Synovial gene signatures associated with the development of rheumatoid arthritis in at risk individuals: A prospective study
Bibliographic record
Abstract
OBJECTIVE: To identify molecular changes in synovium before arthritis development in individuals at risk of developing rheumatoid arthritis (RA). MATERIALS AND METHODS: We included 67 IgM rheumatoid factor and/or anti-citrullinated protein antibody positive individuals with arthralgia but without arthritis. Synovial biopsies were collected after which individuals were prospectively followed for at least 2 years during which 17 developed arthritis. An exploratory genome-wide transcriptional profiling study was performed in 13 preselected individuals to identify transcripts associated with arthritis development (n = 6). Findings were validated using quantitative real-time PCR and immunohistochemistry in the total cohort. RESULTS: Microarray-based survival analyses identified 5588 transcripts whose expression levels in synovium were significantly associated with arthritis development. Pathway analysis revealed that synovial tissue of at risk individuals who later developed arthritis display higher expression of genes involved in adaptive immune response-related pathways compared to at risk individuals who did not develop arthritis. Lower expression was observed for genes involved in extracellular matrix receptor interaction, Wnt-mediated signal transduction and lipid metabolism. Two-way hierarchical clustering analyses of a 27-gene signature separated the total at risk cohort into two groups, where pre-RA individuals preferred to cluster together. Immunohistochemistry studies revealed more podoplanin positive cells and lower lipid droplet staining in synovial tissue from pre-RA individuals. CONCLUSION: Synovial alterations in adaptive immune response and lipid metabolism are associated with future development of arthritis. Since this data show synovial changes without overt cellular infiltration, these may be attributed to preclinical changes in resident synovial tissue cells such as fibroblasts, macrophages and tissue resident T cells.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".