Diagnostic performance of body mass index to identify obesity as defined by body adiposity in children and adolescents: a systematic review and meta-analysis

Pediatr Obes. 2015 Jun;10(3):234-44. doi: 10.1111/ijpo.242. Epub 2014 Jun 25.


Background: The ideal means of identifying obesity in children and adolescents has not been determined although body mass index (BMI) is the most widely used screening tool.

Objective: We performed a systematic review and meta-analysis of studies assessing the diagnostic performance of BMI to detect adiposity in children up to 18 years.

Methods: Data sources were EMBASE, MEDLINE, Cochrane, Database of Systematic Reviews Cochrane CENTRAL, Web of Science and SCOPUS up to March 2013. Studies providing measures of diagnostic performance of BMI and using body composition technique for body fat percentage measurement were included.

Results: Thirty-seven eligible studies that evaluated 53 521 patients, with mean age ranging from 4 to 18 years were included in the meta-analysis. Commonly used BMI cut-offs for obesity showed pooled sensitivity to detect high adiposity of 0.73 (confidence interval [CI] 0.67-0.79), specificity of 0.93 (CI 0.88-0.96) and diagnostic odds ratio of 36.93 (CI 20.75-65.71). Males had lower sensitivity. Moderate heterogeneity was observed (I(2) = 48%) explained in meta-regression by differences across studies in race, BMI cut-off, BMI reference criteria (Center for Disease Control vs. International Obesity Task Force) and reference standard method assessing adiposity.

Conclusion: BMI has high specificity but low sensitivity to detect excess adiposity and fails to identify over a quarter of children with excess body fat percentage.

Keywords: Body mass index; meta-analysis; paediatric obesity; systematic review.

Publication types

  • Meta-Analysis
  • Review
  • Systematic Review

MeSH terms

  • Adiposity
  • Adolescent
  • Body Composition
  • Body Mass Index
  • Child
  • Female
  • Humans
  • Male
  • Pediatric Obesity / diagnosis*
  • Pediatric Obesity / prevention & control
  • Predictive Value of Tests
  • Sensitivity and Specificity