BACKGROUND: The aim of this study was to develop a risk score to predict the 4-year risk of diabetes in a middle-aged Korean cohort. METHODS AND RESULTS: Participants without diabetes (6,342 participants, aged 40-69 years) were included and biennial follow ups were conducted. A logistic regression analysis was used to construct the models. The basic model was based on simple information such as age, parental or sibling history of diabetes, smoking status, body mass index, and hypertension, while clinical model 1 was constructed by adding biochemical tests such as fasting plasma glucose, high-density lipoprotein-cholesterol and triglycerides to the basic model; clinical model 2 further added glycated hemoglobin (HbA(1c)) to clinical model 1. The model accuracy was assessed using area under a receiver operating characteristic (AROC) curve and the Hosmer-Lemeshow statistics. Both net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated to determine the contribution of HbA(1c). Two clinical models improved model discrimination (AROC=0.75 and 0.77) when compared with the basic model (AROC=0.65). The addition of HbA(1c) to clinical model 1 increased AROC by only 0.02 despite its high impact on the prediction of diabetes (odds ratio=2.66). However, the NRI and IDI were significantly improved with the addition of HbA(1c) Therefore, a risk score system was developed to estimate the 4-year risk of diabetes based on clinical model 2. CONCLUSIONS: A risk score derived from simple biochemical examinations including HbA(1c) can help identify those at a high risk of diabetes in a middle-aged Korean cohort.
BACKGROUND: The aim of this study was to develop a risk score to predict the 4-year risk of diabetes in a middle-aged Korean cohort. METHODS AND RESULTS:Participants without diabetes (6,342 participants, aged 40-69 years) were included and biennial follow ups were conducted. A logistic regression analysis was used to construct the models. The basic model was based on simple information such as age, parental or sibling history of diabetes, smoking status, body mass index, and hypertension, while clinical model 1 was constructed by adding biochemical tests such as fasting plasma glucose, high-density lipoprotein-cholesterol and triglycerides to the basic model; clinical model 2 further added glycated hemoglobin (HbA(1c)) to clinical model 1. The model accuracy was assessed using area under a receiver operating characteristic (AROC) curve and the Hosmer-Lemeshow statistics. Both net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated to determine the contribution of HbA(1c). Two clinical models improved model discrimination (AROC=0.75 and 0.77) when compared with the basic model (AROC=0.65). The addition of HbA(1c) to clinical model 1 increased AROC by only 0.02 despite its high impact on the prediction of diabetes (odds ratio=2.66). However, the NRI and IDI were significantly improved with the addition of HbA(1c) Therefore, a risk score system was developed to estimate the 4-year risk of diabetes based on clinical model 2. CONCLUSIONS: A risk score derived from simple biochemical examinations including HbA(1c) can help identify those at a high risk of diabetes in a middle-aged Korean cohort.
Authors: Y Heianza; Y Arase; S D Hsieh; K Saito; H Tsuji; S Kodama; S Tanaka; Y Ohashi; H Shimano; N Yamada; S Hara; H Sone Journal: Diabetologia Date: 2012-09-07 Impact factor: 10.122