Periodontitis: Microbial Dysbiosis, Non‐Resolving Inflammation, or Both?
Bibliographic record
Abstract
The central question addressed in this review revisits the historical chicken-and-egg debate: "In periodontitis, does microbial dysbiosis drive inflammation, or does inflammation shape the subgingival microbiome?" This question is reframed through the lens of inflammation resolution. Specialized pro-resolving mediators (SPMs) provide a mechanistic framework for understanding how inflammation intersects with microbial dysbiosis. Derived from omega-3 and omega-6 fatty acids, SPMs actively promote the resolution of inflammation through binding of specific cell surface receptors rather than nonspecifically suppressing it, highlighting their therapeutic potential as side-effect-free host modulators, with implications beyond periodontitis to other chronic inflammatory diseases. The evidence reviewed shows how SPMs can: (1) control inflammation by resolution rather than inhibition, (2) reverse microbial dysbiosis as a consequence of inflammation control, and (3) promote tissue regeneration through diverse biological pathways. Whether the primary dysregulation in periodontitis lies solely in resolution failure or involves additional-possibly still unidentified-mechanisms, remains unclear. All individuals harbor periodontal pathobionts, yet only a subset develop severe disease. Why do some individuals with significant biofilm accumulation maintain attachment levels, while others with reasonable plaque control become edentulous? This remains one of the most significant unanswered questions in periodontology. What is evident, however, is the need for a paradigm shift. While bacteria initiate the inflammatory process in all individuals, it is the host response that ultimately determines the progression to periodontitis.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".