Analysis of serum metabolic profile by ultra-performance liquid chromatography-mass spectrometry for biomarkers discovery: application in a pilot study to discriminate patients with tuberculosis.
Analysis of serum metabolic profile by ultra-performance liquid chromatography-mass spectrometry for biomarkers discovery: application in a pilot study to discriminate patients with tuberculosis.
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DOI：
10.4103/0366-6999.149188
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发表时间：
2015-01-20
影响因子：
6.1
通讯作者：
Liu SY
中科院分区：
文献类型：
--
作者：
Feng S;Du YQ;Zhang L;Zhang L;Feng RR;Liu SY
结核病是一种以多系统受累为特征的慢性消耗性炎症性疾病，可导致患者代谢紊乱。代谢特征已在几种疾病的研究中得到利用。然而，在代谢谱的基础上成功用于结核病诊断的血清并不多。正交偏最小二乘判别分析能够区分结核病患者与健康受试者和结核病以外的疾病患者。因此，建立了TB特异性代谢谱。使用Mann-Whitney U检验鉴定用于区分活动性TB与非TB疾病的潜在生物标志物的簇。计算代谢物的多元逻辑回归分析，以确定允许有效区分活动性TB患者与对照受试者的合适生物标志物组。在271名参与者中，发现12种代谢物有助于区分结核活动组和对照组。这些代谢产物主要参与以下三种生物分子的代谢途径：脂肪酸、氨基酸和脂质。3D、7 D和11 D-植烷酸、二十二烷酸和苏氨酰-γ-谷氨酸的受试者工作特征曲线显示出极好的效率，曲线下面积（AUC）值为0.904（95%置信区间[CI]：0863-0.944）、0.93（95% CI：0.893-0.966）和0.964（95% CI：00.941-0.988）。最大和最小的AUC分别为0.964和0.720，表明这些生物标志物可能参与疾病机制。溶血磷脂酰胆碱（18：0）、山嵛酸、苏氨酰-γ-谷氨酸和二磷酸前角鲨烯的组合用于代表区分活动性TB患者与对照受试者的最合适的生物标志物组，AUC值为0.991。代谢分析结果确定了新的血清生物标志物，可以区分结核病和非结核病。基于代谢组学的分析为结核病的生物学提供了具体的见解，并可能为结核病诊断提供新的途径。
Tuberculosis (TB) is a chronic wasting inflammatory disease characterized by multisystem involvement, which can cause metabolic derangements in afflicted patients. Metabolic signatures have been exploited in the study of several diseases. However, the serum that is successfully used in TB diagnosis on the basis of metabolic profiling is not by much. Orthogonal partial least-squares discriminant analysis was capable of distinguishing TB patients from both healthy subjects and patients with conditions other than TB. Therefore, TB-specific metabolic profiling was established. Clusters of potential biomarkers for differentiating TB active from non-TB diseases were identified using Mann–Whitney U-test. Multiple logistic regression analysis of metabolites was calculated to determine the suitable biomarker group that allows the efficient differentiation of patients with TB active from the control subjects. From among 271 participants, 12 metabolites were found to contribute to the distinction between the TB active group and the control groups. These metabolites were mainly involved in the metabolic pathways of the following three biomolecules: Fatty acids, amino acids, and lipids. The receiver operating characteristic curves of 3D, 7D, and 11D-phytanic acid, behenic acid, and threoninyl-γ-glutamate exhibited excellent efficiency with area under the curve (AUC) values of 0.904 (95% confidence interval [CI]: 0863–0.944), 0.93 (95% CI: 0.893–0.966), and 0.964 (95% CI: 00.941–0.988), respectively. The largest and smallest resulting AUCs were 0.964 and 0.720, indicating that these biomarkers may be involved in the disease mechanisms. The combination of lysophosphatidylcholine (18:0), behenic acid, threoninyl-γ-glutamate, and presqualene diphosphate was used to represent the most suitable biomarker group for the differentiation of patients with TB active from the control subjects, with an AUC value of 0.991. The metabolic analysis results identified new serum biomarkers that can distinguish TB from non-TB diseases. The metabolomics-based analysis provides specific insights into the biology of TB and may offer new avenues for TB diagnosis.