## 8 Homo- and Heteronuclear Two-Dimensional (2D) NMR Analysis of Human Saliva
### Sample Size Considerations and MV Power Calculations
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & **PC1** & **PC2** & **PC3** & **PC4** & \
\begin{tabular}{c} **Mean Salivary Level (\(\mathbf{\mu}\)mol./L) * \\ \end{tabular} &
\begin{tabular}{c} **Mean Blood** \\ **Level (\(\mathbf{\mu}\)mol./L) * \\ \end{tabular} \\ \hline \(n\)-Cap & 0.19 & **0.85** & 0.23 & 0.12 & 7–8 & 17 ** \\ \hline \(n\)-But & 0.28 & **0.91** & 0.14 & 0.00 & 12–1470 & 1.0 ** \\ \hline Prop & 0.21 & **0.84** & 0.02 & 0.12 & 38–6420 & 0.9 ** \\ \hline Val & 0.11 & 0.17 & **0.60** & 0.31 & 4–48 & 130–266 \\ \hline _iso_-But & 0.26 & **0.38** & 0.08 & **0.56** & 117–885 & 2.3 ** \\ \hline
3-D-HB & 0.21 & **0.67** & 0.29 & **0.48** & 3–16 & 36–323 \\ \hline Lac & **0.42** & 0.34 & 0.08 & **0.59** & 96–20,310 & 740–2980 \\ \hline Ace & **0.48** & **0.67** & 0.30 & **0.42** & na & 1 (WB) \\ \hline Ala & 0.30 & **0.86** & 0.32 & 0.24 & 14–145 & 200–585 \\ \hline Thy & **0.46** & **0.54** & 0.24 & **0.52** & 2–9 & na \\ \hline A & 0.31 & **0.77** & 0.01 & 0.25 & 112–14,640 & 27–69 \\ \hline N-Ac-1 & **0.84** & 0.18 & 0.12 & **0.47** & n/a & n/a \\ \hline N-Ac-2 & **0.79** & 0.22 & 0.09 & **0.54** & n/a & n/a \\ \hline N-Ac-3 & **0.76** & **0.39** & 0.28 & **0.42** & n/a & n/a \\ \hline
5-AV & 0.32 & **0.68** & 0.19 & 0.31 & 74–470 & na \\ \hline Glu & **0.78** & 0.26 & 0.34 & **0.37** & 12–14 & 24–177 \\ \hline Pyr & **0.76** & **0.47** & 0.23 & 0.30 & 9–5350 & 34–67 \\ \hline Gln & **0.82** & **0.35** & 0.17 & **0.35** & 5–42 & 491–658 \\ \hline Suc & **0.69** & **0.40** & 0.31 & 0.25 & 9–2260 & 6–16 \\ \hline MA & 0.30 & 0.10 & **0.82** & 0.27 & 101 ** & 19 * \\ \hline DMA & 0.08 & **0.39** & **0.81** & 0.20 & 11–65 & 1–48 \\ \hline \end{tabular}
\end{table} 
Table 5: Loading vectors of salivary metabolites on (i.e., linear correlations with) components (PCs) 1–4 for a factor analysis model performed with a total of 31 \({}^{1}\)H NMR-determined analytes. The TSP-normalized dataset was glog-transformed and Pareto-scaled prior to principal factor analysis, and Varimax rotation with Kaiser normalization was then applied for the purpose of further analysis. The significance threshold of component (PC) loadings vectors was set at 0.35 in view of the large sample size of this investigation. Abbreviations: as Figure 10, with na, not available; n/a, not applicable. * Values obtained from the _HMDB_[47]; ** only one observation available. Taurine most notably loads on PC4 very highly, but it also offers a small but significant contribution towards host source-derived PC1. This \(\beta\)-amino acid is found in many tissues at high (usually several mmol./L) levels [157]; however, its presence in human saliva is not simply explicable, although its salivary concentrations are similar to those of blood plasma (Table 5). Taurine is not detectable in glandular saliva [30] but it is found in GCF at high levels, which are likely to arise from immune cell sources since they are able to accumulate this metabolite at concentrations of up to 50 mmol./L [158]. Figure 11: Agglomerative hierarchical clustering (AHC) analysis of metabolite variables of a large  
healthy control WMS supernatant dataset showing the clear distinction of these into four separate clusters from the dissimilarity threshold index (dotted horizontal line), which are colored blue, black, green, and violet. The two larger green and violet clusters are further split into two sub-clusters, and one for the latter contains all bacterial-derived malodorous methylamine species with the amino acid valine (far right-hand side), whilst the other contains all oral microbiome-derived organic acid anions, specifically _n_-caproate, _n_-butyrate, _iso_-butyrate, propionate, 5-aminovalerate, and acetate, plus acetoin and the amino acids alanine, phenylalanine, and tyrosine. TSP-normalized metabolite levels were glog-transformed and Pareto-scaled prior to AHC analysis, which was conducted using [PERSON]'s agglomeration method and Euclidean distance as a dissimilarity measure.