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Diabetic Medicine

Year: 2009  |  Volume: 26  |  Issue: 10  |  Page No.: 996 - 1002

Risk prediction models for the development of diabetes in Mauritian Indians

W. G. Gao, Q. Qiao, J. Pitkaniemi, S. Wild, D. Magliano, J. Shaw, S. Soderberg, P. Zimmet, P. Chitson, S. Knowlessur, G. Alberti and J. Tuomilehto


Aims To develope risk prediction models of future diabetes in Mauritian Indians. MethodsThree thousand and ninety-four Mauritian Indians (1141 men, aged 20–65 years) without diabetes in 1987 or 1992 were followed up to 1992 or 1998. Subjects underwent repeated oral glucose tolerance tests and diabetes was diagnosed according to 2006 World Health Organization/International Diabetes Federation criteria. Cox regression models for interval censored data were performed using data from 1544 randomly selected participants. Predicted probabilities for diabetes were calculated and validated in the remaining 1550 subjects. ResultsOver 11 years of follow-up, there were 511 cases of diabetes. Among variables tested, family history of diabetes, obesity (body mass index, waist circumference) and glucose were significant predictors of diabetes. Predicted probabilities derived from a simple model fitted with sex, family history of diabetes and obesity ranged from 0.05 to 0.64 in men and 0.03 to 0.49 in women. To predict the onset of diabetes, area under the receiver operating characteristic (ROC) curve (AROC) of predicted probabilities was 0.62 (95% confidence interval, 0.56–0.68) in men and 0.64 (0.59–0.69) in women. At a cut-off point of 0.12, the sensitivity and specificity were 0.72 (0.71–0.74) and 0.47 (0.45–0.49) in men and 0.77 (0.75–0.78) and 0.50 (0.48–0.52) in women, respectively. Addition of fasting plasma glucose (FPG) to the model improved the prediction slightly [AROC curve 0.70 (0.65–0.76) in men, 0.71 (0.67–0.76) in women]. ConclusionsA diabetes prediction model based on obesity and family history yielded moderate discrimination in Mauritian Indians, which was slightly inferior to the model with the FPG but may be useful in low-income countries to promote identification of people at high risk of diabetes.

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