Progression from healthy periodontium to gingivitis and periodontitis: Insights from bioinformatics‐driven proteomics – A systematic review with meta‐analysis
Bibliographic record
Abstract
AIM: The current study aimed to: (1) systematically review the published literature regarding the proteomics analyses of saliva and gingival crevicular fluid (GCF) in healthy humans and gingivitis and/or periodontitis patients; and (2) to identify the differentially expressed proteins (DEPs) based on the systematic review, and comprehensively conduct meta-analyses and bioinformatics analyses. METHODS: An online search of Web of Science, Scopus, and PubMed was performed without any restriction on the year and language of publication. After the identification of the DEPs reported by the included human primary studies, gene ontology (GO), the Kyoto encyclopedia of genes and genomes pathway (KEGG), protein-protein interaction (PPI), and meta-analyses were conducted. The risk of bias among the included studies was evaluated using the modified Newcastle-Ottawa quality assessment scale. RESULTS: The review identified significant differences in protein expression between healthy individuals and those with gingivitis and periodontitis. In GCF, 247 proteins were upregulated and 128 downregulated in periodontal diseases. Saliva analysis revealed 79 upregulated and 70 downregulated proteins. There were distinct protein profiles between gingivitis and periodontitis, with 159 and 31 unique upregulated proteins in GCF, respectively. Meta-analyses confirmed significant upregulation of various proteins in periodontitis, including ALB and MMP9, while CSTB and GSTP1 were downregulated. AMY1A and SERPINA1 were upregulated in periodontitis saliva. HBD was upregulated in gingivitis GCF, while DEFA3 was downregulated. PPI analysis revealed complex networks of interactions among DEPs. GO and KEGG pathway analyses provided insights into biological processes and pathways associated with periodontal diseases. CONCLUSION: The ongoing MS-based proteomics studies emphasize the need for a highly sensitive and specific diagnostic tool for periodontal diseases. Clinician acceptance of the eventual diagnostic method relies on its ability to provide superior or complementary information to current clinical assessment procedures. Future research should prioritize the multiplex measurement of multiple biomarkers simultaneously to enhance diagnostic accuracy and large study cohorts are necessary to ensure the validity and reliability of research findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".