Comparison of metabolic syndrome prevalence using six different definitions in overweight pre-pubertal children enrolled in a weight management study

Golley, Rebecca K.; Magarey, Anthea; Steinbeck, Katharine; Baur, Louise A.; Daniels, Lynne

doi:10.1038/sj.ijo.0803195

Cited by 103 publications

(103 citation statements)

References 37 publications

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“…Overall, CV risk may be characterized in terms of the CV risk factors present, although their relative contributions and whether there is synergy between the individual factors underlying the concept of the Metabolic Syndrome have been questioned, as have the diagnostic criteria. 36,37 High WC and BMI levels were confirmed in the present study as being associated with clusters of three-or-more or fouror-more of the six CV risk factors measured. The most strongly associated individual factor of the six was fasting insulin, followed by TG and HDL, with systolic more strongly associated than diastolic BP, and LDL cholesterol and blood glucose only weakly or insignificantly associateda hierarchy of individual associations similar to that noted previously.…”

Section: Discussionsupporting

confidence: 78%

Waist circumference and body mass index in Chinese children: cutoff values for predicting cardiovascular risk factors

et al. 2006

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Background: Body mass index (BMI) and waist circumference (WC) correlate with cardiovascular (CV) risk factors in childhood which track into adulthood. WC provides a measure of central obesity, which has been specifically associated with CV risk factors. Reference standards for WC, and for WC and BMI risk threshold values are not established in Chinese children. Objectives: To construct reference percentile charts of WC, establish relationships between WC, BMI and other risk factors, and propose WC and BMI threshold values predictive of CV risk factors in Hong Kong ethnic Chinese children. Methods: Weight, height, waist and hip circumference were measured in 2593 (52% boys, 47% girls) randomly sampled Hong Kong school children aged 6-12 years. In 958 of these and 97 additional overweight children (n ¼ 1055), the relationships between WC, BMI, waist/hip and waist/height ratio and six age-adjusted CV risk factors (485% percentile levels of blood pressure (BP), fasting triglycerides, low-density lipoprotein (LDL) cholesterol, glucose and insulin levels, and o15% percentile levels of high-density lipoprotein (HDL) cholesterol) were studied. Receiver-operating characteristic analysis was employed to derive optimal age-adjusted sex-specific WC and BMI thresholds for predicting these measures of risk. Results: WC percentiles were constructed. WC correlated slightly more than BMI with CV risk factors and most strongly with insulin and systolic BP, but poorly or not with LDL and glucose. Optimal WC and BMI risk thresholds for predicting four of these six CV risk factors were ca. the 85th percentiles (sensitivities B0.8, specificities B0.87) with age-specific cutoff values in girls/ boys from B57/58 to B71/76 cm and 17/18 to 22/23 kg/m 2 . Conclusion: These are the first set of WC reference data for Chinese children. WC risk cutoff values are proposed which, despite a smaller waist in Chinese children, are similar to those reported for American children. WC percentiles may reflect population risk.

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Section: Discussionsupporting

confidence: 78%

Waist circumference and body mass index in Chinese children: cutoff values for predicting cardiovascular risk factors

et al. 2006

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“…18,19,22,24,27,29 This figure is definitely alarming, but we know that the prevalence recorded strongly depends on the definition chosen, with the estimate being much higher if insulin is part of the definition. 34 Regardless of insulin, a low-discrimination power parameter is the presence of obesity itself in the definition, either directly via BMI or indirectly via WC. It is easy to understand that, when BMI is used as part of the diagnosis, screening for the MS in obese children could show high prevalence; however, even when WC replaces BMI, prevalence remains high, owing to the use of a WC cutoff value (75-90th percentile) that includes the great majority of obese children.…”

Section: Major Concernsmentioning

confidence: 99%

“…Moreover, the available definitions in children 21,22 do not consider the pathophysiological basis of the clustering of CVD risk-factors of metabolic origin. 21,34 Although, progress is being made in understanding the complex gene-environment interaction and associated developmental origin of risk-factors clustering. 88,89 Following the recent recommendation of Grundy 14 regarding metabolic risk-factor clustering in adults, we propose a new comprehensive approach for Metabolic Individual Risk-factor And Clustering Estimation (MIRACLE) in children and adolescents.…”

