An innovative prognostic model for predicting diabetes risk in the Thai population

Division of Endocrinology and Metabolism, Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand.
Diabetes research and clinical practice (Impact Factor: 2.54). 08/2011; 94(2):193-8. DOI: 10.1016/j.diabres.2011.07.019
Source: PubMed


To estimate the prevalence and type 2 diabetes, and to develop a prognostic model for identifying individuals at high risk of undiagnosed type 2 diabetes.
The study was designed as a cross-sectional investigation with 4314 participants of Thai background, aged between 15 and 85 years (mean age: 48). Fasting plasma glucose was initially measured, and repeated if the first measurement was more than 126 mg/dl. Type 2 diabetes was diagnosed using the World Health Organization's criteria. Logistic regression model was used to develop prognostic models for men and women separately. The prognostic performance of the model was assessed by the area under the receiver operating characteristic curve (AUC) and a nomogram was constructed from the logistic regression model.
The overall prevalence of type 2 diabetes was 7.4% (n = 125/1693) in men and 3.4% (n = 98/2621) in women. In either gender, the prevalence increased with age and body mass index (BMI). Gender, age, BMI and systolic blood pressure (SBP) were independently associated with type 2 diabetes risk. Based on the estimated parameters of model, a nomogram was constructed for predicting diabetes separated by gender. The AUC for the model with 3 factors was 0.75.
These data suggest that the combination of age, BMI and systolic blood pressure could help identify Thai individuals at high risk of undiagnosed diabetes.

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Available from: Praew Kotruchin, Aug 18, 2015
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    ABSTRACT: Prediction algorithms are increasingly advocated in diabetes screening strategies, particularly in developing countries. We conducted a systematic review to assess the application and applicability of existing non-invasive prevalent diabetes risk models to populations within Africa. systematic review data sources A systematic search of English literatures in Medline via PubMed from 1999 until June, 2014. Study selection Included studies had to report on the development, validation or implementation of a model that was primarily constructed to predict prevalent undiagnosed diabetes using non-laboratory based predictors. Data were extracted on the type of statistical model, type and range of predictors in the model, performance measures in both internal and external validation, and whether the model was developed from, validated or implemented in an African population. Twenty-three studies reporting on non-invasive prevalent diabetes models were identified. Ten from Europe (some with multiethnic populations), nine models were developed among Asian population, two from the USA and two from the Middle-East. The c-statistics for these models ranged from 0.65 to 0.88 in the development studies, and from 0.63 to 0.80 in the validation studies. Twenty models were validated, and none in Africa. Among predictors commonly included in models, parental/family history of diabetes and personal history of hypertension appear to be more prone to measurement errors in the African context. Existing prevalent diabetes prediction models have not been applied to African populations, and issues with the measurement of key predictors make their applicability likely inaccurate. Copyright © 2015. Published by Elsevier Ltd.
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