EDITORIALS

PEDIATRIC METABOLIC SYNDROME: SMOKE

AND MIRRORS OR TRUE MAGIC?

Clustering of cardiovascular (CV) risk factors in adoles-cence is not a new phenomenon. However, interestin CV risk factor clustering has been renewed due to

the recent surge in attention to the metabolic syndrome. In thepast three years, research on this phenomenon has skyrocketed,with annual publications on the subject more than triplingbetween 2002 and 2004.

Despite this explosion of research, however, serious de-bate remains as to the definition of metabolic syndrome andeven whether it actually exists.1,2 Since Reaven’s descriptionof insulin resistance syndrome (IRS) in 1988, this clusteringof CV risks has gone by more than 15 different names.3 Here-after, I will refer to it as metabolic syndrome.

Metabolic syndrome reflects a wide array of factors,including adiposity, dyslipidemia, hypertension, hyperin-sulinemia, impaired glucose metabolism, microalbuminuria,abnormalities in fibrinolysis, and inflammation.3,4 This abun-dance of factors makes defining metabolic syndrome a chal-lenge. Three health organizations have created clinicalcriteria for defining this syndrome in adults. These includethe World Health Organization (WHO),5 the NationalCholesterol Education Panel’s Adult Treatment Panel III(ATP III),6 and the American Association of ClinicalEndocrinologists (AACE).7 These definitions differ signifi-cantly. The AACE lists 12 clinical criteria for the diagnosisof what they specifically refer to as IRS. No required numberof these risks is specified for diagnosis; rather, this is left toclinical judgment. With the development of diabetes, the di-agnosis of IRS becomes inapplicable. In contrast, both theWHO and ATP III specify a minimum number of risk factorsrequired for the diagnosis of metabolic syndrome and do notexclude those with type 2 diabetes mellitus. In fact, theWHO explicitly requires the presence of insulin resistance.In contrast, the ATP III does not include insulin resistancein its definition, focusing instead on 5 metabolic risk factorsas a way to identify obese persons at greatest risk for medical

AACE American Association of Clinical EndocrinologistsATP III Adult Treatment Panel IIICI Confidence intervalCV CardiovascularHDL-C High-density lipoprotein cholesterolIRS Insulin resistance syndromeWHO World Health Organization

Editorials

complications.2 Both the WHO and ATP III definitionsrequire the presence of at least 3 risk factors for a diagnosisof metabolic syndrome. Although there are shared risk factorsbetween theWHO and ATP III definitions, the defining cut-off points differ. Moreover, the WHO definition, which wasmodified in 1999,8 condenses some risk factors into parame-ters, whereas each risk factor is independent in the ATP IIIdefinition.

The differences in these definitions have importantimplications for case identification. A person with hyperglyce-mia, hypertriglyceridemia, and low high-density lipoproteincholesterol (HDL-C) levels would have metabolic syndromeby the ATP III criteria but not by the WHO crtieria. In con-trast, a person with hyperinsulinemia, low HDL-C, and obe-sity would have metabolic syndrome by theWHO criteria, butnot by the ATP III criteria. These concerns about differingspecificity have been borne out by studies contrasting theWHO and ATP III definitions in adults9,10 and adolescents11

that found important differences between definitions in caseidentification and in prognostic ability.

Even setting aside differences in definitional criteria,there remain differences in how each risk factor within a def-inition is operationalized. Although true for both adult andpediatric definitions, these differences are especially problem-atic for pediatric populations, for whom no accepted defini-tion of metabolic syndrome exists. Almost all of the pediatricmetabolic syndrome studies use a definition based on age- andsex-specific percentiles for the various cutoff points of the 5ATP III risk factors. However, these cutoff points range from75% to 97.5% (5% to 25% for HDL-C), depending on the studyand the risk factor of inter-est.12-17 A major problemwith this approach is thatthe pediatric percentiles donot adjust for the transitionto adulthood at which pointthe adult criteria, which arenot based on percentiles ofdistribution, will be applied.For example, the pediatricdefinitions often use the 90%cutoff point for waist circum-ference. For 18-year-olds, the

Reprint requests: Elizabeth Goodman,MD, Brandeis University, SchneiderInstitute for Social Policy, Heller Grad-uate School, 415 South Street Mall,Stop 035, Waltham, MA 02454-9110.E-mail: goodman@brandeis.edu.

