Obesity and the Metabolic Syndrome in Children and Adolescents
Ram Weiss, M.D., James Dziura, Ph.D., Tania S. Burgert, M.D., William V. Tamborlane, M.D., Sara E. Taksali, M.P.H., Catherine W. Yeckel, Ph.D., Karin Allen, R.N., Melinda Lopes, R.N., Mary Savoye, R.D., John Morrison, M.D., Robert S. Sherwin, M.D., and Sonia Caprio, M.D.
Background The prevalence and magnitude of childhood obesityare increasing dramatically. We examined the effect of varyingdegrees of obesity on the prevalence of the metabolic syndromeand its relation to insulin resistance and to C-reactive proteinand adiponectin levels in a large, multiethnic, multiracialcohort of children and adolescents.
Methods We administered a standard glucose-tolerance test to439 obese, 31 overweight, and 20 nonobese children and adolescents.Baseline measurements included blood pressure and plasma lipid,C-reactive protein, and adiponectin levels. Levels of triglycerides,high-density lipoprotein cholesterol, and blood pressure wereadjusted for age and sex. Because the body-mass index variesaccording to age, we standardized the value for age and sexwith the use of conversion to a z score.
Results The prevalence of the metabolic syndrome increased withthe severity of obesity and reached 50 percent in severely obeseyoungsters. Each half-unit increase in the body-mass index,converted to a z score, was associated with an increase in therisk of the metabolic syndrome among overweight and obese subjects(odds ratio, 1.55; 95 percent confidence interval, 1.16 to 2.08),as was each unit of increase in insulin resistance as assessedwith the homeostatic model (odds ratio, 1.12; 95 percent confidenceinterval, 1.07 to 1.18 for each additional unit of insulin resistance).The prevalence of the metabolic syndrome increased significantlywith increasing insulin resistance (P for trend, <0.001)after adjustment for race or ethnic group and the degree ofobesity. C-reactive protein levels increased and adiponectinlevels decreased with increasing obesity.
Conclusions The prevalence of the metabolic syndrome is highamong obese children and adolescents, and it increases withworsening obesity. Biomarkers of an increased risk of adversecardiovascular outcomes are already present in these youngsters.
In 1988, Reaven and colleagues1 described "the metabolic syndrome"as a link between insulin resistance and hypertension, dyslipidemia,type 2 diabetes, and other metabolic abnormalities associatedwith an increased risk of atherosclerotic cardiovascular disease2in adults. Recent studies suggest that the metabolic syndromemay originate in utero.2,3
Obesity, which is the most common cause of insulin resistancein children,4 is also associated with dyslipidemia,5 type 2diabetes,6 and long-term vascular complications.7,8,9 In a sampleof adolescents in the United States who were included in thethird National Health and Nutrition Examination Survey (NHANESIII), conducted between 1988 and 1994, the prevalence of themetabolic syndrome was 6.8 percent among overweight adolescentsand 28.7 percent among obese adolescents.10 However, these ratesmay underestimate the current extent of the problem, becauseboth the magnitude and the prevalence of childhood obesity haveincreased in the past decade.11
We examined the effect of different degrees of obesity in childrenon the prevalence of the metabolic syndrome and its relationshipto insulin resistance. Because high C-reactive protein and interleukin-6levels and low adiponectin levels are independent risk factorsfor atherosclerosis in obese, insulin-resistant adults,12,13we also examined the relationship between childhood obesityand these putative surrogate markers of future cardiovasculardisease.
Methods
Study Population
We studied 439 obese children and adolescents beginning in 1999.Subjects were eligible if they were healthy, were between 4and 20 years of age, and had a body-mass index (BMI, the weightin kilograms divided by the square of the height in meters)that exceeded the 97th percentile for their age and sex.14 Exclusioncriteria were the known presence of diabetes and the use ofmedication that alters blood pressure or glucose or lipid metabolism.Parents provided information about race or ethnic group: 179subjects were white (40.8 percent), 135 were black (30.8 percent),120 were Hispanic (27.3 percent), and 5 subjects were classifiedas other. Twenty nonobese siblings of obese subjects (BMI, <85thpercentile) and 31 overweight siblings (BMI, 85th to 97th percentile)were recruited as comparison groups. The Yale University Schoolof Medicine human investigation committee approved the study.Written informed consent from parents and written assent fromchildren (where appropriate) and adolescents were obtained.
