The predictive value of the individual components of the metabolic syndrome for insulin resistance in obese children

Horm Res Paediatr. 2011;76(3):156-64. doi: 10.1159/000327371. Epub 2011 Jul 16.

Abstract

Background/aims: The usefulness of the concept of the metabolic syndrome (MS) in its current form has recently been questioned, and its association with insulin resistance is unknown. We assessed whether a multivariate model based on all components of MS expressed on a continuous scale would be a better predictor of a common marker of insulin resistance than the current dichotomous MS definitions.

Methods: Data from 78 obese Dutch teenagers (age 13.0 ± 2.1 years) were used for model development, and the model was validated in 40 obese Hindustani children (age 12.6 ± 2.0 years). MS components and homeostasis model assessment-insulin resistance (HOMA-IR) were expressed as standard deviation scores (SDSs), based on gender- and age-specific reference values.

Results: Using the three dichotomous models, the prevalence of MS was found to be 36, 65 and 18%, with low mutual agreement. None of these dichotomous models was a significant predictor for increased HOMA-IR SDS. The multivariate model incorporating MS components expressed as SDSs explained 58% of the variance of increased HOMA-IR SDS. In the validation group, the predicted and observed HOMA-IR SDS (2.4 ± 1.2 vs. 2.6 ± 2.2) did not differ significantly.

Conclusion: A multivariate prediction model based on MS components expressed as SDSs has a good predictive value for increased HOMA-IR SDS.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Adolescent
  • Child
  • Diagnostic Techniques, Endocrine
  • Female
  • Humans
  • Insulin Resistance* / physiology
  • Male
  • Metabolic Syndrome / complications
  • Metabolic Syndrome / diagnosis*
  • Metabolic Syndrome / etiology
  • Netherlands
  • Obesity / complications
  • Obesity / diagnosis*
  • Predictive Value of Tests
  • Regression Analysis
  • Risk Factors