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Assessment of Child Anthropometry in a Large Epidemiologic Study
09:36

Assessment of Child Anthropometry in a Large Epidemiologic Study

Published on: February 2, 2017

Prediction of metabolic syndrome in children through anthropometric indicators

Aparecido Pimentel Ferreira1, Cristiane Batisti Ferreira, Ciro José Brito

  • 1Universidade Paulista, Brasilia, Brazil. cidopimentel@yahoo.com.br

Arquivos Brasileiros De Cardiologia
|January 19, 2011
PubMed

Insights

This study identified key anthropometric indicators for predicting metabolic syndrome in children. Waist circumference, body fat percentage, and BMI are effective predictors, aiding early diagnosis and intervention.

Area of Science:

  • Pediatrics
  • Metabolic Health
  • Anthropometry

Background:

  • Diagnosing metabolic syndrome in children requires complex measurements, posing challenges for large-scale studies.
  • There's a critical need for accessible, accurate, and cost-effective tools for early metabolic syndrome detection in pediatric populations.

Purpose of the Study:

  • To determine the prevalence of metabolic syndrome in children aged 7-11 years.
  • To evaluate the predictive capability of various anthropometric indicators for metabolic syndrome.

Main Methods:

  • A cross-sectional study involving 109 children (ages 7-11).
  • Metabolic syndrome diagnosed using the age-adjusted National Cholesterol Education Program criteria.
  • Evaluated predictors included body mass index (BMI), waist circumference (WC), waist-to-hip ratio (WHR), conicity index (C index), and body fat percentage.

Main Results:

  • Metabolic syndrome prevalence was 13.3% in boys and 36% in girls.
  • Significant anthropometric indicators identified: BMI (0.81), WC (0.79), and body fat percentage (0.79).
  • Waist-to-hip ratio (WHR) showed weaker predictive value (0.37).

Conclusions:

  • Waist circumference (>78 cm), body fat percentage (>41%), and BMI (>24.5 kg/m²) predict metabolic syndrome in children.
  • Conicity index and WHR were not found to be reliable predictors in this cohort.
Abstract

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