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. 2014 May 4;5(3):290-6.
doi: 10.1111/jdi.12155. Epub 2013 Oct 30.

Accurate method to estimate insulin resistance from multiple regression models using data of metabolic syndrome and oral glucose tolerance test

Chung-Ze Wu  1 , Jiunn-Diann Lin  2 , Te-Lin Hsia  3 , Chun-Hsien Hsu  4 , Chang-Hsun Hsieh  5 , Jin-Biou Chang  6 , Jin-Shuen Chen  7 , Chun Pei  8 , Dee Pei  3 , Yen-Lin Chen  9
Affiliations

Accurate method to estimate insulin resistance from multiple regression models using data of metabolic syndrome and oral glucose tolerance test

Chung-Ze Wu et al. J Diabetes Investig. .

Abstract

Aims/introduction: 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.

Results: In general, our models had higher r (2) 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 r (2) (0.505 in NGT, 0.556 in AGT, respectively).

Conclusions: Our modified BIGTT models proved to be accurate and easy methods for estimating IR, and can be used in clinical practice and research.

Keywords: Insulin resistance; Oral glucose tolerance test; Steady‐state plasma glucose.

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Figures

Figure 1
Figure 1
The (a) plasma glucose and (b) insulin concentrations during oral glucose tolerance test in normal glucose tolerance (NGT) and abnormal glucose tolerance test (AGT) groups.
Figure 2
Figure 2
The linear relationship between calculated insulin resistance from model 5 and steady‐state plasma glucose (SSPG) in (a) the normal glucose tolerance group (NGT) and (b) the abnormal glucose tolerance group (AGT).
See this image and copyright information in PMC

References

    1. Himsworth HP, Kerr RB. Insulin‐sensitive and insulin insensitive types of diabetes mellitus. Clin Sci 1939; 4: 119–152
    1. Beck‐Nielsen H, Henriksen JE, Vaag A, et al Pathophysiology of non‐insulin‐dependent diabetes mellitus (NIDDM). Diabetes Res Clin Pract 1995; 28(Suppl): S13–S25 - PubMed
    1. Abdul‐Ghani MA, DeFronzo RA. Pathophysiology of prediabetes. Curr Diab Rep 2009; 9: 193–199 - PubMed
    1. Reaven GM. Banting lecture 1988. Role of insulin resistance in human disease. Diabetes 1998; 37: 1595–1607 - PubMed
    1. DeFronzo RA, Tobin JD, Andres R. Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Physiol 1979; 237: E214–E223 - PubMed