Combined carrier status of prothrombin 20210A and factor XIII-A Leu34 alleles as a strong risk factor for myocardial infarction: evidence of a gene-gene interaction
Bibliographic record
Abstract
Studies associating the prothrombin 20210G>A (FII 20210A), factor V Leiden (FVL), and factor XIII Leu34 (FXIII-A Leu34) alleles with myocardial infarction (MI) have yielded conflicting results. Complicated gene-gene interactions, small sample sizes, and heterogeneous genetic and environmental backgrounds may contribute to opposing findings. Simultaneous analysis of multiple gene variants in a large sample size from a genetically isolated population may overcome these weaknesses. Genotyping was performed in 500 MI patients and 500 control subjects from the genetically isolated Newfoundland population to determine the prevalence of the FII 20210A, FVL, and FXIII-A Leu34 variants and their association with MI. Gene-gene interactions were also analyzed. The prevalence of the FII 20210A allele was higher in MI patients (3.2%) than in control subjects (1.0%; P =.015). The FII 20210A allele was also 5.6-fold higher in MI patients younger than 51 years than in age-matched control subjects (P =.04). FVL showed 3.9-fold higher prevalence in young patients than in patients older than 50 years (P =.004) and 2.7-fold higher than in age-matched control subjects (P =.007). Furthermore, the prevalence of combined carriers of the FXIII-A L34 and FII 20210A alleles was 12-fold higher in MI patients than in control subjects (P =.002) and with 92% penetrance. There was disequilibrium of the FXIII-A Leu34 allele to MI patients carrying the FII 20210A allele as a genetic background. Based on our data, we determined that (1) the FII 20210A allele is a risk factor for MI, possibly important for early onset; (2) FVL may predispose for early-onset MI; (3) the FXIII-A Leu34 allele predisposes for MI in males only; however, (4) interaction between the FII 20210A and FXIII-A Leu34 alleles forms a synergistic coeffect that strongly predisposes for MI, placing combined carriers at high risk for MI.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".