Circulating miR-126-3p is a mechanistic biomarker for knee osteoarthritis
Bibliographic record
Abstract
Abstract As a chronic joint disease, osteoarthritis (OA) is a major contributor to pain and disability worldwide, and yet there are currently no validated soluble biomarkers or disease-modifying treatments. Since microRNAs are promising mechanistic biomarkers that can be therapeutically targeted, we aimed to prioritize reproducible circulating microRNAs in knee OA. We performed secondary analysis on two microRNA-sequencing datasets and found circulating miR-126-3p to be elevated in radiographic knee OA compared to non-OA individuals. This finding was validated in an independent cohort (N=145), where miR-126-3p showed an area under the receiver operating characteristic curve of 0.91 for distinguishing knee OA. Measuring miR-126-3p in six primary human knee OA tissues, subchondral bone, fat pad and synovium exhibited the highest levels, and cartilage the lowest. Following systemic miR-126-3p mimic treatment in a surgical mouse model of knee OA, we found reduced disease severity. Following miR-126-3p mimic treatment in human knee OA tissue explants, we found direct inhibition of genes associated with angiogenesis and indirect inhibition of genes associated with osteogenesis, adipogenesis, and synovitis. These findings suggest miR-126-3p becomes elevated during knee OA and mitigates disease processes to attenuate severity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".