Modifiable risk factors linked to the development of rheumatoid arthritis: evidence, immunological mechanisms and prevention
Bibliographic record
Abstract
Rheumatoid Arthritis (RA) is a common autoimmune disease that targets the synovial joints leading to arthritis. Although the etiology of RA remains largely unknown, it is clear that numerous modifiable risk factors confer increased risk to developing RA. Of these risk factors, cigarette smoking, nutrition, obesity, occupational exposures and periodontal disease all incrementally increase RA risk. However, the precise immunological mechanisms by which these risk factors lead to RA are not well understood. Basic and translational studies have provided key insights into the relationship between inflammation, antibody production and the influence in other key cellular events such as T cell polarization in RA risk. Improving our general understanding of the mechanisms which lead to RA will help identify targets for prevention trials, which are underway in at-risk populations. Herein, we review the modifiable risk factors that are linked to RA development and describe immune mechanisms that may be involved. We highlight the few studies that have sought to understand if modification of these risk factors reduces RA risk. Finally, we speculate that modification of risk factors may be an appealing avenue for prevention for some at-risk individuals, specifically those who prefer lifestyle interventions due to safety and economic reasons.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".