The role of synovial fluid markers of catabolism and anabolism in osteoarthritis, rheumatoid arthritis and asymptomatic organ donors
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study was to correlate the level of anabolic and catabolic biomarkers in synovial fluid (SF) from patients with rheumatoid arthritis (RA), patients with osteoarthritis (OA) and asymptomatic organ donors. METHODS: SF was collected from the knees of 45 OA, 22 RA patients and 20 asymptomatic organ donors. Eight biomarkers were selected and analyzed by using an enzyme-linked immunosorbent assay: interleukin (IL)-1, IL-6, IL-8 and IL-11; leukemia-inhibitory factor (LIF); cartilage oligomeric protein (COMP); osteocalcin; and osteogenic protein 1 (OP-1). Data are expressed as medians (interquartile ranges). The effects of sex and disease activity were assessed on the basis of the Western Ontario and McMaster Universities index score for patients with OA and on the basis of white blood cell count, erythrocyte sedimentation rate and C-reactive protein level for patients with RA. RESULTS: The mean ages (± SD) of the patients were as follows: 53 ± 9 years for patients with OA, 54 ± 11 years for patients with RA and 52 ± 7 years for asymptomatic organ donors. No effect of participants' sex was identified. In the SF of patients with RA, four of five cytokines were higher than those in the SF of patients with OA and those of asymptomatic organ donors. The most significant differences were found for IL-6 and IL-8, where IL-6 concentration in SF of patients with RA was almost threefold higher than that in patients with OA and fourfold higher than that in asymptomatic donor controls: 354.7 pg/ml (1,851.6) vs. 119.4 pg/ml (193.2) vs. 86.97 pg/ml (82.0) (P < 0.05 and P < 0.05, respectively). IL-8 concentrations were higher in SF of patients with RA than that in patients with OA as well as that in asymptomatic donor controls: 583.6 pg/ml (1,086.4) vs. 429 pg/ml (87.3) vs. 451 pg/ml (170.1) (P < 0.05 and P < 0.05, respectively). No differences were found for IL-11 in the SF of patients with RA and that of patients with OA, while a 1.4-fold difference was detected in the SF of patients with OA and that of asymptomatic donor controls: 296.2 pg/ml (257.2) vs. 211.6 pg/ml (40.8) (P < 0.05). IL-1 concentrations were the highest in the SF of RA patients (9.26 pg/ml (11.1)); in the SF of asymptomatic donors, it was significantly higher than that in patients with OA (9.083 pg/ml (1.6) vs. 7.76 pg/ml (2.6); P < 0.05). Conversely, asymptomatic donor control samples had the highest LIF concentrations: 228.5 pg/ml (131.6) vs. 128.4 pg/ml (222.7) in the SF of patients with RA vs. 107.5 pg/ml (136.9) in the SF of patients with OA (P < 0.05). OP-1 concentrations were twofold higher in the SF of patients with RA than those in patients with OA and threefold higher than those in asymptomatic donor control samples (167.1 ng/ml (194.8) vs. 81.79 ng/ml (116.0) vs. 54.49 ng/ml (29.3), respectively; P < 0.05). The differences in COMP and osteocalcin were indistinguishable between the groups, as were the differences between active and inactive OA and RA. CONCLUSIONS: Activation of selected biomarkers corresponds to the mechanisms that drive each disease. IL-11, LIF and OP-1 may be viewed as a cluster of biomarkers significant for OA; while profiling of IL-1, IL-6, IL-8, LIF and OP-1 may be more significant in RA. Larger, better-defined patient cohorts are necessary to develop a biomarker algorithm for prognostic use.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".