Fig 1.
The volcano plot displays the log2(MFC) between minimum and maximum concentrations values versus the-log10(P-value) calculated from statistical hypothesis testing. The horizontal blue line indicates the selected significance level of 0.001. The vertical blue line indicates the threshold for classification as moderate predictor (MFC > 1.20). The vertical green line denotes the classification threshold for a strong predictor (MFC > 1.40). Acetylcarnitine (C2), propionylcarnitine (C3) and alanine could be selected as strong biomarker candidates. Valerylcarnitine (C5), arginine, glucose, butyrylcarnitine (C4), methylmalonylcarnitine (C3-DC-M), and hydroxyvalerylcarnitine (C5-OH) were identified as moderate biomarker candidates.
Table 1.
Selection of dynamic biomarker candidates.
Fig 2.
Kinetic signatures of acylcarnitines.
Kinetic signatures of the 11 selected acylcarnitines are depicted. Dynamic curves were characterized by polynomial fitting of 9th degree to the median concentration values of the analyzed metabolites. For visualization, relative changes (in %) of metabolite concentrations in reference to their initial concentration at rest are displayed. An early response pattern is shown for valerylcarnitine (C5) with a decrease in relative concentration of approx. 16%. Late response profiles include acetylcarnitine (C2), propionylcarnitine (C3) and butyrylcarnitine (C4).
Fig 3.
Kinetic signatures of amino acids.
Kinetic signatures of the 18 selected amino acids. Methionine yields a halving interval response pattern with a plateau (sigmoid characteristics). Alanine and arginine show a late response pattern.
Fig 4.
A delayed response pattern is apparent in glucose, decreasing in relative concentration (-12%) towards the end of exercise with a steep increase (up to 13%) after the end of exercise during the recovery phase.
Table 2.
Metabolite groups with similar patterns.
Fig 5.
Kinetic shape templates for the classification of similar dynamic patterns.
Table 3.
Anthropometric data.
Fig 6.
Flow chart of the selected data analysis and biomarker discovery workflow (according to the workflow described by Baumgartner & Graber, 2008 [42]). Intermediate discovery steps include the technical validation of raw data, preprocessing of data, selection of dynamic biomarker candidates, modeling and characterization of metabolite kinetic patterns, identification of metabolite groups with similar kinetic behavior, specification of observed kinetic shape templates, classification of dynamic biomarker candidates, and subsequently the biochemical interpretation of findings.
Fig 7.
A) Concentration curves of all test persons after linear interpolation. B) Box plot representation of concentration curves of all test persons.
Fig 8.
Colored heatmap, visualizing the results of hierarchical cluster analysis. Concentration values are scaled and centered for each metabolite by row, resulting in an improved color representation. Relative workload values (x-axis) are visualized in linear order, resulting in a colored representation of the polynomially fitted concentration curves for each metabolite.