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DOI: 10.1530/eje.1.02206
European Journal of Endocrinology, Vol 155, Issue 2, 313-319
Copyright © 2006 by European Society of Endocrinology
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CLINICAL STUDY

Frequency of the metabolic syndrome in obese Spanish pediatric population

Marta López-Capapé, Milagros Alonso, Esmeralda Colino, Carmen Mustieles, José Corbatón and Raquel Barrio

Pediatric Endocrine and Radiology Units, Ramón y Cajal Hospital, University of Alcalá, Ctra, Colmenar Km. 9,100, 28034 Madrid, Spain

(Correspondence should be addressed to R Barrio; Email: rbarrio.hrc{at}salud.madrid.org)


   Abstract
Top
 Abstract
Introduction
 Patients and methods
 Results
 Discussion
 References
 
Objective: Obesity is associated with insulin-resistance (IR), type 2 diabetes (T2D) and a constellation of cardiovascular risk factors at the early years of life. These features define the so-called metabolic syndrome (MS).

Aims: To assess the frequency of the MS among obese pediatric Spanish population and analyse the individual contribution and the predictive potential of individual components to the development of the syndrome.

Patients and methods: A total of 429 patients, 220 boys and 209 girls, aged 4–18 years, with a body mass index of > 2 standard deviation scores for Spanish normative charts, were included in the study. Forty-seven percent were prepubertal and ten percent had Hispanic ethnicity. HbA1c, lipids, liver enzymes and uric acid levels were determined from blood and a standard 2-h oral glucose tolerance test was performed. MS was defined by the National Cholesterol Education Program’s Adult Treatment Panel III criteria modified by Cook as having at least three features among: obesity, low high-density lipoprotein (HDL), hypertriglyceridemia, hypertension (HTA) or impaired glucose metabolism (IGM). We defined IR as homeostatic model assessment of IR index and/or fasting insulin levels> 95th centile of the control population.

Results: Almost 18% of the patients had MS, with significantly higher frequency in Hispanic (32%) than in Caucasian (16%) population. There were no differences by sex or pubertal status. Prevalence of low HDL, HTA, hypertriglyceridemia and IGM were 27, 23, 16 and 7% respectively. No silent T2D was identified. According to International Obesity Task Force charts, 22% of the patients were overweight and not obese, but no differences in the frequency of individual features of MS between these two groups were observed. Among IR patients (35% of our population), the frequency of MS reached 28%. IR predicted the presence of MS independently from age and race.

Conclusion: MS is present in 18% of our obese pediatric population. IR is closely associated with the components of MS and strongly predicts its development.


   Introduction
Top
 Abstract
 Introduction
Patients and methods
 Results
 Discussion
 References
 
Obesity is the sixth most important risk factor contributing to the overall burden of disease worldwide (1) and is associated with a constellation of metabolic derangements during the pediatric age (2). The prevalence of overweight and obesity is increasing rapidly worldwide in all age groups (3, 4). During 2001–2002, 16.5% of children aged 6–19 years in the US were overweight (3). In Europe, the number of children affected by overweight and obesity is now rising to at least 400 000 cases per year, and already affecting almost one in four children across the European Union (5). The highest prevalence is shown in southern European countries (20–35%). In Spain, prevalence of pediatric obesity increased from 13 to 35% in males and from 16 to 32% in females during 1985–2002 (6).

Childhood obesity has significant adverse health consequences (7–10); it is associated with dyslipidemia, hypertension(HTA) and glucose intolerance and predisposes to early cardiovascular disease. This constellation of metabolic disturbances has been defined as metabolic syndrome (MS). Insulin resistance (IR) has been implicated in the pathogenesis of this syndrome and obesity is, also in children, the most common feature associated with IR.

Although the term MS is widely used both in research and clinical practice, it has been defined imprecisely (11). While the concept of MS exists for at least 80 years, it was not until 1988 (12) that initiatives developed toward an internationally recognised definition for MS. Definitions by the World Health Organization (WHO), The National Cholesterol Education Program’s Adult Treatment Panel III (NCEP-ATP III), The European Group for the Study of Insulin Resistance, The American College of Endocrinology and The International Diabetes Federation agree on the essential components of the syndrome but differ in some diagnostic criteria (13). There is no standard pediatric definition of MS, since it has not been well characterized in children in terms of diagnostic criteria and prevalence. Cook et al. (14) and de Ferranti et al. (15) modified the adult NCEP-ATP III criteria for adolescents. However, Viner et al. (16) define MS following WHO criteria adapted for children and, consequently, used hyperinsulinism as an additional component of MS.

