Metabolomics and cardiovascular biomarker discovery.
Metabolomics and cardiovascular biomarker discovery.
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DOI：
10.1373/clinchem.2011.169573
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发表时间：
2012-01
影响因子：
9.3
通讯作者：
Gerszten RE
中科院分区：
文献类型：
--
作者：
Rhee EP;Gerszten RE
代谢组学是对生物标本中低分子量生化化合物的系统分析，已越来越多地应用于生物标志物的发现。由于没有单一的分析方法可以适应整个代谢组的化学多样性，各种方法，如核磁共振波谱（NMR）和质谱（MS）已被采用，后者与一系列分离技术，包括气相色谱和液相色谱相结合。核磁共振可以在不使用外源标准的情况下为选定的代谢物提供结构信息和绝对定量，而质谱往往具有更高的分析灵敏度，可以对代谢物组进行更广泛的调查。核磁共振和质谱都可以用于表征代谢物数据，无论是以有针对性的方式还是以非有针对性的模式识别方式。除了技术方面的考虑外，仔细的样本选择和研究设计对于最大限度地减少对代谢组的潜在混杂影响也很重要，包括饮食、药物和合并症。为此，代谢物分析已被应用于小规模干预中的人类生物标志物发现，在小规模干预中，个体表型非常好，能够作为自己的生物对照，以及在更大的流行病学队列中。了解代谢物如何相互关联以及与糖尿病和肾衰竭等疾病的既定风险标志物之间的关系，对于评估这些代谢物作为临床有用生物标志物的潜在价值至关重要。应用于实验和流行病学研究设计，代谢物分析已经开始强调伴随人类疾病的代谢紊乱的广度。模型系统的实验工作和与其他功能基因组方法的整合将需要在选定的生物标志物和疾病发病机制之间建立因果关系。
Metabolomics, the systematic analysis of low molecular weight biochemical compounds in a biological specimen, has been increasingly applied to biomarker discovery. Because no single analytical method can accommodate the chemical diversity of the entire metabolome, various methods such as nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) have been employed, with the latter coupled to an array of separation techniques including gas and liquid chromatography. Whereas NMR can provide structural information and absolute quantification for select metabolites without the use of exogenous standards, MS tends to have much higher analytical sensitivity, enabling broader surveys of the metabolome. Both NMR and MS can be used to characterize metabolite data either in a targeted manner or in a nontargeted, pattern-recognition manner. In addition to technical considerations, careful sample selection and study design are important to minimize potential confounding influences on the metabolome, including diet, medications, and comorbitidies. To this end, metabolite profiling has been applied to human biomarker discovery in small-scale interventions, in which individuals are extremely well phenotyped and able to serve as their own biological controls, as well as in larger epidemiological cohorts. Understanding how metabolites relate to each other and to established risk markers for diseases such as diabetes and renal failure will be important in evaluating the potential value of these metabolites as clinically useful biomarkers. Applied to both experimental and epidemiological study designs, metabolite profiling has begun to highlight the breadth metabolic disturbances that accompany human disease. Experimental work in model systems and integration with other functional genomic approaches will be required to establish a causal link between select biomarkers and disease pathogenesis.
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影响因子：
30.8
作者：
通讯作者：
--
影响因子：
9.9
作者：
Shaham, Oded;Wei, Ru;Wang, Thomas J.;Ricciardi, Catherine;Lewis, Gregory D.;Vasan, Ramachandran S.;Carr, Steven A.;Thadhani, Ravi;Gerszten, Robert E.;Mootha, Vamsi K.
通讯作者：
Mootha, Vamsi K.
DOI：
10.1111/j.1365-2362.2010.02398.x
发表时间：
2011-03-01
影响因子：
5.5
作者：
Sato, Emiko;Kohno, Masahiro;Tanaka, Noriaki
通讯作者：
Tanaka, Noriaki
影响因子：
3.6
作者：
Dunn, Warwick B.;Broadhurst, David I.;Neyses, Ludwig
通讯作者：
Neyses, Ludwig
影响因子：
7.1
作者：
O'Sullivan, Aifric;Gibney, Michael J.;Brennan, Lorraine
通讯作者：
Brennan, Lorraine