Gut permeability and osteoarthritis, towards a mechanistic understanding of the pathogenesis: a systematic review
Bibliographic record
Abstract
the disruption of tight junction competency, are proposed to explain a gut-joint axis through the interaction with the host immune system. Since previous studies and methods have underestimated the role of the gut-joint axis in OA and have only focussed on the characterisation of microbiota phenotypes, this systematic review aims to appraise the current evidence concerning the influence of gut permeability in the pathogenesis of OA. We propose that the tight junction disruption may be due to an increase in zonulin activity as already demonstrated for many other chronic inflammatory disorders. After years of unreliable quantification, one study optimised the methodology, showing a positive validated correlation between plasma lipopolysaccharide (LPS), obesity, joint inflammation, and OA severity. Chemokines show a prominent role in pain development. Our systematic review confirms preliminary evidence supporting a gut-joint axis in OA pathogenesis and progression. Being modifiable by several factors, the gut microbiota is a promising target for treatment. We propose a pathogenetic model in which dysbiosis is correlated to the bipartite graph of tight junctions and bacterially-produced products, aiming to direct future studies in the search of other bacterial products and tight junction disassembly regulators.KEY MESSAGESPrevious studies and methods have underestimated the impact of the gut-joint axis in osteoarthritis and have focussed on the characterisation of microbiota phenotypes rather than clear molecular mediators of disease.Gut dysbiosis is related to higher levels of bacterial toxins that elicit cartilage and synovium inflammatory pathways.Future research may benefit from focussing on both tight junctions and bacterially-produced products.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".