Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany.
German Centre for Cardiovascular Research, partner site Greifswald, Greifswald, Germany.
J Clin Endocrinol Metab. 2019 Dec 1;104(12):6357-6370. doi: 10.1210/jc.2019-01104.
Impaired glucose tolerance (IGT) is one of the presymptomatic states of type 2 diabetes mellitus and requires an oral glucose tolerance test (OGTT) for diagnosis. Our aims were twofold: (i) characterize signatures of small molecules predicting the OGTT response and (ii) identify metabolic subgroups of participants with IGT.
Plasma samples from 827 participants of the Study of Health in Pomerania free of diabetes were measured using mass spectrometry and proton-nuclear magnetic resonance spectroscopy. Linear regression analyses were used to screen for metabolites significantly associated with the OGTT response after 2 hours, adjusting for baseline glucose and insulin levels as well as important confounders. A signature predictive for IGT was established using regularized logistic regression. All cases with IGT (N = 159) were selected and subjected to unsupervised clustering using a k-means approach.
In total, 99 metabolites and 22 lipoprotein measures were significantly associated with either 2-hour glucose or 2-hour insulin levels. Those comprised variations in baseline concentrations of branched-chain amino ketoacids, acylcarnitines, lysophospholipids, or phosphatidylcholines, largely confirming previous studies. By the use of these metabolites, subjects with IGT segregated into two distinct groups. Our IGT prediction model combining both clinical and metabolomics traits achieved an area under the curve of 0.84, slightly improving the prediction based on established clinical measures. The present metabolomics approach revealed molecular signatures associated directly to the response of the OGTT and to IGT in line with previous studies. However, clustering of subjects with IGT revealed distinct metabolic signatures of otherwise similar individuals, pointing toward the possibility of metabolomics for patient stratification.
糖耐量受损（IGT）是 2 型糖尿病的前驱状态之一，需要进行口服葡萄糖耐量试验（OGTT）进行诊断。我们的目的有两个：（i）确定预测 OGTT 反应的小分子特征；（ii）确定 IGT 患者的代谢亚组。
使用质谱法和质子核磁共振波谱法测量了来自无糖尿病的波罗的海健康研究 827 名参与者的血浆样本。线性回归分析用于筛选与 2 小时后 OGTT 反应相关的代谢物，调整基线血糖和胰岛素水平以及重要的混杂因素。使用正则化逻辑回归建立预测 IGT 的特征。选择所有 IGT 病例（N=159），并使用 k-均值方法进行无监督聚类。
共有 99 种代谢物和 22 种脂蛋白指标与 2 小时血糖或 2 小时胰岛素水平显著相关。这些指标包括支链氨基酸酮酸、酰基辅酶 A、溶血磷脂或磷脂酰胆碱的基线浓度变化，这在很大程度上证实了之前的研究。通过使用这些代谢物，IGT 患者分为两个不同的组。我们结合临床和代谢组学特征的 IGT 预测模型的曲线下面积为 0.84，略高于基于既定临床指标的预测。本代谢组学方法揭示了与 OGTT 反应直接相关的分子特征以及与先前研究一致的 IGT 特征。然而，IGT 患者的聚类显示出不同的代谢特征，提示代谢组学可能用于患者分层。
