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学科主题: 物理化学
题名: Fatty acid metabolic profiles and biomarker discovery for type 2 diabetes mellitus using graphical index of separation combined with principal component analysis and partial least squares-discriminant analysis
作者: Xu, Wenjuan1;  Zhang, Liangxiao1;  Huang, Yuhong2;  Yang, Qianxu1;  Xiao, Hongbin1;  Zhang, Deqin2
通讯作者: 肖红斌 ;  DeqinZhang
关键词: Fatty acid metabolic profile ;  Potential biomarker ;  Type 2 diabetes mellitus ;  Graphical index of separation (GIOS) ;  Partial least squares-discriminant analysis (PLS-DA)
刊名: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
发表日期: 2012-08-15
DOI: 10.1016/j.chemolab.2012.06.008
卷: 118, 页:173-179
收录类别: SCI
文章类型: Article
部门归属: 18
项目归属: 1804
产权排名: 1,1
WOS标题词: Science & Technology ;  Technology ;  Physical Sciences
类目[WOS]: Automation & Control Systems ;  Chemistry, Analytical ;  Computer Science, Artificial Intelligence ;  Instruments & Instrumentation ;  Mathematics, Interdisciplinary Applications ;  Statistics & Probability
研究领域[WOS]: Automation & Control Systems ;  Chemistry ;  Computer Science ;  Instruments & Instrumentation ;  Mathematics
英文摘要: Metabolite profiles and biomarker discovery benefit clinical diagnosis of type 2 diabetes mellitus (T2DM) and reflection of metabolite variations in pathophysiology. Fatty acids play an important role in the occurrence and development of diabetes mellitus. The fasting plasma glucose (FPG) values of T2DM patients are partially adjusted to normal range (FPG < 7.0 mmol/L) after the intervention of drug or other treatments. However, whether these patients return to normal is still unknown. In this study, graphical index of separation (GIOS) was employed to discover the potential biomarkers of fatty acids for T2DM and subsequently investigate whether the fatty acid differences exist between T2DM patients with low and high FPG values (low fasting plasma glucose. FPG < 7.0 mmol/L and high fasting plasma glucose, FPG >= 7.0 mmol/L). As a result, C16:0, C20:2, C20:5, C22:6 and FPG were selected as potential biomarkers, which could significantly decline the misdiagnosis rate of diabetes (from 35/98 to 0/98). Additionally, the partial least squares-discriminant analysis (PLS-DA) was also employed to identify fatty acids responsible for T2DM and subsequently build a discriminant model using significant variables. After Monte Carlo cross validation, the prediction rate of PLS-DA reaches to 89.1%. Moreover, the biomarkers selected by GIOS were compared with those selected by PLS-DA model. The almost accordant results indicate that GIOS is a simple but comprehensible method to screen biomarkers. (C) 2012 Elsevier B.V. All rights reserved.
关键词[WOS]: VARIABLE SELECTION ;  MULTIVARIATE CALIBRATION ;  UNINFORMATIVE VARIABLES ;  ELIMINATION ;  PLS ;  METABONOMICS
语种: 英语
WOS记录号: WOS:000312626800019
Citation statistics: 
内容类型: 期刊论文
URI标识: http://cas-ir.dicp.ac.cn/handle/321008/118216
Appears in Collections:中国科学院大连化学物理研究所_期刊论文

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作者单位: 1.Chinese Acad Sci, Dalian Inst Chem Phys, Key Lab Separat Sci Analyt Chem, Dalian 116023, Peoples R China
2.Tianjin Univ Tradit Chinese Med, Tianjin 300193, Peoples R China

Recommended Citation:
Xu, Wenjuan,Zhang, Liangxiao,Huang, Yuhong,et al. Fatty acid metabolic profiles and biomarker discovery for type 2 diabetes mellitus using graphical index of separation combined with principal component analysis and partial least squares-discriminant analysis[J]. CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS,2012,118:173-179.
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