Association of Uric Acid in Oral Health, Periodontal Disease, and Systemic Disorders: A Systematic Review
Bibliographic record
Abstract
Background: The relationship between periodontitis or periodontal disease (PD) with uric acid (UA) is controversial. Furthermore, a rise in UA levels in common systemic disorders documented to be liked to PD must be understood. Objectives: The objective is to determine the association of UA levels with periodontal health and disease states by a systematic review of evidence from observational studies. Furthermore, we aimed to group secondary research papers which showed systemic diseases linked to periodontitis (SDLP) for the changes in UA levels in serum and discuss the consequences. Materials and Methods: A systematic review was conducted following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement. The literature search was followed by qualitative assessment using the Newcastle–Ottawa scale when evaluating PD with UA associations. A similar strategic search system was used for identifying secondary studies (systematic reviews with meta-analyses or [SRMA]) for identifying UA levels in common SDLP. Results: Out of 212 selected studies, 40 underwent full-text analysis; 6 were eligible for systematic review. The current review summarizes that overall PD cases have significantly more UA levels than healthy individuals or controls from data from three observational studies. In the case of SDLP, around 8 SRMA (conducted on = 164 observational studies) revealed a significant association ( P < 0.05 in all instances) with serum hyperuricemia; cancer incidence and progression were more in women with raised UA than in men. Conclusion: The review showed that UA levels were significantly altered in PD compared to healthy individuals. However, the available evidence is inadequate to establish whether UA is diminished or elevated in periodontal disease. The SDLP all showed a significant association with raised serum UA levels with cancers affecting more women than men with high UA levels.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".