Metabolon, Inc., Durham, NC
Metabolon, Inc., Durham, NC.
Diabetes Care. 2016 Jun;39(6):988-95. doi: 10.2337/dc15-2752. Epub 2016 Apr 5.
Plasma metabolites that distinguish isolated impaired glucose tolerance (iIGT) from isolated impaired fasting glucose (iIFG) may be useful biomarkers to predict IGT, a high-risk state for the development of type 2 diabetes.
Targeted metabolomics with 23 metabolites previously associated with dysglycemia was performed with fasting plasma samples from subjects without diabetes at time 0 of an oral glucose tolerance test (OGTT) in two observational cohorts: RISC (Relationship Between Insulin Sensitivity and Cardiovascular Disease) and DMVhi (Diabetes Mellitus and Vascular Health Initiative). Odds ratios (ORs) for a one-SD change in the metabolite level were calculated using multiple logistic regression models controlling for age, sex, and BMI to test for associations with iIGT or iIFG versus normal. Selective biomarkers of iIGT were further validated in the Botnia study.
α-Hydroxybutyric acid (α-HB) was most strongly associated with iIGT in RISC (OR 2.54 [95% CI 1.86-3.48], P value 5E-9) and DMVhi (2.75 [1.81-4.19], 4E-5) while having no significant association with iIFG. In Botnia, α-HB was selectively associated with iIGT (2.03 [1.65-2.49], 3E-11) and had no significant association with iIFG. Linoleoyl-glycerophosphocholine (L-GPC) and oleic acid were also found to be selective biomarkers of iIGT. In multivariate IGT prediction models, addition of α-HB, L-GPC, and oleic acid to age, sex, BMI, and fasting glucose significantly improved area under the curve in all three cohorts.
α-HB, L-GPC, and oleic acid were shown to be selective biomarkers of iIGT, independent of age, sex, BMI, and fasting glucose, in 4,053 subjects without diabetes from three European cohorts. These biomarkers can be used in predictive models to identify subjects with IGT without performing an OGTT.
能够区分孤立性糖耐量受损（iIGT）与孤立性空腹血糖受损（iIFG）的血浆代谢物，可能成为预测 IGT 的有用生物标志物，IGT 是发生 2 型糖尿病的高危状态。
在口服葡萄糖耐量试验（OGTT）的 0 时间点，对两个观察队列（RISC[胰岛素敏感性与心血管疾病的关系]和 DMVhi[糖尿病和血管健康倡议]）中无糖尿病受试者的空腹血浆样本进行靶向代谢组学分析，共检测 23 种与糖代谢异常相关的代谢物。使用多元逻辑回归模型，控制年龄、性别和 BMI，计算代谢物水平每增加一个标准差的比值比（OR），以检验与 iIGT 或 iIFG 相比正常的相关性。在 Botnia 研究中进一步验证了 iIGT 的选择性生物标志物。
在 RISC（OR 2.54[95%CI 1.86-3.48]，P 值 5E-9）和 DMVhi（OR 2.75[1.81-4.19]，4E-5）中，α-羟基丁酸（α-HB）与 iIGT 的相关性最强，而与 iIFG 无显著相关性。在 Botnia 研究中，α-HB 与 iIGT 显著相关（OR 2.03[1.65-2.49]，P 值 3E-11），与 iIFG 无显著相关性。亚麻酰甘油磷酸胆碱（L-GPC）和油酸也被发现是 iIGT 的选择性生物标志物。在多变量 IGT 预测模型中，将 α-HB、L-GPC 和油酸与年龄、性别、BMI 和空腹血糖联合应用，在三个队列中均显著提高了曲线下面积。
在来自三个欧洲队列的 4053 名无糖尿病受试者中，α-HB、L-GPC 和油酸被证明是 iIGT 的选择性生物标志物，与年龄、性别、BMI 和空腹血糖无关。这些生物标志物可用于预测模型，以在不进行 OGTT 的情况下识别 IGT 患者。
