Evaluation of fasting state-/oral glucose tolerance test-derived measures of insulin release for the detection of genetically impaired β-cell function.
Evaluation of fasting state-/oral glucose tolerance test-derived measures of insulin release for the detection of genetically impaired β-cell function.
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DOI：
10.1371/journal.pone.0014194
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发表时间：
2010-12-02
期刊：
影响因子：
3.7
通讯作者：
Fritsche A
中科院分区：
文献类型：
--
作者：
Herzberg-Schäfer SA;Staiger H;Heni M;Ketterer C;Guthoff M;Kantartzis K;Machicao F;Stefan N;Häring HU;Fritsche A
迄今为止，空腹状态和不同的口服葡萄糖耐量试验（OGTT）衍生的测量方法被用于估计胰岛素释放，需要在大型人类队列中进行合理的努力，例如遗传学研究。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，以检测基因决定的β细胞功能障碍的适用性。对1364名2型糖尿病风险增加的欧洲白人个体进行OGTT研究，测量葡萄糖、胰岛素和c肽，并对已知影响葡萄糖和肠促胰岛素刺激的胰岛素分泌的单核苷酸多态性（snp）进行基因分型。计算一个空腹状态和11个ogtt衍生指数并进行统计评估。在校正混杂变量后，所有测试的snp都与至少两项胰岛素分泌指标显著相关（p≤0.05）。根据各指标的关联统计能力进行排序，将该指标与所有被测snp（或某一子集）的关联所得到的排名进行汇总，得出最终排名。该方法显示曲线下面积（AUC）胰岛素(0-30)/AUCGlucose（0-30）是检测snp依赖性胰岛素释放差异的最佳指标。此外，AUCInsulin(0-30)/AUCGlucose（0-30）、校正胰岛素反应（CIR）、AUCC-Peptide(0-30)/AUCGlucose（0-30）、AUCC-Peptide(0-120)/AUCGlucose（0-120）、两个不同的0-30 min胰岛素增量反应公式，即胰岛素生成指数（IGI）2和IGI1，以及胰岛素30 min的排名显著高于稳态模型评估β-细胞功能（HOMA-B; p<0.05）。AUCC-Peptide(0-120)/AUCGlucose（0-120）在检测与肠促胰岛素刺激的胰岛素分泌相关的snp方面排名最高。在所有分析中，HOMA-β显示出最高的排名总和，因此得分最后。在AUCInsulin(0-30)/AUCGlucose（0-30）、CIR、AUCC-Peptide(0-30)/AUCGlucose（0-30）、AUCC-Peptide(0-120)/AUCGlucose（0-120）、IGI2、IGI1和胰岛素30 min的情况下，基于早期胰岛素和c肽对口服葡萄糖反应的胰岛素分泌动态测量比空腹测量（如HOMA-B）更适合评估基因决定的β细胞功能障碍。主要影响肠促胰岛素轴的基因可能最好通过AUCC-Peptide(0-120)/ auc葡萄糖（0-120）检测。
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. 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)/AUCGlucose(0-30) as the best-ranked index to detect SNP-dependent differences in insulin release. Moreover, AUCInsulin(0-30)/AUCGlucose(0-30), corrected insulin response (CIR), AUCC-Peptide(0-30)/AUCGlucose(0-30), AUCC-Peptide(0-120)/AUCGlucose(0-120), two different formulas for the incremental insulin response from 0–30 min, i.e., the insulinogenic indices (IGI)2 and IGI1, and insulin 30 min were significantly higher-ranked than homeostasis model assessment of β-cell function (HOMA-B; p<0.05). AUCC-Peptide(0-120)/AUCGlucose(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. With AUCInsulin(0-30)/AUCGlucose(0-30), CIR, AUCC-Peptide(0-30)/AUCGlucose(0-30), AUCC-Peptide(0-120)/AUCGlucose(0-120), IGI2, IGI1, 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 AUCC-Peptide(0-120)/AUCGlucose(0-120).
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影响因子：
8.2
作者：
KADOWAKI, T;MIYAKE, Y;KOSAKA, K
通讯作者：
KOSAKA, K
影响因子：
3.7
作者：
Staiger, Harald;Machicao, Fausto;Kantartzis, Konstantinos;Schaefer, Silke A.;Kirchhoff, Kerstin;Guthoff, Martina;Silbernagel, Guenther;Stefan, Norbert;Fritsche, Andreas;Haering, Hans-Ulrich
通讯作者：
Haering, Hans-Ulrich
影响因子：
7.7
作者：
SLUITER, WJ;ERKELENS, DW;DOORENBOS, H
通讯作者：
DOORENBOS, H
影响因子：
16.2
作者：
Stumvoll, M;Mitrakou, A;Gerich, J
通讯作者：
Gerich, J
影响因子：
7.7
作者：
Müssig K;Staiger H;Machicao F;Kirchhoff K;Guthoff M;Schäfer SA;Kantartzis K;Silbernagel G;Stefan N;Holst JJ;Gallwitz B;Häring HU;Fritsche A
通讯作者：
Fritsche A