Key result
A predictive model incorporating metabolic syndrome components, fasting plasma insulin, and 2-hour oral glucose tolerance test data accurately identified insulin resistance in normoglycemic patients with an area under the curve of 85.1%.
Why the study?
Do predictive models incorporating metabolic syndrome components and OGTT data accurately identify insulin resistance in subjects with normal glucose tolerance?
Cross-Sectional (n=292)
No
Do predictive models incorporating metabolic syndrome components and OGTT data accurately identify insulin resistance in subjects with normal glucose tolerance?
Absolute Event Rate: 0.851% vs 0.716%
p-value: p=<0.001
A prediction equation using metabolic syndrome components and fasting plasma insulin can effectively identify insulin resistance in normoglycemic individuals, potentially aiding in early cardiovascular risk stratification.
INTRODUCTION: 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). MATERIALS AND METHODS: 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. RESULTS: 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). CONCLUSION: 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.
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Lin et al. (2014) conducted a cross-sectional in Normal glucose tolerance (n=292). Predictive model using metabolic syndrome components and OGTT data (Model 4) vs. Body mass index alone (Model 0) was evaluated on Area under the receiver operating characteristic curve (aROC) for predicting insulin resistance (95% CI 0.802-0.900, p=<0.001). A predictive model incorporating metabolic syndrome components, fasting plasma insulin, and 2-hour oral glucose tolerance test data accurately identified insulin resistance in normoglycemic patients with an area under the curve of 85.1%.
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