Connecting proteomics and genomics to identify causal biomarkers specific for aortic valve stenosis
Bibliographic record
Abstract
Abstract Introduction Aortic valve stenosis (AS) is a progressive disease characterized by the calcification and narrowing of the aortic valve, leading to significant morbidity and mortality. Early detection and risk stratification remain a major clinical challenge. Identifying specific biomarkers could significantly improve risk prediction and disease management. Objective This study aims to (1) identify novel plasma protein biomarkers for incident AS, (2) establish causal relationships using genetic approaches, and (3) prioritize biomarkers specific to the aortic valve tissue. Methods We assessed the association between 2,923 unique plasma proteins measured using the Olink Explore assay and AS incidence in 52,632 UK Biobank participants with 487 incident AS cases over a median follow-up of 13 years. Multivariable Cox proportional hazards models were used to evaluate associations adjusted for cardiovascular risk factors. A stratified analysis was performed to investigate the association in men and women separately. For causal inference, we used protein quantitative trait loci (pQTL) Mendelian Randomization (MR). We then verified the aortic valve specificity of the proteins using our transcriptomic dataset of 500 human aortic valves. Using expression QTL (eQTL)-based MR, we evaluated the causal role of gene expression in AS and performed colocalization analyses. Results Our fully adjusted model identified 55 proteins significantly associated with AS incidence, with GDF15 showing the strongest association (HR=1.93 per SD, 95% CI: 1.67–2.23, P value = 3.3E-19). In sex-stratified analyses, 10 proteins showed a significant association with AS incidence in women, including 5 more strongly associated in women (CD38, CD80, IGFBPL1, NFASC, SERPINA9, P interaction <0.05), whereas 16 proteins were identified in men including 1 protein (REG1A) with a significantly stronger association in men. pQTL-MR suggested a potential causal role for 4 proteins (PCSK9, CHI3L1, NFASC, PRSS8). Transcriptomic integration confirmed high aortic valve expression for 11 candidates, with three genes demonstrating significant associations in aortic valve eQTL MR analyses, including LTBP2, for which the pQTL and eQTL colocalized. Conclusion The integration of proteomics and genomics allowed the identification of potential biomarkers and drug targets for AS, showing evidence of causality and tissue-specific expression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".