Division of Endocrinology, Diabetology, Angiology, Nephrology, and Clinical Chemistry, Department of Internal Medicine, University Hospital Tübingen and Eberhard Karls University Tübingen, Tübingen, Germany.
PLoS One. 2010 Dec 2;5(12):e14194. doi: 10.1371/journal.pone.0014194.
To date, fasting state- and different oral glucose tolerance test (OGTT)-derived measures are used to estimate insulin release with reasonable effort in large human cohorts required, e.g., for genetic studies. Here, we evaluated twelve common (or recently introduced) fasting state-/OGTT-derived indices for their suitability to detect genetically determined β-cell dysfunction.
METHODOLOGY/PRINCIPAL FINDINGS: A cohort of 1364 White European individuals at increased risk for type 2 diabetes was characterized by OGTT with glucose, insulin, and C-peptide measurements and genotyped for single nucleotide polymorphisms (SNPs) known to affect glucose- and incretin-stimulated insulin secretion. One fasting state- and eleven OGTT-derived indices were calculated and statistically evaluated. After adjustment for confounding variables, all tested SNPs were significantly associated with at least two insulin secretion measures (p≤0.05). The indices were ranked according to their associations' statistical power, and the ranks an index obtained for its associations with all the tested SNPs (or a subset) were summed up resulting in a final ranking. This approach revealed area under the curve (AUC)(Insulin(0-30))/AUC(Glucose(0-30)) as the best-ranked index to detect SNP-dependent differences in insulin release. Moreover, AUC(Insulin(0-30))/AUC(Glucose(0-30)), corrected insulin response (CIR), AUC(C-Peptide(0-30))/AUC(Glucose(0-30)), AUC(C-Peptide(0-120))/AUC(Glucose(0-120)), two different formulas for the incremental insulin response from 0-30 min, i.e., the insulinogenic indices (IGI)(2) and IGI(1), and insulin 30 min were significantly higher-ranked than homeostasis model assessment of β-cell function (HOMA-B; p<0.05). AUC(C-Peptide(0-120))/AUC(Glucose(0-120)) was best-ranked for the detection of SNPs involved in incretin-stimulated insulin secretion. In all analyses, HOMA-β displayed the highest rank sums and, thus, scored last.
CONCLUSIONS/SIGNIFICANCE: With AUC(Insulin(0-30))/AUC(Glucose(0-30),) CIR, AUC(C-Peptide(0-30))/AUC(Glucose(0-30)), AUC(C-Peptide(0-120))/AUC(Glucose(0-120)), IGI(2), IGI(1), and insulin 30 min, dynamic measures of insulin secretion based on early insulin and C-peptide responses to oral glucose represent measures which are more appropriate to assess genetically determined β-cell dysfunction than fasting measures, i.e., HOMA-B. Genes predominantly influencing the incretin axis may possibly be best detected by AUC(C-Peptide(0-120))/AUC(Glucose(0-120)).
迄今为止,人们使用空腹状态和不同的口服葡萄糖耐量试验(OGTT)衍生指标来估计胰岛素的释放,这需要在大型人类队列中进行合理的努力,例如遗传研究。在这里,我们评估了 12 种常见的(或最近引入的)空腹状态/OGTT 衍生指数,以评估它们在检测遗传决定的β细胞功能障碍方面的适用性。
方法/主要发现:一个患有 2 型糖尿病风险增加的 1364 名白种欧洲个体的队列,通过 OGTT 进行了葡萄糖、胰岛素和 C 肽测量,并对已知影响葡萄糖和肠促胰岛素刺激胰岛素分泌的单核苷酸多态性(SNP)进行了基因分型。计算并统计了一种空腹状态和 11 种 OGTT 衍生指数。在调整混杂变量后,所有测试的 SNP 均与至少两种胰岛素分泌测量值显著相关(p≤0.05)。根据它们与所有测试 SNP 的关联的统计能力对指数进行了排序,并且将一个指数为其与所有测试 SNP(或子集)的关联获得的排名进行了求和,从而得到最终排名。这种方法揭示了 AUC(胰岛素(0-30))/AUC(葡萄糖(0-30))作为检测胰岛素释放中 SNP 依赖性差异的最佳排序指数。此外,AUC(胰岛素(0-30))/AUC(葡萄糖(0-30))、校正胰岛素反应(CIR)、AUC(C-肽(0-30))/AUC(葡萄糖(0-30))、AUC(C-肽(0-120))/AUC(葡萄糖(0-120))、0-30 分钟时的两个不同的增量胰岛素反应公式,即胰岛素原指数(IGI)(2)和 IGI(1),以及 30 分钟胰岛素的排名均高于β细胞功能的稳态模型评估(HOMA-B;p<0.05)。AUC(C-肽(0-120))/AUC(葡萄糖(0-120))是检测涉及肠促胰岛素刺激胰岛素分泌的 SNP 的最佳排序指数。在所有分析中,HOMA-β 显示出最高的排名总和,因此得分最低。
结论/意义:使用 AUC(胰岛素(0-30))/AUC(葡萄糖(0-30))、CIR、AUC(C-肽(0-30))/AUC(葡萄糖(0-30))、AUC(C-肽(0-120))/AUC(葡萄糖(0-120))、IGI(2)、IGI(1)和 30 分钟胰岛素,基于口服葡萄糖对早期胰岛素和 C 肽反应的胰岛素分泌的动态测量比空腹测量(即 HOMA-B)更适合评估遗传决定的β细胞功能障碍。可能通过 AUC(C-肽(0-120))/AUC(葡萄糖(0-120))来最佳检测主要影响肠促胰岛素轴的基因。