Circulating microRNA profiles in early-stage osteoarthritis and rheumatoid arthritis
Bibliographic record
Abstract
Osteoarthritis (OA) and rheumatoid arthritis (RA) are prevalent joint diseases, yet early diagnosis remains challenging with existing methods. Circulating microRNAs are promising biomarkers for detection and differentiation of arthritis subtypes. This study aimed to profile plasma microRNAs from early OA (N = 22), early RA (N = 12), and non-OA/RA (N = 50) individuals using microRNA-sequencing. Principal component analysis revealed distinct clustering of early OA from both early RA and non-OA/RA, but not for early RA and non-OA/RA. A total of 170 differentially expressed microRNAs were identified in early OA versus the other groups, with no significant differences found between early RA and non-OA/RA. Stepwise filtering followed by RT-qPCR validation in independent samples identified six microRNAs: miR-16-5p and miR-29c-3p were upregulated in early OA compared to both early RA and non-OA/RA, while miR-744-5p, miR-382-5p, miR-3074-5p, and miR-11400 were upregulated in early RA compared to the other two groups. Additionally, three novel microRNAs were identified using bioinformatic tools-one enriched in early OA and two in early RA. Target prediction and pathway analyses revealed that early OA microRNAs were linked to extracellular matrix degradation pathways, and early RA microRNAs were linked to immune signaling. These findings highlight six known and three novel circulating microRNAs with potential as biomarkers to distinguish early OA from early RA.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".