The purpose of our study was to establish the frequency of the MS and its individual components in obese Spanish pediatric and adolescent population and to identify clinical or biochemical factors related with MS.


   Patients and methods
Top
 Abstract
 Introduction
 Patients and methods
Results
 Discussion
 References
 
We studied 429 children and adolescents (220 boys, 209 girls) with a mean age of 11.2 ± 2.8 years, who were referred to our Pediatric Endocrine Unit for the evaluation of obesity. Inclusion criteria were the existence of obesity as defined by body mass index (BMI) greater than 2 standard deviation scores (SDS) for age and sex upon Spanish normative charts (17) and age between 4 and 18 years, in the absence of previously diagnosed diabetes or medical treatment for hyperglycemia.

Detailed medical, personal and family histories of MS were obtained from all subjects, including birth weight for gestational age. A complete physical examination was performed with special attention to the existence of acanthosis nigricans. The height was measured using a Harpenden stadiometer. The weight status was evaluated as BMI (calculated as the weight in kilograms divided by the square of the height in meters) and the BMI SDS. Obesity in our study was defined by BMI SDS exceeding 2SDS according to Spanish charts. To explore the impact of the definition of obesity used by other international studies on the frequency of MS features, we also applied the obesity criteria by Cole (International Obesity Task Force) (18) to our BMI data. Pubertal development was assessed by physical examination according to Tanner staging (I–V). Sitting blood pressure (BP) was measured with the monitor Critikon Dinamap 8100 (Vital Signs), three times after the subject had rested at least 5 min. The lowest BP was chosen and was evaluated using the percentiles of International Task Force for BP (19).

All participants and their parents gave their written informed consent. The study was approved by the Ethical Committee of our Institution.

Metabolic evaluation

Before the implementation of weight-maintenance diets and personally adapted exercise programs in our patients, and after at least 3 days under 50% calorie intake of carbohydrates per day and 12-h overnight fast, lipids, liver enzymes, uric acid, HbA1c, glucose and insulin levels were obtained from blood and a standard 2-h oral glucose tolerance test (OGTT) was performed. Blood samples were obtained every 30 min for 120 min for the measurement of plasma glucose and insulin levels after the oral administration of 1.75 g of glucose per kilogram of body weight (maximal dose, 75 g). Plasma glucose concentrations were measured by the AEROSET c8000 analyzer using the glucose oxidase method. Insulin was measured by immunoassay, which does not importantly cross-react with proinsulin, using the Immunolite 2000 Analyzer. The intraassay coefficient of variation (CV) values for controls at 7.67 and 12.5 µU/ml were 5.5 and 4.0% respectively, whereas the interassay values were 7.3 and 4.9% respectively.

The IR was determined by the plasma insulin levels and the use of a homeostatic model assessment of IR (HOMAIR = G (mmol/l) x I (µU/ml)/22.5) (20). The HOMAIR index was also evaluated in 69 healthy, non-obese children and adolescents with familiar short stature and without personal or familiar history of MS with a mean age of 12 ± 3 years.

The 95th percentile of HOMAIR index in these prepubertal and pubertal patients was 2.4 and 3 respectively.

The fasting level of insulin > 10.5 in prepubertal and > 15 µU/ml in pubertal patients (> 95th percentile of control group) were considered hyperinsulinemic levels. We defined IR when HOMAIR was ≥95th percentile and/or when hyperinsulinemic levels were observed.

Insulin sensitivity was calculated as quantitative insulin-sensitivity check index (QUICKI) (21, 22).

The insulinogenic index (IGI) (23) was estimated as a marker of early ß-cell insulin response.

Plasma lipids levels were measured by enzymatic method with the use of AEROSET/ARCHITECT c8000 System (Abbott). Fasting levels of high-density lipoprotein (HDL) cholesterol and triglycerides were expressed in SDS and adjusted for age and sex according to Lopez’ study in Spanish pediatric population (24).

