Accurate method to estimate insulin resistance from multiple regression models using data of metabolic syndrome and oral glucose tolerance test. Issue 3 (30th October 2013)

• Record Type: Journal Article
• Authors: Wu, Chung‐Ze; Lin, Jiunn‐Diann; Hsia, Te‐Lin; Hsu, Chun‐Hsien; Hsieh, Chang‐Hsun; Chang, Jin‐Biou; Chen, Jin‐Shuen; Pei, Chun; Pei, Dee; Chen, Yen‐Lin
• Is Part Of: Journal of diabetes investigation. Volume 5:Issue 3(2014:Jun.)
• Page Start: 290
• Page End: 296
• Publication Date: 2013-10-30
• Subjects: Diabetes -- Periodicals; Diabetes -- Research -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462005
• DOI: 10.1111/jdi.12155
• Languages: English
• ISSNs: 2040-1116

Abstract

<abstract abstract-type="main" id="jdi12155-abs-0001"> <title>Abstract</title> <sec id="jdi12155-sec-0001" sec-type="section"> <title>Aims/Introduction</title> <p>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.</p> </sec> <sec id="jdi12155-sec-0002" sec-type="section"> <title>Materials and Methods</title> <p>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.</p> </sec> <sec id="jdi12155-sec-0003" sec-type="section"> <title>Results</title> <p>In general, our models had higher <italic>r</italic><sup>2</sup> 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 <italic>r</italic><sup>2</sup> (0.505 in NGT, 0.556 in AGT, respectively).</p> </sec> <sec id="jdi12155-sec-0004" sec-type="section"> <title>Conclusions</title> <p>Our modified BIGTT models proved to be accurate and easy methods for estimating IR, and can be used in clinical practice and research.</p> </sec> </abstract>