Defining the Metabolic Syndrome in Children and Adolescents: Will the Real Definition Please Stand Up?

Division of Adult and Community Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention , Atlanta, GA, USA.
The Journal of pediatrics (Impact Factor: 3.79). 03/2008; 152(2):160-4. DOI: 10.1016/j.jpeds.2007.07.056
Source: PubMed


To review the use of definitions of the metabolic syndrome in studies of children and adolescents and to review results from studies that used factor analysis to examine structure among cardiometabolic variables.
Literature review.
In 27 publications, authors used 40 unique definitions of the metabolic syndrome. Most of these definitions were adaptations of the adult definition developed by the National Cholesterol Education Program. In 11 studies that used exploratory factor analysis, the number of components ranged from 5 to 19, and the number of factors identified ranged from 1 to 5.
The use of multiple definitions of the metabolic syndrome argues strongly for the development of a standard pediatric definition.

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    • "The metabolic syndrome represents a cluster of cardiometabolic abnormalities, including abdominal obesity, insulin resistance, glucose intolerance, dyslipidaemia and elevated blood pressure [4,9]. The definitions of paediatric metabolic syndrome have been criticised, because they are based on artificial cut-offs for the individual features of metabolic syndrome used in adults [10,11]. Therefore, studies among children or adolescents have often employed composite cardiometabolic risk scores that have been calculated using the features of metabolic syndrome as continuous variables [12-15]. "
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    ABSTRACT: Lower levels of physical activity (PA) and sedentary behaviour (SB) have been associated with increased cardiometabolic risk among children. However, little is known about the independent and combined associations of PA and SB as well as different types of these behaviours with cardiometabolic risk in children. We therefore investigated these relationships among children. The subjects were a population sample of 468 children 6-8 years of age. PA and SB were assessed by a questionnaire administered by parents and validated by a monitor combining heart rate and accelerometry measurements. We assessed body fat percentage, waist circumference, blood glucose, serum insulin, plasma lipids and lipoproteins and blood pressure and calculated a cardiometabolic risk score using population-specific Z-scores and a formula waist circumference + insulin + glucose + triglycerides - HDL cholesterol + mean of systolic and diastolic blood pressure. We analysed data using multivariate linear regression models. Total PA was inversely associated with the cardiometabolic risk score (beta = -0.135, p = 0.004), body fat percentage (beta = -0.155, p < 0.001), insulin (beta = -0.099, p = 0.034), triglycerides (beta = -0.166, p < 0.001), VLDL triglycerides (beta = -0.230, p < 0.001), VLDL cholesterol (beta = -0.168, p = 0.001), LDL cholesterol (beta = -0.094, p = 0.046) and HDL triglycerides (beta = -0.149, p = 0.004) and directly related to HDL cholesterol (beta = 0.144, p = 0.002) adjusted for age and gender. Unstructured PA was inversely associated with the cardiometabolic risk score (beta = -0.123, p = 0.010), body fat percentage (beta = -0.099, p = 0.027), insulin (beta = -0.108, p = 0.021), triglycerides (beta = -0.144, p = 0.002), VLDL triglycerides (beta = -0.233, p < 0.001) and VLDL cholesterol (beta = -0.199, p < 0.001) and directly related to HDL cholesterol (beta = 0.126, p = 0.008). Watching TV and videos was directly related to the cardiometabolic risk score (beta = 0.135, p = 0.003), body fat percentage (beta = 0.090, p = 0.039), waist circumference (beta = 0.097, p = 0.033) and systolic blood pressure (beta = 0.096, p = 0.039). Resting was directly associated with the cardiometabolic risk score (beta = 0.092, p = 0.049), triglycerides (beta = 0.131, p = 0.005), VLDL triglycerides (beta = 0.134, p = 0.009), VLDL cholesterol (beta = 0.147, p = 0.004) and LDL cholesterol (beta = 0.105, p = 0.023). Other types of PA and SB had less consistent associations with cardiometabolic risk factors. The results of our study emphasise increasing total and unstructured PA and decreasing watching TV and videos and other sedentary behaviours to reduce cardiometabolic risk among children.Trial registration: Identifier: NCT01803776.
