome and its individual components were calculated for both short- and long-term cohorts. Contingency table analysis or t tests as appropriate were used to compare differences between groups. Means ± sd are reported unless otherwise indicated. All statistical tests were two tailed, and P values of <0.05 were considered significant.

Results

Short-term follow-up cohort

The mean age of the sample was 14.0 ± 2.0 (range 7.0–18.0) years at baseline; 75% were age over 12 yr. The cohort was 63% female and predominantly African-American (57.3%) and Caucasian (41.8%), with a small percentage (0.9%) of Asian subjects. For the short-term follow-up cohort, 94% of subjects had evidence for clinical puberty (for girls, Tanner breast stage > I; for boys, testis volume ≥4 ml). Eighty percent of girls had Tanner IV or V breast development; 55% of boys had testis volumes of 10 ml or more.

Metabolic risk factors and weight status

The means and sd of BMI, BMI Z-score, and each of the individual components of metabolic syndrome are given in Table 2. As per study design, all subjects in the short-term cohort were obese (BMI ≥95th percentile) and had at least one comorbid condition associated with obesity.

Table 2.

Means of metabolic risks in short-term (n = 220) and long-term (n = 146) cohorts

Short-term, baseline
Short-term, follow-up
Long-term, baseline
Long-term, follow-up
Mean sd Mean sd Mean sd Mean sd
BMI (kg/m2) 40.9 9.3 40.6 9.3 21.0 5.3 27.6c 8.2
BMI Z-score 2.56 0.32 2.54 0.32 1.14 1.15 1.30 1.08
Waist circumference (cm) 117.7 17.8 75.1 14.5 92.7c 18.7
Waist circumference Z-score 2.21 0.90 0.87 1.09 0.89 0.90
SBP (mm Hg) 122.2 13.4 121.0 14.4 109.2 12.0 116.4c 12.9
SBP Z-score 1.02 1.48 0.94 1.35 0.46 1.12 0.41 1.46
DBP (mm Hg) 67.0 8.0 65.3 8.0 63.5 7.8 64.1 8.0
DBP Z-score 0.08 0.85 0.02 0.73 0.26 0.70 −0.12b 0.83
Triglycerides (mg/dl) 109.4 79.9 122.0 83.0 84.2 56.5 88.4b 50.0
Triglyceride Z-score 0.58 1.88 0.68 1.97 −0.40 1.07 −0.09b 1.13
HDL-C (mg/dl) 45.2 9.1 41.2a 8.4 46.2 11.3 47.6 10.5
HDL-C Z-score −1.32 1.65 −1.52 1.69 −0.21 2.59 −0.44 2.02
Fasting glucose (mg/dl) 89.1 14.0 93.6a 18.2 87.3 9.4 86.6 9.0
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In the short-term cohort, waist circumference was measured only at the follow-up visit. DBP, Diastolic blood pressure; SBP, systolic blood pressure. 

a

P < 0.01; 

b

P < 0.001; 

c

P < 0.0001 for comparison of baseline and follow-up values by paired t tests. 

Short-term stability and incidence of the metabolic syndrome diagnosis and individual components of metabolic syndrome

The mean follow-up interval between visits was 19.8 ± 13.2 (range 1–60) days. The interquartile range for follow-up interval in the short-term cohort was 15.5 d; 55% were seen again within 15 d and 82% within 30 d. Table 3 provides the baseline and follow-up prevalence of metabolic syndrome in the short-term cohort. At baseline, 38 of 220 (17.3%) of the short-term subjects had a metabolic syndrome diagnosis. At follow-up, 70 of 220 (31.8%) of the short-term subjects had metabolic syndrome. As presented in Table 3 and Fig. 1, metabolic syndrome was unstable (present at the first measurement but absent at the second measurement) in almost one third of cases. In the short term, incidence may also reflect true instability for the diagnosis of metabolic syndrome. Among the short-term cohort, 24% who were metabolic syndrome negative at baseline met criteria for metabolic syndrome at follow-up. There was no significant association between short-term follow-up interval and metabolic syndrome instability (P = 0.50). Each individual component of metabolic syndrome was unstable in the short term in at least 22% of cases.

