The Estimation of First-Phase Insulin Secretion by Using Components of the Metabolic Syndrome in a Chinese Population

Aims. There are two phases of insulin secretion, the first (FPIS) and second phase (SPIS). In this study, we built equations to predict FPIS with metabolic syndrome (MetS) components and fasting plasma insulin (FPI). Methods. Totally, 186 participants were enrolled. 75% of participants were randomly...

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Main Authors: Jiunn-diann Lin, Chun-Hsien Hsu, Yao-Jen Liang, Wei-Cheng Lian, Chang-Hsun Hsieh, Chung-Ze Wu, Dee Pei, Yen-Lin Chen
Format: Article
Language:English
Published: Wiley 2015-01-01
Series:International Journal of Endocrinology
Online Access:http://dx.doi.org/10.1155/2015/675245
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author Jiunn-diann Lin
Chun-Hsien Hsu
Yao-Jen Liang
Wei-Cheng Lian
Chang-Hsun Hsieh
Chung-Ze Wu
Dee Pei
Yen-Lin Chen
author_facet Jiunn-diann Lin
Chun-Hsien Hsu
Yao-Jen Liang
Wei-Cheng Lian
Chang-Hsun Hsieh
Chung-Ze Wu
Dee Pei
Yen-Lin Chen
author_sort Jiunn-diann Lin
collection DOAJ
description Aims. There are two phases of insulin secretion, the first (FPIS) and second phase (SPIS). In this study, we built equations to predict FPIS with metabolic syndrome (MetS) components and fasting plasma insulin (FPI). Methods. Totally, 186 participants were enrolled. 75% of participants were randomly selected as the study group to build equations. The remaining 25% of participants were selected as the external validation group. All participants received a frequently sampled intravenous glucose tolerance test, and acute insulin response after the glucose load (AIRg) was obtained. The AIRg was considered as FPIS. Results. When MetS components were only used, the following equation was built: log (FPIS) = 1.477 − 0.119 × fasting plasma glucose (FPG) + 0.079 × body mass index (BMI) − 0.523 × high-density lipoprotein cholesterol (HDL-C). After FPI was added, the second equation was formulated: log (FPIS) = 1.532 − 0.127 × FPG + 0.059 × BMI - 0.511 × HDL-C + 0.375 × log (FPI), which provided a better accuracy than the first one. Conclusions. Using MetS components, the FPIS could be estimated accurately. After adding FPI into the equation, the predictive power increased further. We hope that these equations could be widely used in daily practice.
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institution Kabale University
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spelling doaj-art-48264718dae14fa28c753042aaa0e6e42025-02-03T05:43:33ZengWileyInternational Journal of Endocrinology1687-83371687-83452015-01-01201510.1155/2015/675245675245The Estimation of First-Phase Insulin Secretion by Using Components of the Metabolic Syndrome in a Chinese PopulationJiunn-diann Lin0Chun-Hsien Hsu1Yao-Jen Liang2Wei-Cheng Lian3Chang-Hsun Hsieh4Chung-Ze Wu5Dee Pei6Yen-Lin Chen7Division of Endocrinology, Department of Internal Medicine, Shuang Ho Hospital, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, TaiwanDepartment of Family Medicine, Cardinal Tien Hospital, School of Medicine, Fu-Jen Catholic University, New Taipei City 242, TaiwanDepartment of Life-Science, Fu-Jen University, New Taipei City 242, TaiwanDivision of Endocrinology and Metabolism, Department of Internal Medicine, Buddhist Dalin Tzu Chi General Hospital, School of Medicine, Hualien 970, TaiwanDivision of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, Taipei 114, TaiwanDivision of Endocrinology, Department of Internal Medicine, Shuang Ho Hospital, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, TaiwanDivision of Endocrinology and Metabolism, Department of Internal Medicine, Cardinal Tien Hospital, Medical School, Fu-Jen Catholic University, New Taipei City 242, TaiwanDepartment of Pathology, Cardinal Tien Hospital, Medical School, Fu-Jen Catholic University, New Taipei City 242, TaiwanAims. There are two phases of insulin secretion, the first (FPIS) and second phase (SPIS). In this study, we built equations to predict FPIS with metabolic syndrome (MetS) components and fasting plasma insulin (FPI). Methods. Totally, 186 participants were enrolled. 75% of participants were randomly selected as the study group to build equations. The remaining 25% of participants were selected as the external validation group. All participants received a frequently sampled intravenous glucose tolerance test, and acute insulin response after the glucose load (AIRg) was obtained. The AIRg was considered as FPIS. Results. When MetS components were only used, the following equation was built: log (FPIS) = 1.477 − 0.119 × fasting plasma glucose (FPG) + 0.079 × body mass index (BMI) − 0.523 × high-density lipoprotein cholesterol (HDL-C). After FPI was added, the second equation was formulated: log (FPIS) = 1.532 − 0.127 × FPG + 0.059 × BMI - 0.511 × HDL-C + 0.375 × log (FPI), which provided a better accuracy than the first one. Conclusions. Using MetS components, the FPIS could be estimated accurately. After adding FPI into the equation, the predictive power increased further. We hope that these equations could be widely used in daily practice.http://dx.doi.org/10.1155/2015/675245
spellingShingle Jiunn-diann Lin
Chun-Hsien Hsu
Yao-Jen Liang
Wei-Cheng Lian
Chang-Hsun Hsieh
Chung-Ze Wu
Dee Pei
Yen-Lin Chen
The Estimation of First-Phase Insulin Secretion by Using Components of the Metabolic Syndrome in a Chinese Population
International Journal of Endocrinology
title The Estimation of First-Phase Insulin Secretion by Using Components of the Metabolic Syndrome in a Chinese Population
title_full The Estimation of First-Phase Insulin Secretion by Using Components of the Metabolic Syndrome in a Chinese Population
title_fullStr The Estimation of First-Phase Insulin Secretion by Using Components of the Metabolic Syndrome in a Chinese Population
title_full_unstemmed The Estimation of First-Phase Insulin Secretion by Using Components of the Metabolic Syndrome in a Chinese Population
title_short The Estimation of First-Phase Insulin Secretion by Using Components of the Metabolic Syndrome in a Chinese Population
title_sort estimation of first phase insulin secretion by using components of the metabolic syndrome in a chinese population
url http://dx.doi.org/10.1155/2015/675245
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