Abstract 13260: Large Scale Plasma Proteomics Identifies MMP-12 as a Novel Biomarker of Aortic Stenosis Progression
Bibliographic record
Abstract
Background: Aortic stenosis (AS) is associated with significant morbidity and mortality and is increasing in prevalence. Limited data exist regarding circulating biomarkers of AS risk. Methods: Among Atherosclerosis Risk in Communities study participants with available proteomics (Somascan v4) at study Visit 5 (2011-13; n=4,899; age 76 ± 5 years, 57% women), we used multivariable linear regression to evaluate the association of 4,877 plasma proteins with peak aortic valve (AV) velocity and AV dimensionless index. We then tested their association, when assessed at study Visit 3 (1993-95; n=11,430; age 60 ± 6, 54% women), with incident AV-related hospitalization post-Visit 3 (median follow-up 22, IQR 14 - 25 years) using multivariable Cox PH regression models. For the resulting candidate proteins, we assessed the association of Visit 5 protein levels with change in AV peak velocity over 6 years from Visit 5 to 7 (2018-19; n=2,314) and with quantitative AV calcification by cardiac CT at Visit 7 (n=1,804); associations with incident adjudicated AS in the Cardiovascular Health Study (CHS; n=3,413); and differences in AV tissue expression in normal, fibrotic, and calcific segments of explanted stenotic human AVs (n=3). Results: We identified 52 plasma proteins with consistent associations with AV peak velocity, AV dimensionless index, and incident AV hospitalization. Of these 52 proteins, MMP12 was also associated with magnitude of increase in AV peak velocity between Visits 5 and 7 (Figure), and with magnitude of AV calcification by CT at Visit 7 (adjusted OR 1.25 [95% CI 1.19-1.32], p=1.7x10 -17 ). Higher MMP12 was also associated with incident moderate or severe AS in CHS, an independent cohort. MMP12 expression was greater in calcific compared to fibrotic or normal AV tissue segments. Conclusions: Plasma MM12 is a potential novel circulating biomarker of AS risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".