Application of Metabolomics in Traditional Chinese Medicine Differentiation of Deficiency and Excess Syndromes in Patients with Diabetes Mellitus

被引:28
|
作者
Wu, Tao [2 ,3 ]
Yang, Ming [4 ]
Wei, Hua-Feng [5 ]
He, Song-Hua [5 ]
Wang, Shun-Chun [1 ]
Ji, Guang [3 ]
机构
[1] Shanghai Univ Tradit Chinese Med, Inst Chinese Mat Med, Shanghai 201203, Peoples R China
[2] Shanghai Univ Tradit Chinese Med, Ctr Chinese Med Therapy & Syst Biol, Shanghai 201203, Peoples R China
[3] Shanghai Univ Tradit Chinese Med, Longhua Hosp, Inst Digest Dis, Shanghai 200032, Peoples R China
[4] Shanghai Univ Tradit Chinese Med, Longhua Hosp, Dept Medicament, Shanghai 200032, Peoples R China
[5] Shanghai Univ Tradit Chinese Med, Longhua Hosp, Dept Internal Med, Shanghai 200032, Peoples R China
基金
中国国家自然科学基金; 上海市自然科学基金;
关键词
GLUCOSE-TOLERANCE TEST; ORTHOGONAL PROJECTIONS; LATENT STRUCTURES; FATTY-ACID; PLASMA; METABONOMICS; BIOMARKERS; MODEL; PROFILES; PLATFORM;
D O I
10.1155/2012/968083
中图分类号
R [医药、卫生];
学科分类号
10 ;
摘要
Metabolic profiling is widely used as a probe in diagnosing diseases. In this study, the metabolic profiling of urinary carbohydrates was investigated using gas chromatography/mass spectrometry (GC/MS) and multivariate statistical analysis. The kernel-based orthogonal projections to latent structures (K-OPLS) model were established and validated to distinguish between subjects with and without diabetes mellitus (DM). The model was combined with subwindow permutation analysis (SPA) in order to extract novel biomarker information. Furthermore, the K-OPLS model visually represented the alterations in urinary carbohydrate profiles of excess and deficiency syndromes in patients with diabetes. The combination of GC/MS and K-OPLS/SPA analysis allowed the urinary carbohydrate metabolic characterization of DM patients with different traditional Chinese medicine (TCM) syndromes, including biomarkers different from non-DM patients. The method presented in this study might be a complement or an alternative to TCM syndrome research.
引用
收藏
页数:11
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