Correlation of bone metabolism with serum pain mediators and inflammatory cytokines in knee osteoarthritis and their role in predicting poor rehabilitation outcomes: A prospective cohort study
Bibliographic record
Abstract
Background: The present study was designed to investigate how bone metabolism markers - specifically Procollagen Type I N-Terminal Propeptide (PINP) and C-Telopeptide of Type I Collagen (CTX) - correlate with serum pain mediators (Prostaglandin E2 [PG E2], Norepinephrine [NE], Substance P [SP]) and inflammatory cytokines (Interleukin-1p [IL-ip], Interleukin-6 [IL-6], Tumor Necrosis Factor-a [TNF-a]) in individuals with knee osteoarthritis (KOA). A further aim was to develop a multi-biomarker predictive model for identifying patients at risk of suboptimal rehabilitation outcomes to guide targeted clinical management. Methods: In this prospective cohort study, 184 KOA patients and an 180 healthy controls were enrolled between January 2023 and May 2024. Using baseline serum, biomarker levels were assessed; Enzyme-Linked Immunosorbent Assay (ELISA) was employed for all analytes except SP, which was determined by radioimmunoassay. The primary endpoint, suboptimal rehabilitation outcome, was determined as either a <30% enhancement in the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score or a <2-point decrease on the Visual Analogue Scale (VAS). Model construction utilized multivariate logistic regression, with its performance evaluated through receiver operating characteristic (ROC) curve analysis, obtaining the area under the curve (AUC), sensitivity, and specificity. Results: KOA patients exhibited significantly lower serum PINP levels but elevated levels of CTX, PGE2, NE, SP, IL-1p, IL-6, and TNF-a compared to controls (P< 0.05). PINP demonstrated significant inverse correlations with the pain and inflammatory biomarkers, while CTX showed strong positive correlations with them (P< 0.05). The integrative logistic regression model, integrating PINP, CTX, inflammatory cytokines, and pain mediators, demonstrated excellent predictive value for poor outcomes, with an AUC of 0.862, 83.58% sensitivity, and 78.63% specificity (P< 0.05). Conclusions: Dysregulated bone metabolism (characterized by low PINP and high CTX) in KOA is significantly linked to heightened expression of pain-associated and inflammatory markers. A combined panel of these biomarkers serves as an effective tool for predicting individuals likely to experience suboptimal rehabilitation results.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".