Radiographic Bone Damage in Chronic Gout Is Negatively Associated with the Inflammatory Cytokines Soluble Interleukin 6 Receptor and Osteoprotegerin
Bibliographic record
Abstract
OBJECTIVE: We investigated the risk factors for radiographic bone damage to foot joints in patients with chronic gout among various patient characteristics and serum inflammatory cytokines such as interleukin 1ß (IL-1ß), IL-6, soluble IL-6 receptor (sIL-6R), osteoprotegerin (OPG), and receptor activator of nuclear factor-κB ligand (RANKL). METHODS: Fifty consecutive male patients with gout and 54 age-matched healthy male controls were enrolled. Serum levels of cytokines including IL-1ß, IL-6, sIL-6R, OPG, and RANKL were measured using ELISA. Radiographic damage indices including erosion scores, narrowing scores, and total scores for foot joints were assessed according to a modified Sharp-van der Heijde system. RESULTS: There were significant differences in serum IL-1ß, IL-6, sIL-6R, OPG, and RANKL levels between patients with gout and the controls, after adjustment for confounding factors such as age, body mass index, blood urea nitrogen, creatinine, triglyceride, and fasting blood glucose (p = 0.034 for IL-1ß, p < 0.001 for IL-6, p = 0.040 for sIL-6R, p = 0.002 for OPG, and p = 0.018 for RANKL). Radiographic damage indices (erosion, narrowing, and total scores) were negatively associated with serum sIL-6R and OPG levels in multivariable-adjusted regression analysis. Serum sIL-6R levels in patients without radiographic damage were higher than in those with damage (p = 0.006). CONCLUSION: Radiographic damage in patients with chronic gouty arthritis was negatively associated with serum sIL-6R and OPG. Further study on the role of inflammatory cytokines in the pathogenesis of radiographic damage in gout is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".