A self-assessment predictive model for type 2 diabetes or impaired fasting glycaemia derived from a population-based survey.

Abstract

There is no cure for diabetes and its prevention is interesting for both people and health policy makers. The aim of this study was to construct a simple scoring system to predict diabetes and suggest a self assessment predictive model for type 2 diabetes in Iran. This study was a part of a comprehensive population based survey performed in Ilam province during 2011-2012, including 2158 cases≥25years. All demographic and laboratory results were entered into the prepared sheets and were analysed using SPSS 16. By identification of relative risks of diabetes and IFG, a predictive model was constructed and proposed for these abnormalities. Totally, 2158 people comprising 72% female, 60% from urban regions, mean age of 45.5±14years were investigated and the average height, weight, FBS and waist of participants were as follows respectively: 164±8.9cm, 68.4±12.3kg, 5.7±2.8mmol/l (102.6±49.9mg/dl) and 82.3±14.3cm. The prevalence of IFG, diabetes and hyperglycaemia among all participants were 7.8%, 11.8% and 19.6% respectively. Regression analysis revealed familial history of diabetes, place of life, age, hypertension, daily exercise, marital status, gender, waist size, smoking, and BMI as the most relevant risk factors for diabetes and hyperglycemia. A self-assessment predictive model was constructed for general population living in the west of Iran. This is the first self-assessment predictive model for diabetes in Iran.