Chemometrics in metabolomics-A review in human disease diagnosis

被引:422
作者
Madsen, Rasmus [1 ,2 ]
Lundstedt, Torbjorn [3 ,4 ]
Trygg, Johan [1 ,2 ]
机构
[1] Umea Univ, Dept Chem, S-90187 Umea, Sweden
[2] Umea Univ, KBC, S-90187 Umea, Sweden
[3] AcureOmics AB, Umea, Sweden
[4] Uppsala Univ, BMC, Uppsala, Sweden
关键词
Chemometrics; Metabolomics; Human disease diagnosis; Cancer; Diabetes; Theranostics; CHROMATOGRAPHY-MASS-SPECTROMETRY; MAGNETIC-RESONANCE-SPECTROSCOPY; HUMAN HEPATOCELLULAR-CARCINOMA; MINIMUM REPORTING STANDARDS; EXHALED BREATH CONDENSATE; NMR-BASED METABONOMICS; PATTERN-RECOGNITION; IDENTIFYING DIFFERENCES; ORTHOGONAL PROJECTIONS; QUANTITATIVE-ANALYSIS;
D O I
10.1016/j.aca.2009.11.042
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Metabolomics is a post genomic research field concerned with developing methods for analysis of low molecular weight compounds in biological systems, such as cells, organs or organisms. Analyzing metabolic differences between unperturbed and perturbed systems, such as healthy volunteers and patients with a disease, can lead to insights into the underlying pathology. In metabolomics analysis, large amounts of data are routinely produced in order to characterize samples. The use of multivariate data analysis techniques and chemometrics is a commonly used strategy for obtaining reliable results. Metabolomics have been applied in different fields such as disease diagnosis, toxicology, plant science and pharmaceutical and environmental research. In this review we take a closer look at the chemometric methods used and the available results within the field of disease diagnosis. We will first present some current strategies for performing metabolomics studies, especially regarding disease diagnosis. The main focus will be on data analysis strategies and validation of multivariate models, since there are many pitfalls in this regard. Further, we highlight the most interesting metabolomics publications and discuss these in detail; additional studies are mentioned as a reference for the interested reader. A general trend is an increased focus on biological interpretation rather than merely the ability to classify samples. In the conclusions, the general trends and some recommendations for improving metabolomics data analysis are provided. (C) 2009 Elsevier B.V. All rights reserved.
引用
收藏
页码:23 / 33
页数:11
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