A multivariate screening strategy for investigating metabolic effects of strenuous physical exercise in human serum
A multivariate screening strategy for investigating metabolic effects of strenuous physical exercise in human serum
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DOI：
10.1021/pr070007g
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发表时间：
2007-01-01
影响因子：
4.4
通讯作者：
Antti, Henrik
中科院分区：
文献类型：
--
作者：
Pohjanen, Elin;Thysell, Elin;Antti, Henrik
提出了一种新的无假设的多变量筛选方法，用于人体血清运动代谢的研究。血清气相色谱/飞行时间质谱（GC/TOFMS）数据进行处理，采用分层多变量曲线分辨率（H-MCR），正交偏最小二乘判别分析（OPLS-DA）被用来模拟剧烈运动的急性效应相关的系统变异。使用数据库比较鉴定潜在的代谢生物标志物。进行了广泛的验证，包括预测性H-MCR、7倍全交叉验证和OPLS-DA模型的预测、用于突出感兴趣的代谢物的变量排列以及用于检查代谢物显著性的成对t检验。在原始GC/TOFMS数据中验证了潜在生物标志物的浓度变化。血清样本中共分离出420种潜在代谢产物。基于420种分解代谢物的相对浓度，获得了运动前和运动后受试者之间差异的有效多变量模型。共有34种代谢物被突出显示为潜在的生物标志物，所有代谢物均具有统计学显著性（p< 8.1E-05）。例如，两个潜在的标记物被鉴定为甘油和天冬酰胺。还在原始GC/TOFMS数据中验证了这两种代谢物的浓度变化。该策略有助于解释和验证人血清中的代谢相互作用，并揭示已知或新的人体运动生理学机制的潜在标志物的身份。解决代谢研究的多元方式可以帮助增加对背后的综合生物学的理解，以及解开与运动生理学有关的新的机制解释。
A novel hypothesis-free multivariate screening methodology for the study of human exercise metabolism in blood serum is presented. Serum gas chromatography/ time-of-flight mass spectrometry ( GC/TOFMS) data was processed using hierarchical multivariate curve resolution ( H-MCR), and orthogonal partial least-squares discriminant analysis ( OPLS-DA) was used to model the systematic variation related to the acute effect of strenuous exercise. Potential metabolic biomarkers were identified using data base comparisons. Extensive validation was carried out including predictive H-MCR, 7-fold full cross-validation, and predictions for the OPLS-DA model, variable permutation for highlighting interesting metabolites, and pairwise t tests for examining the significance of metabolites. The concentration changes of potential biomarkers were verified in the raw GC/TOFMS data. In total, 420 potential metabolites were resolved in the serum samples. On the basis of the relative concentrations of the 420 resolved metabolites, a valid multivariate model for the difference between pre- and postexercise subjects was obtained. A total of 34 metabolites were highlighted as potential biomarkers, all statistically significant ( p< 8.1E-05). As an example, two potential markers were identified as glycerol and asparagine. The concentration changes for these two metabolites were also verified in the raw GC/TOFMS data. The strategy was shown to facilitate interpretation and validation of metabolic interactions in human serum as well as revealing the identity of potential markers for known or novel mechanisms of human exercise physiology. The multivariate way of addressing metabolism studies can help to increase the understanding of the integrative biology behind, as well as unravel new mechanistic explanations in relation to, exercise physiology.