TY - JOUR
T1 - Identification of Impaired Second-Phase Insulin Secretion in Various Degrees of Glucose Tolerance in a Chinese Population
AU - Lin, Jiunn Diann
AU - Wu, Chung Ze
AU - Pei, Dee
AU - Lian, Wei Cheng
AU - Hsu, Chun Hsien
AU - Hsieh, Chang Hsun
AU - Chen, Yen Lin
N1 - Publisher Copyright:
© 2016, Mary Ann Liebert, Inc.
PY - 2016/9/1
Y1 - 2016/9/1
N2 - Aim: Impaired insulin sensitivity and insulin secretion (ISEC) are major pathophysiologies of type 2 diabetes (T2DM). ISEC has two phases: the first and second phases (second ISEC). In this study, we derived equations to identify patients with second ISEC deficiency (ISEC-D). Methods: Data from 96 patients, namely 19 with a normal fasting plasma glucose (FPG) level, 21 with prediabetes, and 56 with T2DM, were enrolled. They underwent a modified low-dose graded glucose infusion test, which was originally proposed by Polonsky et al. The test results were interpreted as the slopes of the changes of plasma insulin against the glucose levels, which were considered second ISEC. Patients with the lowest quartile of the slopes were defined as having ISEC-D. We built three models: Model 0: FPG, Model 1: FPG + waist circumference, and Model 2: Model 1 + fasting plasma insulin. The area under the receiver operating characteristic (aROC) curve was used to determine the predictive power of these models. Results: Among the metabolic syndrome components, FPG had the largest aROC curve (78.2%). Although aROC curves of Models 1 and 2 (85.2% and 91.5%, respectively) were higher than the aROC curve of FPG, no difference was observed between Models 1 and 0. By contrast, the aROC curve of Model 2 was higher compared with Model 1. Conclusions: FPG showed the largest aROC curve. Model 2 had the highest predictive power, which could identify patients with ISEC-D with a sensitivity and specificity of 94.3% and 82.6%, respectively. These two models could be conveniently used in daily practice.
AB - Aim: Impaired insulin sensitivity and insulin secretion (ISEC) are major pathophysiologies of type 2 diabetes (T2DM). ISEC has two phases: the first and second phases (second ISEC). In this study, we derived equations to identify patients with second ISEC deficiency (ISEC-D). Methods: Data from 96 patients, namely 19 with a normal fasting plasma glucose (FPG) level, 21 with prediabetes, and 56 with T2DM, were enrolled. They underwent a modified low-dose graded glucose infusion test, which was originally proposed by Polonsky et al. The test results were interpreted as the slopes of the changes of plasma insulin against the glucose levels, which were considered second ISEC. Patients with the lowest quartile of the slopes were defined as having ISEC-D. We built three models: Model 0: FPG, Model 1: FPG + waist circumference, and Model 2: Model 1 + fasting plasma insulin. The area under the receiver operating characteristic (aROC) curve was used to determine the predictive power of these models. Results: Among the metabolic syndrome components, FPG had the largest aROC curve (78.2%). Although aROC curves of Models 1 and 2 (85.2% and 91.5%, respectively) were higher than the aROC curve of FPG, no difference was observed between Models 1 and 0. By contrast, the aROC curve of Model 2 was higher compared with Model 1. Conclusions: FPG showed the largest aROC curve. Model 2 had the highest predictive power, which could identify patients with ISEC-D with a sensitivity and specificity of 94.3% and 82.6%, respectively. These two models could be conveniently used in daily practice.
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U2 - 10.1089/met.2015.0128
DO - 10.1089/met.2015.0128
M3 - Article
C2 - 27303892
AN - SCOPUS:84984846156
SN - 1540-4196
VL - 14
SP - 347
EP - 353
JO - Metabolic Syndrome and Related Disorders
JF - Metabolic Syndrome and Related Disorders
IS - 7
ER -