Division of Endocrinology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.
Division of Endocrinology and Metabolism, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Metab Syndr Relat Disord. 2019 Nov;17(9):465-471. doi: 10.1089/met.2019.0028. Epub 2019 Oct 7.
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. 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. 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, < 0.001), whereas IR had the least influence on ΔPG (β = -0.040, = 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 = 1/(1 - e), where = -11.88 + 312.89 × (GE) -1.22 × log(SPIS) +1.63 × log(FPIS). In this equation, refers to the risk of HFG. 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.
本研究评估了胰岛素抵抗(IR)、第一时相胰岛素分泌(FPIS)、第二时相胰岛素分泌(SPIS)和葡萄糖效应(GE)在确定中国 2 型糖尿病(T2D)患者空腹血糖(FPG)与餐后血糖(PPG)差值(ΔPG)中的相对影响。共纳入 1213 例 T2D 患者(女性 479 例)。采用我们之前建立的方程评估 IR、FPIS、SPIS 和 GE。ΔPG 定义为 FPG-PPG。采用多元线性回归分析四种糖尿病致病因素(DFs)的相对贡献,结果显示,在ΔPG 值方面,GE 的贡献最大(β=0.171, < 0.001),而 IR 对ΔPG 的影响最小(β=-0.040, = 0.439)。采用二元逻辑回归分析 DFs,以确定ΔPG≥0(高空腹血糖,HFG)的情况。建立了三个模型:模型 0:SPIS,模型 1:SPIS+FPIS,模型 2:模型 1+GE。模型 2 具有最准确的预测能力;模型 2 的方程为=1/(1−e),其中=−11.88+312.89×(GE)−1.22×log(SPIS)+1.63×log(FPIS)。在该方程中,指 HFG 的风险。对于中国患者,GE 在确定ΔPG 方面的影响最大,其次是 FPIS、SPIS 和 IR。该模型提示 FPIS、SPIS 和 GE 较高的患者 HFG 发生率较高。