Section: Minor Concernsmentioning

confidence: 99%

“…[15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33] The results of these studies varied according to the selected diagnostic criteria used, as established by a recent comparison study. 34 Moreover, in an evaluation of the different prevalence yields using different definition, Goodman et al 35 found that ATP III definition identified half as many children and adolescents as having MS as did WHO definition in the same population.…”

Section: Introductionmentioning

confidence: 99%

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Metabolic risk-factor clustering estimation in children: to draw a line across pediatric metabolic syndrome

et al. 2007

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Background: The diagnostic criteria of the metabolic syndrome (MS) have been applied in studies of obese adults to estimate the metabolic risk-associated with obesity, even though no general consensus exists concerning its definition and clinical value. We reviewed the current literature on the MS, focusing on those studies that used the MS diagnostic criteria to analyze children, and we observed extreme heterogeneity for the sets of variables and cutoff values chosen. Objectives: To discuss concerns regarding the use of the existing definition of the MS (as defined in adults) in children and adolescents, analyzing the scientific evidence needed to detect a clustering of cardiovascular risk-factors. Finally, we propose a new methodological approach for estimating metabolic risk-factor clustering in children and adolescents. Results: Major concerns were the lack of information on the background derived from a child's family and personal history; the lack of consensus on insulin levels, lipid parameters, markers of inflammation or steato-hepatitis; the lack of an additive relevant effect of the MS definition to obesity per se. We propose the adoption of 10 evidence-based items from which to quantify metabolic risk-factor clustering, collected in a multilevel Metabolic Individual Risk-factor And CLustering Estimation (MIRACLE) approach, and thus avoiding the use of the current MS term in children. Conclusion: Pediatricians should consider a novel and specific approach to assessing children/adolescents and should not simply derive or adapt definitions from adults. Evaluation of insulin and lipid levels should be included only when specific references for the relation of age, gender, pubertal status and ethnic origin to health risk become available. This new approach could be useful for improving the overall quality of patient evaluation and for optimizing the use of the limited resources available facing to the obesity epidemic.

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“…An increasing number of children and adolescents are now described as affected by metabolic syndrome (MS), [1][2][3][4][5][6][7][8] and as the main consequence they should be addressed to a specific treatment. The prevalence of MS is 0.2% in 10-year-old and 1.4% in 15-year-old subjects in Europe, 9 and B4-7% in US children and adolescents.…”

Section: Introductionmentioning

confidence: 99%

Physical activity as the main therapeutic tool for metabolic syndrome in childhood

Brambilla¹,

Pozzobon

Pietrobelli

2010

Int J Obes

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Physical activity (PA) and diet directly influence obesity and metabolic syndrome (MS) as important determinants of body composition. Understanding how PA relates to MS in youth is of great importance, and could offer a common strategy for clinical and public health approaches to control this condition. The underlying disorder of MS is a condition of insulin resistance, and a strong relationship between PA level and insulin sensitivity is clearly ascertained. The type, duration, frequency and intensity of PA affect fuel metabolism, in particular carbohydrate and lipid oxidation. The possible modulation of metabolism because of increased fat oxidation by PA is the basis for both prevention and restoration of insulin resistance and MS in obese children. In daily clinical practice, diet followed by pharmacologic treatment are usually the approaches taken, whereas PA is often considered just a suggestion. Although diet and PA have different effects on body composition, with both contributing to fat loss, only PA increases muscle mass and thus has a direct effect on metabolic function, expressed by changes in cardiovascular risk factors. Therefore, it is important to remember their complementary but different targets in daily clinical practice, such as body weight control for diet and metabolic health for PA. In this review, we have summarized the literature on the relationship between PA and MS in pediatrics. Then, we have analyzed the possibility of using PA for MS treatment, as an alternative to drugs, by discussing the results of intervention studies, reasons for low compliance to PA, related benefits, adherence difficulties and costs. Finally, we have tried to suggest recommendations for a multiple-step PA strategy in children and adolescents at risk for MS, by considering PA as the 'key' player in treatment.

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Comparison of metabolic syndrome prevalence using six different definitions in overweight pre-pubertal children enrolled in a weight management study

Cited by 103 publications

References 37 publications

Waist circumference and body mass index in Chinese children: cutoff values for predicting cardiovascular risk factors

Waist circumference and body mass index in Chinese children: cutoff values for predicting cardiovascular risk factors

Metabolic risk-factor clustering estimation in children: to draw a line across pediatric metabolic syndrome

Physical activity as the main therapeutic tool for metabolic syndrome in childhood

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