Epigenetics and precision medicine in cardiovascular patients: from basic concepts to the clinical arena
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide and also inflict major burdens on morbidity, quality of life, and societal costs. Considering that CVD preventive medications improve vascular outcomes in less than half of patients (often relative risk reductions range from 12% to 20% compared with placebo), precision medicine offers an attractive approach to refine the targeting of CVD medications to responsive individuals in a population and thus allocate resources more wisely and effectively. New tools furnished by advances in basic science and translational medicine could help achieve this goal. This approach could reach beyond the practitioners 'eyeball' assessment or venerable markers derived from the physical examination and standard laboratory evaluation. Advances in genetics have identified novel pathways and targets that operate in numerous diseases, paving the way for 'precision medicine'. Yet the inherited genome determines only part of an individual's risk profile. Indeed, standard genomic approaches do not take into account the world of regulation of gene expression by modifications of the 'epi'genome. Epigenetic modifications defined as 'heritable changes to the genome that do not involve changes in DNA sequence' have emerged as a new layer of biological regulation in CVD and could advance individualized risk assessment as well as devising and deploying tailored therapies. This review, therefore, aims to acquaint the cardiovascular community with the rapidly advancing and evolving field of epigenetics and its implications in cardiovascular precision medicine.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".