Does the BMI reflect body fat in obese children and adolescents? A study using the TOBEC method

Int J Obes Relat Metab Disord. 2001 Feb;25(2):279-85. doi: 10.1038/sj.ijo.0801511.

Abstract

Objective: Due to the fact that obesity is defined as excess of body fat mass, we tested the hypothesis whether the body mass index (BMI) can be used as a valid measure for the detection of the degree of obesity in individual obese children and adolescents.

Methods: A total of 204 obese children and adolescents (105 boys, 99 girls) aged 6-17 y, using total body electrical conductivity (TOBEC) for fat measurement, were included into this study. A multiple regression analysis was performed with percentage body fat (PBF) as dependent variable and BMI, age and sex as independent variables. First- and second-order interaction terms were also included. Since all interaction terms showed a significant influence on PBF, regression analysis was performed separately for boys and girls, dividing each group into two age subgroups (subjects younger than 10 y, and subjects 10 y or older).

Results: BMI and PBF were observed to be positively correlated (overall: r=0,65; P=0.0001; boys r=0.63 and girls: r=0.68). Through a multiple regression analysis 57% of the variance of PBF could be explained by the independent variables. In boys younger than 10 y 73% and in girls younger than 10 y 63% of the variance of PBF was explained by the BMI. In subjects 10 y or older the association was poor (boys: 27%; girls: 38%). It should be emphasized that there is a wide range in the relationship between PBF and BMI in the obese subjects.

Conclusion: From these results we conclude that BMI might be a useful parameter for epidemiological studies: however in the individual pediatric patient, especially from 10 y onwards, it gives only a limited insight to the degree of obesity based on the definition.

MeSH terms

  • Adipose Tissue*
  • Adolescent
  • Body Mass Index*
  • Child
  • Electric Impedance*
  • Female
  • Humans
  • Male
  • Obesity / diagnosis*
  • Regression Analysis
  • Reproducibility of Results