A comparison of the Slaughter skinfold-thickness equations and BMI in predicting body fatness and cardiovascular disease risk factor levels in children
ABSTRACT Although estimation of percentage body fat with the Slaughter skinfold-thickness equations (PBFSlaughter) is widely used, the accuracy of this method has not been well studied.
The objective was to determine the accuracy of the Slaughter skinfold-thickness equations.
We compared agreement between PBFSlaughter and derived from dual-energy X-ray absorptiometry (PBFDXA) in 1169 children in the Pediatric Rosetta Body Composition Project and the relation to cardiovascular disease risk factors, as compared with body mass index (BMI), in 6725 children in the Bogalusa Heart Study.
PBFSlaughter was highly correlated (r = 0.90) with PBFDXA, but it markedly overestimated levels of PBFDXA in children with large skinfold thicknesses. In the 65 boys with a sum of skinfold thicknesses (subscapular- plus triceps-skinfold thicknesses) ≥50 mm, PBFSlaughter overestimated PBFDXA by 12 percentage points. The comparable overestimation in girls with a high skinfold sum was 6 percentage points. We also found that, after adjustment for sex and age, BMI showed slightly stronger associations with lipid, lipoprotein, insulin, and blood pressure values than did PBFSlaughter.
These results indicate that PBFSlaughter, which was developed among a group of much thinner children and adolescents, is fairly accurate among nonobese children, but markedly overestimate the body fatness of children who have thick skinfold thicknesses. Furthermore, PBFSlaughter has no advantage over sex- and age-adjusted BMIs at identifying children who are at increased risk of cardiovascular disease based on lipid, lipoprotein, insulin, and blood pressure values.
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ABSTRACT: Most of the complications of juvenile obesity are due to metabolic disturbances induced by an excessive accumulation of fat which leads to chronic diseases like type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD). Finding effective ways of identifying obese paediatric patients who are at increased risk of developing cardiovascular and metabolic complications has been recognised to be a promising strategy to improve prevention of complications of early obesity. Moreover, correctly identifying obese children who are already affected by metabolic co-morbidities should be a clinical priority. According to the state of the art summarised in this review, traditional metabolic variables included in the definitions of metabolic syndrome (MS), pre-diabetes, non-alcoholic fatty liver disease (NAFLD)/non-alcoholic steato-hepatitis and, in obese girls, the presence of polycystic ovary syndrome are the best available longitudinal predictors of CVD and T2DM among obese children and adolescents. In clinical practice, traditional metabolic variables included in the definitions of MS should be assessed in all obese children and adolescents; fasting metabolic variables have been proposed to identify obese patients likely to be affected by impaired glucose tolerance or T2DM, and ultrasound has proved to be a valid surrogate for biopsy in the diagnosis of NAFLD. Further large longitudinal and cross-sectional studies are needed to improve our chances of identifying obese youth at the highest metabolic risk. © 2014 S. Karger AG, Basel.Hormone Research in Paediatrics 06/2014; 82(1). DOI:10.1159/000362237 · 1.71 Impact Factor
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ABSTRACT: Context: The use of BMI to assess risk for cardiometabolic disease in the pediatric population may be limited by a failure to differentiate between fat and lean body mass. Objectives: To identify biologically based criteria for the definition of obesity using fat (FMI) and lean body mass index (LBMI) and to compare the ability of FMI and LBMI to BMI to identify the presence of metabolic syndrome (MetSyn). Design: Cross-sectional study using National Health and Nutrition Examination Survey (NHANES) 1999-2006 data. Participants: 3004 participants aged 12-20 years with DXA body composition and fasting laboratory data. Main Outcome Measures: Adjusted odds ratios for MetSyn according to FMI and LBMI status, and area under the curve (AUC) for identification of MetSyn. Results: Receiver operating characteristic (ROC) curve analyses identified the 80th percentile for FMI and the 74th percentile for LBMI as the optimal cutpoints for the identification of MetSyn. There was no difference in the AUC for FMI (0.867; 95% CI: 0.838,0.891) vs. BMI (0.868; 95% CI:0.837,0.894) Z-scores for MetSyn discrimination. Separate multivariate regression models identified odds ratios for the identification of MetSyn of 6.2 (95% CI: 3.3,11.5) for BMI-Z, 6.4 (95% CI: 3.7,11.1) for FMI-Z, and 4.6 (95% CI:3,7.1) for LBMI-Z. Models containing both FMI-Z and LBMI-Z revealed that greater LBMI-Z was associated with greater odds of low HDL (1.5; 95% CI: 1.2,1.9), high blood pressure (1.8; 95% CI: 1.1,2.9), and insulin resistance (1.8; 95% CI: 1.4,2.5), independent of FMI-Z. Conclusions: The use of FMI and LBMI do not improve upon BMI for the identification of MetSyn in the pediatric population. Unexpectedly, higher LBMI was associated with greater odds of multiple cardiometabolic risk factors independent of FMI. The use of FMI and LBMI allow for the independent evaluation of relationships between body compartments and disease and warrants future research.Journal of Clinical Endocrinology & Metabolism 06/2014; 99(9):jc20141684. DOI:10.1210/jc.2014-1684 · 6.31 Impact Factor
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ABSTRACT: Objective To assess the accuracy of body mass index (BMI), Z score of the BMI, waist circumference, and waist-to-height ratio in selecting obese children with fasting metabolic impairments or impaired glucose tolerance. Study design In a cohort of 883 obese children and adolescents (age 8-18 years), we assessed the associations of anthropometric indices with traditional metabolic complications of obesity (impaired fasting glucose, impaired glucose tolerance, hypertension, high triglycerides, low high-density lipoprotein-cholesterol). The accuracy of anthropometric indices as markers of metabolic impairment was assessed by receiver operating characteristic analysis and the areas under the receiver operating characteristics curves (AUROCs) of anthropometric indices were compared with each other by the DeLong test. Results BMI, Z score of the BMI, waist circumference, and waist-to-height ratio were associated with metabolic impairments but showed low to moderate accuracy in discriminating both single and clustered metabolic impairments. The AUROCs ranged from 0.55-0.70. The 4 anthropometric indices did not show significantly different AUROCs as predictors of clustered metabolic risk factors (all P values of DeLong tests: >.05). Conclusions Commonly used anthropometric indices are not satisfactory markers of metabolic comorbidity among obese children and adolescents and should not be adopted as screening tools for the metabolic assessment of this category of patients.Journal of Pediatrics 08/2014; 165(6). DOI:10.1016/j.jpeds.2014.07.004 · 3.74 Impact Factor