代谢组学检测法应用于肌张力障碍诊断的研究

Application of nuclear magnetic resonance metabolomics method in the diagnosis of dystonia

目的应用核磁共振代谢组学技术研究肌张力障碍患者血清中的小分子代谢物(分子量小于1 000)与正常人血清中小分子代谢物的差异,筛选肌张力障碍患者血清中的特征代谢物,建立肌张力障碍患者血清代谢图谱的诊断模型,从代谢组学角度探讨肌张力障碍发病的可能机制,为肌张力障碍的早期检测、诊断、干预提供依据。方法采集2012年7月至12月在我院就诊的18例肌张力障碍患者的血清,并分别采集4例男性3例女性的正常血清,应用核磁共振技术检测血清中小分子的代谢谱,并对谱图的积分数据进行主成分分析(PCA)和偏最小二乘法判别分析,以辨识血清代谢产物的变化。结果肌张力障碍患者与健康人血清中小分子代谢物的质谱磁共振图谱有明显差异,其中主成分分析模式识别法未能区分实验组和健康对照组,而用偏最小二乘法判别分析模式识别方法可明显区分两组,并能提供两组之间差别所对应的代谢物质。结论采用代谢组学技术研究肌张力障碍患者血清代谢组与健康人存在明显差异;偏最小二乘法判别分析模式识别法明显优于主成分分析法,能够去除非实验因素的影响,提高分类效果。通过对代谢组学数据分析,可以发现肌张力障碍患者的代谢产物异常,有望发现特征性生物标志物,为进一步干预提供依据,具有较好的临床应用价值。

Objective To probe into the difference of small molecule metabolites （molecular weight less than 1 000） in the norInal serum and the serum in patients with dystonia by nuclear magnetic resonance （1H-NMR） metabolomics technology, screen out the featured metabolites of dystonia patients serum, and establish diagnosis model of serum metabolic profiles, and discuss the possible mechanisms of dystonia. Methods The serum of 18 dystonia patients, who were admitted to our hospital from July to December 2012, was collected. The normal serum of 4 male patients and 3 female patients was used to detect the serum medium and small molecular metabolic spectrum by 1H-NMR technology. The spectra of the integral data with principal component analysis （PCA） and partial least squares discriminant analysis （PLS-DA） were applied to detect the changes of the serum metabolites. Results There was significant difference in NMR spectrum of small molecule motabolites in serum between dystonia patients and healthy people. PCA pattern recognition method failed to distinguish the experimental group from healthy controls, and PLS-DA pattern recognition method could distinguish successfully the experimental group from healthy controls, and the metabolic substances which corresponded to the difference between two groups could be detected. Conclusion There is significant difference in serum between dystonia patients and healthy control by 1H-NMR metabolomics technology. PLS-DA pattern recognition method is better than PCA method, which improves the effect of classification and screens out the non-experimental factors. Metabolomics data analysis is used to detect abnormal metabolites of dystonia patients, which is helpful to find characteristic biomarkers and provide the basis for fitrther intervention.