Blood, tissue and imaging biomarkers in calcific aortic valve stenosis
Bibliographic record
Abstract
PURPOSE OF REVIEW: Calcific aortic valve stenosis is the most prevalent valvular heart disease in the high-income countries. To this date, no medical therapy has been proven to prevent or to stop the progression of aortic valve stenosis. The physiopathology of aortic valve stenosis is highly complex and involves several signalling pathways, as well as genetic related factors, which delay the elaboration of effective pharmacotherapies. Moreover, it is difficult to predict accurately the progression of the valve stenosis and finding the optimal timing for aortic valve replacement remains challenging. Therefore, the present review makes an inventory of the most recent and promising circulating and imaging biomarkers related to the underlying mechanisms involved in the physiopathology of aortic valve stenosis, as well as the biomarkers associated with the left ventricular (LV) remodelling and subsequent dysfunction in patients with aortic valve stenosis. RECENT FINDINGS: Over the last decade, several blood, tissue and imaging biomarkers have been investigated in aortic valve stenosis patients. At the aortic valve level, these biomarkers are mostly associated and/or involved with processes such as lipid infiltration and oxidation, chronic inflammation and fibrocalcific remodelling of the valve. Moreover, recent findings suggest that aging and sex hormones might interact with these multiple processes. Several studies demonstrated the usefulness of circulating biomarkers such as lipoprotein(a), brain natriuretic peptides and high-sensitivity cardiac troponin, which are very close to clinical routine. Furthermore, noninvasive imaging biomarkers including positron emission tomography and cardiac magnetic resonance, which provide a detailed view of the disease activity within the aortic valve and its repercussion on the left ventricle, may help to improve the understanding of aortic valve stenosis physiopathology and enhance the risk stratification. Other biomarkers such as von Willebrand factor and microRNAs are promising but further studies are needed to prove their additive value in aortic valve stenosis. SUMMARY: Most of the biomarkers are used in research and thus, are still being investigated. However, some biomarkers including plasma level of lipoprotein(a), F-sodium fluoride, brain natriuretic peptides and high-sensitivity cardiac troponin can be or are very close to be used for the clinical management of patients with aortic valve stenosis. Moreover, a multibiomarker approach might provide a more global view of the disease activity and improve the management strategies of these patients.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".