The clinical utility of identifying pediatric metabolic syndrome (MetS) has been questioned recently because of evidence demonstrating marked short-term instability in the categorical diagnosis.^1–3 Although instability of the diagnosis is an important concern, particularly in relation to considerations of pharmacotherapy in children and adolescents (herein referred to as youth), it is only 1 component in prediction. An equally important consideration concerns whether pediatric MetS identifies those at increased risk of subsequent disease later in life. Adults with MetS are at increased risk of type 2 diabetes mellitus (T2DM)⁴ and cardiovascular disease,⁴ but the evidence base for youth is not well established. For example, although some studies suggest that pediatric MetS predicts adult MetS,^5–7 few studies have examined the link between MetS in youth and risk of future cardiovascular disease⁸ and T2DM in adulthood.⁷ Furthermore, the existing data are limited by very small case numbers and did not fully consider the contribution of each MetS component to risk prediction.⁹ It is therefore evident that the current understanding of youth MetS and its components and their association with adult cardiometabolic-related outcomes is in its infancy, and there is clearly a need for data from large-scale longitudinal studies on the utility of identifying pediatric MetS.

Clinical Perspective on p 1611

The present study is based on 2 prospective cohorts, the Bogalusa Heart Study (BHS) and the Cardiovascular Risk in Young Finns Study, that both have MetS risk factor variables measured in youth (baseline) and again in adulthood (follow-up). Our aims were to determine the status of pediatric MetS as a risk factor for adult MetS, subclinical atherosclerosis (carotid intima-media thickness [cIMT]), and T2DM and compare and contrast this prediction with its individual components. A secondary aim was to determine the long-term (childhood to adulthood) stability of MetS. These aims are in accord with the directions for future research detailed in the February 2009 Scientific Statement from the American Heart Association on MetS in children and adolescents.¹

Methods

For the BHS, youth aged 9 to 18 years who participated in either the 1984–1985 or 1987–1988 surveys and attended either the 2001–2002 or 2003–2007 adult surveys (then aged 25 to 41 years) were included in the analyses (n=374). To harmonize the study designs, we included from Young Finns those who participated in the 1986 survey when aged 9, 12, 15, or 18 years and in either the 2001 or 2007 adult follow-ups (then aged 24 to 39 years; n=1407). For individuals who participated in multiple baseline (in the case of BHS) or follow-up surveys, we used those measures that provided the longest time period between baseline and follow-up. Each study received ethical approval and obtained written informed consent from participants. Measures available at baseline and follow-up included height and weight, blood pressure, lipids and lipoproteins, glucose, and insulin. Waist circumference and ultrasound examinations of the carotid artery were collected at follow-up only. Study samples and protocols have been described in detail previously.^10,11 We encourage readers to view the online-only Data Supplement for a more comprehensive description of methods.

Classification of the MetS in Childhood

Because there is no universal definition of pediatric MetS, we took an approach used in previous reports that characterize pediatric MetS according to multiple alternate definitions.² We used body mass index (BMI) as the measure of adiposity because waist circumference was not available for either cohort at baseline. For the first 2 definitions, we generated age-, sex-, race- (BHS), cohort-, and study year–specific Z scores of BMI, systolic and diastolic blood pressures, high-density lipoprotein cholesterol, triglycerides, and glucose. For the modified National Cholesterol Education Program (NCEP) definition, a participant was categorized as having MetS if he/she had any 3 of the following 5 components: BMI ≥75th percentile, systolic or diastolic blood pressure ≥75th percentile, high-density lipoprotein cholesterol ≤25th percentile, triglycerides ≥75th percentile, or glucose ≥75th percentile. For the modified International Diabetes Federation (IDF) definition, the same cut points as those for the modified NCEP definition were used, but the combination of the components differed. The modified IDF required elevated BMI plus any 2 of the remaining 4 components to be classified as having MetS. The third and fourth definitions utilized age- and sex-standardized pediatric cut points available in the literature to denote each component risk factor. For example, overweight or obesity was defined according to the Cole classification¹²; prehypertension or hypertension was defined according to the fourth report on high blood pressure in children and adolescents from the National High Blood Pressure Education Program¹³; low high-density lipoprotein cholesterol and high triglycerides were defined with the use of cut points recently proposed from growth-curve data that were linked to adult definitions¹⁴; and hyperglycemia was defined as plasma glucose ≥5.60 mmol/L (100 mg/dL) because growth-curve data linking youth glucose levels to adult hyperglycemia have shown levels to remain consistent in the pediatric setting.¹⁵ The pediatric NCEP definition required any 3 of these 5 criteria, whereas the pediatric IDF definition required overweight or obesity plus any 2 of the remaining 4 components. To complement the dichotomous definitions, a continuous MetS risk score (cMetS) was created with the use of the methods described by Wijndaele et al.¹⁶ Similar to previous studies in which this method was used,^16,17 2 principal components were identified (see Table I in the online-only Data Supplement). The principal components were then summed, with weights determined by the relative proportion of variance explained, to compute cMetS, where a higher score is indicative of a less favorable MetS profile.¹⁶

