Interrelationships between anthropometric variables and overweight in childhood and adolescence

Am J Hum Biol. 2014 Jul-Aug;26(4):502-10. doi: 10.1002/ajhb.22554. Epub 2014 Apr 30.


Objectives: To answer the questions: how does body mass index (BMI) correlate to five overweight related anthropometric variables during different ages in childhood, and which anthropometric variables contribute most to variation in BMI during childhood?

Methods: Data on BMI, height (H), sitting height (SH), waist circumference (WC), waist to height ratio (WHtR), waist to sitting height ratio (WSHtR), subscapular skinfold (SSF), and triceps skinfold (TSF), from 4,576 Norwegian children 4.00-15.99 years of age, were transformed to standard deviation scores (SDS) and studied using correlation and multiple regression analyses.

Results: The correlations between BMI SDS and the standardized anthropometric variables were in general strong and positive. For all variables, the correlations were weakest in the youngest age group and highest between 7 and 12 years. WC SDS and WHtR SDS were most strongly correlated with BMI SDS through all ages and in both sexes. A model with seven anthropometric variables adjusted for age and sex explained 81.4% of the variation in BMI SDS. When adjusted for all other variables, WC SDS contributed most to the variation in BMI SDS (b = 0.467, CI [0.372, 0.562]). Age group, but not sex, contributed significantly to variation in BMI SDS.

Conclusion: The interrelationships between BMI SDS and five standardized overweight related anthropometric variables were dependent on age, being weakest in the youngest age group. Independent of sex and age, WC SDS was in this study superior to other anthropometric variables in contributing to variation in BMI SDS during childhood.

MeSH terms

  • Adolescent
  • Anthropometry*
  • Body Mass Index*
  • Child
  • Child, Preschool
  • Cohort Studies
  • Female
  • Humans
  • Male
  • Norway / epidemiology
  • Overweight / epidemiology*
  • Overweight / etiology
  • Prevalence
  • Regression Analysis