Evaluating the potential of matrix metalloproteinase as a diagnostic biomarker in rheumatoid arthritis and periodontitis: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Matrix metalloproteinases (MMPs) play a crucial role in the pathogenesis of several chronic diseases including rheumatoid arthritis (RA) and periodontitis (PD). RA patients with periodontitis (RA-PD) are associated with elevated inflammatory burden due to increased production of proinflammatory cytokines. Controlling upregulated MMPs activity in these patients may have potential therapeutic effects. Therefore, aim of this study is to address the focused question: "Do RA subjects with concurrent PD have different levels of MMPs in comparison to RA alone, PD alone and HC subjects?" METHODS: The systematic review was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search from 4 electronic databases (EMBASE, Medline, Web of Science, and Cochrane library) and manual search was performed from inception to July 2023. Quality assessment of each article was done using Newcastle-Ottawa Scale. Meta-analyses derived results were summarized as standardized mean difference (SMD) with 95% confidence intervals. RESULTS: A total of 879 articles were extracted. Following screening and full text assessment, 9 studies were included. MMP-1, MMP-3, MMP-8, MMP-9, and MMP-13 were consistently elevated in RA-PD subjects. MMP-8 levels were found to be higher in RA-PD subjects compared with RA alone, PD alone, and HC in 3 studies reporting GCF levels (SMD = 1.2; Z = 2.07; P = .04) and 2 studies reporting serum levels (SMD = 0.87; Z = 4.53; P < .00001). CONCLUSION: RA-PD group showed significantly higher MMP levels in their serum and GCF compared with HC, RA, and PD alone individuals. MMP-8 may serve as a reliable biomarker in the diagnosis and management of RA-PD subjects.
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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 | 0.024 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.048 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".