Procedures
The subjects consumed a diet containing at least 250 g of carbohydratesper day for three days before the study and refrained from vigorousphysical activity. They were evaluated at 8 a.m., after a 12-hour,overnight fast. Their weight and height were measured, and theirBMI was calculated. Blood pressure was measured three timeswhile the subjects were seated, and the last two measurementswere averaged for analysis. The physical examination includeddetermination of the stage of puberty according to the criteriaof Tanner.15
Baseline blood samples were obtained from subjects while theywere fasting, with the use of an indwelling venous line formeasurement of levels of glucose, insulin, lipids, adiponectin(in the 328 most recently enrolled subjects), C-reactive protein,and interleukin-6 (in the 293 most recently enrolled subjects).An oral glucose-tolerance test was then performed with the administrationof 1.75 g of glucose per kilogram of body weight (maximal dose,75 g).16
Definitions
The criteria we used to diagnose the metabolic syndrome weremodified from those of the National Cholesterol Education Program'sAdult Treatment Panel17 and the World Health Organization.18Because body proportions normally change during pubertal developmentand may vary among persons of different races and ethnic groups,differences in waist-to-hip ratios are difficult to interpretin children. We therefore defined obesity on the basis of athreshold BMI z score of 2.0 or more, adjusted for age and sex.The subjects were then classified as moderately obese (a z scoreof 2.0 to 2.5) or severely obese (a z score above 2.5). Elevatedsystolic or diastolic blood pressure was defined as a valuethat exceeded the 95th percentile for age and sex.19
Abnormalities in the fasting levels of triglycerides and high-densitylipoprotein (HDL) cholesterol were adjusted for age, sex, andrace or ethnic group (>95th percentile for triglycerides;<5th percentile for HDL cholesterol).20 Impaired glucosetolerance was defined as a glucose level greater than 140 mgper deciliter (7.8 mmol per liter) but less than 200 mg perdeciliter (11.1 mmol per liter) at two hours.21 Like adults,the children and adolescents in our study were classified ashaving the metabolic syndrome if they met three or more of thefollowing criteria for age and sex: they had a BMI above the97th percentile (z score, 2.0 or more), a triglyceride levelabove the 95th percentile, an HDL cholesterol level below the5th percentile, systolic or diastolic blood pressure above the95th percentile, and impaired glucose tolerance. The degreeof insulin resistance was determined with the use of a homeostaticmodel (homeostatic model assessment: insulin resistance).22Scores ordinarily range from 0 to 15, with higher scores indicatinggreater insulin resistance, and are calculated as the productof the fasting plasma insulin level (in microunits per milliliter)and the fasting plasma glucose level (in millimoles per liter),divided by 22.5.
Biochemical Analysis
Plasma glucose levels were measured with the use of the YSI2700 STAT Analyzer (Yellow Springs Instruments), and lipid levelswere measured with the use of an AutoAnalyzer (model 747–200,Roche–Hitachi). Plasma insulin and adiponectin levelswere measured with the use of a radioimmunoassay (Linco Laboratories).C-reactive protein levels were measured with the use of an ultrasensitiveassay (Kamiya Biomedical) (intraassay coefficient of variation,1.24 percent; interassay coefficient of variation, 4.2 percent).Interleukin-6 levels were measured with the use of highly sensitivesolid-phase enzyme-linked immunosorbent assay kits (R&DSystems) (lower limit of detection, 0.1 pg per milliliter; intraassayand interassay coefficients of variation, 3.3 percent and 3.6percent, respectively).
Statistical Analysis
The data are expressed either as frequencies or as means with95 percent confidence intervals. Distributions of continuousvariables were examined for skewness and kurtosis and were logarithmicallytransformed, when appropriate. Geometric means are reportedfor insulin levels obtained from fasting subjects and for insulinresistance and triglyceride levels. Differences across weightcategories, insulin-resistance categories, and racial or ethnicgroups and between the sexes in the anthropometric, cardiovascular,and metabolic variables were assessed with the use of linearregression. Mantel–Haenszel chi-square statistics wereused to evaluate trends in proportions across weight and insulin-resistancecategories. Tests for departure from linear trend23 were performedfor analyses of differences in means and proportions acrossweight and insulin-resistance categories, with pairwise comparisonsfor both variables evaluated with the use of Holm's adjustmentin the case of a significant departure from linearity.24 Whenthe few obese subjects with lean or overweight siblings whoalso participated in the study were excluded from the analysis,means and variances were unaltered, indicating a negligibleeffect of correlation between data on siblings. Thus, the dataare presented without adjustment for the correlation of siblingdata.
