Cardiovascular biomarkers for risk stratification in primary prevention
Bibliographic record
Abstract
The foundation of preventive cardiology relies on the assessment of cardiovascular (CV) risk using scores based on traditional risk factors. However, risk models based on these risk factors provide only moderate discrimination and some are poorly calibrated, highlighting a need for improved approaches to risk assessment. Cardiovascular biomarkers, including cardiac troponins, natriuretic peptides, and inflammatory markers, can be used to reclassify CV risk, especially in individuals at intermediate risk, providing opportunities for optimization of primary prevention strategies. This review provides an overview of the characteristics of a circulating biomarker that would promote its clinical use and on the existing evidence behind CV biomarkers for risk stratification. Hundreds of studies have described robust associations between CV biomarkers and incident CV events. However, the incremental value, when biomarkers were added to conventional risk factor models, has generally been modest in terms of improvement in model performance. The review also describes emerging proteomic techniques that enable high-throughput analysis of circulating proteins, presenting opportunities for improved CV risk prediction. Despite encouraging findings, challenges remain in integrating these biomarkers into clinical practice, and there is a need for evidence from clinical trials to demonstrate their cost-effective impact on reducing CV events. While biomarkers hold promise for enhancing CV prevention strategies, their routine application in clinical settings requires further innovation and investigation to establish clear treatment guidelines and optimize patient outcomes.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".