• L Xu, C Q Jiang, C M Schooling, W S Zhang, K K Cheng, and T H Lam.
• School of Public Health, The University of Hong Kong, Hong Kong.
• Prev Med. 2014 Dec 1; 69: 63-8.

ObjectiveTo recalibrate and modify the Framingham diabetes mellitus (DM) function and establish a simple point score for predicting near-term incident diabetes in a large sample of Chinese.MethodsA total of 16,043 participants aged 50years or above without diabetes at baseline from the Guangzhou Biobank Cohort Study (GBCS) were recruited from 2003 to 2008 and followed up until 31 December 2012, with an average follow-up period of 4.1years. A randomly selected sub-sample of 8000 participants was used to calculate the predictive model and the remaining sample including 8043 participants was used for validating the prediction model.ResultsDuring follow-up, 5.2% (95% confidence interval (CI) 4.6-5.9) of men and 5.2% (95% CI 4.8-5.6) of women developed diabetes. A GBCS point score prediction model was constructed based on the Framingham DM function risk factors using the randomly selected sub-sample. Compared with the Framingham DM risk score (AUC 0.740, 95% CI 0.715-0.766), the GBCS point score prediction model predicted the development of diabetes well, with an AUC of 0.779 (95% CI 0.756-0.801, P for comparison <0.001). Validation analysis showed that the new GBCS function had satisfactory predictive ability for actual DM incidence and improved the calibration substantially. The original Framingham DM score underestimated diabetes incidence in the GBCS sample.ConclusionsThe constructed GBCS point score prediction model based on GBCS coefficients could be more useful for identifying high risk individuals in Chinese populations than the original Framingham DM score.Copyright © 2014 Elsevier Inc. All rights reserved.

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