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

Calvin Woon-Loong Chin1, Elian Hui San Chia5, Stefan Ma2, Derrick Heng2, Maudrene Tan3, Jeanette Lee5, E Shyong Tai45 and Agus Salim5*

Author Affiliations

1 Department of Cardiology, National Heart Centre, Singapore, Singapore

2 Epidemiology and Disease Control Division, Ministry of Health, Singapore, Singapore

3 Office of Clinical Research, SingHealth, Singapore, Singapore

4 Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

5 Saw Swee Hock School of Public Health, National University of Singapore, MD3, 16 Medical Drive, Singapore 117597, Singapore

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BMC Medical Research Methodology 2012, 12:48  doi:10.1186/1471-2288-12-48

Published: 13 April 2012



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.


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.


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

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.


We conclude that adoption of the ARIC model for Asian populations is feasible and highly recommended when local prospective data is unavailable.