Principal-component factor analysis was used to investigatethe relations among the correlated risk factors for the metabolicsyndrome in 470 obese and overweight children. Extraction ofthe initial set of uncorrelated components was accomplishedwith the principal-factor method, and then orthogonal rotationof components was used to facilitate interpretation. Eight variablesrelated to the metabolic syndrome were included in the factoranalysis. The number of components retained was based on Screeplot analysis and Eigen values greater than 1 (with the componentsaccounting for more of the total variance than any single variable).Factor loading — the product-moment correlation (a measureof linear association) between an observed variable and an underlyingfactor — was used to interpret the factor structure. Loadingsare equivalent to Pearson correlation coefficients, with a higherloading indicating a stronger relation between a factor andan observed variable.25 We defined factor loadings from 0.2through 0.4 as indicating marginal correlations and loadingsabove 0.4 as indicating strong correlation. Multivariable logisticregression was performed to identify variables that were significantlyrelated to the odds of having the metabolic syndrome. The resultsare reported as odds ratios with 95 percent confidence intervals.All analyses were performed with the use of SAS software (version8.2, SAS Institute).
Results
Anthropometric and Metabolic Phenotype
Anthropometric and metabolic data are shown in Table 1. Valuesfor glucose, insulin, insulin resistance, triglycerides, C-reactiveprotein, interleukin-6, and systolic blood pressure, as wellas the prevalence of impaired glucose tolerance, increased significantlywith increasing obesity, whereas HDL cholesterol and adiponectinlevels decreased with increasing obesity (Table 1). Moderatelyand severely obese black subjects had lower triglyceride andhigher HDL cholesterol levels than similar white and Hispanicsubjects. The percentage of subjects with impaired glucose toleranceincreased directly with the severity of obesity in subjectsin all racial and ethnic groups, a trend that persisted afteradjustment for sex, pubertal status, and race or ethnic group.The severity of obesity and the prevalence of the metabolicsyndrome were strongly associated after adjustment for raceand ethnic group (P=0.009) and for race and ethnic group andsex (P=0.03).
Table 1. Baseline Anthropometric and Metabolic Characteristics of the Study Cohort.
The overall prevalence of the metabolic syndrome was 38.7 percentin moderately obese subjects and 49.7 percent in severely obesesubjects; no overweight or nonobese subject met the criteriafor the metabolic syndrome. The prevalence of the metabolicsyndrome in severely obese black subjects was 39 percent. Whenwe analyzed our data according to the commonly accepted criteriaof the National Cholesterol Education Program26 (which are notspecific to any race or ethnic group), the prevalence of themetabolic syndrome among severely obese black subjects was only27 percent.
Factor Analysis
As shown in Table 2 and Table 3, three factors were sufficientto explain correlations between variables — obesity andglucose metabolism, the degree of dyslipidemia, and blood pressure.The three factors explained 58 percent of the total variancein the data (27 percent of the variance was explained by thefirst factor, an additional 17 percent by the second factor,and another 14 percent by the third factor). The first factorwas obesity and glucose metabolism, reflecting strong correlationwith the z score for the body-mass index, insulin resistance,and fasting and two-hour plasma glucose levels. The second factorwas dyslipidemia, reflecting a positive correlation of insulinresistance with the triglyceride level and a negative correlationof insulin resistance with the HDL cholesterol level. The thirdfactor was blood pressure, reflecting a positive correlationwith systolic and diastolic blood pressure. When the C-reactiveprotein level was incorporated into the analysis (for 293 subjects),it loaded significantly only with the obesity and glucose metabolismfactor.
Table 3. Principal-Factor Analysis and Oblique Analysis of the Whole Cohort of Obese and Overweight Children and Adolescents, According to Risk Factors for the Metabolic Syndrome.