J Pediatr 2006;148:149-51.0022-3476/$ - see front matter

Copyrightª 2006 Elsevier Inc. All rightsreserved.

10.1016/j.jpeds.2005.08.057

See related article, p 176.

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adult cutoff points (102 cm for men and 88 cm for women) fallsomewhere between the 75th and 90th percentiles for boysand slightly above the 75th percentile for girls.18 Thus an18-year-old could be classified as having central obesity basedon adult definitions of metabolic syndrome but not be consid-ered to have central obesity by the pediatric definition. Suchdifferences in criteria between adolescents and adults createdifficulties in understanding the developmental trajectory ofcardiovascular risk as adolescents age into the adulthood.

The use of different cutoff points can also have strikingeffects on reported prevalence rates. Two studies assessingmetabolic syndrome prevalence in NHANES III among12- to 19-year-olds, published about a year apart, differed inthe cutoff points used to define the metabolic syndromecomponents.13,19 The differences in the reported prevalencesbetween the 2 studies were dramatic. Mexican-Americanshad the highest prevalence in both studies, but the first studyreported a prevalence of 5.6% (95% confidence interval [CI],3.6% to 7.5%), whereas the second study reported a prevalencemore than twice as high (12.9%; 95% CI, 10.4% to 15.4%).These CIs do not overlap, suggesting that the difference in re-ported rates is significant. Similar differences and nonoverlap-ping confidence intervals were seen for European-Americanadolescents (4.8% vs 10.9%). The prevalences were muchcloser for African-American youth (2.0% vs 2.5%).

The article by Retnakaran et al in this issue ofThe Journal provides an assessment of metabolic syndrome inNative Canadian adolescents taking part in the Sandy LakeHealth and Diabetes Project.20 This is the first report of met-abolic syndrome in a First Nations population, a group thathas been experiencing alarming increases in obesity and is atincreased risk for type 2 diabetes mellitus and CV disease.21

Retnakaran et al use a definition of metabolic syndrome de-rived from the NHANES II study discussed earlier.19 Theirreported prevalence is 18.6%. As the authors note, this rateis high for a population-based study. In addition to the defini-tional issues noted earlier, a higher prevalence of obesity in thisethnic group may be influencing the prevalence in this popu-lation;21 metabolic syndrome has consistently been shown tobe much higher in obese youth.11,13,17

Obesity clearly plays an important role in the pathogen-esis of metabolic syndrome, but the underlying ‘‘cause’’ of thisrisk factor clustering remains elusive. Investigators have usedfactor analysis over the past 7 years as a means to assess theunderlying structure and cause of metabolic syndrome. Bothpediatric and adult studies generally find 2 to 4 factors whenassessing the traditional risks associated with metabolicsyndrome. However, when the number of risks included inthe analysis expands, the constellation of factors changes.Retnakarnan et al’s factor analysis, which included severalnontraditional CV risk factors, revealed 5 factors. This is alarger number than has been reported in previous pediatricfactor analytic studies, including a population-based studyand a clinic-based study, each of which used expanded bio-markers of CV risk in their factor analysis.14,22

Making sense of this widely divergent number of factorsand changing constellation of included risks is a daunting task.

