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BMC Med Res Methodol. 2012; 12: 48.
Published online 2012 April 13. doi:  10.1186/1471-2288-12-48
PMCID: PMC3353252

The ARIC predictive model reliably predicted risk of type II diabetes in Asian populations

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.


Articles from BMC Medical Research Methodology are provided here courtesy of BioMed Central