Accurate method to estimate insulin resistance from multiple regression models using data of metabolic syndrome and oral glucose tolerance test
Article first published online: 30 OCT 2013
© 2013 The Authors. Journal of Diabetes Investigation published by Asian Association of the Study of Diabetes (AASD) and Wiley Publishing Asia Pty Ltd
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
Journal of Diabetes Investigation
Volume 5, Issue 3, pages 290–296, May 2014
How to Cite
J Diabetes Invest 2014; 5: 290–296
- Issue published online: 4 MAY 2014
- Article first published online: 30 OCT 2013
- Manuscript Accepted: 6 AUG 2013
- Manuscript Revised: 14 JUL 2013
- Manuscript Received: 1 APR 2013
- Insulin resistance;
- Oral glucose tolerance test;
- Steady-state plasma glucose
How to measure insulin resistance (IR) accurately and conveniently is a critical issue for both clinical practice and research. In the present study, we tried to modify the β-cell function, insulin sensitivity, and glucose tolerance test (BIGTT) in patients with normal glucose tolerance (NGT) and abnormal glucose tolerance (AGT) by oral glucose tolerance test (OGTT) and metabolic syndrome (MetS) components.
Materials and Methods
There were 327 participants enrolled and divided into NGT or AGT. Data from 75% of the participants were used to build the models, and the remaining 25% were used for external validation. Steady-state plasma glucose (SSPG) concentration derived from the insulin suppression test was regarded as the standard measurement for IR. Five models were built from multiple regression: model 1 (MetS model with sex, age and MetS components); model 2 (simple OGTT model with sex, age, plasma glucose, and insulin concentrations at 0 and 120 min during OGTT); model 3 (full OGTT model with sex, age, and plasma glucose and insulin concentrations at 0, 30, 60, 90, 120, and 180 min during OGTT); model 4 (simple combined model): model 1 and model 2; and model 5 (full model): model 1 and 3.
In general, our models had higher r2 compared with surrogates derived from OGTT, such as homeostasis model assessment-insulin resistance and quantitative insulin sensitivity check index. Among them, model 5 had the highest r2 (0.505 in NGT, 0.556 in AGT, respectively).
Our modified BIGTT models proved to be accurate and easy methods for estimating IR, and can be used in clinical practice and research.