Table 3.

Prevalence and stability of metabolic syndrome in the short and long term

Baseline prevalence, % (CI), n Instability, % (CI), n Incidence, % (CI), n Follow-up prevalence, % (CI), n
Short-term cohort
 Metabolic syndrome diagnosis 17.3 (12.5–22.9), 220 31.6 (17.5–48.7), 38 24.2 (18.1–31.3), 182 31.8 (25.7–38.4), 220
 Waist circumference 96.8 (93.6–98.7), 220
 Triglycerides 16.4 (11.7–21.9), 220 30.6 (16.3–48.1), 36 13.6 (9.0–19.4), 184 22.7 (17.4–28.8), 220
 HDL-C 14.1 (9.8–19.4), 220 22.6 (9.6–41.1), 31 18.5 (13.3–24.8), 189 26.8 (21.1–33.2), 220
 Blood pressure 43.2 (36.5–50.0), 220 44.2 (34.0–54.8), 95 34.4 (26.1–43.4), 125 43.6 (37.0–50.5), 220
 Fasting glucose 7.7 (4.6–12.1), 220 47.1 (23.0–72.2), 17 15.8 (11.0–21.5), 203 18.6 (13.7–24.4), 220
Long-term cohort
 Metabolic syndrome diagnosis 7.5 (3.8–13.1), 146 45.5 (16.7–76.6), 11 5.2 (2.1–10.4), 135 8.9 (4.8–14.7), 146
 Waist circumference 39.0 (31.1–47.5), 146 15.8 (7.5–27.9), 57 18.0 (10.6–27.5), 189 43.8 (35.6–52.3), 146
 Triglycerides 6.9 (3.3–12.2), 146 70.0 (34.8–93.3), 10 5.5 (2.4–10.5), 136 7.5 (3.8–13.1), 146
 HDL-C 15.8 (10.3–22.7), 146 43.5 (23.2–65.5), 23 15.8 (10.3–22.7), 123 24.7 (17.9–32.5), 146
 Blood pressure 20.1 (14.3–28.0), 146 56.7 (37.4–74.5), 30 12.3 (7.5–18.8), 116 21.2 (14.9–28.8), 146
 Fasting glucose 2.1 (0.4–5.9), 146 100.0 (29.2–100.0), 3 2.7 (0.8–6.9), 143 2.7 (0.8–6.9), 146
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Waist circumference was measured only at follow-up visit for the short-term cohort. CI, Confidence interval. 

Figure 1.

Figure 1

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Percentage of participants with persistent, baseline-only, and incident metabolic syndrome in short-term and long-term follow-up cohorts.

Long-term follow-up cohort

The long-term sample had a mean age of 8.8 ± 1.6 (5.5–12.8) years at baseline, and 90.5% had age under 11 yr. The sample was 54.1% female and was composed primarily of Caucasian (61.0%) and African-American (32.9%) subjects, with a small percentage of biracial and Hispanic participants (6.2%). At baseline, 62% were prepubertal (breast Tanner stage I for girls; testes volume <4 ml for boys).

Metabolic risk factors and weight status at baseline

The long-term follow-up cohort’s mean and sd values for BMI, BMI Z-score, and each of the individual components of metabolic syndrome at baseline are given in Table 2. Due to targeted recruitment of at-risk children, the long-term cohort also contained many subjects with obesity, giving a baseline prevalence of BMI in the 95th or higher percentile of 39.0%. At baseline, metabolic syndrome was diagnosed in 11 of 146 (7.5%) of the cohort (Table 3). These cases were found exclusively among participants who had BMI that exceeded the 99th percentile. Among the 11 long-term cohort baseline metabolic syndrome-positive cases, three of the girls with metabolic syndrome had evidence for the onset of puberty. Among the 55 children who had signs of puberty at their baseline visit, the incidence for metabolic syndrome was 5.5%.

