Thrombospondin-4 correlates with MRI measures of structural damage and pain sensitisation: a new biomarker in knee osteoarthritis
Bibliographic record
Abstract
BACKGROUND: We hypothesised thrombospondin-4 (TSP-4), a molecule mediating pain sensitisation in peripheral nerve injury, is associated with pain sensitisation in OA. METHODS: A cross-sectional study of clinical, imaging and fluid biomarkers from knee OA participants was conducted. TSP-4 was assessed by immunohistochemistry (IHC) for OA tissue samples and by ELISA in serum samples. Type II collagen degradation products (CTX-II), linked to OA structural damage, was determined from urine samples. A general linear model (GLM) was used to: a) investigate how patient-reported WOMAC (Western Ontario and McMaster Universities OsteoArthritis Index) pain/stiffness subscales and pain sensitisation measured by painDETECT, related to the Hospital Anxiety and Depression Scale (HADS), structural damage quantified from MRI and X-rays, CTX-II and TSP-4; b) how TSP-4 related to structural damage. We used linear discriminant analysis (LDA) to determine a classifier for pain-sensitisation from clinical and wet-biomarkers. RESULTS: TSP-4 was expressed in cartilage, bone marrow lesion (BML) and synovial tissue from OA samples. Upregulated TSP-4 protein was observed in cartilage, synovial tissue and BMLs in a perivascular distribution and in fibrotic tissue. Serum TSP-4 was significantly higher (p = 0.001) in those with pain sensitisation (painDETECT level ≥19) compared with non-sensitised participants. Serum TSP-4 was significantly increased with Hoffa's synovitis (p < 0.001) and number of BMLs (p < 0.001 to p < 0.05). LDA provided classification accuracy of 80 % for pain sensitisation using TSP-4, CTX-II and HADS, supporting the biopsychosocial model of pain in OA. CONCLUSION: Our data suggests TSP-4 is associated with pain sensitisation in OA and is a biomarker stratifying for pain sensitisation.
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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 | 0.001 | 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) | 0.002 | 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".