Epigenetics of Cardiovascular Disease: A New ‘Beat' in Coronary Artery Disease
Bibliographic record
Abstract
Genome-wide association studies have become a powerful tool in the identification of disease-associated variants. Unfortunately, many of these studies have found that the estimated variability in cardiovascular disease (CVD) risk cannot be fully explained by traditional paradigms of genetic variation in protein-coding genes. Moreover, traditional views do not sufficiently explain the well-known link between CVD and environmental influence. We posit that epigenetics, defined as chromatin-based mechanisms important in the regulation of gene expression that do not involve changes in the DNA sequence per se, represents the missing link. The nuclear-based mechanisms that contribute to epigenetic gene regulation can be broadly separated into three unique but highly interrelated processes, namely DNA methylation and hydroxymethylation, histone density and post-translational modifications, and ribonucleic acid-based mechanisms. Together, they complement the cis/trans perspective on transcriptional control paradigms in blood vessels. Moreover, epigenetics provides a molecular basis for understanding how the environment influences the genome to modify CVD risk over the lifetime of a cell and its offspring. This review is an introduction to epigenetic function and CVD, with a focus on endothelial cell biology. Additionally, we highlight emerging concepts on epigenetic gene regulation that are highly relevant to atherosclerosis and coronary artery disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".