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尿代谢组学分析在肾移植后急性 T 细胞介导排斥反应的非侵入性诊断中的应用。

Urinary metabolomic profiling for noninvasive diagnosis of acute T cell-mediated rejection after kidney transplantation.

机构信息

Department of Molecular Medicine, School of Medicine, Kyungpook National University, Daegu, Republic of Korea; Department of Clinical Pharmacology, Kyungpook National University Hospital, Daegu, Republic of Korea.

Department of Internal Medicine, School of Medicine, Kyungpook National University, Kyungpook National University Hospital, Daegu, Republic of Korea.

出版信息

J Chromatogr B Analyt Technol Biomed Life Sci. 2019 Jun 15;1118-1119:157-163. doi: 10.1016/j.jchromb.2019.04.047. Epub 2019 Apr 24.

Abstract

To improve early renal allograft function, it is important to develop a noninvasive diagnostic method for acute T cell-mediated rejection (TCMR). This study aims to explore potential noninvasive urinary biomarkers to screen for acute TCMR in kidney transplant recipients (KTRs) using untargeted metabolomic profiling. Urinary metabolites, collected from KTRs with stable graft function (STA) or acute TCMR episodes, were analyzed using liquid chromatography-mass spectrometry (LC-MS). Multivariate statistical analyses were performed to discriminate differences in urinary metabolites between the two groups. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of potential urinary biomarkers. Statistical analysis revealed the differences in urinary metabolites between the two groups and indicated several statistically significant metabolic features suitable for potential biomarkers. By comparing the retention times and mass fragmentation patterns of the chemicals in metabolite databases, samples, and standards, six of these features were clearly identified. ROC curve analysis showed the best performance of the training set (area under the curve value, 0.926; sensitivity, 90.0%; specificity, 84.6%) using a panel of five potential biomarkers: guanidoacetic acid, methylimidazoleacetic acid, dopamine, 4-guanidinobutyric acid, and L-tryptophan. The diagnostic accuracy of this model was 62.5% for an independent test dataset. LC-MS-based untargeted metabolomic profiling is a promising method to discriminate between acute TCMR and STA groups. Our model, based on a panel of five potential biomarkers, needs to be further validated in larger scale studies.

摘要

为了改善早期肾移植功能,开发一种用于急性 T 细胞介导排斥反应(TCMR)的非侵入性诊断方法非常重要。本研究旨在通过非靶向代谢组学分析,探索潜在的尿生物标志物,用于筛选肾移植受者(KTR)中的急性 TCMR。使用液相色谱-质谱联用(LC-MS)分析来自具有稳定移植物功能(STA)或急性 TCMR 发作的 KTR 的尿液代谢物。采用多变量统计分析方法对两组之间尿液代谢物的差异进行判别。采用接收者操作特征(ROC)曲线分析评估潜在尿生物标志物的诊断性能。统计分析显示两组间尿液代谢物的差异,并指出了几种具有潜在生物标志物特征的统计学显著代谢物。通过比较代谢物数据库、样本和标准品中化学物质的保留时间和质谱碎片模式,明确鉴定出其中的六种特征。ROC 曲线分析显示,使用一组五个潜在生物标志物(胍基乙酸、甲基咪唑乙酸、多巴胺、4-胍基丁酸和 L-色氨酸),训练集的表现最佳(曲线下面积值为 0.926;灵敏度为 90.0%;特异性为 84.6%)。该模型对独立测试数据集的诊断准确率为 62.5%。基于 LC-MS 的非靶向代谢组学分析是一种有前途的方法,可用于区分急性 TCMR 和 STA 组。我们的模型基于一组五个潜在生物标志物,需要在更大规模的研究中进一步验证。

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