Classification of MetS in Adulthood

To classify adult MetS, we used the recent definition proposed in a joint statement of the IDF Task Force on Epidemiology and Prevention, National Heart, Lung, and Blood Institute, American Heart Association, World Heart Federation, International Atherosclerosis Society, and International Association for the Study of Obesity.¹⁸

Classification of High cIMT in Adulthood

As detailed previously,¹⁹ the most consistent cIMT measurement recorded across study centers was the maximum measurement at the far wall of the left common carotid artery. We defined high cIMT in adulthood as a maximum cIMT ≥90th percentile for age-, sex-, race- (BHS), study year–, and cohort-specific values. In sensitivity analyses, we had essentially similar results using standardized cut points corresponding to the 70th, 75th, 80th, and 85th cIMT percentiles (data not shown).

Classification of T2DM in Adulthood

Participants were classified as having T2DM if they (1) had a fasting plasma glucose ≥7.0 mmol/L (≥125 mg/dL); or (2) reported receiving oral hypoglycemic agents and/or insulin injections and did not have type 1 diabetes mellitus; or (3) reported a history of physician-diagnosed T2DM, which is consistent with the World Health Organization definition.²⁰ Women who reported having physician-diagnosed diabetes mellitus only during the term of their pregnancy were considered to have had gestational diabetes and were classified as not currently having T2DM provided that their plasma glucose levels were not ≥7.0 mmol/L (≥125 mg/dL).

Statistical Analyses

Stability of MetS Between Youth and Adulthood

Stability of MetS definitions between youth and adulthood is presented according to 3 groups: (1) persistent MetS (MetS-positive youth who were also MetS positive as adults); (2) instable MetS (those MetS positive at baseline but MetS negative at follow-up); and (3) incident MetS (MetS-negative youth who were MetS positive as adults). The number of participants in each of these 3 groups is expressed as a proportion of the total MetS cases identified (total cases from youth and adulthood) and is presented graphically, which is consistent with previous reports.^2,3

Utility of Pediatric MetS in Predicting Adult Outcomes

Relative risks and 95% confidence intervals estimated with the use of log binomial regression or Poisson regression with robust standard errors were used to examine associations between MetS phenotypes (number of MetS components in youth; youth MetS status; cMetS score) and outcomes of (1) adult MetS, (2) adult high cIMT, and (3) adult T2DM. Analyses were performed for both cohort-stratified and cohort-pooled data. All estimates were adjusted for length of follow-up. We adjusted for length of follow-up to account for any within-cohort differences observed between length of follow-up and risk of adult outcomes, as we have previously observed and detailed.¹⁹ Race was also included as a covariate for BHS analyses. For pooled estimates, we included a 2-level variable for cohort. Interactions between cohort and the predictor variables were assessed by including product terms as additional covariates. The associations between each MetS component and the adult outcomes were examined with the use of 2 models. Model 1 adjusted for length of follow-up and cohort; model 2 additionally included all MetS components.

