| 摘要: | 
			 
		     | 目的  从代谢组学角度分析并寻找2型糖尿病(T2DM)患者可能的代谢标记物。方法  选取30例初诊、或有糖尿病史经药物治疗控制不理想且无并发症的T2DM 患者,另选取30例性别、年龄匹配的健康者为正常对照。收集清晨空腹中段尿,以气相色谱- 质谱联用(GC-MS)技术对尿液样本进行代谢图谱分析,正交偏最小二乘法判别研究尿液内源性化合物在两组间的差异。结果  T2DM 组和正常对照组尿液代谢谱明显分离。与正常对照组比较,T2DM 组尿液2,3,4- 三羟基丁酸、肌醇、D- 葡萄糖、D- 葡萄糖酸及尿素含量升高(P<0.05 或0.01),马尿酸含量减少(P<0.01)。结论  代谢组学检查提示T2DM 患者尿液中代谢标志物为2,3,4- 三羟基丁酸、肌醇、马尿酸、D-葡萄糖、D- 葡萄糖酸及尿素,观察这些标志物含量的变化有助于T2DM 的临床诊断及发病机制研究。 | 
			
	         
				| 关键词:  代谢组学  2型糖尿病  气相色谱- 质谱联用  正交偏最小二乘法判别 | 
			 
                | DOI: | 
            
                | 分类号: | 
			 
             
                | 基金项目:浙江省钱江人才计划 | 
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                | Urinary metabonomics in type 2 diabetic patients | 
           
			
                | SU Junmei,GE Weihong,XU Guangyan,ZHANG Yu | 
           
		   
		   
                | Zhejiang University of Traditional Chinese Medicine | 
		   
             
                | Abstract: | 
			
                | Objective To investigate the urinary metabonomics in patients with type 2 diabetes mellitus. Methods Thirty patients with type 2 diabetes mellitus were enrolled as study group, and 30 health subjects served as controls. The morning midstreamurine samples were collected. Gas chromatography-mass spectrometry(GC-MS) was used to analyzemetabonomics of the urine samples.Orthogonal partial least-squares discriminant analysis(OPLS-DA) was appliedto identify potential biomarkers. Results The difference of metabonomics spectra was observed between diabetic patients and normal controls. Compared with the normal controls, the contents of 3-hydroxybutyrate, inositol, D-glucose, D-gluconic acid, urea were increased (P<0.05 or P<0.01) and hippuric acid decreased significantly (P<0.01) in type 2 diabetic patients. Conclusion The 3-hydroxybutyrate, inositol, hippuric acid, D-glucose, D-gluconic acidandurea in the urinemight be the candidate biomarkers for type 2 diabetes mellitus. | 
	       
                | Key words:  Metabonomics  Type 2 diabetes mellitus  Gas chromatography-mass spectrometry  Orthogonal partial least -squares discriminant analysis |