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>

Comparison of oral glucose insulin sensitivity with other insulin sensitivity surrogates from oral glucose tolerance tests in Chinese

<p><b>INTRODUCTION</b>There is no single method of measuring insulin resistance that is both accurate and can be easily performed by general researchers. We validate the accuracy of oral glucose insulin sensitivity (OGIS) in the Chinese by comparing the OGIS120 and OGIS180, homeostasis model assessment of insulin resistance (HOMA-IR), and quantitative insulin sensitivity check index (OUICKI) with steady-state plasma glucose (SSPG) in different glucose tolerance subjects.</p><p><b>MATERIALS AND METHODS</b>We enrolled 515 subjects, aged between 20 and 75 years old, during routine health evaluations. All subjects were divided into normal, obese, impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and type 2 diabetes (T2D) groups. Participants had a 3-hour oral glucose tolerance test (OGTT) and SSPG with an insulin suppression test. The relationships between SSPG and OGIS120, OGIS180, HOMA-IR, and QUICKI were evaluated.</p><p><b>RESULTS</b>The normal group had the highest OGIS120, OGIS180 and lowest SSPG as compared with the other 4 groups. OGIS180, HOMA-IR and QUICKI in all 5 groups were significantly related to SSPG (r = 0.397-0.621, all P <0.05). OGIS120 in all 5 groups was not significantly related to SSPG (r = 0.003-0.226). Additionally, the r value of OGIS180 against SSPG was not higher than the other 2 insulin sensitivity surrogates from OGTT.</p><p><b>CONCLUSIONS</b>Although OGIS180 was more accurate in estimating insulin sensitivity than OGIS120 in the Chinese, it was not superior to the traditional surrogates such as HOMA-IR or QUICKI.</p>

Identifying Subjects with Insulin Resistance by Using the Modified Criteria of Metabolic Syndrome

The objectives of this cohort analysis were to explore the relationship between insulin resistance (IR) and the criteria for metabolic syndrome (MetS) and to evaluate the ability to detect IR in subjects fulfilling those criteria. We enrolled 511 healthy subjects (218 men and 283 women) and measured their blood pressure (BP), body mass index, high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), and fasting plasma glucose levels. Insulin suppression testing was done to measure insulin sensitivity as the steady-state plasma glucose (SSPG) value. Subjects with an SSPG value within the top 25% were considered to have IR. The commonest abnormality was a low HDL-C level, followed by high BP. The sensitivity to detect IR in subjects with MetS was about 47%, with a positive predictive value of about 64.8%, which has higher in men than in women. In general, the addition of components to the criteria for MetS increased the predictive value for IR. The most common combination of components in subjects with MetS and IR were obesity, high BP, and low HDL-C levels. All of the components were positive except for HDL-C, which was negatively correlated with SSPG. The correlation was strongest for obesity, followed by high TG values. In subjects with MetS, sensitivity for IR was low. However, body mass index and TG values were associated with IR and may be important markers for IR in subjects with MetS.

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