Cardiovascular Risk Factors Clustering Features of Insulin Resistance Syndrome (Syndrome X) In a Biracial (Black-White) Population of Children, Adolescents, and Young Adults: The Bogalusa Heart Study

Chen, Wei; Srinivasan, Sathanur R.; Elkasabany, Abdalla; Berenson, Gerald S.

doi:10.1093/oxfordjournals.aje.a010069

Cited by 237 publications

(209 citation statements)

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“…In Table 1 we present 15 papers, [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29] all of which used different sets of variables, number of criteria and cutoff points to define the risk-factors associated with the MS. [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60] The main differences concern the techniques used to estimate adiposity (body mass index (BMI) and/or waist circumference (WC)), and the variable(s) chosen to evaluate glucose metabolism (fasting glucose, …”

Section: Actual Featuresmentioning

confidence: 99%

“…12 Nonetheless, the three proposed definitions have been used in several studies carried out in pediatric populations, which reported high-prevalence of the MS in obese children and adolescents. [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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Section: Actual Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2007

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“…While earlier studies in childhood had focused on the clustering of risk factors for cardiovascular diseases [7, 8], recent studies had reported the prevalence of MeS during childhood [9–11]. Several definitions have been proposed for MeS in children and some of these definitions followed the ATP III guidelines [9–14].…”

Section: Introductionmentioning

confidence: 99%

Metabolic Syndrome and Its Individual Components among Jordanian Children and Adolescents

Khader

Batieha

Jaddou

et al. 2010

International Journal of Pediatric Endocrinology

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This study aimed to determine the prevalence of metabolic syndrome (MeS) and its individual components in Jordanian children and adolescents aged 7–18 years and determine the factors that are associated with clustering of metabolic abnormalities. MeS was defined using the International Diabetes Federation (IDF) definition. The prevalence of MeS was estimated from 512 subjects who had complete information on all MeS components. The prevalence of MeS according to IDF criteria was 1.4% in subjects aged between 10 and 15.9 years and 3.6% in subjects aged between 16 and 18 years. When categorized according to body mass index (BMI), the prevalence of the MeS was 15.1% in obese subjects, compared to 0.3% in subjects with normal BMI, and 3.0% in overweight subjects. In conclusion, our results indicate that although the prevalence of MeS is low in Jordanian children and adolescents, a large proportion of them had one or two metabolic abnormalities.

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“…[10][11][12] Two 10,12 of the three common definitions include measures of insulin resistance, reflecting the proposed causal or mediating role insulin action plays in the development of MS. 13,14 In the paediatric literature there is no single definition of MS. [15][16][17] Barriers to a consistent, accepted definition for children and adolescents include use of adult cut points or a single set of cut points for all ages of childhood, the fact that disturbances seen in the metabolic indicators in most children are quantitatively moderate, lack of a normal range for insulin concentration across childhood, the insulin resistance of puberty, and lack of central obesity (waist) cut points linked to obesity morbidity or MS for children. If hyperinsulinaemia alone is used as a marker of MS in children, 18 it ignores the clustering of several metabolic indicators which is potentially of greater clinical significance.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2006

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Objectives: To assess the implications of variation in Metabolic Syndrome (MS) definition (biochemical and anthropometric indicators) on MS prevalence estimates in a population of overweight and mildly obese children. Design: Cross-sectional study. Subjects: Ninety-nine (64 girls) overweight or mildly obese, but otherwise healthy, pre-pubertal 6-9-year olds recruited for a randomized controlled trial of weight management. Measures: Height, weight and waist circumference were measured with BMI and waist z-scores calculated. Fasting cholesterol and fractions, glucose and insulin were measured, together with systolic and diastolic blood pressure (BP). Anthropometric and metabolic indicators were classified as normal or elevated using adult-or child-specific cut points with clustering of MS indicators also assessed using two adult and three child-specific definitions. Results: A total of 0-4% of subjects were classified with MS when adult definitions were applied. This increased to between 39 and 60% using child-specific definitions, varying according to whether hyperinsulinaemia was central to the MS classification. Systolic BP, triglycerides, total cholesterol, high-density lipoprotein cholesterol and waist z-score increased across insulin quartiles (Po0.05). The use of body mass index and waist circumference in the MS definition classified the same subjects. Conclusions: The classification of MS in children depends strongly on the definition chosen, with MS prevalence estimates higher if insulin is part of the definition and child-specific cut points for metabolic indicators are used. Hyperinsulinaemia and MS are common consequences of childhood obesity but they are not commonly part of the assessment or management plan for weight management in children. There is a need for the establishment of normal insulin ranges and consistent definition of MS in childhood and adolescence.

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Cardiovascular Risk Factors Clustering Features of Insulin Resistance Syndrome (Syndrome X) In a Biracial (Black-White) Population of Children, Adolescents, and Young Adults: The Bogalusa Heart Study

Cited by 237 publications

References 0 publications

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

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

Metabolic Syndrome and Its Individual Components among Jordanian Children and Adolescents

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

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