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A Risk Score for Predicting Incident Diabetes in the Thai Population

Aekplakorn, Wichai, Bunnag, Pongamorn, Woodward, Mark, Sritara, Piyamitr, Cheepudomwit, Sayan, Yamwong, Sukit, Yipintsoi, Tada, Rajatanavin, Rajata
Diabetes care 2006 v.29 no.8 pp. 1872-1877
Thai people, laboratory techniques, glucose, parents, models, high density lipoprotein cholesterol, hypertension, fasting, regression analysis, risk factors, noninsulin-dependent diabetes mellitus, triacylglycerols, waist circumference, body mass index, glucose tolerance, siblings, prediction, Thailand
OBJECTIVE:--The objective of this study was to develop and evaluate a risk score to predict people at high risk of diabetes in Thailand. RESEARCH DESIGN AND METHODS--A Thai cohort of 2,677 individuals, aged 35-55 years, without diabetes at baseline, was resurveyed after 12 years. Logistic regression models were used to identify baseline risk factors that predicted the incidence of diabetes; a simple model that included only those risk factors as significant (P < 0.05) when adjusted for each other was developed. The coefficients from this model were transformed into components of a diabetes score. This score was tested in a Thai validation cohort of a different 2,420 individuals. RESULTS:--A total of 361 individuals developed type 2 diabetes in the exploratory cohort during the follow-up period. The significant predictive variables in the simple model were age, BMI, waist circumference, hypertension, and history of diabetes in parents or siblings A cutoff score of 6 of 17 produced the optimal sum of sensitivity (77%) and specificity (60%). The area under the receiver-operating characteristic curve (AUC) was 0.74. Adding impaired fasting glucose or impaired glucose tolerance status to the model slightly increased the AUC to 0.78; adding low HDL cholesterol and/or high triglycerides barely improved the model. The validation cohort demonstrated similar results. CONCLUSIONS:--A simple diabetes risk score, based on a set of variables not requiring laboratory tests, can be used for early intervention to delay or prevent the disease in Thailand. Adding impaired fasting glucose or impaired glucose tolerance or triglyceride and HDL cholesterol status to this model only modestly improves the predictive ability.