Insulin Resistance
To test the effect of insulin resistance on the prevalence ofthe metabolic syndrome, we categorized the subjects accordingto three insulin-resistance categories, using the 33rd and 66thpercentiles as cutoffs, and race or ethnic group, with adjustmentfor the degree of obesity (Figure 1). The prevalence of themetabolic syndrome increased significantly with increasing insulinresistance (P for trend, <0.001) after adjustment for raceor ethnic background and obesity group. The prevalence was lowerin black subjects than in white subjects (P<0.001) but notthan in Hispanic subjects (P=0.20), and it was higher in severelyobese subjects than in moderately obese subjects (P=0.03).
Figure 1. Effect of Insulin Resistance on the Prevalence of the Metabolic Syndrome in White Subjects (Panel A), Hispanic Subjects (Panel B), and Black Subjects (Panel C), According to the Degree of Obesity.
Subjects were grouped into three categories of insulin resistance, with cutoffs at the 33rd and 66th percentiles.
Multiple Logistic-Regression Analysis
For the multiple logistic-regression analysis of risk factorsassociated with the metabolic syndrome in overweight and obesechildren and adolescents, we incorporated age, sex, z scorefor BMI, race or ethnic group, and insulin-resistance levelinto the model. The overall significance of the model was P<0.001.Increasing insulin-resistance levels according to the homeostatic-modelassessment were significantly related to the risk of the metabolicsyndrome (odds ratio for each increase of one unit, 1.12; 95percent confidence interval, 1.07 to 1.18). Each half-unit increasein the z score for the body-mass index (one half of 1 SD) wasassociated with a significant increase in the risk of the metabolicsyndrome (odds ratio, 1.55; 95 percent confidence interval,1.16 to 2.08). White subjects had a higher risk of the metabolicsyndrome than black subjects (odds ratio, 2.20; 95 percent confidenceinterval, 1.35 to 3.59); there was no significant differencein risk between Hispanic subjects and black subjects. Girlswere at lower risk for the metabolic syndrome than boys (oddsratio, 0.59; 95 percent confidence interval, 0.39 to 0.89).When the z score for the body-mass index was excluded, the oddsratios associated with each unit of increase in insulin resistance,female sex, and white race as compared with black race did notchange significantly.
Proinflammatory and Antiinflammatory Markers and Insulin Resistance
C-reactive protein levels (Figure 2A ) were significantly relatedto the degree of obesity (P<0.001) but not to the level ofinsulin resistance (P=0.12). The levels tended to rise withthe number of components of the metabolic syndrome in this cohort,but the trend did not reach statistical significance.
Figure 2. C-Reactive Protein and Adiponectin Levels According to the Degree of Obesity and the Insulin-Resistance Category.
Panel A shows C-reactive protein levels. P<0.001 for the association with the obesity group, and P=0.12 for the association with insulin-resistance category. P=0.64 for the interaction between the obesity group and the insulin-resistance category. Panel B shows adiponectin levels. P=0.04 for the association with the obesity group, and P=0.005 for the association with the insulin-resistance category. P=0.07 for the interaction between the obesity group and the insulin-resistance category. After stratification according to the obesity group, the effect of the insulin-resistance category was evident in moderately obese subjects; those in the highest category of insulin resistance had significantly lower adiponectin levels than those in the middle and low categories (P=0.04 and P=0.002, respectively, with Holm's adjustment).
Adiponectin levels decreased with increasing obesity (Table 1).When the subjects were stratified according to obesity groupand insulin-resistance category (Figure 2B), the adiponectinlevels were significantly associated with the obesity category(P=0.04) and insulin-resistance category (P=0.005); the adiponectinlevels were lowest in subjects with the highest level of insulinresistance. There was an interaction between obesity and insulinresistance, but it was not statistically significant (P=0.07).After stratification according to obesity group, the effectof insulin-resistance category was evident in the moderatelyobese group; subjects in the highest category of insulin resistancehad significantly lower adiponectin levels than those in themiddle and low categories (P=0.04 and P=0.002, respectively,with Holm's adjustment). In contrast, adiponectin levels inthe severely obese group did not vary significantly accordingto the insulin-resistance category. Adiponectin levels werenegatively correlated with C-reactive protein levels (R=–0.18,P=0.005).
Interleukin-6 levels rose significantly with the degree of obesity(Table 1) and were correlated with C-reactive protein levels(R=0.37, P<0.001) but not with the degree of insulin resistance.The relation between interleukin-6 and C-reactive protein levelspersisted after adjustment for the z score for the body-massindex (R=0.29, P<0.001).
