Circulating miR-126-3p is a mechanistic biomarker for knee osteoarthritis
Bibliographic record
Abstract
Osteoarthritis is a major contributor to pain and disability worldwide, yet there are currently no validated soluble biomarkers or disease-modifying treatments. Given that microRNAs are promising mechanistic biomarkers that can be therapeutically targeted, in this study, we aimed to identify and prioritize reproducible circulating microRNAs associated with radiographic knee osteoarthritis. Across four independent cohorts, we find circulating miR-126-3p is elevated in knee osteoarthritis versus controls. Across six primary human knee osteoarthritis tissues, miR-126-3p is highest in subchondral bone, fat pad and synovium, and lowest in cartilage. Following both intravenous and intra-articular miR-126-3p mimic treatment in a surgical mouse model of knee osteoarthritis, we show reduced disease severity in males. In human knee osteoarthritis biospecimens, miR-126-3p mimic treatment reduces genes and markers associated with angiogenesis, as well as genes linked to osteogenesis, adipogenesis, and synovitis—processes secondary to angiogenesis. Our findings indicate that miR-126-3p is elevated in knee osteoarthritis and mitigates disease severity, supporting its potential as a biomarker and therapeutic target. Though it is the most common joint disease, osteoarthritis has no molecular biomarkers or disease-modifying therapies. Here, the authors show miR-126-3p is a mechanistic biomarker that regulates angiogenesis and mitigates knee osteoarthritis 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".