The characteristic expression of circulating MicroRNAs in osteoporosis: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective To evaluate the characteristics of the circulating microRNA expression profiles in patients with osteoporosis. Methods A systematic literature search was performed using the Web of Science, PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), VIP, and WANFANG databases from inception until 1 March 2024. The search strategy employed keywords, encompassing “osteoporosis”, “bone loss”, or “osteopenia” and “miRNA” or “microRNA”. The Newcastle-Ottawa Scale (NOS) quality assessment scale was used to evaluate the methodological quality. Heterogeneity tests and statistical analyses of all data were performed by Stata 16.0. The differences in microRNA levels between groups were illustrated by the weighted mean difference (WMD) and 95% confidence interval (95% CI). Results A total of 27 studies were included and analyzed in the meta-analysis, with 2,263 participants. The results showed that miR-21-5p (WMD 0.88, 95% CI: 0.22 to 1.55), miR-125b-5p (WMD 6.63, 95% CI: 0.19 to 13.08), miR-483-5p (WMD 6.43, 95% CI: 3.26 to 9.61), miR-133a (WMD 1.43, 95% CI: 1.39 to 1.47), miR-422a (WMD 1, 95% CI: 0.28 to 1.72), and miR-214-3p (WMD 2.03, 95% CI: 0.14 to 3.92) were significantly upregulated, and miR-497-5p (WMD -0.57, 95% CI: -0.98 to -0.17) was significantly downregulated. Conclusion miR-21-5p , miR-125b-5p , miR-483-5p , miR-133a , miR-497-5p , miR-422a , and miR-214-3p might serve as potential diagnostic biomarkers for osteoporosis. In the future, integrating these miRNAs to build a diagnostic model might be a promising diagnosis strategy for osteoporosis. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/ , identifier CRD42023481209.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.037 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".