The ability of each MetS definition in youth to predict MetS, high cIMT, and T2DM in adulthood was assessed with the use of sensitivity, specificity, positive predictive value, negative predictive value, and area under receiver operating characteristic curves. Because we found high BMI in youth to be the major contributing component in the prediction of adult outcomes, we also provide these data for high BMI. In addition, to predict adult outcomes of MetS, high cIMT, and T2DM, we performed comparisons between 3 models: (1) high youth BMI (referent model); (2) modified NCEP (or pediatric NCEP) MetS definition; and (3) modified IDF (or pediatric IDF) MetS definition. Differences in area under receiver operating characteristic curves between models were estimated with the use of the DeLong algorithm.²¹ Net reclassification improvement was also calculated to determine the extent to which MetS definitions reassigned participants to a risk status that better reflected their final outcome (case or control).²² All statistical analyses were performed with the use of STATA 10 with statistical significance inferred at a 2-tailed P value ≤0.05.

Results

Participant Characteristics

Key baseline and follow-up characteristics are displayed in Table 1. Mean (SD) length of follow-up between baseline and follow-up was 24.4 (3.7) years and ranged from 14 to 27 years. The prevalence of youth MetS differed according to definition. Those with T2DM at follow-up included 25 BHS participants (2 black males, 10 black females, 4 white males, 9 white females; prevalence in blacks=9.5%, whites=5.3%, overall=6.7%) and 11 Young Finns participants (4 males and 7 females; prevalence=0.8%).

Stability of MetS Between Youth and Adulthood

The proportions of participants who had persistent MetS, incident MetS, or instable MetS diagnosis with the use of different youth MetS definitions is displayed in the Figure. Of those with MetS at either baseline or follow-up, those with persistent MetS accounted for ≈20% based on the modified definitions and ≈7% based on the pediatric definitions. Irrespective of the youth definition employed, the major proportion of participants with MetS had acquired it since youth.

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Utility of Pediatric MetS in Predicting Adult Outcomes

Adult MetS

Pooled analyses suggested youth with MetS to have between 2.7 and 3.4 times greater risk of adult MetS compared with those without baseline MetS (all P<0.05; complete data not shown). The risk of adult MetS tended to increase as the number of youth MetS components increased (P for trend <0.001), whereas 1-SD increase in youth cMetS score increased the risk of adult MetS (relative risk=1.5; 95% confidence interval, 1.4 to 1.6).

Adult High cIMT

Relative risks for pediatric MetS definitions in predicting high cIMT in adulthood are displayed in Table 2. Pediatric MetS definitions were associated with ≈2-fold increase in risk for developing high cIMT in adulthood. The risk of high cIMT increased as the number of youth MetS components increased. As youth cMetS increased, risk of high cIMT in adulthood increased in each cohort but was stronger in the BHS than the Young Finns Study (P for interaction <0.01). In light of this interaction, the pooled estimate should be interpreted with caution. Effect estimates from pooled analyses that additionally adjusted for baseline low-density lipoprotein cholesterol and smoking were essentially similar.

Adult T2DM

Relative risks of T2DM in adulthood according to youth MetS are shown in Table 3. Pooled analyses showed that youth with MetS had 2 to 3 times the risk of developing T2DM in adulthood compared with those without youth MetS. There was a trend toward increased risk of T2DM as MetS components increased, but this effect was driven by Young Finns (P for interaction <0.001). A 1-SD increase in cMetS in youth was associated with 30% excess risk of T2DM in adulthood.

Youth MetS Components in Predicting Adult Outcomes

Table 4 displays relative risks from pooled data for predicting MetS, high cIMT, and T2DM in adulthood according to each component of youth MetS (cohort-stratified data were essentially similar; data not shown). High BMI was the only consistent component associated with increased risk of adult outcomes in multivariable models. Insulin was a multivariable predictor of adult MetS but not high cIMT or T2DM; BMI remained a strong predictor of all outcomes with the inclusion of insulin (Table II in the online-only Data Supplement). Risk estimates in which high BMI was used (Table 4) were similar to those of MetS phenotypes (Tables 2 and 3) in predicting adult outcomes.

Comparison Between High BMI and MetS Definitions

The prevalence of modified NCEP and modified IDF MetS among youth with BMI ≥75th percentile was 49.3%; the prevalence of pediatric NCEP and pediatric IDF MetS among youth classified as overweight or obese¹² was 14.8%. Data that compare high BMI with MetS definitions in youth in predicting adult outcomes are displayed in Table 5 and Table III in the online-only Data Supplement. Prediction of adult outcomes by BMI in youth was either equal to or superior than the prediction provided by any of the youth MetS definitions. Substantial gains in sensitivity at relatively modest trade-offs in specificity were observed with the use of high BMI or overweight or obesity in youth, which translated to improved discrimination (area under receiver operating characteristic curves). As evidenced by negative net reclassification index values, the accuracy of classification was reduced significantly (all P<0.03) by using either of the youth MetS definitions in place of high BMI.