    Full-text · Article · Apr 2014 · International Journal of Behavioral Nutrition and Physical Activity
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    • "Hyperglycemia had the highest prevalence according to Ford et al. 2007, Agudelo et al. 2008, and the IDF (2.8%). The prevalence of high blood pressure was 10.3% for the cutoff defined by Cook et al. 2003, Ford et al. 2007, and Agudelo et al. 2008; high WC, according to de Ferranti et al. 2004, was present in 18.9% versus 3.4% with the other definitions. Finally, the BMI ‡ 95th percentile criterion, used to define obesity in adolescents by Agudelo et al. 2008, had a prevalence of 29.1%. "
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    ABSTRACT: Background: Despite the increasing prevalence of metabolic syndrome in adolescents, there is no consensus for its diagnosis. Methods: A cross-sectional study was conducted to compare the prevalence of metabolic syndrome in adolescents by different definitions, evaluate their concordance, and suggest which definition to apply in this population. A total of 851 adolescents between 10 and 18 years of age were evaluated. Anthropometric (weight, height, waist circumference), biochemical (glucose, lipid profile), and blood pressure data were taken. The prevalence of metabolic syndrome was determined by the definitions of the International Diabetes Federation (IDF) and four published studies by Cook et al., de Ferranti et al., Agudelo et al., and Ford et al. Concordance was determined according to the kappa index. Results: The prevalence of metabolic syndrome was 0.9%, 3.8%, 4.1%, 10.5%, and 11.4%, according to the IDF, Cook et al., Ford et al., Agudelo et al., and de Ferranti et al. definitions, respectively. The most prevalent components were hypertriglyceridemia and low high-density lipoprotein cholesterol, whereas the least prevalent components were abdominal obesity and hyperglycemia. The highest concordance was found between the definitions by Cook et al. and Ford et al. (kappa=0.92), whereas the greatest discordance was between the de Ferranti et al. and IDF definitions (kappa=0.14). Conclusions: Metabolic syndrome and its components were conditions present in the adolescents of this study. In this population, with a high prevalence of dyslipidemia and a lower prevalence of abdominal obesity and hyperglycemia, the recommendation to diagnose metabolic syndrome would be that used by Ford et al.
    Full-text · Article · Feb 2014 · Metabolic syndrome and related disorders
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    • "In the long-standing Bogalusa Heart study, body mass indexes (BMI) performed in childhood and adolescence, as a measurement for obesity, predicted intimamedia thickness in adults [6]. Obesity is not only related to modifiable cardiovascular risk factors, but also to several other health-related conditions that may persist or worsen in adulthood [7] [8]. This includes asthma, orthopedic disorders, depression and anxiety, liver abnormalities, and endocrine related issues including type 2 diabetes, hyperlipidemia, and polycystic ovarian syndrome (PCOS) [9]. "
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    ABSTRACT: Background. Obesity studies are often performed on population data. We sought to examine the incidence of obesity and its associated comorbidities in a single freestanding children's hospital. Methods. We performed a retrospective analysis of all visits to Boston Children's Hospital from 2000 to 2012. This was conducted to determine the incidence of obesity, morbid obesity, and associated comorbidities. Each comorbidity was modeled independently. Incidence rate ratios were calculated, as well as odds ratios. Results. A retrospective review of 3,185,658 person-years in nonobese, 26,404 person-years in obese, and 25,819 person-years in the morbidly obese was conducted. Annual rates of all major comorbidities were increased in all patients, as well as in our obese and morbidly obese counterparts. Incidence rate ratios (IRR) and odds ratios (OR) were also significantly increased across all conditions for both our obese and morbidly obese patients. Conclusions. These data illustrate the substantial increases in obesity and associated comorbid conditions. Study limitations include (1) single institution data, (2) retrospective design, and (3) administrative undercoding. Future treatment options need to address these threats to longevity and quality of life.
    Full-text · Article · Feb 2014
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