The gut microbiota in spondyloarthritis: an update
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review provides an updated overview of the gut microbiota's involvement in spondyloarthritis (SpA) from a clinical perspective. It explores mechanisms by which the gut microbiota may influence SpA pathogenesis and considers the therapeutic implications of targeting the microbiome in SpA treatment. RECENT FINDINGS: The pathogenesis of SpA is multifactorial, involving genetic predisposition, external factors and dysregulation of the immune system. Recent studies have identified alterations in the gut microbiome of patients with SpA, including changes in microbial diversity and specific taxa linked to disease activity. HLA-B27 status seems to influence gut microbiota composition, potentially impacting disease progression. In HLA-B27 transgenic rats, the association between gut microbiota and SpA development has been confirmed, supporting findings from human studies. A compromised gut barrier, influenced by proteins like zonulin, may allow microbial antigens to translocate, triggering immune responses associated with SpA. SUMMARY: These findings highlight the potential for microbiota-modulating therapies, such as probiotics, prebiotics, diet and exercise, in managing SpA. However, methodological variability in human studies exposes the need for more rigorous research to better understand these associations. This may offer the opportunity to refine treatment strategies, offering a personalized approach to managing the disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".