Abstract
INTRODUCTION: Insulin resistance and defects in pancreatic beta cells are the two major pathophysiologic abnormalities that underlie type 2 diabetes. In addition, visceral fat area (VFA) is reported to be a stronger predictor for diabetes than body mass index (BMI). Here, we tested whether the performance of diabetes prediction models could be improved by adding HOMA-IR and HOMA-β and replacing BMI with VFA. RESEARCH DESIGN AND METHODS: We developed five prediction models using data from a cohort study (5578 individuals, of whom 94.7% were male, and 943 had incident diabetes). We conducted a baseline model (model 1) including age, sex, BMI, smoking, dyslipidemia, hypertension, and HbA1c. Subsequently, we developed another four models: model 2, predictors in model 1 plus fasting plasma glucose (FPG); model 3, predictors in model 1 plus HOMA-IR and HOMA-β; model 4, predictors in model 1 plus FPG, HOMA-IR, and HOMA-β; model 5, replaced BMI with VFA in model 2. We assessed model discrimination and calibration for the first 10 years of follow-up. RESULTS: The addition of FPG to model 1 obviously increased the value of the area under the receiver operating characteristic curve from 0.79 (95% CI 0.78, 0.81) to 0.84 (0.83, 0.85). Compared with model 1, model 2 also significantly improved the risk reclassification and discrimination, with a continuous net reclassification improvement index of 0.61 (0.56, 0.70) and an integrated discrimination improvement index of 0.09 (0.08, 0.10). Adding HOMA-IR and HOMA-β (models 3 and 4) or replacing BMI with VFA (model 5) did not further materially improve the performance. CONCLUSIONS: This cohort study, primarily composed of male workers, suggests that a model with BMI, FPG, and HbA1c effectively identifies those at high diabetes risk. However, adding HOMA-IR, HOMA-β, or replacing BMI with VFA does not significantly improve the model. Further studies are needed to confirm our findings.