Association between polymorphisms of CYP2J2 and EPHX2 genes and risk of coronary artery disease
Bibliographic record
Abstract
OBJECTIVE: Common polymorphisms within cytochrome P450 2J2 (CYP2J2) and epoxide hydrolase 2 (EPHX2), which are involved in the generation or hydrolysis of epoxyeicosatrienoic acids, may determine susceptibility to the development of cardiovascular disease. To derive a more precise estimation of their relationship, we undertook a case-control study as well as a meta-analysis to assess possible associations of coronary artery disease (CAD) risk with CYP2J2 and EPHX2 genetic variations. METHODS: Associations among four single nucleotide polymorphisms in CYP2J2 and five in EPHX2 with CAD were examined in a total of 1344 cases and 1267 ethnically and geographically matched controls. To further confirm the effect of two functional variants (G-50T and R287Q) in the development of CAD, we conducted a meta-analysis including seven studies on G-50T polymorphism and six studies on R287Q polymorphism before June 2010. RESULTS: No significant association between common polymorphisms within these two genes and CAD was observed in our sample, either using methods of single-locus analysis or haplotype-based analysis. In addition, no association was detected in our meta-analysis between these two functional variants and the risk of developing CAD. CONCLUSION: This case-control study as well as meta-analysis suggested no association between CYP2J2 G-50T and EPHX2 R287Q and the risk of developing CAD.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".