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

Impact of Clinical Characteristics of Individual Metabolic Syndrome on the Severity of Insulin Resistance in Chinese Adults

The impact the metabolic syndrome (MetS) components on the severity of insulin resistance (IR) has not been reported. We enrolled 564 subjects with MetS and they were divided into quartiles according to the level of each component; and an insulin suppression test was performed to measure IR. In males, steady state plasma glucose (SSPG) levels in the highest quartiles, corresponding to body mass index (BMI) and fasting plasma glucose (FPG), were higher than the other three quartiles and the highest quartiles, corresponding to the diastolic blood pressure and triglycerides, were higher than in the lowest two quartiles. In females, SSPG levels in the highest quartiles, corresponding to the BMI and triglycerides, were higher than in all other quartiles. No significant differences existed between genders, other than the mean SSPG levels in males were greater in the highest quartile corresponding to BMI than that in the highest quartile corresponding to HDL-cholesterol levels. The factor analysis identified two underlying factors (IR and blood pressure factors) among the MetS variables. The clustering of the SSPG, BMI, triglyceride and HDLcholesterol was noted. Our data suggest that adiposity, higher FPG and triglyceride levels have stronger correlation with IR and subjects with the highest BMI have the highest IR.