Circulating Protein Fragments of Cartilage and Connective Tissue Degradation Are Diagnostic and Prognostic Markers of Rheumatoid Arthritis and Ankylosing Spondylitis
Bibliographic record
Abstract
Inflammation driven connective tissue turnover is key in rheumatic diseases, such as ankylosing spondylitis (AS). Few biomarkers are available for measuring disease prognosis or the efficacy of interventions applied in these tissue-related conditions. Type II collagen is the primary structural protein of cartilage and type III collagen of connective tissues, and obvious targets for the collagenalytic, which increase during tissue inflammation. The objective of the study was to investigate the diagnostic and prognostic utility of cartilage, C2M, and synovial, C3M, turnover biomarkers in AS. Serum samples were retrieved from patients suffering from AS (n = 103), RA (n = 47) and healthy controls (n = 56). AS progressors were defined as having new vertebral syndesmophytes or more that 3 unit change in mSASSS over a two-year period. Type II collagen degradation markers in serum were measured by the C2M ELISA, and type III collagen degradation by the C3M ELISA. Logistic regression and dichotomized decision tree were used to analyze the prognostic value of the markers individually or in combination. Both C2M and C3M levels were significantly higher in RA patients than in healthy controls (p<0.0001). Diagnostic utility was analyzed by ROC and areas under the curve (AUCs) were 72% and 89% for C2M and C3M, respectively. Both C2M and C3M, were significantly higher in serum samples from AS patient than from healthy controls (p<0.0001). The AUCs of C2M and C3M, respectively, were 70% and 81% for AS. A combination of C2M and C3M, dichotomized according to best cut-offs for individual markers, could correctly identify 80% of the progressors and 61% of the non-progressors. The present study is the first to show that specific biomarkers of cartilage and connective tissue degradation facilitate both diagnosis and prediction of progression of RA and AS.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 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".