Identification of insulin resistance in subjects with normal glucose tolerance

Jiunn-Diann Lin, Jin Biou Chang, Chung-Ze Wu, Dee Pei, Chang H sun Hsieh, An-Tsz Hsieh, Yen Lin Chen, Chun Hsin Hsu, Chuan C hieh Liu

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Abstract

INTRODUCTION: Decreased insulin action (insulin resistance) is crucial in the pathogenesis of type 2 diabetes. Decreased insulin action can even be found in normoglycaemic patients, and they still bear increased risks for cardiovascular disease. In this study, we built models using data from metabolic syndrome (Mets) components and the oral glucose tolerance test (OGTT) to detect insulin resistance in subjects with normal glucose tolerance (NGT).

MATERIALS AND METHODS: In total, 292 participants with NGT were enrolled. Both an insulin suppression test (IST) and a 75-g OGTT were administered. The steady-state plasma glucose (SSPG) level derived from the IST was the measurement of insulin action. Participants in the highest tertile were defined as insulin-resistant. Five models were built: (i) Model 0: body mass index (BMI); (ii) Model 1: BMI, systolic and diastolic blood pressure, triglyceride; (iii) Model 2: Model 1 + fasting plasma insulin (FPI); (iv) Model 3: Model 2 + plasma glucose level at 120 minutes of the OGTT; and (v) Model 4: Model 3 + plasma insulin level at 120 min of the OGTT.

RESULTS: The area under the receiver operating characteristic curve (aROC curve) was observed to determine the predictive power of these models. BMI demonstrated the greatest aROC curve (71.6%) of Mets components. The aROC curves of Models 2, 3, and 4 were all substantially greater than that of BMI (77.1%, 80.1%, and 85.1%, respectively).

CONCLUSION: A prediction equation using Mets components and FPI can be used to predict insulin resistance in a Chinese population with NGT. Further research is required to test the utility of the equation in other populations and its prediction of cardiovascular disease or diabetes mellitus.

Original languageEnglish
Pages (from-to)113-119
Number of pages7
JournalAnnals of the Academy of Medicine, Singapore
Volume43
Issue number2
Publication statusPublished - Feb 1 2014

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Insulin Resistance
Insulin
Glucose
Glucose Tolerance Test
Body Mass Index
ROC Curve
Fasting
Cardiovascular Diseases
Blood Pressure
Type 2 Diabetes Mellitus
Population
Diabetes Mellitus
Triglycerides
Research

ASJC Scopus subject areas

  • Medicine(all)

Cite this

Lin, J-D., Chang, J. B., Wu, C-Z., Pei, D., Hsieh, C. H. S., Hsieh, A-T., ... Liu, C. C. H. (2014). Identification of insulin resistance in subjects with normal glucose tolerance. Annals of the Academy of Medicine, Singapore, 43(2), 113-119.

Identification of insulin resistance in subjects with normal glucose tolerance. / Lin, Jiunn-Diann; Chang, Jin Biou; Wu, Chung-Ze; Pei, Dee; Hsieh, Chang H sun; Hsieh, An-Tsz; Chen, Yen Lin; Hsu, Chun Hsin; Liu, Chuan C hieh.

In: Annals of the Academy of Medicine, Singapore, Vol. 43, No. 2, 01.02.2014, p. 113-119.

