Obesity indicators and cardiometabolic status in 4-y-old children1–3

Background: In adults and adolescents, obesity is positively associated with cardiovascular disease risk factors; however, evidence in preschool children is scarce. Objective: The objective was to assess the relations between obesity indicators and cardiometabolic risk factors in 324 Chilean children 4 y of age. Design: We collected anthropometric measurements and calculated general indicators of obesity [weight, body mass index (BMI), sum of 4 skinfold thicknesses, percentage fat, and body fat index] and central obesity (waist circumference, waist-to-hip ratio, waist-toheight ratio, and truncal fatness based on skinfold thickness). We measured blood sample concentrations of C-reactive protein, interleukin- 6, homeostasis model assessment of insulin resistance, triglycerides, and total, LDL, and HDL cholesterol. We used correlation and multiple linear regression analyses. Results: The prevalence of obesity (BMI-for-age z score .2, World Health Organization 2006), central obesity ( 90th percentile, third National Health and Nutrition Examination Survey), and lipid disorders was high (13%, 11%, and 20%, respectively), and 70% of the children had at least one cardiometabolic risk factor. Most correlations between obesity and central obesity indicators were moderate to strong (0.40 , r , 0.96). Obesity was positively but weakly associated with C-reactive protein in both sexes and with homeostasis model assessment of insulin resistance only in girls (all r , 0.3, P , 0.05). Obesity indicators were unrelated to interleukin-6 and lipid concentrations (P . 0.05). Overall, obesity indicators explained, at most, 8% of the variability in cardiometabolic risk factors. Conclusions: Obesity and central obesity were common, and most of the children had at least one cardiometabolic risk factor, particularly lipid disorders. Obesity and central obesity indicators were highly intercorrelated and, overall, were weakly related to cardiometabolic status. At this age, body mass index and waist circumference were poor predictors of cardiometabolic status.Supported by the Ellison Medical Foundation/International Nutrition Foundation and the Chilean National Science and Technology Fund (Fondecyt) project no. 1060785

Relationship between body composition and postural control in prepubertal overweight/obese children: A cross-sectional study

Background: Excess body weight during childhood causes reduced motor functionality and problems in postural control, a negative influence which has been reported in the literature. Nevertheless, no information regarding the effect of body composition on the postural control of overweight and obese children is available. The objective of this study was therefore to establish these relationships. Methods: A cross-sectional design was used to establish relationships between body composition and postural control variables obtained in bipedal eyes-open and eyes-closed conditions in twenty-two children. Centre of pressure signals were analysed in the temporal and frequency domains. Pearson correlations were applied to establish relationships between variables. Principal component analysis was applied to the body composition variables to avoid potential multicollinearity in the regression models. These principal components were used to perform a multiple linear regression analysis, from which regression models were obtained to predict postural control. Findings: Height and leg mass were the body composition variables that showed the highest correlation with postural control. Multiple regression models were also obtained and several of these models showed a higher correlation coefficient in predicting postural control than simple correlations. These models revealed that leg and trunk mass were good predictors of postural control. More equations were found in the eyes open than eyes-closed condition. Interpretation: Body weight and height are negatively correlated with postural control. However, leg and trunk mass are better postural control predictors than arm or body mass. Finally, body composition variables are more useful in predicting postural control when the eyes are ope