2007
DOI: 10.1021/pr070007g
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A Multivariate Screening Strategy for Investigating Metabolic Effects of Strenuous Physical Exercise in Human Serum

Abstract: 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.… Show more

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Cited by 104 publications
(96 citation statements)
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References 37 publications
(48 reference statements)
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“…Although many studies have assessed the effect of strenuous exercise on the metabolite profile in blood, none identified the N-lactoyl-amino acids (44)(45)(46). Nevertheless, many of the studies report multiple unidentified exercise-dependent metabolites, most likely including N-lactoyl-amino acids.…”
Section: Discussionmentioning
confidence: 99%
“…Although many studies have assessed the effect of strenuous exercise on the metabolite profile in blood, none identified the N-lactoyl-amino acids (44)(45)(46). Nevertheless, many of the studies report multiple unidentified exercise-dependent metabolites, most likely including N-lactoyl-amino acids.…”
Section: Discussionmentioning
confidence: 99%
“…A wide range of metabolites present in plasma and serum has been determined using liquid chromatography mass spectrometry and/or gas-chromatography mass spectrometry collected pre-and post-exercise. In most cases, a wide range of metabolites have been reported (Pohjanen et al 2007, Lewis et al 2010, Peake et al 2014 to exist and that with exercise changes in metabolites related to carbohydrate, triglyceride and markers of the tricarboxylic acid have been reported. Peake et al (2014), these studies were not hypothesis driven and are more observational in nature.…”
Section: Figurementioning
confidence: 99%
“…Results can be prioritized based on cross-validation, and only those that meet a preset threshold of predictive ability are considered potential 'true positives.' Crossvalidation methods have been widely used over the last decade in genomic, 41,42 metabolomic, 43 proteomic, 44,45 and transcriptomic 46 -48 studies as a method for discriminating between true associations and false-positive associations.…”
Section: Statisticsmentioning
confidence: 99%