Abstract 4141341: Association of Polygenic Risk Scores with Aortic Valve Calcium: The Multi-Ethnic Study of Atherosclerosis (MESA)
Bibliographic record
Abstract
Background: Aortic valve calcification (AVC) is the primary underlying process leading to aortic stenosis. Whether polygenic risk scores (PRS) are associated with AVC beyond traditional atherosclerotic cardiovascular disease risk factors (ASCVD) is unknown. Methods: This study included 6,812 Multi-Ethnic Study of Atherosclerosis participants who had AVC measured via CT at Visit 1 and single-nucleotide polymorphism (SNP) genotype data. Using previously published PRS for coronary artery disease (CAD), coronary artery calcium (CAC), and ASCVD risk factors we calculated a weighted PRS for each participant that was standardized within each ancestry group. The cross-sectional association of the individual PRS with AVC >0 was examined using multivariable logistic regression modeling with Bonferroni correction. Results: The mean age was 62 years old, 53% were women, and 913 (13.4%) of participants had AVC >0 at baseline. The PRS for CAD (HR 1.17, 95% CI 1.07-1.26), SBP (HR 1.13, 95% CI 1.04-1.24), LDL-C (HR 1.16, 95% CI 1.07-1.26), and lipoprotein(a) [Lp(a)] (HR 1.11, 95% CI 1.02-1.20) were significantly associated with AVC, while the other PRS including CAC (HR 1.02, 95% CI 0.94-1.10) and CRP (HR 0.97, 95% CI 0.89-1.05) were not (Table). In sex stratified analyses, the PRS for CAD, LDL-C, and Lp(a) were significantly associated with AVC >0 for both women and men (p<0.05), while the SBP PRS for women was HR 1.14, 95% CI 0.99-1.32 and for men was HR 1.11, 95% CI 0.99-1.25. Conclusions: Our results further confirm the role of atherogenic lipids in the pathogenesis of AVC and suggest that SBP may also be an important risk factor for AVC, while also suggesting that inflammation may not be an independent risk factor for AVC >0. Additionally, the lack of association for the CAC PRS with AVC >0 demonstrates that significant differences exist in the calcification pathways for AVC and CAC.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".