## 8 Homo- and Heteronuclear Two-Dimensional (2D) NMR Analysis of Human Saliva
### Sample Size Considerations and MV Power Calculations
Although there remains much overlap between the 48 groups of five daily samples collected from each participant, there is clearly some evidence available that the WMS sample metabolite patterns of at least some sample donors were reproducibly distinctive from those of others, as displayed in the 2D plot shown in Figure 9. The PLS-DA model applied to all the healthy control participants also provided at least some evidence for the distinctiveness of participant clusters (Figure 9, for example, that for  
Investigation of Distinctive Metabolite Network Pools Putative Arising from Host or Salivary Microbiome Sources: Factor Analysis of Component Loading Vectors and Patterns  
For this very large healthy control-only participant dataset, we also elected to explore the loadings vectors (correlations) of individual metabolite variables on (with) each PC/factor isolated in a factor analysis technique model applied. For this purpose, we employed Varimax rotation and Kaiser normalization strategies, and the best model generated was that containing a total of 4 PCs. Table 5 lists these loadings vectors for the total of 31 \({}^{1}\)H NMR-assigned and determined biomolecule variables, along with ranges of values for their mean salivary and blood plasma concentrations referenced from the _Human Metabolome Database (HMDB)_[47] and further sources. Primarily, a comparison of already established mean salivary concentrations with those of plasma demonstrates that the concentrations of \(n\)-butyrate, _iso_-butyrate, propionate, acetate, 5-aminovalerate, pyruvate, succinate, methylamine, and trimethylamine are much or even substantially higher in the former biofluid than they are in the latter, and this is more than sufficiently convincing to confirm that all these metabolites have a predominant  
Figure 9: (**a**) Orthogonal T score [1] vs. T score [1] plot for an OPLS-DA model applied to n = 6 randomly selected WMS sample donors only, showing some reproducible phenotypic distinctions between at least some of the study participants (coded as participants 4, 7, 14, 28, 29, and 36). A corresponding PLS-DA model yielded a Q\({}^{2}\) value of 0.44 for a consideration of three orthogonal components, and a permutation test featuring 2000 permutations gave a highly significant \(p\) value of 0.007. (**b**) PC3 vs. PC2 vs. PC1 scores plot for a PLS-DA model applied to all sample participants (n = 48), again showing some discrimination between the WMS samples of donors (different colorations correspond to different study participants). Although the maximal Q\({}^{2}\) value obtained was only 0.15 for a six-component model (which unfortunately is also extremely restricted by the very large number of participant classifications used for distinction purposes, i.e., 48), a permutation test involving 2000 permutations gave rise to a very highly significant \(p\) value of \(<\)5 \(\times\) 10\({}^{-4}\). oral or oral microbiome source and that any supply form the host system via plasma and parotid saliva can certainly be considered to be minimal. Since lactate and formate have higher salivary levels than those of plasma, it also appears that the presence of larger amounts of these biomolecules in the former biofluid is also at least partially ascribable to an oral or oral microbiome origin, although, as noted above, it has been previously suggested that lactate is derived from a host source [20]. However, the factor analysis model applied below indicates that this metabolite may arise from both sources. PC1 featured strong loading contributions from lactate, acetoin, thymine, all N-acetylated metabolites 1-3, glutamate, glutamine, pyruvate, succinate, glycine, and tyrosine, along with a weaker loading contribution from urea.