Sclerostin and Antisclerostin Antibody Serum Levels Predict the Presence of Axial Spondyloarthritis in Patients with Inflammatory Bowel Disease
Bibliographic record
Abstract
OBJECTIVE: The early diagnosis of inflammatory bowel disease (IBD)-associated spondyloarthritis (SpA/IBD) in patients affected by IBD represents a major topic in clinical practice; in particular, to date there are no available serum biomarkers revealing the presence of joint inflammation in these patients. Sclerostin (SOST), an antagonist of the Wnt/β-catenin pathway, and antisclerostin-immunoglobulin G (anti-SOST-IgG) have been recently studied in patients with ankylosing spondylitis (AS) as a putative marker of disease activity. METHODS: SOST and anti-SOST-IgG serum levels were assayed in 125 patients with IBD, 85 with axial or peripheral SpA, and in control groups (patients with AS and rheumatoid arthritis, and healthy individuals). The diagnostic performance in discriminating the presence of SpA/IBD was assessed for both candidate biomarkers. RESULTS: Patients affected by SpA/IBD with axial involvement displayed significantly lower levels of SOST and higher levels of anti-SOST-IgG compared to patients with only peripheral arthritis, IBD, and controls. Moreover, SOST and anti-SOST-IgG serum levels were inversely correlated and were associated with the duration of articular symptoms. Both biomarkers showed good accuracy in predicting the presence of axial SpA in patients with IBD. CONCLUSION: We demonstrated that in patients with IBD, SOST and anti-SOST-IgG might represent novel biomarkers to assess the presence of axial joint involvement. Moreover, the development of anti-SOST-IgG and the subsequent decrease of SOST serum levels could play a role in the pathogenesis of SpA/IBD.
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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 | 0.002 |
| 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 | 0.001 | 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".