Waist circumference seems to be the best predictor of children with the metabolic syndrome in paediatric clinical settings.
Aim: To identify the best anthropometric predictor of the metabolic syndrome in children. Methods: Screening performance was evaluated in a clinical setting. The study included 140 children: 72 non‐obese and 68 with non‐syndromal obesity. Body mass index (BMI), waist circumference and triceps/subscapular skinfolds ratio were used as predictor variables, and systolic blood pressure, diastolic blood pressure, glucose, uric acid, fasting insulin, triglycerides and HDL‐C as metabolic syndrome variables. Results: The areas under the receiver operating characteristic (ROC) curves were 0.849 (95% CI: 0.780,0.919) for BMI, 0.868 (95% CI: 0.801,0.934) for waist circumference and 0.834 (95% CI: 0.757,0.910) for the triceps/subscapular skinfolds ratio. No statistically significant differences were found for the three areas under the ROC curves. The point on the ROC curve closest to 1 corresponded to the 65th percentile for BMI, to the 70th percentile for waist, and to the 40th percentile for the triceps/subscapular skinfolds ratio. Conclusion: Waist circumference seems to be the best predictor of children with the metabolic syndrome in paediatric clinical settings.
The aim of the study was to establish the best cut-off value for the homeostatic model assessment (HOMA) index in identifying children and adolescents with the metabolic syndrome. The study included 72 non-obese and 68 obese children aged 7 to 16 years. Obesity is defined using the criteria proposed by Cole et al., being included as metabolic syndrome variables waist circumference, systolic blood pressure, diastolic blood pressure and seric values of glucose, uric acid, fasting insulin, leptin, triglycerides and HDL-cholesterol. Children were considered as having the metabolic syndrome when four or more characteristics showed abnormal values. The HOMA index was calculated as the product of the fasting plasma insulin level (microU/mL) and the fasting plasma glucose level (mmol/L), divided by 22.5. HOMA index cut-offs from the 5th to the 95th percentile were used. A receiver operating characteristic (ROC) curve was generated using the different HOMA cut-offs for the screening of the metabolic syndrome. The areas under the ROC curve, 95% confidence intervals, and the point to the ROC curve closest to 1, were calculated. The area under the ROC curve was 0.863 (95% C.I.: 0.797, 0.930). The point closest to 1 corresponds to the 60th percentile of the HOMA index distribution in our sample. HOMA index value at the 60th percentile was 2.28. Cut-off values corresponding to a range of HOMA index from the 50 to the 75 percentile, showed similar distances to 1. HOMA index values for percentiles 50 to 75 ranged from 2.07 to 2.83. In conclusion, HOMA index could be a useful tool to detect children and adolescents with the metabolic syndrome. HOMA cut-off values need to be defined in the paediatric population; however, values near to 3 seem to be adequate.
Metabolic syndrome is characterized by a clustering of metabolic abnormalities: insulin resistance - hyperinsulinemia, dyslipidemia (high triglycerides and low HDL - cholesterol serum concentrations), impaired glucose tolerance and/or type 2 diabetes, and hypertension. The aim of this study was to analyse the role of different variables of metabolic syndrome, including leptin, in 74 non-obese children and 68 children with non-syndromal obesity. As metabolic syndrome variables, we have included body mass index, waist circumference, trunk-to-total skinfolds (%), systolic blood pressure, diastolic blood pressure, glucose, uric acid, fasting insulin, triglycerides and high-density lipoprotein-cholesterol (HDL-C). Factor analysis showed 4 factors in each group. In non-obese children, waist circumference, BMI, fasting insulin, triglycerides, trunk-to-total skinfolds (%), leptin and uric acid loaded positively on factor 1, and HDL-C loaded negatively on this factor; systolic and diastolic blood pressure had high positive loadings in factor 2; HDL-C and leptin showed positive loadings and triglycerides and uric acid, negative loadings in factor 3; and, finally, glucose and insulin showed positive loadings in factor 4. These four factors explained 72.16 % of the total variance in the non-obese group. In obese children, BMI, waist circumference, leptin, diastolic blood pressure and systolic blood pressure loaded positively on factor 1; diastolic blood pressure, trunk-to-total skinfolds (%), uric acid and systolic blood pressure showed high positive loadings in factor 2; fasting insulin, glucose and triglycerides showed positive loadings in factor 3; and, finally, triglycerides showed positive loadings and HDL-C negative loadings in factor 4. These four factors explained 74.18 % of the total variance in the obese group. Our results point to a different homeostatic control of metabolic syndrome characteristics in obese and non-obese children. Leptin seems to play a key underlying role in metabolic syndrome, especially in the obese group.
Resting energy expenditure (REE) is the largest component of total daily energy expenditure. Objectives of this study were to examine whether differences in REE exist after obesity develops in a group of children and adolescents, and to determine the effects of body composition, gender, age, pubertal development and parental obesity on REE. In 116 Caucasian children and adolescents (57 obese and 59 non-obese), aged 7.8 to 16.6 years, REE was assessed by open-circuit indirect calorimetry and different anthropometric variables and bioelectrical impedance were obtained (weight, height, skinfold thicknesses, waist and hip circumferences). Anthropometric indices and body compartments were calculated: the body mass index, surface area (SA), fat-free mass (FFM), fat-mass (FM) and percentage of FM. Differences between obese and non-obese subjects were tested and stepwise multiple regression analysis was performed with REE as dependent variable. Results show that REE was significantly higher in obese than in non-obese children and adolescents but REE/FFM ratio was not significantly different between these groups. In the non-obese group, FFM explained 73.1% of the variability in REE and gender, age and SA added 3.8%, 2.6%, and 2.6% to it, respectively. In the obese group, FFM was also the most powerful predictor of REE with 72.3%, followed by waist circumference and age with 2.5% and 2.1%, respectively. These results show that REE differences between obese and lean children do not seem to justify the maintenance of obesity. The main determinant of REE is FFM in both groups. No significant contribution of FM, pubertal development or parental obesity in REE was found in children and adolescents.
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