TY - JOUR
T1 - Influence of Diabetogenic Factors on Fasting and Postprandial Glucose Levels in Patients with Type 2 Diabetes Mellitus
AU - Chang, Yuan Tung
AU - Wu, Chung Ze
AU - Hsieh, Chang Hsun
AU - Chang, Jin Biou
AU - Liang, Yao Jen
AU - Chen, Yen Lin
AU - Pei, Dee
AU - Lin, Jiunn Diann
N1 - Publisher Copyright:
© Copyright 2019, Mary Ann Liebert, Inc., publishers 2019.
PY - 2019/11
Y1 - 2019/11
N2 - Background: This study evaluated the relative influence of insulin resistance (IR), first-phase insulin secretion (FPIS), second-phase insulin secretion (SPIS), and glucose effectiveness (GE) in determining the difference between fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) (ΔPG), in a Chinese population with type 2 diabetes (T2D) mellitus. Methods: In total, we enrolled 1213 participants with T2D (479 women). IR, FPIS, SPIS, and GE were estimated by using equations we built previously. ΔPG was defined as FPG - PPG. Results: The relative contribution of the four diabetogenic factors (DFs) was analyzed by multiple linear regression, and GE was the greatest contributor in the ΔPG value (β = 0.171, P < 0.001), whereas IR had the least influence on ΔPG (β = -0.040, P = 0.439). DFs were analyzed by using binary logistic regression to ascertain if ΔPG ≥0 (high fasting plasma glucose, HFG). Three models were built: Model 0: SPIS, Model 1: SPIS + FPIS, and Model 2: Model 1 + GE. Model 2 had the most accurate predictive power; the equation for Model 2 is P = 1/(1 - e-x), where x = -11.88 + 312.89 × (GE) -1.22 × log(SPIS) +1.63 × log(FPIS). In this equation, P refers to the risk of HFG. Conclusions: For Chinese patients, GE had the most profound effect in determining ΔPG, followed by FPIS, SPIS, and IR. The model suggested that participants with high FPIS, SPIS, and GE would have a high incidence of HFG.
AB - Background: This study evaluated the relative influence of insulin resistance (IR), first-phase insulin secretion (FPIS), second-phase insulin secretion (SPIS), and glucose effectiveness (GE) in determining the difference between fasting plasma glucose (FPG) and postprandial plasma glucose (PPG) (ΔPG), in a Chinese population with type 2 diabetes (T2D) mellitus. Methods: In total, we enrolled 1213 participants with T2D (479 women). IR, FPIS, SPIS, and GE were estimated by using equations we built previously. ΔPG was defined as FPG - PPG. Results: The relative contribution of the four diabetogenic factors (DFs) was analyzed by multiple linear regression, and GE was the greatest contributor in the ΔPG value (β = 0.171, P < 0.001), whereas IR had the least influence on ΔPG (β = -0.040, P = 0.439). DFs were analyzed by using binary logistic regression to ascertain if ΔPG ≥0 (high fasting plasma glucose, HFG). Three models were built: Model 0: SPIS, Model 1: SPIS + FPIS, and Model 2: Model 1 + GE. Model 2 had the most accurate predictive power; the equation for Model 2 is P = 1/(1 - e-x), where x = -11.88 + 312.89 × (GE) -1.22 × log(SPIS) +1.63 × log(FPIS). In this equation, P refers to the risk of HFG. Conclusions: For Chinese patients, GE had the most profound effect in determining ΔPG, followed by FPIS, SPIS, and IR. The model suggested that participants with high FPIS, SPIS, and GE would have a high incidence of HFG.
KW - first-phase insulin secretion
KW - glucose effectiveness
KW - insulin resistance
KW - second-phase insulin secretion
KW - type 2 diabetes mellitus
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U2 - 10.1089/met.2019.0028
DO - 10.1089/met.2019.0028
M3 - Article
C2 - 31589092
AN - SCOPUS:85074446464
SN - 1540-4196
VL - 17
SP - 465
EP - 471
JO - Metabolic Syndrome and Related Disorders
JF - Metabolic Syndrome and Related Disorders
IS - 9
ER -