The ARIC predictive model reliably predicted risk of type II diabetes in Asian populations Academic Article uri icon

abstract

  • BACKGROUND: Identification of high-risk individuals is crucial for effective implementation of type 2 diabetes mellitus prevention programs. Several studies have shown that multivariable predictive functions perform as well as the 2-hour post-challenge glucose in identifying these high-risk individuals. The performance of these functions in Asian populations, where the rise in prevalence of type 2 diabetes mellitus is expected to be the greatest in the next several decades, is relatively unknown. METHODS: Using data from three Asian populations in Singapore, we compared the performance of three multivariate predictive models in terms of their discriminatory power and calibration quality: the San Antonio Health Study model, Atherosclerosis Risk in Communities model and the Framingham model. RESULTS: The San Antonio Health Study and Atherosclerosis Risk in Communities models had better discriminative powers than using only fasting plasma glucose or the 2-hour post-challenge glucose. However, the Framingham model did not perform significantly better than fasting glucose or the 2-hour post-challenge glucose. All published models suffered from poor calibration. After recalibration, the Atherosclerosis Risk in Communities model achieved good calibration, the San Antonio Health Study model showed a significant lack of fit in females and the Framingham model showed a significant lack of fit in both females and males. CONCLUSIONS: We conclude that adoption of the ARIC model for Asian populations is feasible and highly recommended when local prospective data is unavailable.

authors

  • Chin, Calvin Woon-Loong
  • Chia, Elian Hui San
  • Ma, Stefan
  • Heng, Derrick
  • Tan, Maudrene
  • Lee, Jeanette
  • Tai, E Shyong
  • Salim, Agus

publication date

  • December 2012

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