Plasma Proteomic Assessment of Calcific Aortic Valve Disease in Older Adults
Bibliographic record
Abstract
Background Calcific aortic valve disease (CAVD), and ensuing severe aortic stenosis (AS), is the foremost valvular disorder of aging, yet preventive therapies are lacking. A better understanding of the molecular underpinnings of aortic valve calcification (AVC) is necessary to develop pharmacologic interventions. Methods and Results We undertook large‐scale plasma proteomics in a cohort study of adults ≥65 years old, the CHS (Cardiovascular Health Study), to identify individual proteins associated with echocardiographic AVC and incident moderate/severe AS. Proteomics measurements were performed with the aptamer‐based SomaLogic platform of ~5000 proteins. Significant proteins were validated in a second cohort, the AGES‐RS (Age, Gene/Environment Susceptibility‐Reykjavik Study), which assessed AVC and AS by computed tomography. The potential causal associations of replicated proteins were tested in 2‐sample Mendelian randomization using identified cis protein quantitative trait loci in consortia having computed tomography‐quantified AVC or AS as outcomes. Six proteins showed Bonferroni‐corrected significant relationships with AVC in CHS. Three of these, CXCL‐12 (C‐X‐C chemokine ligand 12), KLKB1 (kallikrein), and leptin, replicated in AGES‐RS, of which the former 2 are novel. Only 1 protein, CXCL6, which showed a near‐significant association with AS in the replication cohort, was significantly (positively) associated with incident AS. Mendelian randomization analysis was conducted for KLKB1, CXCL12, and CXCL6, which supported a causal relationship for higher KLKB1 with lower AVC (beta=−0.25, P =0.009). Conclusions This study of older adults newly identified and largely replicated associations of 3 circulating proteins with calcific aortic valve disease, of which the relationship of plasma KLKB1 may have a causal basis. Additional investigation is necessary to determine if KLKB1 could be harnessed for calcific aortic valve disease therapeutics.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".