# Metabolomics Approach for Sweat Mapping the Performance of Soccer Players in Pretoria, South Africa
## 3 Results
### Univariate and Multivariate Data Analyses of Sweat Metabolome  
To exclude imprecise effects and predict the potential significant metabolites of pre- and post-exercise-induced sweat, partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) models were utilized to compare changes in the sweat metabolites. The PLS-DA and OPLS-DA score plots shown in Figure 2 demonstrate a separation between pre- and post-exercise-induced sweat samples. The predictive ability and fitness of PLS-DA and OPLS-DA models are represented in Figure 2, Figures S2 and S3, Supplementary Materials.  
Statistical and chemometric analyses, employed parameters of variable importance in projection (VIP) values, fold change (FC) values, and \(p\)-values to elucidate possible signature metabolites of pre- and post-exercise-induced sweat samples. Concerning the univariate analysis, the volcano plots were plotted by using fold change (FC) \(>\) 2.0 and \(p\) \(<\) 0.05, as the principle for the selection of the degree of difference metabolites, as shown in Figure 3. A total of 385 metabolites were not significant, while significant down and significant upregulated metabolites were 14 and 5, respectively. The upregulated metabolites includes derivatives of silanamine, N,N'-methanetetraylbis[1,1,1-trimethyl, Benzimidazole [2,1-a]isoquinoline, glycerol, and 2 analytes and the down regulated metabolites includes derivatives of niacin, 2-[(Trimethylsilyl)oxy)propan-1-ol, L-Serine, L-Valine, meso-Erythrid, d-Galactose, 2,3,4,5,6-pentakis-O-(trimethylsilyl)-, o-methyloxyme, (1E), L-Glutamic acid, bis(trimethylsilyl)) ester, L-Alanine, 2-Octanol, triethanolamine, methyl galactoside, methyl tetradecanoate, and analyte 164 UM93. However, after the false discovery rate (FDR) correction, niacin was the only metabolite discovered to have a significant association with exercise-induced fatigue with the Benjamini-Hochberg-adjusted \(p\)-value of 0.032 (Table 2).  
Further analysis of the total 57 signature metabolites with a VIP greater than 1.5 was carried out. The top 15 signature metabolites with the lowest \(p\)-value ranking and greater VIP as shown in Figure 4. Derivatives of niacin, Analyte 817 UM103, 2-[(Trimethylsilyl)oxy]propan-1-ol, L-Serine, pyroglutamic acid, L-Valine, L-Aspartic acid, meso-Erythrid, d-Galactose, 2,3,4,5,6-pentakis-O-(trimethylsilyl)-, o-methyloxyme, (1E), L-Isoleucine, L-Glutamic acid, bis(trimethylsilyl) ester, L-Alanine and 2-Octanol were mostly abundant pre-exercise, while silanamine, N,N'-methanetetraylbis[1,1,1-trimethyl and Benzimidazole[2,1-a]isoquinoline metabolites were mostly abundant post-exercise  
Figure 1: Total ion chromatograms (TICs) of pre- and post-exercise-induced sweat metabolites profiles.  
(Figure 4). The identified signature metabolites belonging to carboxylic acids, ketone, alcohols, aldehydes, aromatics, alkenes, hexoses, hydroxy fatty acids, tetracycline N-heterocycles, aldopentose, benzenes, alkanes, phenols and heterocyclic (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Metabolites ID** & **Molecular Weight** & \
\begin{tabular}{c} **Retention Time 10** \\ **Rtimin** \\ \end{tabular} & **Formula** & **VIP** & **FC log2** &
\begin{tabular}{c} **Paired f Test** \\ **\(p\)-Value** \\ \end{tabular} & **FOR Critical** & **FDR** \\ \hline Nación, TMS derivative & 195.29 & 15.87 & C\({}_{4}\)H\({}_{13}\)NO\({}_{3}\)Si & 3.55 & 5.449 & 0.001 & 0.002 & 0.032 \(\pm\) 14 \\ Analye 817 UM103 & - & - & - & 2.65 & 3.667 & 0.  
Figure 4: Distribution of variable importance in projection (VIP) values (VIP > 1.5) of sweat metabolites induced pre- and post-exercise. The colored boxes on the right signify the relative concentrations of the related sweat metabolites.  
Figure 3: Volcano plots analysis of changes in sweat metabolites induced pre- and post-exercise. The fold change (FC) was calculated as log2.