Combined Polymorphisms in Oxidative Stress Genes Predict Coronary Artery Disease and Oxidative Stress in Coronary Angiography Patients
Bibliographic record
Abstract
Oxidative stress has been implicated in all stages of atherosclerosis, but how inherited variations in oxidative stress genes influence the severity of cardiovascular disease is not known. We tested associations between polymorphisms in candidate oxidative stress genes, plasma oxidative stress biomarkers, and cardiovascular mortality in an angiography cohort. Single nucleotide polymorphisms (SNPs) across 15 genes were selected by linkage disequilibrium tagging. Genotyping was performed using customized arrayed primer extension micro-arrays, with automated genotype calling methods. Effects of SNPs and haplotypes on plasma oxidative stress and coronary artery disease (CAD) were estimated using a stochastic estimation maximization algorithm. Proportionate hazards analyses were used to determine effects of single and combined genetic markers on cardiovascular mortality risk, and on the following oxidative stress biomarkers: myeloperoxidase (MPO), nitrotyrosine, oxidized low-density lipoprotein, and antioxidant capacity. Oxidative stress gene SNPs associated with CAD were combined into an oxidative stress risk allele score, which predicted disease presence (1.5-fold risk increase per allele, P < 0.001). Combined risk alleles were also associated with elevated plasma MPO (P < 0.003), an oxidative stress biomarker that predicts cardiovascular mortality. Genetic markers that represent lifetime oxidative stress burden may implicate specific oxidative stress pathways in the pathogenesis of atherosclerosis, and offer therapeutic opportunities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".