A prediction model for type 2 diabetes risk among Chinese people.
OBJECTIVE
A range of prediction rules for the onset of type 2 diabetes have been proposed. However, most studies have been conducted in white groups and it is not clear whether these models apply to Asian populations. The purpose of this study was to construct a simple points model for predicting incident diabetes among Chinese people.


METHODS
We estimated the 10 year risk of diabetes in a cohort study of middle-aged and elderly participants who were free from diabetes at baseline. Cox regression coefficients were used to construct the simple points model and the discriminatory ability of the resulting prediction rule was determined using AUC and net reclassification improvement and integrated discrimination improvement statistics. Fivefold random splitting was used to test the internal validity and obtain bootstrap estimates of the AUC.


RESULTS
Of the 2,960 participants without diabetes at the baseline examination, 548 developed type 2 diabetes during a median 10 year follow-up period. Age (four points), elevated fasting glucose (11 points), body mass index (eight points), triacylglycerol (five points), white blood cell count (four points) and a higher HDL-cholesterol (negative four points) were found to strongly predict diabetes incidence in a multivariate model. The estimated AUC for the model was 0.702 (95% CI 0.676-0.727). This model performed better than existing prediction models developed in other populations, including the Prospective Cardiovascular Münster, Cambridge, San Antonia and Framingham models for diabetes risk.


CONCLUSIONS
We have constructed a model for predicting the 10 year incidence of diabetes in Chinese people that could be useful for identifying individuals at high risk of diabetes in the Chinese population.
Chien
K
K
Department of Nutrition, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA.
Cai
T
T
Hsu
H
H
Su
T
T
Chang
W
W
Chen
M
M
Lee
Y
Y
Hu
F B
FB
D000328:Adult; D044466:Asian People / Q000706:statistics & numerical data; D001794:Blood Pressure; D008076:Cholesterol, HDL / Q000097:blood; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000453:epidemiology* / Q000503:physiopathology; D005260:Female; D006801:Humans; D006943:Hyperglycemia / Q000097:blood; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D015999:Multivariate Analysis; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D018570:Risk Assessment; D012307:Risk Factors; D013624:Taiwan / Q000453:epidemiology; D014280:Triglycerides / Q000097:blood
D008076:Cholesterol, HDL; D014280:Triglycerides