气相色谱-质谱用于2型糖尿病患者血清代谢特征研究

Serum Metabonomics Study on Type 2 Diabetes by Using Gas Chromatography/Mass Spectrometry

目的研究2型糖尿病患者的血清代谢特征。方法采用气相色谱-质谱(GC/MS)联用技术对健康人及2型糖尿病患者的血清代谢物谱进行分析测定。对采集的图谱进行峰提取、峰匹配和过滤后,采用SIMCA-P 11.0软件及SPSS13.0软件进行数据分析。结果 2型糖尿病患者与健康人的血清代谢物谱之间存在明显差异,偏最小二乘-判别分析(PLS-DA)得分散点图中疾病组与健康对照组得到了良好区分;单元统计分析发现,在2型糖尿病组与健康对照组间共有22个变量存在着显著性差异,主要涉及糖代谢、脂代谢及氨基酸代谢。在糖类代谢物中,葡萄糖、半乳糖含量在糖尿病患者血清中明显高于正常对照组;在脂类代谢物中,甘油和游离脂肪酸(FFA C16:0、FFA C18:1、FFA C18:2)含量高于正常对照组;氨基酸类代谢物中,缬氨酸及其代谢物2-酮戊二酸、亮氨酸、异亮氨酸以及代谢物2-酮异己酸的浓度较正常组显著升高,而甘氨酸、丝氨酸、苏氨酸、色氨酸、苯丙氨酸和天冬酰胺的浓度显著降低。结论 2型糖尿病患者的血清代谢谱发生了明显改变,差异代谢物的紊乱反映了糖尿病状态下糖、脂、氨基酸代谢途径的改变,这些代谢物在2型糖尿病的发病过程中可能具有重要的病理意义,同时也为临床诊断提供了依据。

Objective To investigate the serum metabolic characteristics of type 2 diabetes patients. Methods The serum metabol- ic profiles of healthy controls and type 2 diabetes patients were determined by using gas ehromatography coupled with mass spectrome- try （GC/MS）. Firstly, obtained raw chromatograms were processed by peak extraction, peak match and filtration. Secondly, the data were analyzed through the SIMCA-P 11.0 software and SPSS13.0 software. Results There were obvious metabolic differences between type 2 diabetes and healthy controls. The scores plot by PLS-DA showed that the type 2 diabetes group was distinctly differentiated with the healthy controls. By using univariate data analysis, 22 variables had significant differences between the two groups, and these metabo- lites variations are related to the dysfunctions in glucose metabolism, lipid metabolism and amino acid metabolism. Compared with the healthy controls, the type 2 diabetes group showed elevated serum concentrations of valine, 2-ketoisovalerate, 2-ketoisocaproate, leu- cine, isoleucine, 2-hydroxybutyrate , 2-ketoglutarate, galactose, glucose, FFA C16：0, FFA C18：2, FFA C18：1 and glycerol, but de- creased levels of glycine, tryptophan, serine, threonine, phenylalanine and asparagine. Conclusions The results revealed that type 2 di- abetes patients presented perturbations in glucose metabolism, lipid metabolism and amino acid metabolism. This study was helpful for revealing the complex pathological mechanism in type 2 diabetes and providing a basis for clinical diagnosis.