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Utility of Body Mass Index in Identifying Excess Adiposity in Youth Across the Obesity Spectrum.

ABSTRACT: OBJECTIVES:To determine the proportion of youth within a given body mass index (BMI) obesity category with excess adiposity using dual energy x-ray absorptiometry (DXA). Furthermore, to examine whether mean differences in cardiometabolic risk factors based upon various excess adiposity cutpoints were present. STUDY DESIGN:DXA data from the National Health and Nutrition Examination Survey 1999-2006 (n?=?10 465; 8-20 years of age) were used for this analysis. Obesity categories were defined using Centers for Disease Control and prevention definitions for age and sex. Excess adiposity was defined using cohort-specific cutpoints at 75th, 85th, and 90th percentiles of DXA body fat (%) by age and sex using quantile regression models. Additionally, we examined differences in cardiometabolic risk factors among youth (BMI percentile >85th) above and below various excess adiposity cutpoints. RESULTS:Nearly all youth with class 3 obesity (100% male, 100% female; 97% male, 99% female; and 95% male, 96% female; using the 75th, 85th, and 90th DXA percentiles, respectively) and a high proportion of those with class 2 obesity (98% male, 99% female; 92% male, 91% female; and 76% male, 76% female) had excess adiposity. Significant discordance was observed between BMI categorization and DXA-derived excess adiposity among youth with class 1 obesity or overweight. Elevated cardiometabolic risk factors were present in youth with excess adiposity, regardless of the cutpoint used. CONCLUSIONS:BMI correctly identifies excess adiposity in most youth with class 2 and 3 obesity but a relatively high degree of discordance was observed in youth with obesity and overweight. Cardiometabolic risk factors are increased in the presence of excess adiposity, regardless of the cutpoint used.

SUBMITTER: Ryder JR

PROVIDER: S-EPMC5905327 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature

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Utility of Body Mass Index in Identifying Excess Adiposity in Youth Across the Obesity Spectrum.

Ryder Justin R JR Kaizer Alexander M AM Rudser Kyle D KD Daniels Stephen R SR Kelly Aaron S AS

The Journal of pediatrics 20160802

<h4>Objectives</h4>To determine the proportion of youth within a given body mass index (BMI) obesity category with excess adiposity using dual energy x-ray absorptiometry (DXA). Furthermore, to examine whether mean differences in cardiometabolic risk factors based upon various excess adiposity cutpoints were present.<h4>Study design</h4>DXA data from the National Health and Nutrition Examination Survey 1999-2006 (n = 10 465; 8-20 years of age) were used for this analysis. Obesity categories were ...[more]

PMID: 27496270

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Project description:ObjectiveThe worldwide prevalence of obesity mandates a widely accessible tool to categorize adiposity that can best predict associated health risks. The body adiposity index (BAI) was designed as a single equation to predict body adiposity in pooled analysis of both genders. We compared body adiposity index (BAI), body mass index (BMI), and other anthropometric measures, including percent body fat (PBF), in their correlations with cardiometabolic risk factors. We also compared BAI with BMI to determine which index is a better predictor of PBF.MethodsThe cohort consisted of 698 Mexican Americans. We calculated correlations of BAI, BMI, and other anthropometric measurements (PBF measured by dual energy X-ray absorptiometry, waist and hip circumference, height, weight) with glucose homeostasis indices (including insulin sensitivity and insulin clearance from euglycemic clamp), lipid parameters, cardiovascular traits (including carotid intima-media thickness), and biomarkers (C-reactive protein, plasminogen activator inhibitor-1 and adiponectin). Correlations between each anthropometric measure and cardiometabolic trait were compared in both sex-pooled and sex-stratified groups.ResultsBMI was associated with all but two measured traits (carotid intima-media thickness and fasting glucose in men), while BAI lacked association with several variables. BAI did not outperform BMI in its associations with any cardiometabolic trait. BAI was correlated more strongly than BMI with PBF in sex-pooled analyses (r?=?0.78 versus r?=?0.51), but not in sex-stratified analyses (men, r?=?0.63 versus r?=?0.79; women, r?=?0.69 versus r?=?0.77). Additionally, PBF showed fewer correlations with cardiometabolic risk factors than BMI. Weight was more strongly correlated than hip with many of the cardiometabolic risk factors examined.ConclusionsBAI is inferior to the widely used BMI as a correlate of the cardiometabolic risk factors studied. Additionally, BMI's relationship with total adiposity may not be the sole determinate of its association with cardiometabolic risk.

Project description:BackgroundAccurate obesity classification is important so that appropriate intervention can be instituted to modify metabolic risk factors. Commonly utilized body mass index (BMI) and percentage body fat (PBF) are influenced by lean mass whereas fat mass index (FMI) measures only body fat. This study compares the prevalence of obesity and metabolic risk factors with FMI, BMI and PBF using DXA (dual-energy x-ray absorptiometry).Methods489 women randomly recruited from the electoral roll were stratified into 4 age groups; 40-49, 50-59, 60-69 and 70-79 years from 2000 to 2001. Clinical data and DXA body composition were obtained. Statistical analyses were performed using Medcalc v15 (Ostend, Belgium) with significance level at p = 0.05 (two-tailed).ResultsThere was higher prevalence of obesity using PBF compared to BMI and FMI (p<0.001). This difference was greater from age 50-59 (p<0.05) which may be explained by age-related lean mass loss. PBF over-classified obesity in over 35% of normal and 95% of overweight categories compared to FMI and BMI. BMI has a sensitivity of 78.9% and specificity of 98.3% for obesity using FMI as the standard. BMI under-classified obesity in the overweight category by 14.9% compared to FMI. There was no difference in diabetes, dyslipidemia, hypertension and metabolic syndrome prevalence within the BMI-obesity and FMI-obesity categories (p>0.05).ConclusionPBF classified more obesity than BMI and FMI because of its low pre-determined threshold. The greater difference with PBF compared to BMI and FMI from the 50-59 decade onwards can be attributed to age-related lean mass loss. BMI had the lowest sensitivity for obesity diagnosis. BMI under-classified obesity in the overweight category compared to FMI due to its inability to differentiate lean mass. However, there was no significant difference in the prevalence of metabolic risk factors between BMI and FMI-obesity categories indicating that fat location may influence metabolic dysregulation.

Dataset Information

Utility of Body Mass Index in Identifying Excess Adiposity in Youth Across the Obesity Spectrum.

Publications

Utility of Body Mass Index in Identifying Excess Adiposity in Youth Across the Obesity Spectrum.

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