Aims: Type 2 diabetes mellitus (T2DM) is characterized by both decreased insulin sensitivity and impaired insulin secretion. The 2 phases of insulin secretion are the first-phase insulin secretion (1st ISEC) and the secondphase insulin secretion. In this study, we tried to build clinical-metabolic models to predict the 1st ISEC deficiency (ISEC-D) in non-diabetic subjects so that early intervention could be started. Design and Settings: A cross-sectional study was conducted in the clinical research department of a hospital in Taiwan from 2010 to 2011. Methods: A total of 89 subjects without diabetes were enrolled in the study, including 49 with normal glucose tolerance and 40 pre-diabetes. A frequently sampled intravenous glucose tolerance test was done to determine insulin sensitivity and acute insulin response after the glucose load, which is regarded as the 1st ISEC. Subjects with the lowest tertile of the 1st ISEC were defined as ISEC-D. From the simplest to the most complex, 3 models were build: Model 0: fasting plasma glucose (FPG); Model 1: FPG + body mass index (BMI) + High-density lipoprotein cholesterol (HDL-C); Model 2: Model 1+ fasting plasma insulin (FPI). The area under the receiveroperating characteristic curve (aROC curve) was used to determine the predictive power among these models. An optimal cut-off value was also determined. Results: Among metabolic syndrome (MetS) components (FPG, BMI, and HDL-C), FPG had the greatest aROC curve (70.9%). Moreover, the aROC curves of Models 1 and 2 were all significantly greater than that of FPG (80.4% and 82.3%, respectively). Their aROC curves were also greater than that of the homeostasis model assessment b-cell (HOMA-b) function, which is the most commonly used method to evaluate b-cell function. Conclusion: By using only MetS components, ISEC-D could be predicted with an acceptable sensitivity of 84.0% and a specificity of 74.0%. However, after adding FPI into the Model, the predictive power of Model 2 did not increase. These model-derived MetS components could be widely used in clinical settings and early detection of non-diabetic subjects with high risk for T2DM.
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