Research output: Contribution to journalArticle

Lin, J-D, Chang, JB, Wu, C-Z, Pei, D, Hsieh, CHS, Hsieh, A-T, Chen, YL, Hsu, CH & Liu, CCH 2014, 'Identification of insulin resistance in subjects with normal glucose tolerance', Annals of the Academy of Medicine, Singapore, vol. 43, no. 2, pp. 113-119.
Lin, Jiunn-Diann ; Chang, Jin Biou ; Wu, Chung-Ze ; Pei, Dee ; Hsieh, Chang H sun ; Hsieh, An-Tsz ; Chen, Yen Lin ; Hsu, Chun Hsin ; Liu, Chuan C hieh. / Identification of insulin resistance in subjects with normal glucose tolerance. In: Annals of the Academy of Medicine, Singapore. 2014 ; Vol. 43, No. 2. pp. 113-119.
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abstract = "INTRODUCTION: Decreased insulin action (insulin resistance) is crucial in the pathogenesis of type 2 diabetes. Decreased insulin action can even be found in normoglycaemic patients, and they still bear increased risks for cardiovascular disease. In this study, we built models using data from metabolic syndrome (Mets) components and the oral glucose tolerance test (OGTT) to detect insulin resistance in subjects with normal glucose tolerance (NGT).MATERIALS AND METHODS: In total, 292 participants with NGT were enrolled. Both an insulin suppression test (IST) and a 75-g OGTT were administered. The steady-state plasma glucose (SSPG) level derived from the IST was the measurement of insulin action. Participants in the highest tertile were defined as insulin-resistant. Five models were built: (i) Model 0: body mass index (BMI); (ii) Model 1: BMI, systolic and diastolic blood pressure, triglyceride; (iii) Model 2: Model 1 + fasting plasma insulin (FPI); (iv) Model 3: Model 2 + plasma glucose level at 120 minutes of the OGTT; and (v) Model 4: Model 3 + plasma insulin level at 120 min of the OGTT.RESULTS: The area under the receiver operating characteristic curve (aROC curve) was observed to determine the predictive power of these models. BMI demonstrated the greatest aROC curve (71.6{\%}) of Mets components. The aROC curves of Models 2, 3, and 4 were all substantially greater than that of BMI (77.1{\%}, 80.1{\%}, and 85.1{\%}, respectively).CONCLUSION: A prediction equation using Mets components and FPI can be used to predict insulin resistance in a Chinese population with NGT. Further research is required to test the utility of the equation in other populations and its prediction of cardiovascular disease or diabetes mellitus.",
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N2 - INTRODUCTION: Decreased insulin action (insulin resistance) is crucial in the pathogenesis of type 2 diabetes. Decreased insulin action can even be found in normoglycaemic patients, and they still bear increased risks for cardiovascular disease. In this study, we built models using data from metabolic syndrome (Mets) components and the oral glucose tolerance test (OGTT) to detect insulin resistance in subjects with normal glucose tolerance (NGT).MATERIALS AND METHODS: In total, 292 participants with NGT were enrolled. Both an insulin suppression test (IST) and a 75-g OGTT were administered. The steady-state plasma glucose (SSPG) level derived from the IST was the measurement of insulin action. Participants in the highest tertile were defined as insulin-resistant. Five models were built: (i) Model 0: body mass index (BMI); (ii) Model 1: BMI, systolic and diastolic blood pressure, triglyceride; (iii) Model 2: Model 1 + fasting plasma insulin (FPI); (iv) Model 3: Model 2 + plasma glucose level at 120 minutes of the OGTT; and (v) Model 4: Model 3 + plasma insulin level at 120 min of the OGTT.RESULTS: The area under the receiver operating characteristic curve (aROC curve) was observed to determine the predictive power of these models. BMI demonstrated the greatest aROC curve (71.6%) of Mets components. The aROC curves of Models 2, 3, and 4 were all substantially greater than that of BMI (77.1%, 80.1%, and 85.1%, respectively).CONCLUSION: A prediction equation using Mets components and FPI can be used to predict insulin resistance in a Chinese population with NGT. Further research is required to test the utility of the equation in other populations and its prediction of cardiovascular disease or diabetes mellitus.

AB - INTRODUCTION: Decreased insulin action (insulin resistance) is crucial in the pathogenesis of type 2 diabetes. Decreased insulin action can even be found in normoglycaemic patients, and they still bear increased risks for cardiovascular disease. In this study, we built models using data from metabolic syndrome (Mets) components and the oral glucose tolerance test (OGTT) to detect insulin resistance in subjects with normal glucose tolerance (NGT).MATERIALS AND METHODS: In total, 292 participants with NGT were enrolled. Both an insulin suppression test (IST) and a 75-g OGTT were administered. The steady-state plasma glucose (SSPG) level derived from the IST was the measurement of insulin action. Participants in the highest tertile were defined as insulin-resistant. Five models were built: (i) Model 0: body mass index (BMI); (ii) Model 1: BMI, systolic and diastolic blood pressure, triglyceride; (iii) Model 2: Model 1 + fasting plasma insulin (FPI); (iv) Model 3: Model 2 + plasma glucose level at 120 minutes of the OGTT; and (v) Model 4: Model 3 + plasma insulin level at 120 min of the OGTT.RESULTS: The area under the receiver operating characteristic curve (aROC curve) was observed to determine the predictive power of these models. BMI demonstrated the greatest aROC curve (71.6%) of Mets components. The aROC curves of Models 2, 3, and 4 were all substantially greater than that of BMI (77.1%, 80.1%, and 85.1%, respectively).CONCLUSION: A prediction equation using Mets components and FPI can be used to predict insulin resistance in a Chinese population with NGT. Further research is required to test the utility of the equation in other populations and its prediction of cardiovascular disease or diabetes mellitus.

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