HbA1c was measured by HPLC Menarini technique standardized to the Diabetes Control and Complication Trial (normal range 4.05–6.05%). Plasma liver enzyme levels were measured with the use of AEROSET/ARCHITECT c8000 System (Abbott). Serum alanine amino transferase (ALAT) levels ≥40 U/l, were defined as increased. The existence of fatty liver was diagnosed by ultrasound scan, the study being performed by the same pediatric radiologist. Primary non-alcoholic fatty liver disease was diagnosed when other hepatic diseases like B, C and autoimmune hepatitis, a1-anti-trypsin defect, Wilson’s disease and alcohol consumption were excluded.

The MS was defined by modified Cook’s criteria for adolescents (14) as having at least three of the following features: low HDL-cholesterol (≤40 mg/dl), hypertriglyceridemia (TG 110 mg/dl), obesity (BMI ≥2SDS for age and sex for Spanish normative charts (17)), HTA as diastolic and/or systolic BP > 95th percentile for age, sex and height according to the Task Force (19) and impaired glucose metabolism (IGM) according to the American Diabetes Association (ADA) criteria for children and adolescents (25). Impaired fasting glucose (IFG) was defined by levels, ≥100 mg/dl (5.6 mmol/l) but < 126 mg/dl (7 mmol/l), impaired glucose tolerance (IGT) as glucose levels between 140 and 199 mg/dl (7.8–11 mmol/l) 2 h after oral glucose load and diabetes (T2D) as fasting glucose≥126 mg/dl (7 mmol/l) or ≥200 mg/dl (11.1 mmol/l) after oral glucose.

Statistical analysis

The values are presented either as mean ± S.D. or as frequencies. BMI, lipids and lipoprotein values are adjusted for age and sex by using the SDS (z), calculated according to the formula z = x–x'/S.D. The following statistical tests were used: the unpaired Student’s t-test, the {chi}2-test for categorical variables, non-parametric test (U Mann–Whitney) for variables that were not normally distributed. Multivariable logistic regression analysis was performed using statistically significant variables. Maximum model was performed for age, race, BMI SDS, HOMA, existence of acanthosis nigricans, HbA1c, {gamma}-glutamyl transferase (GGT) and uric acid to identify their independent contributions to the MS. Co-linearity was assessed by Belsley criteria (26). The odds ratios (OR) are presented together with 95% confidence intervals (CI). Statistical significance was set at P< 0.05. The analyses were performed using SPSS statistical package version 12.0 (SPSS, Inc., Chicago, IL, USA).


   Results
Top
 Abstract
 Introduction
 Patients and methods
 Results
Discussion
 References
 
The average age of the 429 children and adolescents included in our study was 11 ± 3 years with a mean BMI of 27.3 ± 3.5 kg/m2. Forty-seven percent of them were prepubertal. Patients were mainly Caucasian with the exception of 10%, who were from Hispanic origin. The baseline clinical and biochemical characteristics of the total population grouped according to the presence or absence of MS are shown in Table 1Go.


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  		Table 1 Clinical and biochemical characteristics of our population cohort. Values are presented as mean ± S.D. or percentage.
 
Seventy-six patients (18%) had diagnostic criteria for MS. Nearly 50% of our obese pediatric population had at least one additional cardiovascular risk factor. Abnormal glucose homeostasis was identified in 29 patients (7%). Twenty (4.6%) had IFG and 11 (2.4%) IGT. Two patients had both glucose metabolism abnormalities and no subjects had silent T2D. HTA was detected in 23% of our population (22.8% systolic and 0.6% diastolic HTA). Frequency of low-HDL cholesterol and hypertriglyceridemia was 27 and 16% respectively (Fig. 1Go).


Figure 1
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  		Figure 1 Frequency of individual components of MS in total population, patients with insulin resistance (IR; n = 205) compared with those without IR (NIR; n = 224). Low HDL, ≤40 mg/dl. High TG, tryglicerides > 110 mg/dl. High BP, blood pressure > p95. IGM, impaired glucose metabolism (IFG (fasting glucose 100 and < 126 mg/dl (5.6–7 mmol/l)) and/or IGT (glucose 2 h OGTT > 140 and < 200 mg/dl (7.8–11 mmol/l)) *P< 0.05, **P< 0.005; ns, non-significant.