The Metabolic Syndrome Phenotype after Two Years of Follow-up
Seventy-seven subjects underwent a second comprehensive assessmentafter a mean (±SD) interval of 21.5±10.5 months.Twenty-four of the 34 subjects in this group who had met thecriteria for the metabolic syndrome initially met these criteriaat the time of the second evaluation as well. The 10 who didnot meet the criteria on follow-up were among the subjects whohad a lower BMI initially (z score, 2.42±0.07 vs. 2.62±0.06;P=0.06), had gained less weight (3.74±2.6 kg vs. 11.93±2.9kg, P=0.05), and tended to have decreased insulin resistance(a reduction from 9.68±1.14 to 7.54±0.82, P=0.07).The syndrome developed over time in 16 of 43 children who didnot have the metabolic syndrome at the time of the first evaluation.The baseline z score for the body-mass index in these 16 subjectswas similar to that in the 10 subjects who had improvement duringfollow-up (2.39±0.11 and 2.42±0.07, respectively;P=0.86), yet they gained significantly more weight (16.91±4.4kg vs. 3.74±2.6 kg, P=0.02). In eight subjects, all ofwhom had impaired glucose tolerance at the first evaluation,type 2 diabetes developed during follow-up.
Discussion
Our findings suggest that the metabolic syndrome is far morecommon among children and adolescents than previously reportedand that its prevalence increases directly with the degree ofobesity. Moreover, each element of the syndrome worsens withincreasing obesity — an association that is independentof age, sex, and pubertal status. Our study shows that, as inobese adults,27 insulin resistance in obese children is stronglyassociated with specific adverse metabolic factors. C-reactiveprotein and interleukin-6 levels, which are putative biomarkersof inflammation and potential predictors of adverse cardiovascularoutcomes, rose with the degree of obesity, whereas adiponectinlevels, a biomarker of insulin sensitivity, decreased.
The degree of obesity in children and adolescents has importantclinical implications, because the risk of death from all causesamong adults with severe obesity is twice that among moderatelyobese adults.28 Data on the prevalence of severe obesity inchildren and adolescents do not exist, to our knowledge. Ourresults show a significant adverse effect of worsening obesityon each component of the metabolic syndrome, underscoring thedeleterious effect of increasing BMI in this age group.
We slightly modified the criteria used to assess adults foruse in defining the metabolic syndrome in children and adolescents.An increase in waist circumference is used to define centralobesity in adults. Although waist circumference in childrenis a good predictor of visceral adiposity,29 it may not be usefulfor detecting differences in body proportions that are relatedto puberty and variations among racial and ethnic groups,30and no normative values exist for children and adolescents.In studies of lean and obese adolescents, we found that theBMI correlated strongly with the visceral lipid depot (R=0.72,P<0.001) (data not shown). The BMI correlates with bloodpressure better than does waist circumference and performs similarlyfor dyslipidemia.31 Therefore, we chose a z score of 2.0 ormore for the BMI as a criterion for the metabolic syndrome.The obese cohort was divided on the basis of the 50th percentile(a z score of 2.5) in order to classify the patients as moderatelyobese or severely obese. We selected impaired glucose toleranceas a criterion for the metabolic syndrome, because impairedfasting glucose (levels above 100 mg per deciliter [5.6 mmolper liter]) is rare in childhood. Blood pressure and fastinglipid levels were compared with population norms adjusted forage and sex.
When black subjects and subjects belonging to other racial andethnic groups were analyzed according to the same criteria forserum lipid levels, the prevalence of the metabolic syndromewas substantially lower than it was among the white subjects.However, when the analysis was performed with lipid thresholdsspecific to black subjects (who have a more favorable lipidprofile than white subjects in the same age group), the prevalenceof the metabolic syndrome and the effect of obesity were similarto those in the white and Hispanic subjects. Thus, the use ofcriteria specific to race or ethnic group for the metabolicsyndrome in children appears to be warranted. The rates of prevalenceof the metabolic syndrome according to our criteria were higherthan the rates reported by Cook et al.,10 which may be explainedin part by a greater degree of obesity in our cohort.
