Peroxisome proliferator-activated receptor Pro12Ala polymorphism and the risks of gestational diabetes mellitus
Bibliographic record
Abstract
BACKGROUND: Peroxisome proliferator-activated receptors-λ (PPAR-λ) is a member of nuclear receptor superfamily and acts as a ligand-dependent transcription factor often found in the adrenal gland, the spleen, and adipose tissue. The Pro12Ala polymorphism of PPAR-λ has been associated with the risks of gestational diabetes mellitus (GDM); however, association studies have provided conflicting results. The aim of this Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) compliant meta-analysis is to reach a more up-to-date and accurate estimation of the relationship between Pro12Ala genetic polymorphisms and the risks of GDM. METHODS: Eligible studies were retrieved by searching PubMed, EMBASE, Web of Science, Ovid, WanFang, and Chinese National Knowledge Databases and selected according to a pre-defined inclusion criterion. The risk of bias was assessed using the Newcastle-Ottawa quality assessment scale. The per-allele odds ratio (OR) of risk allele proline (Pro) was compared between cases and controls in each study to describe the association between the Pro allele and an individual's risk of GDM. The ORs were pooled using both the random-effects model (the DerSimonian and Laird method) and the fixed effects model (the Mantel-Haenszel method) and the 95% confidence interval (95% CI) was calculated using Woolf method. RESULTS: The final meta-analysis included a total of 11 articles of 12 data sets consisting of 7054 controls and 2980 GDM cases. Our results demonstrate that the Pro allele is not associated with GDM [OR: across multiple populations, 95% CI: 0.98-1.24; P(Z) = 0.01; P(Q) = 0.003]. In the stratified analysis by ethnicity, significantly increased risks were found for the Chinese (OR = 2.36; 95% CI: 1.47-3.78) and Korean (OR = 1.39; 95% CI: 1.00-1.93) populations. CONCLUSION: These data suggest the potential role of Pro allele in the pathogenesis of GDM in Asian populations. Although the funnel plot of included studies showed assymetry, the results using the "trim and fill" method did not alter the conclusion of this study.
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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) | 0.009 | 0.022 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".