OBJECTIVE： To explore the plasma metabolite pro- files in patients with the syndrome of phlegm and blood stasis in hyperlipidemia and atherosclerosis （As）, and to search for the metabolic biomarkers of the syndrome. METHODS： The plasma metabolite profiles of 31 patients with the syndrome of phlegm and blood stasis in hyperlipidemia and As, 6 patients with syn- dromes without phlegm and blood stasis, and 10 healthy subjects were analyzed by gas chromatog- raphy-mass spectrometry （GC-MS）. Partial least squares-discriminant analyses （PLS-DA） were used to carry out the pattern-recognition analyses of the data. The plasma metabolic biomarkers of patients were obtained by variable importance plot value （VlP value） and Student＇s t-test. The structures ofbiomarkers were defined by the National Institute of Standards and Technology （NIST） database. RESULTS： PLS-DA score plots of plasma metabo- Iomes did not show overlap between the phlegm-blood stasis syndrome group and syn- dromes without phlegm and blood stasis group, whereas significant differences in the concentra- tions in the plasma of 5 metabolites were found （P〈 0.05）. They were identified as urine, isoleucine, gluc- uronic acid, palmitic acid and glycerol by searching in NIST database. The concentrations of four metab- olites in the plasma of patients with syndrome of phlegm and blood stasis were higher than those with syndromes without phlegm and blood stasis, whereas the glycerol concentration was lower. CONCLUSION： Compared with patients with syn- dromes without phlegm and blood stasis, five me- tabolites showed abnormal levels in patients with the syndrome of phlegm and blood stasis. These metabolites could be diagnostic and prognostic biomarkers.
利用气相色谱-质谱联用技术(GC-MS)和图模型分析方法,寻找脂代谢异常患者可能的血浆代谢标志物群。采用GC-MS技术对37例脂代谢异常患者和10例健康人的血浆样品进行分析,得到血浆代谢物的表达谱。偏最小二乘-判别分析(Partial least squares-discriminant analysis,PLS-DA)得分图可区分脂代谢异常患者与健康人,运用PLS-DA载荷图及t检验发现有9个代谢物在两组间存在显著性差异。经NIST谱库检索,它们分别为缬氨酸、甘氨酸、丙氨酸、焦谷氨酸、葡萄糖醛酸、半乳糖、甘露糖、亚油酸和甘油。在脂代谢异常患者血浆中,除甘油浓度显著高于健康人外,其余8种代谢物浓度均明显低于健康人。图模型分析结果发现这些代谢物与脂代谢异常临床常用诊断指标之间具有很好的相关性。它们可能是脂代谢异常疾病早期诊断和预后新的特异性代谢标志物群。