Discussion

This study addresses 2 important areas of MetS in youth outlined in the recent Scientific Statement from the American Heart Association, as follows: (1) the long-term stability of MetS definitions from youth to adulthood and (2) whether MetS definitions are able to predict future disease.¹ We found that despite instability in the diagnosis of youth MetS over a mean 24-year period, dichotomous definitions of MetS in youth predict important disease outcomes, such as adult MetS, high cIMT, and T2DM in early to middle adulthood. Our analyses also revealed that high BMI alone was as good as and in some cases superior to dichotomous pediatric MetS definitions in predicting adult MetS, high cIMT, and T2DM. These findings have important clinical implications.

MetS is a subject of controversy in both adult²³ and pediatric settings.¹ From a pediatric perspective, the American Heart Association has declined provision of a consensus definition on MetS partly because of the unclear potential of youth with MetS to maintain the diagnosis even over relatively short periods.^1–3 Using 3 different definitions to diagnose MetS in 15-year-old subjects from a population-based sample, Goodman et al² showed that despite consistent risk factor clustering, the diagnosis of MetS was stable in only 50% of cases over a 3-year period. Among obese youth aged 6 to 17 years, Gustafson et al³ found that only 30% and 45% of those with baseline MetS were confirmed after 60-day and 1.5-year follow-ups, respectively, which is striking considering the short duration of follow-up but also because we would expect that MetS in obese youth would be maintained at a comparatively high level. Although the MetS definitions used in this study cannot be compared directly with those used in these previous reports, our estimates of mean 24-year stability were in the same order of magnitude, ranging from 40% to 60% depending on the youth MetS definition employed. This suggests that although long-term stability is low, it does not appear to be substantially worse than short-term stability. This finding is not surprising given the known influence of pubertal stage on a number of MetS components²⁴ that may have contributed to the low short-term stability observed in both prior studies. In addition, although tracking of risk factor levels is known to decrease as the interval between measurements becomes longer,^25,26 the ability to predict future values from baseline levels tends to decrease substantially in the first days to weeks, followed by a more modest decline over several years.²⁶

Although stability of dichotomous MetS definitions may not be substantially worse over a longer follow-up, the clinical utility of categorical MetS definitions is limited, especially given that ≈40%, 20%, and 5% of youth identified as having MetS would have MetS, high cIMT, or T2DM, respectively, in adulthood at age 24 to 41 years. These data are consistent with recent reports on longitudinal data.^27,28 Although these studies were concerned primarily with testing different risk thresholds and combinations of risk factors for categorizing youth MetS, an interesting finding in both studies was that whereas sensitivity and positive predictive values were low, specificity and negative predictive values were consistently high across the criteria examined. Our findings are in agreement with these results. Rather than considering identification of youth with MetS as a means of identifying those who will develop important outcomes in adulthood, it may be more apt to use these definitions, should they be adopted in a clinical setting, as a basis for identifying those not at risk so that further attention can be focused on those with unclear potential for developing MetS, high cIMT, or T2DM in early to middle adulthood.²⁸ This interpretation appears relevant given our findings that those with youth MetS, irrespective of instability in the categorical diagnosis and poor clinical prediction, were at significantly increased risk of MetS, high cIMT, and T2DM in adulthood. However, we acknowledge that a substantially longer follow-up period is needed to judge the clinical utility with respect to T2DM given that the increase in T2DM incidence begins to rise only after age 50 years.

Perhaps the most important finding from this study was that high BMI predicts each outcome as well as or better than the categorical MetS definitions considered in this study. This finding has clinical relevance. At pediatric visits for health care, BMI can easily and accurately be determined with the use of minimum equipment, which would allow the immediate identification of youth at heightened risk (with the use of Cole's international tables) who might benefit from therapeutic lifestyle intervention aimed at weight control. Other benefits include the need not to subject a child to a blood draw and aversion of costs and time associated with laboratory analysis. A caveat to the clinical application of these findings is that a substantial number of contemporary youth will be identified as at risk.

