Biomarkers of Bone Metabolism in Ankylosing Spondylitis in Relation to Osteoproliferation and Osteoporosis
Bibliographic record
Abstract
OBJECTIVE: To identify biomarkers for bone metabolism in patients with ankylosing spondylitis (AS) and to determine the relationship between these biomarkers and disease activity, back mobility, osteoproliferation, and bone mineral density (BMD). METHODS: Serum levels of Wingless protein (Wnt-3a), Dickkopf-1 (DKK-1), sclerostin, soluble receptor activator of nuclear factor-κB ligand (sRANKL), and osteoprotegerin were assessed using ELISA. Ankylosing Spondylitis Disease Activity Score-C reactive protein, Bath Ankylosing Spondylitis Disease Activity Index, Bath Ankylosing Spondylitis patient global score, and C-reactive protein (CRP) were used as disease activity measures, and Bath Ankylosing Spondylitis Metrology Index (BASMI) as a measure of spinal mobility. Lateral spine radiographs were scored for chronic AS-related changes (mSASSS). BMD was measured with dual-energy x-ray absorptiometry. RESULTS: Two hundred four patients with AS (NY criteria; 57% men), with a mean age of 50 ± 13 years and disease duration 15 ± 11 years, and 80 age and sex-matched controls were included. The patients with AS had significantly higher serum levels of Wnt-3a (p < 0.001) and lower levels of sclerostin (p = 0.014) and sRANKL (p = 0.047) compared with the controls. High CRP was associated with low sclerostin (r(S) = -0.21, p = 0.003) and DKK-1 (r(S) = -0.14, p = 0.045). In multiple linear regression analyses, increasing BASMI and mSASSS were independently associated with older age, male sex, high CRP, and elevated serum levels of Wnt-3a. In addition, mSASSS remained associated with a high number of smoking pack-years after adjusting for age. Low BMD of femoral neck was associated with high mSASSS after adjusting for age. CONCLUSION: Serum levels of Wnt-3a are elevated in AS and associated with increased BASMI and mSASSS, independent of age, indicating that Wnt-3a could be a biomarker for the osteoproliferative process.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".