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

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

Research output: Contribution to journalArticle

6 Citations (Scopus)

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

Original languageEnglish
Pages (from-to)290-296
Number of pages7
JournalJournal of Diabetes Investigation
Volume5
Issue number3
DOIs
Publication statusPublished - 2014

Fingerprint

Glucose Tolerance Test
Insulin Resistance
Glucose
Insulin
Research
Homeostasis

Keywords

  • Insulin resistance
  • Oral glucose tolerance test
  • Steady-state plasma glucose

ASJC Scopus subject areas

  • Endocrinology, Diabetes and Metabolism
  • Internal Medicine

Cite this

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

In: Journal of Diabetes Investigation, Vol. 5, No. 3, 2014, p. 290-296.

Research output: Contribution to journalArticle

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. / Accurate method to estimate insulin resistance from multiple regression models using data of metabolic syndrome and oral glucose tolerance test. In: Journal of Diabetes Investigation. 2014 ; Vol. 5, No. 3. pp. 290-296.
@article{9198061fcbeb468b96f9c015f9ce3b92,
title = "Accurate method to estimate insulin resistance from multiple regression models using data of metabolic syndrome and oral glucose tolerance test",
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 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). 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",
author = "Chung-Ze Wu and Jiunn-Diann Lin and Hsia, {Te Lin} and Hsu, {Chun Hsien} and Hsieh, {Chang Hsun} and Chang, {Jin Biou} and Chen, {Jin Shuen} and Chun Pei and Dee Pei and Chen, {Yen Lin}",
year = "2014",
doi = "10.1111/jdi.12155",
language = "English",
volume = "5",
pages = "290--296",
journal = "Journal of Diabetes Investigation",
issn = "2040-1116",
publisher = "Blackwell Publishing Asia Pty Ltd",
number = "3",

}

TY - JOUR

T1 - Accurate method to estimate insulin resistance from multiple regression models using data of metabolic syndrome and oral glucose tolerance test

AU - Wu, Chung-Ze

AU - Lin, Jiunn-Diann

AU - Hsia, Te Lin

AU - Hsu, Chun Hsien

AU - Hsieh, Chang Hsun

AU - Chang, Jin Biou

AU - Chen, Jin Shuen

AU - Pei, Chun

AU - Pei, Dee

AU - Chen, Yen Lin

PY - 2014

Y1 - 2014

N2 - 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 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). Conclusions: Our modified BIGTT models proved to be accurate and easy methods for estimating IR, and can be used in clinical practice and research.

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

KW - Insulin resistance

KW - Oral glucose tolerance test

KW - Steady-state plasma glucose

UR - http://www.scopus.com/inward/record.url?scp=84899976484&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84899976484&partnerID=8YFLogxK

U2 - 10.1111/jdi.12155

DO - 10.1111/jdi.12155

M3 - Article

AN - SCOPUS:84899976484

VL - 5

SP - 290

EP - 296

JO - Journal of Diabetes Investigation

JF - Journal of Diabetes Investigation

SN - 2040-1116

IS - 3

ER -