Accurate method to estimate insulin resistance from multiple regression models using data of metabolic syndrome and oral glucose tolerance test |
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| Authors: | Chung‐Ze Wu Jiunn‐Diann Lin Te‐Lin Hsia Chun‐Hsien Hsu Chang‐Hsun Hsieh Jin‐Biou Chang Jin‐Shuen Chen Chun Pei Dee Pei Yen‐Lin Chen |
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| Affiliation: | 1. Division of Endocrinology and Metabolism, Department of Internal Medicine, Shuang Ho Hospital, , Taipei, Taiwan;2. Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, , Taipei, Taiwan;3. Department of Internal Medicine, Cardinal Tien Hospital, School of Medicine, Fu Jen Catholic University, , Taipei, Taiwan;4. Department of Family Medicine, Cardinal Tien Hospital, School of Medicine, Fu Jen Catholic University, , Taipei, Taiwan;5. Division of Endocrinology and Metabolism, Tri‐Service General Hospital, National Defense Medical Center, , Taipei, Taiwan;6. Division of Clinical Pathology, Department of Pathology, Tri‐Service General Hospital, National Defense Medical Center, , Taipei, Taiwan;7. Department of Medical Laboratory Science and Biotechnology, Yuanpei University, , Hsinchu, Taiwan;8. Division of Nephrology, Department of Internal Medicine, Tri‐Service General Hospital, National Defense Medical Center, , Taipei, Taiwan;9. Graduate School of Gerontic Technology and Service Management, Nan Kai University of Technology, , Nan Tou County, Taiwan;10. Department of Pathology, Cardinal Tien Hospital, School of Medicine, Fu Jen Catholic University, , Taipei, Taiwan;11. School of Medicine, Catholic Fu Jen University, , New Taipei, Taiwan |
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| Abstract: | Aims/IntroductionHow 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 MethodsThere 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.ResultsIn 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).ConclusionsOur modified BIGTT models proved to be accurate and easy methods for estimating IR, and can be used in clinical practice and research. |
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| Keywords: | Insulin resistance Oral glucose tolerance test Steady‐state plasma glucose |
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