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

Author:

Lin Jiunn-diann1,Hsu Chun-Hsien2,Liang Yao-Jen3,Lian Wei-Cheng4,Hsieh Chang-Hsun5,Wu Chung-Ze1,Pei Dee6,Chen Yen-Lin7

Affiliation:

1. Division of Endocrinology, Department of Internal Medicine, Shuang Ho Hospital, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110, Taiwan

2. Department of Family Medicine, Cardinal Tien Hospital, School of Medicine, Fu-Jen Catholic University, New Taipei City 242, Taiwan

3. Department of Life-Science, Fu-Jen University, New Taipei City 242, Taiwan

4. Division of Endocrinology and Metabolism, Department of Internal Medicine, Buddhist Dalin Tzu Chi General Hospital, School of Medicine, Hualien 970, Taiwan

5. Division of Endocrinology and Metabolism, Department of Internal Medicine, Tri-Service General Hospital, Taipei 114, Taiwan

6. Division of Endocrinology and Metabolism, Department of Internal Medicine, Cardinal Tien Hospital, Medical School, Fu-Jen Catholic University, New Taipei City 242, Taiwan

7. Department of Pathology, Cardinal Tien Hospital, Medical School, Fu-Jen Catholic University, New Taipei City 242, Taiwan

Abstract

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.

Publisher

Hindawi Limited

Subject

Endocrine and Autonomic Systems,Endocrinology,Endocrinology, Diabetes and Metabolism

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