 
Patients with MS were significantly older than patients without MS (Table 1Go). Percentage of prepubertal patients in the MS group was lower (38 vs 49%) but the difference was not statistically significant. In addition, no significant differences between boys and girls were found. There was a significantly higher frequency of MS in the Hispanic population of our cohort with respect to Caucasian patients (32 vs 16%). We did not find increased risk of MS among patients with parental obesity or familiar history of MS (data not shown). Subjects with MS had markedly higher IR and lower insulin sensitivity parameters than those without MS; the former also had significantly higher GGT, uric acid and HbA1c levels (Table 1Go).

In multivariable logistic regression analysis with MS as the dependent variable, IR (HOMA), HbA1C, GGT and uric acid significantly predicted the presence of MS independently from confounding variables (age or race). The estimated OR values for the presence of MS are shown in Table 2Go.


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  		Table 2 Factors influencing the presence of MS.
 
IR was found in 35% of the total population (150 patients). The presence of IR significantly influenced the frequency of MS; 29% of IR patients had MS compared with 8% in patients without IR (P< 0.005). All individual components of MS were significantly higher in IR patients than non-IR subjects, except low HDL (see Fig. 1Go); HTA: 29 vs 19% (P< 0.05), low HDL: 31 vs 24% (ns), hypertriglyceridemia: 23 vs 11% (P< 0.005), IFG: 10 vs 2% (P< 0.005) and IGT; 5 vs 1% (P< 0.005). Patients with IR also had higher frequency of hepatic steatosis by ultrasound scan (35 vs 6%, P< 0.005) and higher frequency of increased serum ALAT levels (11 vs 3%, P = 0.05) compared to non-IR obese children.

According to Cole charts (International Obesity Taskforce) (18), 22% of our patients should be classified as overweight and not obese. When we compared the frequency of the different components of MS in overweight vs obese patients, according to the IOTF criteria, we did not find significant differences in these two subgroups with respect to HTA (21 vs 23%), low HDL (25 vs 27%), hypertriglyceridemia (15 vs 16%) or IGM rates (10 vs 6%).


   Discussion
Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
References
 
In the last few decades, the prevalence of obesity has rapidly increased to epidemic proportions around the world (27), having worrying consequences on the health status of the populations and economies of developed and developing countries (28). The MS associated with obesity is an important risk factor for the development of cardiovascular disease, also among children. While an increasing number of studies are available on the prevalence and impact of MS in the adult population, there is a paucity of information on the epidemiology of MS at the pediatric age.

Our study, with a hospital-based study design, represents the first report on the frequency of MS in obese children and adolescents in Spain, as conducted in a large cohort. We found the presence of MS in 18% of children and adolescents in our series. MS frequency was significantly higher among the Hispanic population (32%) than in Caucasians (16%). The overall MS frequency appears to be lower but within the range of MS prevalence identified in other European countries (16, 29), and also lower than that found among North American children and adolescents (14, 15).

In European countries, a few studies using the modified WHO criteria for MS adapted for children, have shown MS prevalence of 33% in UK and 27.2% in a recent study of obese Turkish children and adolescents (30). In contrast, Csabi et al. (29) identified MS only in 8.9% of Hungarian obese children.

In US, the Third National Health and Nutrition Examination Survey study reported the presence of MS in 30% of overweight adolescents (14,15) using the ATP III criteria for the definition of the syndrome. Similarly, Cruz et al. (31) reported a prevalence of 30% MS in 126 overweight Hispanic children and adolescents. However, Butte et al. (32) recently found a lower 20% MS frequency in an American pediatric cohort with the same Hispanic background.

However, the straightforward comparison of pediatric and adolescent MS frequencies found in different countries is not without difficulty. The main reason is the current lack of a standardized and internationally accepted definition for pediatric MS. In a recent article on obese French children, the frequency of MS was found to be 15.9% using the ATP III criteria; however, the frequency reached 42.5% using the WHO definition (33). Discrepancies in the definition of individual components of the syndrome as well as slightly different normality thresholds used in different studies can have an impact on the overall MS prevalence identified (34). Other factors to consider are the specific characteristics of the populations studied, including ethnical background, age range and pubertal status of population samples. Finally, the global severity of obesity within each cohort might influence the prevalence of MS, as suggested by the work of Weiss et al. (35), who report MS prevalence reaching 50% in heavily obese children. Thus, an international definition for pediatric and adolescent MS seems necessary for its uniform application worldwide.