Long-term stability of the metabolic syndrome diagnosis and individual components of metabolic syndrome

The mean follow-up interval was 5.6 (1.5–12.1) years for the long-term cohort; the long-term cohort was seen again at a mean age of 14.4 ± 2.2 yr. The interquartile range for follow-up interval was 1.2 yr; 13% were seen after 1.5–3 yr of follow-up, 71% were seen again after an interval of 4–6 yr, and approximately 3% per year were seen after intervals exceeding 6 yr. At their follow-up visit, 97% had evidence for clinical puberty, with 86% of girls demonstrating Tanner IV or V breast development and 74% of boys with testis volumes of 10 ml or more. At follow-up, 47.3% were classified as obese (Table 2). All but one case of metabolic syndrome was found at follow-up among obese participants; the only subject classified as nonobese who met criteria for metabolic syndrome had BMI at the 93rd percentile.

Table 3 provides the rates of instability, incidence, and follow-up prevalence of metabolic syndrome in the long-term follow-up cohort. At follow-up, 13 of 146 (8.9%) of the long-term subjects had metabolic syndrome. As presented in Table 3 and Fig. 1, about one third of cases were unstable. There was no significant association between long-term follow-up interval and instability for the diagnosis of metabolic syndrome (P = 0.73). High waist circumference was the component most prevalent at both baseline and follow-up. Each individual component of metabolic syndrome was unstable in at least 15% of cases. Impaired fasting glucose, which occurred relatively infrequently in this cohort, also showed 100% instability, because none of the three subjects who met criteria for impaired fasting glucose at baseline met the impaired fasting glucose criterion at follow-up.

Discussion

In samples of obese children and adolescents, among whom metabolic syndrome is highly prevalent (38) and for whom the diagnosis is therefore most likely to be sought, we found the diagnosis of metabolic syndrome was unreliably made in the short term and, if made during childhood, could not be assumed to be maintained over the long term even when BMI remained above the 99th percentile. Within our short-term cohort, both metabolic syndrome and the components of metabolic syndrome also all showed nontrivial rates of both instability and incidence. These data point out the difficulties inherent in making a cutoff-point-based diagnosis of metabolic syndrome using results from a single blood sample.

There was notable instability in the individual component criteria of metabolic syndrome both in the short and long term. For example, the diagnosis of elevated triglycerides at baseline was not confirmed at long-term follow-up in 70% of cases, and both impaired glucose tolerance and high blood pressure were unstable in over 40% of cases in both short- and long-term cohorts. However the criteria are defined, it is axiomatic that any second testing for cutoff-point-based definitions for the components of metabolic syndrome, which are continuous physiological variables, will inherently show variability. This variability may be affected by subject factors including time of day, concurrent illness or other stress, and prior energy and macronutrient intake (including unsuspected consumption on the morning of blood sampling). These factors may potentially be controlled in experimental situations but will significantly impact attempts to identify metabolic syndrome in children seen in clinical situations. Thus, great care should be taken before a metabolic syndrome diagnosis is made. Just as it is recommended that hypertension be diagnosed only after repeated abnormal measurements have been obtained under specified conditions, we believe metabolic syndrome testing circumstances will need to be standardized, and abnormal measurements consistent with this diagnosis will have to be confirmed by follow-up testing before the diagnosis of metabolic syndrome should be made for a pediatric patient.

Both short-term instability and short-term incidence are presumed mostly to be a reflection of test-retest reliability. In long-term studies, however, metabolic syndrome incidence cannot be assumed to be related to test reliability. In children who, at baseline, did not have metabolic syndrome and were prepubertal or in early puberty, the incidence of metabolic syndrome at their long-term follow-up may be attributable to numerous factors, including the appearance of puberty-related insulin resistance (39) and to gain of adipose tissue (38). However, long-term instability (i.e. the disappearance of the diagnosis of metabolic syndrome) in a cohort of children who were not involved in any systematic attempts to alter their metabolic risk is less likely to be related to factors other than the intrinsic imprecision of the measure. Our long-term findings validate the previous study of Goodman et al. (22) examining long-term instability of the clinical diagnosis of metabolic syndrome in adolescents. Goodman et al. (22) found, regardless of the definition of metabolic syndrome applied, that about half of all cases of metabolic syndrome in adolescents were unstable in the long term. In the present study, about 45% of cases of metabolic syndrome in children were unstable in the long term.

