Identification of insulin resistance in subjects with normal glucose tolerance

<p><b>INTRODUCTION</b>Decreased insulin action (insulin resistance) is crucial in the pathogenesis of type 2 diabetes. Decreased insulin action can even be found in normoglycaemic patients, and they still bear increased risks for cardiovascular disease. In this study, we built models using data from metabolic syndrome (Mets) components and the oral glucose tolerance test (OGTT) to detect insulin resistance in subjects with normal glucose tolerance (NGT).</p><p><b>MATERIALS AND METHODS</b>In total, 292 participants with NGT were enrolled. Both an insulin suppression test (IST) and a 75-g OGTT were administered. The steady-state plasma glucose (SSPG) level derived from the IST was the measurement of insulin action. Participants in the highest tertile were defined as insulin-resistant. Five models were built: (i) Model 0: body mass index (BMI); (ii) Model 1: BMI, systolic and diastolic blood pressure, triglyceride; (iii) Model 2: Model 1 + fasting plasma insulin (FPI); (iv) Model 3: Model 2 + plasma glucose level at 120 minutes of the OGTT; and (v) Model 4: Model 3 + plasma insulin level at 120 min of the OGTT.</p><p><b>RESULTS</b>The area under the receiver operating characteristic curve (aROC curve) was observed to determine the predictive power of these models. BMI demonstrated the greatest aROC curve (71.6%) of Mets components. The aROC curves of Models 2, 3, and 4 were all substantially greater than that of BMI (77.1%, 80.1%, and 85.1%, respectively).</p><p><b>CONCLUSION</b>A prediction equation using Mets components and FPI can be used to predict insulin resistance in a Chinese population with NGT. Further research is required to test the utility of the equation in other populations and its prediction of cardiovascular disease or diabetes mellitus.</p>

Predicting glucose intolerance with normal fasting plasma glucose by the components of the metabolic syndrome

Surprisingly, it is estimated that about half of type 2 diabetics remain undetected. The possible causes may be partly attributable to people with normal fasting plasma glucose [FPG] but abnormal postprandial hyperglycemia. We attempted to develop an effective predictive model by using the metabolic syndrome [MeS] components as parameters to identify such persons. All participants received a standard 75-g oral glucose tolerance test, which showed that 106 had normal glucose tolerance, 61 had impaired glucose tolerance, and 6 had diabetes-an-isolated postchallenge hyperglycemia. We tested five models, which included various MeS components. Model 0: FPC; Model 1 [clinical history model]: family history [FH], FPC, age and sex; Model 2 [MeS model]: Model 1 plus triglycerides, high-density lipoprotein cholesterol, body mass index, systolic blood pressure and diastolic blood pressure; Model 3: Model 2 plus fasting plasma insulin [FPI]; Model 4: Model 3 plus homeostasis model assessment of insulin resistance. A receiver-operating characteristic [ROC] curve was used to determine the predictive discrimination of these models. The area under the ROC curve of the Model 0 was significantly larger than the area under the diagonal reference line. All the other 4 models had a larger area under the ROC curve than Model O. Considering the simplicity and lower cost of Model 2, it would be the best model to use. Nevertheless, Model 3 had the largest area under the ROC curve. We demonstrated that Model 2 and 3 have a significantly better predictive discrimination to identify persons with normal FPC at high risk for glucose intolerance