## 9 Challenges and Limitations  
Besides the advantages of untargeted analyses, some limitations should be faced and solved. First of all, saliva has the limitation of having low site specificity for periodontitis, as GCF does. In fact, periodontal inflammation is site-specific, and saliva may not reflect localized metabolic changes, making it limited in the evaluation of the severity of periodontitis [32; 33]. However, other limitations concern the fact that all the present studies lack homogenization [136]. All the existing studies differ in the sample size, case definition, saliva collection methods, analytical platform, and protocols adopted, making it difficult to assess whether a metabolite or a protein variation has evidence to diagnose periodontitis [11]. The cross-sectional nature of some studies makes it difficult to assess the causal relationship between metabolic and protein profile changes with the disease condition and progress. Furthermore, the majority of studies focus on older adults, reducing the applicability of findings to younger populations [136]; thus, there is a need for further research to validate these biomarkers across various age groups. Taken together, these limitations accentuate the importance of introducing standardized methodologies, larger and more varied study populations, and longitudinal approaches to improve the reliability and relevance of metabolomic and proteomic biomarkers in periodontitis research [30; 61]. Moreover, once all the results are taken, they should be categorized using a standardized method for metabolomics and proteomics alone and their combination too to interpret variations correctly, without bias. In particular, for untargeted metabolomics, the limita \begin{table}
\begin{tabular}{c c c c} \hline
**Feature** & **NMR Spectroscopy** & \begin{tabular}{c} **Mass Spectrometry** \\ **(MS)** \\ \end{tabular} & **References** \\ \hline \multirow{4}{*}{Reproducibility} & \multirow{4}{*}{\begin{tabular}{c} High, robust across \\ time and between labs. \\ \end{tabular} } & Moderate; targeted & \multirow{4}{*}{[125; 133; 132]} \\  & & methods are & \\  & & reproducible, and \\  & & time and between labs. & untargeted methods & \\  & & need strict quality & \\  & & control. & \\  & & & control. & \\  & & & high; requires \\ Data Complexity & Moderate; relatively & extensive data & \\  & easier to interpret. & processing and & \\  & & normalization. & \\  & & & high (50–200+ \\ Throughput & \begin{tabular}{c} Moderate \\ (10–20 samples/day); \\ \end{tabular} & \begin{tabular}{c} High (50–200+ \\ samples/day), \\ depending on the \\ platform. \\ \end{tabular} & \multirow{4}{*}{[125]} \\  & & limited by long & \\  & acquisition times in 2D. & & \\  & Stable protocols; recent & Rapidly evolving, & \\ Method Development & \begin{tabular}{c} improvements in 2D \\ NMR and \\ hyperpolarization. \\ \end{tabular} & \begin{tabular}{c} Rapidly evolving, \\ flexible with multiple \\ ionization and \\ \end{tabular} & \multirow{4}{*}{[125]} \\  & & \
\begin{tabular}{c} chromaticographic \\ modes. \\ \end{tabular} & \\  & & & \\ Limitations &
\begin{tabular}{c} Low sensitivity–Long \\ acquisition times for \\ complex spectra. \\ \end{tabular} & - Ion suppression & \\  & & - Requires strict QC & \\  & & - Less robust than NMR & \\ \hline \end{tabular}
\end{table} 
Table 7: _Cont._tions are represented by metabolite identification and analytical variability. Translating analytical parameters into specific metabolite identities is a big problem, often caused by incomplete reference databases. For this reason, untargeted analysis should be followed by a targeted analysis, which helps in identifying metabolites and proteins found by the non-targeted approach. Meanwhile, differences in sample collection, storage, and preparation can impact metabolite stability and reproducibility [137]. On the other hand, proteomics has some limitations and challenges, too.