Toward Precision Medicine in Atherosclerotic Cardiovascular Disease: Insights from Omics Data into Sex Differences
Bibliographic record
Abstract
PURPOSE OF REVIEW: Atherosclerotic cardiovascular disease (ASCVD) remains a leading cause of morbidity and mortality worldwide. Although there is increasing recognition of sex differences in ASCVD epidemiology, pathogenesis, and clinical outcomes, the underlying biological mechanisms are still insufficiently understood. Women often present with distinct disease phenotypes, such as a higher prevalence of fibrous plaques and microvascular dysfunction, compared with the lipid-rich, inflammatory plaques more typical in men. This review examines recent omics research to clarify the molecular basis of these sex-specific patterns and explores their implications for precision cardiovascular medicine. RECENT FINDINGS: Advances in genomics, epigenomics, transcriptomics, proteomics, and metabolomics have shown that sex differences in ASCVD arise from complex hormonal, genetic, epigenetic, and molecular interactions. The variety of available omics approaches offers the potential to discover sex-specific regulatory networks and therapeutic targets, thereby addressing persistent knowledge gaps. However, significant challenges remain, including integrating these diverse omics layers, harmonising datasets across platforms, managing substantial computational demands, and navigating ethical constraints related to data sharing. Multiomics technologies provide unprecedented opportunities to dissect sex-specific mechanisms in ASCVD and to refine individualised risk stratification and therapeutic strategies. Overcoming current analytical and infrastructural barriers through collaborative efforts, standardised methodologies, and responsible data governance will be critical to unlocking the full potential of multiomics in precision cardiovascular medicine. This review synthesises recent evidence across omics domains and underscores their potential to improve ASCVD prevention and treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".