Genetic association between <scp>microRNA</scp> gene polymorphisms and polycystic ovary syndrome susceptibility: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Polycystic ovary syndrome (PCOS) is a prevalent endocrine disorder in women of reproductive age, associated with genetic and environmental factors, including microRNA (miRNA) gene polymorphisms. OBJECTIVE: To evaluate the association between miRNA gene polymorphisms and PCOS. SEARCH STRATEGY: PubMed, Embase, Web of Science, and Scopus databases were searched to September 2024, using MeSH terms including "MicroRNAs", "Polymorphism, Single Nucleotide," and "Polycystic Ovary Syndrome". SELECTION CRITERIA: Case-control studies investigating the relationship between miRNA gene polymorphism and PCOS were included. DATA COLLECTION AND ANALYSIS: Two researchers collected the data independently. The risk of bias was assessed using the Newcastle-Ottawa Scale (NOS). Data synthesis was performed using RevMan 5.4, and the strength of the evidence was evaluated using the grading of recommendations assessment, development, and evaluation (GRADE) approach. MAIN RESULTS: Five case-control studies were included in the systematic review, encompassing 985 patients with PCOS and 1004 healthy controls. The meta-analysis included data from 870 PCOS patients and 889 controls. The GG genotype of miR-146a rs2910164 was significantly associated with a protective effect against PCOS, while the GC and CC genotypes were linked to increased PCOS risk. In contrast, the TT genotype of miR-196a-2 rs11614913 was associated with heightened PCOS susceptibility. However, the certainty of evidence supporting these associations was low, indicating that the true effects may differ from the observed estimates. CONCLUSION: miRNA polymorphisms, specifically the GG genotype of miR-146a rs2910164 and the GC, CC, and TT genotypes of miR-196a-2 rs11614913 seem to be associated with an increased risk of PCOS, warranting further large-scale studies to validate these associations.
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.021 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".