In adults, insulin resistance "drives" the processes underlyingthe metabolic syndrome.32 When adult populations are stratifiedaccording to the degree of insulin resistance, as the childrenwere in our study, the prevalence of the metabolic syndromeincreases directly with insulin resistance.33 Our factor analysisshowed strong loading of insulin resistance to the obesity andglucose metabolism factor and moderate loading to the dyslipidemiafactor, indicating a component of insulin resistance in twoof three factors that account for the majority of the variance.The importance of insulin resistance in the metabolic syndromeis also supported by the results of multiple logistic-regressionanalysis with the use of insulin resistance as an independentfactor and adjustment for the effects of other factors. Thesedata suggest that pathophysiological mechanisms related to themetabolic syndrome in adults are already operative in childhood.
Berenson et al. reported a clustering of components of the metabolicsyndrome with coronary and aortic atherosclerosis in young adults.8We examined the effects of childhood obesity on the C-reactiveprotein level, which is a biomarker of the inflammation associatedwith adverse cardiovascular outcomes34,35 and of altered glucosemetabolism.36 In our cohort, C-reactive protein levels tendedto rise with increases in the z score for the body-mass index— a finding similar to that in another pediatric sample.37Although these levels were at the high end of the normal range,such levels have been associated with adverse outcomes.38 Theinfluence of the z score for the BMI on C-reactive protein levelssuggests that the degree of low-grade inflammation may increaseas children become more obese. However, C-reactive protein levelsdid not correlate significantly with insulin resistance or withthe metabolic syndrome, suggesting that an underlying inflammationmay be an additional factor contributing to adverse long-termcardiovascular outcomes in this population.
We also measured interleukin-6, a well-known regulator of hepaticproduction of C-reactive protein. Interleukin-6 levels increasedwith the degree of obesity. C-reactive protein and interleukin-6levels were strongly related, even after adjustment for thedegree of obesity. Adiponectin, apart from being a biomarkerof insulin sensitivity, has been implicated as having an importantrole in preventing atheromatous plaques. In contrast to C-reactiveprotein levels, adiponectin levels tended to drop with increasesin the z score for the BMI. Low levels of this adipocytokinehave been shown to increase the risk of cardiovascular disease.39Both C-reactive protein and interleukin-6 showed a reciprocaltrend with increasing obesity, suggesting a potentially significanteffect of severe adiposity on adverse cardiovascular outcomes.
Preliminary follow-up of the subjects in the present study suggestedthat the metabolic syndrome phenotype persists over time andtends to progress clinically. In a relatively short period,full-blown type 2 diabetes developed in eight subjects who metthe criteria for the metabolic syndrome. The development oftype 2 diabetes in obese adolescents has been well documented.However, a dramatic increase in the incidence of type 2 diabetesmay represent only the tip of the iceberg and may herald theemergence of an epidemic of advanced cardiovascular diseasedue to the synergistic effects of other components of the metabolicsyndrome, as well as chronic low-grade inflammation, as obeseadolescents become obese young adults.
Supported by grants from the National Institutes of Health (RO1-HD40787,RO1-HD28016, and K24-HD01464, to Dr. Caprio; MO1-RR00125, tothe Yale Children's Clinical Research Center; and MO1-RR06022,to the General Clinical Research Centers Program at Yale UniversitySchool of Medicine) and from the Stephen I. Morse PediatricDiabetes Research Fund (to Dr. Weiss).
Dr. Morrison reports having received grant support from EliLilly.
We are indebted to all the children and adolescents who participatedin the study, to the nursing staff for the excellent care givento our subjects during the study, and to Aida Groszmann, AndreaBelous, and Mary Ann Mitnick for their cooperation and superbtechnical support.
Source Information
From the Department of Pediatrics (R.W., T.S.B., W.V.T., S.E.T., C.W.Y., S.C.), the Children's General Clinical Research Center (J.D., K.A., M.L., M.S.), and the Department of Internal Medicine (R.S.S.), Yale University School of Medicine, New Haven; and Cincinnati Children's Hospital Medical Center, Cincinnati (J.M.).
Address reprint requests to Dr. Caprio at the Department of Pediatrics, Yale University School of Medicine, P.O. Box 802064, New Haven, CT 06520, or at sonia.caprio{at}yale.edu.
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(2008). Recognition and Management of Dyslipidemia in Children and Adolescents. J. Clin. Endocrinol. Metab.
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