The factor structure for metabolic syndrome has been found to be quite stable among adolescents (22), lending credence to the theoretical foundation for the metabolic syndrome diagnosis. Indeed, more than half of all subjects initially diagnosed with metabolic syndrome in the present study continued to meet criteria for metabolic syndrome at long-term follow-up. The cardiometabolic risk factors that comprise metabolic syndrome have also been shown to track reasonably well in young people over time (40,41). Such tracking suggests that large, prospective, population-based investigations may be able to generate weighted risk scores for children and adolescents that use the components of metabolic syndrome as continuous variables to predict the appearance of metabolic abnormalities or adverse cardiovascular outcomes in adulthood without resorting to thresholds. Given that we found stable metabolic syndrome only among 6- to 12-yr-old children who were both severely obese and had evidence for pubertal onset, the current data suggest that both measures of adiposity and assessments of pubertal development will prove to be important components of any such score.

One limitation of the present study is the lack of separate baseline and follow-up waist circumference measurements in the short-term cohort. Thus, the present study may have somewhat underestimated the short-term instability and incidence of pediatric metabolic syndrome. A true estimate of the reproducibility of measures that are taken from an individual requires multiple measurements, not merely two; thus, the data of the present study are insufficient to determine the true reproducibility of the components of metabolic syndrome. Another limitation is the small participation of subjects who were not African-American or Caucasian, which may somewhat limit generalizability of results. A third limitation relates to the study of cohorts enriched for obesity. Although the long-term cohort was not exclusively comprised of obese participants, data for short-term stability derive from obese participants who were selected because they had complications of obesity, including abnormalities that are part of the diagnosis of metabolic syndrome. The observed incidence of metabolic syndrome in the long-term cohort is likely an overestimate of what might be seen in an unselected group, because the studied group underwent a significant increase in body weight and adiposity in excess of average weight change for children of similar age. Because the prevalence of metabolic syndrome is considerably lower in youth who are not selected for having high body mass, short-term instability would also be anticipated to be lower in an unselected cohort. In clinical practice, however, the majority of pediatric-age patients screened for the presence of metabolic syndrome will be those with BMI exceeding the 85th percentile (42). Thus, we believe results of this study have clinical value. Strengths include the large sample size and the selection of youth at high risk for metabolic syndrome so that both short-term and long-term stability could be examined.

Pediatric metabolic syndrome as a research concept may be an important tool for elucidating contributing factors to the development of cardiovascular disease and type 2 diabetes and for evaluating the risks conferred by preexisting cardiometabolic abnormalities (13,14,15); however, there is still lack of consensus as to whether pediatric metabolic syndrome as a construct is better than its individual components as a predictor of cardiovascular disease (15). The current study supports the notion that, due to significant instability, metabolic syndrome as a cutoff-point-based clinical diagnosis in pediatric populations should be approached with caution (43).

In conclusion, we found that metabolic syndrome is unstable in the short term among obese children and adolescents, and as has been reported for adolescents (22), metabolic syndrome is frequently unstable in younger children reevaluated in the long term. These data support the view that the diagnosis of metabolic syndrome in youth, established using dichotomous cutoff points from measurements obtained at a single point in time, may have limited clinical utility.

Footnotes

This work was supported by the Intramural Research Program, NIH, Grant Z01-HD-00641 (to J.A.Y.) from NICHD with supplemental funding from the National Center for Minority Health and Health Disparities. J.A.Y., J.C.H., V.S.H., and N.G.S. are Commissioned Officers in the U.S. Public Health Service, Department of Health and Human Services.

Disclosure Summary: All of the authors (J.K.G., L.B.Y., B.D.E., S.M.B., M.F.K., M.D.R., N.G.S., J.C.H., S.Z.Y., V.S.H., and J.A.Y.) have nothing to declare.

First Published Online October 16, 2009

Abbreviations: BMI, Body mass index; HDL-C, high-density lipoprotein cholesterol.

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