The biological activity of serum bacterial lipopolysaccharides associates with disease activity and likelihood of achieving remission in patients with rheumatoid arthritis
Bibliographic record
Abstract
BACKGROUND: Dysbiotic intestinal and oral microbiota have been implicated in the pathogenesis of rheumatoid arthritis (RA), but the mechanisms how microbiota could impact disease activity have remained elusive. The aim of this study was to assess the association of the biological activity of serum lipopolysaccharides (LPS) with disease activity and likelihood of achieving remission in RA patients. METHODS: We measured Toll-like receptor (TLR) 4-stimulating activity of sera of 58 RA patients with a reporter cell line engineered to produce secreted alkaline phosphatase in response to TLR4 stimulation. Levels of LPS-binding protein, CD14, and CD163 were determined by ELISA assays. RESULTS: The patient serum-induced TLR4 activation (biological activity of LPS) was significantly associated with inflammatory parameters and body mass index at baseline and at 12 months and with disease activity (DAS28-CRP, p<0.001) at 12 months. Importantly, baseline LPS bioactivity correlated with disease activity (p=0.031) and, in 28 early RA patients, the likelihood of achieving remission at 12 months (p=0.009). The level of LPS bioactivity was similar at baseline and 12-month visits, suggesting that LPS bioactivity is an independent patient-related factor. Neutralization of LPS in serum by polymyxin B abrogated the TLR4 signaling, suggesting that LPS was the major contributor to TLR4 activation. CONCLUSION: We describe a novel approach to study the biological activity of serum LPS and their impact in diseases. The results suggest that LPS contribute to the inflammatory burden and disease activity on patients with RA and that serum-induced TLR4 activation assays can serve as an independent prognostic factor. A graphical summary of the conclusions of the study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".