Metabolic Risk Factors and Type 2 Diabetes Incidence in American Indian Children

Kevin M Wheelock1, Madhumita Sinha1, William C Knowler1

  • 1Diabetes Epidemiology and Clinical Research Section, Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona 85014.

Insights

Childhood overweight and impaired glucose tolerance are key predictors of type 2 diabetes. These metabolic risk factors significantly increase long-term diabetes risk in children, especially younger ones.

Area of Science:

  • Pediatric endocrinology
  • Metabolic disease research
  • Diabetes epidemiology

Background:

  • Limited data exist on how childhood metabolic risk factors influence long-term type 2 diabetes risk.
  • Understanding these early predictors is crucial for timely intervention and prevention strategies.

Purpose of the Study:

  • To evaluate four key metabolic risk factors as predictors of type 2 diabetes in children.
  • To determine if the predictive value of these factors differs between younger (5–11 years) and older (12–19 years) children.

Main Methods:

  • Prospective cohort study of 5532 Pima Indian children (aged 5–19) from 1965–2007.
  • Baseline measurements included body mass index (BMI), blood pressure, serum cholesterol, and 2-hour plasma glucose.
  • Participants were followed for diabetes development, with analyses stratified by age group.

Main Results:

  • Diabetes incidence was significantly higher in overweight children (BMI ≥ 85th percentile).
  • Overweight children with impaired glucose tolerance (2-hour glucose ≥ 140 mg/dL) had the highest diabetes risk (79.0%).
  • Body mass index and impaired 2-hour plasma glucose were significant predictors of type 2 diabetes; blood pressure and cholesterol were not.

Conclusions:

  • Childhood body mass index (BMI) and impaired glucose tolerance are strong predictors of future type 2 diabetes.
  • These specific metabolic factors, rather than other components of the metabolic syndrome, are most critical for identifying high-risk children.
Abstract

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