One explanation for why the additional measures incorporated into MetS did not improve prediction may be because 1 measurement of BMI is more accurate than 1 measurement of the laboratory components of MetS. Pediatric guidelines on blood pressure¹³ and lipids²⁹ require multiple measurements before elevated levels are diagnosed owing to laboratory and biological variation. It is possible that multiple laboratory-based and blood pressure measures collected over a period of weeks or months may improve the observed estimates for pediatric MetS, and this is a limitation of this study. In agreement with this hypothesis, Gaziano and colleagues³⁰ have recently shown a non–laboratory-based risk score (including BMI, blood pressure, smoking status, and reported diabetes mellitus status) that predicted cardiovascular disease events as accurately as a risk score that additionally included laboratory-based methods.

Another explanation may be that overweight and obesity precede the clustering of MetS components such that it may be a more sensitive marker in the pediatric setting. Although the specific cause of MetS is unknown, potential mechanisms posit obesity and insulin resistance as initiating factors.^31,32 We found overweight/obesity to remain an independent predictor of adult outcomes in multivariable models, but the corresponding association with insulin disappeared. Although our study cannot establish causality, these data are consistent with reports from the BHS showing a temporal association between degree of baseline adiposity and incidence of hyperinsulinemia in youth and young adults independent of baseline insulin levels³³ and independent of childhood obesity, but not insulin or insulin resistance, in predicting adult MetS.³⁴

Limitations

Several limitations need to be considered. First, because a substantial proportion of participants at baseline did not attend follow-up, bias due to differential loss to follow-up is possible. However, although we have previously shown that nonparticipants at follow-up were more likely to be younger, male, and black (BHS), baseline risk factor levels were similar between those who did and those who did not attend follow-up, suggesting that a major bias is unlikely.^19,35 A second limitation is missing data on baseline waist circumference in both cohorts. The baseline surveys were performed in the 1980s before the importance of abdominal adiposity to clustering of metabolic-related risk factors was known. Although BMI is considered a reasonable alternative to waist circumference,³⁶ it may be a less sensitive measure in the context of this report. Third, the low numbers with T2DM in both cohorts and the use of fasting glucose levels and self-reported data to indicate adult T2DM mean that associations with T2DM should be interpreted cautiously. Fourth, although our data suggest that the identification of meaningful outcomes in adulthood might be accomplished by screening for only youth BMI, we are unable to discount that other elements of youth MetS may be useful in identifying and possibly treating cardiometabolic disorders, and future research should seek to address this gap.

Conclusions

Accumulating evidence is increasingly coming to light on the limitations of using a dichotomous definition of MetS in the pediatric setting that incorporates rudimentary elements shown to have clinical utility in adult settings. Although our data demonstrate that multiple pediatric definitions predict clinically meaningful outcomes, these definitions do so at a level equivalent or inferior to predictions obtained from the status of high BMI in youth. The benefits of screening for only high BMI or overweight and obesity in the pediatric setting are obvious. These data thus contribute to the ongoing debate on the clinical utility of applying dichotomous MetS definitions adapted from the adult literature to the pediatric setting.

Sources of Funding

The BHS was supported financially by National Institutes of Health grants AG-16592 from the National Institute of Aging and HL-38844 from the National Heart, Lung, and Blood Institute. The Cardiovascular Risk in Young Finns study was supported financially by the Academy of Finland (grants 117797, 126925, and 121584), Social Insurance Institution of Finland, Turku University Foundation, special federal grants for Turku University Central Hospital, Juho Vainio Foundation, Finnish Foundation of Cardiovascular Research, Finnish Cultural Foundation, and Orion Farmos Research Foundation. Dr Magnussen's contribution to this article was supported in part by the Emil and Blida Maunulan fund. Dr Kivimäki was supported by the National Heart, Lung, and Blood Institute (R01HL036310–20A2), National Institutes of Health, and the BUPA Foundation specialist research grant. Dr Kähönen was supported by the Tampere University Hospital Medical Fund.

Disclosures

None.

Footnotes