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Howmuch do these analyses really tell us about the underlyingcause or structure of the metabolic syndrome? Very little, Ibelieve. Why? Because the method of data analysis does notsupport making such claims. Principal components analysis(the type of exploratory factor analysis used in these studies)is an empirical, atheoretical method of data reduction. Thistechnique merely uses the linear relationships among variablesto create a smaller number of summary factors that maximizethe explained variance in the observed variables. Althoughinvestigators have used factor-loading patterns to explain thestructure underlying the metabolic syndrome and CV riskclustering, this statistical technique does not provide a testof a hypothetical causal model. The factors are simply a math-ematical transformation of the measured variables, and thus nolatent meaning can be ascribed to them.23 Moreover, the rela-tionships of many of these biomarkers of CV risk, althoughcorrelated, are often not highly linear, which may accountfor the low cumulative variance explained by some of thefactor analyses. Explained variance in the factor analyses byRetnakarnan et al was only 50.7% for traditional metabolicsyndrome risks and 53.9% for both traditional and nontradi-tional risks.

There are also methodological challenges to the use ofexploratory factor analysis. The technique is not standardized,making comparisons across studies difficult. There are differ-ent means for defining factors, including several methodsof factor extraction, inconsistent use of standardization ofobserved variables, and no consensus on the type of rotation orstrength of the rotated factor loading used for interpretation.Factor loadings are measures of shared variance between var-iables and summary factors. The higher the factor loading,the more shared variance exists. A factor loading of 0.4 indi-cates at least 15% shared variance between the variable andsummary factor. Retnakarnan et al used a cutoff point of 0.3for interpreting factors. Although this cutoff point has beenused in other pediatric factor analytic studies, in this instanceit seems generous. A review of these authors’ factor loadingsindicates that most variables have low factor loadings.Insulin, shown in most studies to strongly load onto at least1 factor (and often onto 2 factors), loads only weakly (0.34)onto the glucose tolerance factor. Diastolic blood pressuredoes not make the 0.3 cutoff point for any factor, even theblood pressure factor. This suggests that a more parsimoniousmodel may fit the data better and that the conclusion that5 core traits underlie early development of CV risk may bepremature.

What does drive development of CV risk early in life?Scientific investigations seek answers, and questions such asthis one regarding causality are often the most compelling.For epidemiologic studies, causality is more a philosophicalstate than a tangible reality. Complex statistical techniques,such as exploratory factor analysis, appear to substantiatecausal explanations. Yet exploratory factor analysis on cross-sectional data does not meet most of the basic standards forassessing causality used in epidemiology today. Take timesequencing, for instance. For X to be a cause of Y, X must pre-cede Y. Does insulin resistance precede obesity? Probably not.

The Journal of Pediatrics � February 2006

Obesity likely precedes insulin resistance for most individuals.Yet the etiologic explanation promoted by many factor ana-lytic studies, including the study by Retnakaran et al, is thatmetabolic syndrome is a consequence of insulin resistance.This idea is the most widely accepted hypothesis describingthe pathophysiology underlying metabolic syndrome.24 Prin-cipal components analysis, by its nature, is not able to testthis hypothesis.

Given the complexity of defining metabolic syndrome,the lack of consensus regarding definition, and the questionsregarding its very existence and meaning, it is not surprisingthat there is no general agreement regarding clinical assess-ment of or potential treatment for pediatric metabolic syn-drome. However, both the American Diabetes Associationand the American Heart Association agree that obesity pre-vention and treatment in childhood should be the first-lineapproach to this problem and that a fasting glucose or oralglucose tolerance test should be obtained on children and ad-olescents considered at risk for development of type 2 diabetesmellitus.25 Continued efforts to prevent and treat obesity inchildren and adolescents, and vigilant attention to the earlydiagnosis of diabetes, provide the pediatrician with the mostevidence-based methods for addressing metabolic syndromeand the clustering of CV risks that it represents in childhoodand adolescence. As to the fate of metabolic syndrome,whether it will continue to dominate our thinking or will belaid to ‘‘requiescat in pace,’’1 only time will tell.

Elizabeth Goodman, MDInstitute for Child, Youth, and Family Policy

Heller School for Social Policy and ManagementBrandeis University

Waltham, MA 02454

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