In our study, we used the NCEP-ATP III criteria for the definition of MS (14) with modifications representing the most recent updates on recommendations to diagnose pediatric HTA (ITF, 2004) (19) and alterations in glucose metabolism (ADA, 2004) (25). To define obesity, we used the threshold of > 2SDS of BMI z-score on the national normative charts for growth, available in Spain since 1988 (17). To explore the possible implications of the use of international weight reference values on MS frequency, we also applied the IOTF criteria for obesity (18) to the BMI data from our obese patients. Following IOTF, 22% of our obese patients upon Spanish normative charts would be considered overweight. We compared the frequency of MS (and its individual components) in IOTF-overweight vs IOTF-obese patients and did not find any significant difference (data not shown). This is in agreement with the recent publications showing that discrimination between obesity and overweight using BMI in young children is not a good marker for the prediction of IR and, consequently, for the prediction of metabolic risk (36). Furthermore, the mean BMI z-score of our group of patients with MS and the ones without MS is not significantly different, suggesting that severity of obesity did not play a major role in the development of MS in our cohort. This underlies the significance of factors additional to adiposity (e.g. genetic factors) in the development of MS (37, 38).

IGM is an important feature among the metabolic disturbances of MS and it has been studied in the obese pediatric population (39). In our cohort, we found IGM in 7% of children, either having IGT (2.4%) or IFG (4.6%). No individuals were identified with clinically silent type 2 diabetes (T2D).

The prevalence of IGT found in our cohort seems lower than that in other studies (31, 37, 39). Only the recent reports from Germany (40) and France (33) show a similar IGT frequency among obese patients (2.1 and 3.6% respectively). Other European studies show significantly higher rates of IGT ranging between 19 and 21% (30, 41), but also intermediate IGT frequencies ranging from 11% in British (16) and 7.5% in German (42) and Spanish (43) children to 4.5% in Italian obese children and adolescents (44). These variable IGT rates between studies can be influenced by the particular characteristics of cohorts and probably reflect the different IR indices found in these sample populations.

In contrast to the previous studies suggesting that IFG is rare in children (35), the IFG frequency we found doubles the one found for IGT in our cohort. This could be due to the more strict definition of IFG we applied following the latest recommendations of the ADA (2004) (25), probably allowing a better identification of these initial alterations of glucose metabolism.

IR is the defining pathophysiological defect in MS (45). Obesity leads to fat accumulation not only in adipocytes but also in muscle and liver cells, resulting in IR in these organs (46). Cruz et al. hypothesized that IR would be more closely associated with MS than overall adiposity (47). We found that 35% of our children and adolescents have IR.

The mean HOMAIR index in our obese patients was 2.5 (4.5 in those with IGM), whereas in the Yeste (41) and Sinha (39) cohorts, they were 3.95 (5.85 in those with IGT) and 5 (7.2 in those with IGT) respectively. These data emphasize the importance of IR as a contributing factor in the expression of IGM.

In different studies, IR has been shown to cluster with all the features of MS. We found that the prevalence of MS is significantly higher among our group of IR patients. Prevalence of other factors related to IR like HTA, low HDL, hypertriglyceridemia and IGM were also individually higher among our IR patients. Furthermore, in multivariable regression analysis, IR significantly predicted the presence of MS, independently of several confounding variables (Table 2Go). These findings stress the importance of IR in the development of MS and underline the interest of evaluating IR in obese children.

Interestingly, abnormalities in serum uric acid levels are often found in association with the presence of IR. In agreement with other studies (48), we found that uric acid is a reliable indicator for MS in obese children (Table 2Go). Elevation in serum GGT in obese children compared with normal weight children has also been described (49). The estimated OR values for the presence of MS were significant for HbA1c and GGT levels, suggesting these biochemical parameters could as well be useful predictors for MS.

