Association between Pro12Ala polymorphism of peroxisome proliferator activated receptor .GAMMA.2 gene and gestational diabetes mellitus: a meta-analysis
Bibliographic record
Abstract
Objective To evaluate the association between Pro12Ala polymorphism in peroxisome proliferator activated receptor γ2 (PPARγ2) gene and gestational diabetes mellitus(GDM). Methods Publications on genetic association studies of PPARγ2 and GDM were searched using the PubMed database, The HuGE Navigator, China National Knowledge Infrastructure (CNKI), Wanfang database and VIP Science from the inception of the databases to December 1, 2014. Two reviewers independently selected literature according to the inclusion and exclusion criteria, extracted data and assessed the quality of the data using the Newcastle-Ottawa Scale (NOS) standard. Meta-analysis was performed using RevMan 5.3 software. Results Overall, 13 eligible articles were identified, including seven in English and six in Chinese, with a total of 2 787 GDM cases and 5 408 healthy controls. Quality assessment showed that the quality of the 13 articles was all good, with NOS ≥5. (1) Pro12Ala polymorphism in PPARγ2 (allele Ala or genotype Ala/Ala or Pro/Ala) was shown to be highly associated with GDM occurrence on general evaluation, with an OR(95%CI) of 0.74(0.60-0.93) in the allele model and 0.79(0.65-0.96) in the dominant genetic model (P 0.05, respectively), although there was still a significant correlation in polymerase chain reaction-restriction fragment length polymorphism with an OR(95%CI) of 0.58(0.43-0.79) in the allele model and 0.62(0.45-0.85) in the dominant genetic model (P<0.01, respectively). Conclusions The Ala allele and the Ala/Ala or Pro/Ala genotypes of the Pro12Ala polymorphism in PPARγ2 can decrease the risk of GDM. However, there are differences in the results which are affected by the genotype analysis method or races. Key words: Diabetes, gestational; PPAR gamma; Peroxisome proliferator-activated receptors; Polymorphism, genetic; Meta-analysis
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.054 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".