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基于核磁共振波谱的乙肝患者血清代谢组研究
引用本文:杨永霞,杨生义,梁敏锋,丘翠环.基于核磁共振波谱的乙肝患者血清代谢组研究[J].第二军医大学学报,2010,31(3):288-291.
作者姓名:杨永霞  杨生义  梁敏锋  丘翠环
作者单位:1. 广东药学院基础学院物理与电子学教研室,广州,510006
2. 兰州大学第一临床医院消化科,兰州,730000
3. 南方医科大学南方医院感染内科,广州,510515
摘    要:目的应用核磁共振氢谱(nuclear magnetic resonance 1H,1H NMR)和主成分分析方法研究慢性乙肝患者血清的代谢组变化。方法临床正常人血清8例和慢性乙肝患者血清20例,采用BRUKER500MHz超导核磁共振波谱仪进行检测,并运用PCA分析方法,得出慢性乙肝患者血清代谢组的特征,探讨相关生化过程的改变。结果分析结果显示慢性乙肝患者和正常人血清的代谢组具有明显差异。乙肝患者血清中的脂和葡萄糖是升高的,而乳酸、丙氨酸、缬氨酸、谷氨酰胺以及胆碱类物质则是降低的,且具有统计学意义(P<0.05)。结论通过上述代谢物的变化可以区分正常人和慢性乙肝患者。这种基于核磁共振氢谱和主成分分析的代谢组学方法可以为乙肝的诊断提供可靠的分子水平上的代谢依据。

关 键 词:核磁共振氢谱  乙型肝炎  代谢  主成分分析  
收稿时间:2009/11/2 0:00:00
修稿时间:2/7/2010 12:00:00 AM

1H NMR spectroscopy combined with principal component analysis for metabonomic study on serum of HBV patients
YANG Yong-xi,YANG Sheng-yi,LIANG Min-feng,QIU Cui-huan.1H NMR spectroscopy combined with principal component analysis for metabonomic study on serum of HBV patients[J].Academic Journal of Second Military Medical University,2010,31(3):288-291.
Authors:YANG Yong-xi  YANG Sheng-yi  LIANG Min-feng  QIU Cui-huan
Institution:1. Department of Physics and Electronic Teaching, Basic School of Guangdong Pharmaceutical University, Guangzhou 510006, Guangdong, China;2. Department of Gastroenterology and Hepatology, First Clinical Medicine College of Lanzhou University, Lanzhou 730000, Gansu, China; 3. Department of Infectious Diseases,Nanfang Hospital,Southern Medical University, Guangzhou 510515,Guangdong, China
Abstract:Objective To characterize the metabolite composition(i.e.,metabonome) of the serum samples from chronic hepatitis B patients by using nuclear magnetic resonance 1H(1H NMR) spectroscopy combined with principal component analysis(PCA).Methods The 1H NMR sepctra were obtained for the serum samples from 8 healthy controls and 20 chronic hepatitis B patients,and the spectra data were analyzed by PCA to demonstrate the metabonomic changes.Results We found that there were evident differences in metabonomics betwee...
Keywords:proton nuclear magnetic resonance  hepatitis B  metabolism  principal component analysis  
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