In conclusion, we report a frequency of 18% of MS in Spanish children and adolescents with moderate obesity. Our findings suggest that IR is a good predictor for the development of MS in obese children and adolescents, with independence of age, ethnicity and the severity of obesity. These results provide evidence that, early in life, alterations in glucose metabolism and insulin sensitivity start to develop in obese children, and should be monitored in this segment of population at high cardiovascular risk.


   Acknowledgements
 
We are indebted to patients participating in this study as well as to their families for their collaboration. We acknowledge the contribution of Alfonso Muriel in the statistical analysis. We thank the technical help and dedicated work of nurses Victoria Gallardo and Rosa Contreras in this study. Research funding granted by the Spanish Institute for Research on Rare Genetic Diseases (INERGEN C03/05).


   References
Top
 Abstract
 Introduction
 Patients and methods
 Results
 Discussion
 References
 

    1. Haslam DW & James WP. Obesity. Lancet 2005 366 1197–1209.[CrossRef][Web of Science][Medline]

    2. Dietz WH. Overweight in childhood and adolescence. New England Journal of Medicine 2004 350 855–857.[Free Full Text]

    3. Miller J, Rosenbloom A & Silverstein J. Childhood obesity. Journal of Clinical Endocrinology and Metabolism 2004 89 4211–4218.[Free Full Text]

    4. Hedley AA, Ogden CL, Johnson CL, Carroll MD, Curtin LR & Flegal KM. Prevalence of overweight and obesity among US children, adolescents and adults, 1999–2002. Journal of American Medical Association 2004 291 2847–2850.[Abstract/Free Full Text]

    5. International Obesity Task Force and the European Association for the study of obesity. Obesity in Europe: a case for action. [www document] URL http://www.iotf.org/media/euobesity.pdf.

    6. Moreno LA, Mesana MI, Fleta J, Ruiz JR, González-Gross M, Sarría A, Marcos A & Bueno M. The AVENA study group. Overweight, obesity and body fat composition in Spanish adolescents. Annals of Nutrition and Metabolism 2005 49 71–76.

    7. Reilly JJ, Methven E, McDowell ZC, Hacking B, Alexander D, Stewart L & Kelnar CJ. Health consequences of obesity. Archives of Disease in Childhood 2003 88 748–752.[Abstract/Free Full Text]

    8. Daniels SR, Arnett DK, Eckel RH, Gidding SS, Hayman LL, Kumanyika S, Robinson TN, Scott BJ, St Jeor S & Williams CL. Overweight in children and adolescents. Pathophysiology, consequences, prevention and treatment. Circulation 2005 111 1999–2012.[Abstract/Free Full Text]

    9. Whitlock EP, Williams SB, Gold R, Smith PR & Shipman SA. Screening andinterventions for childhood overweight: a summary of evidence for the US preventive services task force. Pediatrics 2005 116 e125–e144.[Abstract/Free Full Text]

    10. Berenson GS, Srinivasan SR, Bao W, Newman WP, Tracy RE & Wattigney WA. Association between multiple cardiovascular risk factors and atherosclerosis in children and young adults. New England Journal of Medicine 1998 338 1650–1656.[Abstract/Free Full Text]

    11. Kahn R, Buse J, Ferrannini E & Stern M. The metabolic syndrome: time for a critical appraisal. Joint statement from the American Diabetes Association and the European Association for the study of diabetes. Diabetes Care 2005 28 2289–2304.[Abstract/Free Full Text]

    12. Reaven G. Role of insulin resistance in human disease. Diabetes 1988 37 1595–1607.[Abstract]

    13. Eckel RH, Gruñid SM & Zimmet PZ. The metabolic syndrome. Lancet 2005 365 1415–1428.[CrossRef][Web of Science][Medline]

    14. Cook S, Weitzman M, Auinger P, Nguyen M & Dietz WH. Prevalence of a metabolic syndrome phenotype in adolescents. Findings from the third National Heath and nutrition examination survery, 1988–1994. Archives of Pediatric Adolescent Medicine 2003 157 821–827.[Abstract/Free Full Text]

    15. De Ferranti SD, Gauvreau K, Ludwig DS, Neufeld EJ, Newburger JW & Rifai N. Prevalence of the metabolic syndrome in American Adolescents: findings from the third National Health and Nutrition Examination Survey. Circulation 2004 110 2494–2497.[Abstract/Free Full Text]

    16. Viner RM, Segal TY, Lichtarowicz-Krynska E & Hidmarsh P. Prevalence of the insulin resistance syndrome in obesity. Archives of Disease in Childhood 2005 90 10–14.[Abstract/Free Full Text]

    17. Hernández M, Castellet J, Narvaiza JL, Rincón JM, Ruiz I, Sánchez E y cols. Curvas y tablas de crecimiento. Instituto sobre crecimiento y desarrollo fundación F. Orbegozo. Madrid: Garsi, 1988.

    18. Cole TJ, Bellizzi MC, Flegal KM & Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. British Medical Journal 2000 320 1240–1243.[Abstract/Free Full Text]

    19. The fourth report on the diagnosis, evaluation and treatment of high blood pressure in children and adolescents. National High Blood Pressure Education Program Working group on High Blood Pressure in children and adolescents. Pediatrics 2004 114 555–576.[Free Full Text]

    20. Conwell LS, Trost SG, Brown WJ & Batch JA. Indexes of insulin resistance and secretion in obese children and adolescents. Diabetes Care 2004 27 314–319.[Abstract/Free Full Text]

    21. Uwaifo GI, Fallon EM, Chin J, Elberg J, Parikh J & Yanovski JA. Indices of insulin action, disposal and secretion derived from fasting samples and clamps in normal glucose-tolerant black and white children. Diabetes Care 2002 11 2081–2087.

    22. Gungor N, Saad R, Janosky J & Arslanian S. Validation of surrogate of insulinsensitivity and insulin secretion in children and adolescents. Journal of Pediatrics 2004 144 47–55.[CrossRef][Web of Science][Medline]

    23. Yeckel CW, Weiss R, Dziura J, Taksali SE, Dufour S, Burgert TS, Tamorlane WV & Caprio S. Validation of insulin sensitivity indices from oral glucose tolerance test parametres in obese children and adolescents. Journal of Clinical Endocrinology and Metabolism 2004 89 1096–1101.[Abstract/Free Full Text]

    24. Lopez D, Plaza I, Muñoz MT, Madero R, Otero de Becerra J, Hidalgo I, Baeza J, Ceñal MJ, Cobaleda A, Parra I, Ruiz C & Dominguez J. Estudio de Fuenlabrada: lipidos y lipoproteinas en niños y adolescentes. An Esp Pediatr 1989 31 342–349.[Medline]

    25. American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 2004 27 5–10.[CrossRef]

    26. Belsley DA. Conditioning diagnostics: Collinearity and Weak Data Regression. New York: John Wiley & Sons, 1991.

    27. Smyth S & Heron A. Diabetes and obesity: the twin epidemics. Nature Medicine 2006 12 75–80.[CrossRef][Web of Science][Medline]

    28. Yach D, Stucker D & Brownell KD. Epidemiologic and economic consequences of the global epidemics of obesity and diabetes. Nature Medicine 2006 12 62–66.[CrossRef][Web of Science][Medline]

    29. Csabi G, Török K, Jeges S & Molnár D. Presence of metabolic cardiovascularsyndrome in obese children. European Journal of Pediatrics 2000 159 91–94.[CrossRef][Web of Science][Medline]

    30. Atabek ME, Pirgon O & Kurtoglu S. Prevalence of metabolic syndrome in obese Turkish children and adolescents. Diabetes Research and Clinical Practice 2006 72 315–321.[Web of Science][Medline]

    31. Cruz ML, Weigensberg MJ, Huang T, Ball G, Shaibi GQ & Goran MI. The metabolic syndrome in overweight hispanic youth and the role of insulin sensitivity. Journal of Clinical Endocrinology and Metabolism 2004 89 108–113.[Abstract/Free Full Text]

    32. Butte NF, Comuzzie AG, Cole SA, Mehta NR, Cai G, Tejero M, Bastarrachea R & Smith O. Quantitative genetic analysis of the metabolic syndrome in hispanic children. Pediatric Research 2005 58 1243–1248.[Web of Science][Medline]

    33. Druet C, Dabbas M, Baltakse V, Payen C, Jouret B, Baud C, Chevenne D, Ricour C, Tauber M, Polak M, Alberti C & Levy-Marchal C. Insulin resistance and the metabolic syndrome in obese French children. Journal of Clinical Endocrinology 2006 64 672–678.

    34. Goodman E, Daniels SR, Morrison JA, Huang B & Dolan LM. Contrasting prevalence of and demographic disparities in the WHO and NationalCholesterol Education Program Adult Treatment Panel III definitions of metabolic syndrome among adolescents. Journal of Pediatrics 2004 145 445–451.[CrossRef][Web of Science][Medline]

    35. Weiss R, Dziura J, Burgert TS, Tamborlane WV, Taksali SE, Yeckel CW, Allen K, Lopes M, Savoye M, Morrison J, Sherwin RS & Caprio S. Obesity and the metabolic syndrome in children and adolescents. New England Journal of Medicine 2004 350 2362–2374.[Abstract/Free Full Text]

    36. Voss LD, Metcalf BS, Jeffery AN & Wilkin TJ. IOTF thresholds for overweight and obesity and their relation to metabolic risk in children (EarlyBird 20). International Journal of Obesity 2006 30 606–609.[CrossRef][Web of Science][Medline]

    37. Goran MI, Ball GD & Cruz ML. Obesity and risk of T2D and cardiovascular disease in children and adolescents. Journal of Clinical Endocrinology and Metabolism 2003 88 1417–1427.[Abstract/Free Full Text]

    38. Hannon TS, Rao G & Arslanian SA. Childhood obesity and T2D mellitus. Pediatrics 2005 116 473–480.[Abstract/Free Full Text]

    39. Sinha R, Fisch G, Teague B, Tamborlane WV, Banyas B, Allen K, Savoye M, Rieger V, Taksali S, Barbetta G, Sherwin RS & Caprio S. Prevalence of impaired glucose tolerance among children and adolescents with marked obesity. New England Journal of Medicine 2002 346 802–810.[Abstract/Free Full Text]

    40. Wabitsch M, Hauner H, Hertrampf M, Muche R, Hay B, Mayer H, Kratzer W, Debatin KM & Heinze E. Type II diabetes mellitus and impaired glucose regulation in Caucasian children and adolescents with obesity living in Germany. International Journal of Obesity and Related Metabolic Disorders 2004 28 307–313.

    41. Yeste D, Betancourth S, Gussinyé M, Potau N & Carrascosa. Glucose intolerance in obese children and adolescents. Medical Clinica 2005 125 405–408.

    42. Wiegand S, Maikowski U, Blankenstein O, Biebermann H, Tarnow P & Grüters A. T2D and impaired glucose tolerance in European children and adolescents with obesity–a problem that is no longer restricted to minority groups. European Journal of Endocrinology 2004 151 199–206.[Abstract]

    43. Tresaco B, Bueno G, Moreno LA, Garagorri JM & Bueno M. Insulin resistance and impaired glucose tolerance in obese children and adolescents. Journal of Physiology and Biochemistry 2003 59217–224.[Web of Science][Medline]

    44. Invitti C, Guzzaloni G, Gilardini L, Morabito F & Viberti G. Prevalence and concomitants of glucose intolerance in European obese children and adolescents. Diabetes Care 2003 26 118–124.[Abstract/Free Full Text]

    45. De Luca C & Olefsky JM. Stressed out about obesity and insulin resistance. Nature Medicine 2006 12 41–42.[CrossRef][Web of Science][Medline]

    46. Petersen KF & Shulman GI. Etiology of insulin resistance. American Journal of Medicine 2006 119 10S–16S.

    47. Cruz ML & Goran MI. The metabolic syndrome in children and adolescents. Current Diabetes Reports 2004 4 53–62.[Medline]

    48. Denzer C, Muche R, Mayer H, Heinze E, Debatin KM & Wabitsch M. Serum uric acid levels in obese children and adolescents: linkage to testosterone levels and pre-metabolic syndrome. Journal of Pediatric Endocrinology and Metabolism 2003 16 1225–1232.

    49. Strauss RS, Barlow SE & Dietz WH. Prevalence of abnormal serum aminotransferase values in overweight and obese adolescents. Journal of Pediatrics 2000 136 727–733.[CrossRef][Web of Science][Medline]

Received 23 December 2005
Accepted 8 May 2006




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