Abstract 13828: Aptamer Proteomics for Biomarker Discovery in Heart Failure With Preserved Ejection Fraction: The PARADIGM-HF Proteomic Sub-Study
Bibliographic record
Abstract
Introduction: Prognostic markers and biological pathways linked to detrimental clinical outcomes in heart failure with preserved ejection fraction (HFpEF) remain incompletely defined. Goal: To identify serum proteins associated with risk for HF hospitalization (HFH) and cardiovascular (CV) death in patients with HFpEF, including proteins with differential associations with clinical outcomes in HFpEF compared to HFrEF. Methods: We measured serum levels of 4123 unique proteins in 1117 patients with HFpEF in the PARAGON-HF trial using a modified aptamer proteomic assay. Baseline protein concentrations significantly associated with total HFH and CV death were identified by recurrent events regression, accounting for multiple testing, adjusted for age, sex, treatment, and anticoagulant use, and compared to 2515 patients with HFrEF from the PARADIGM-HF and ATMOSPHERE trials. Results: We identified 288 proteins to be robustly associated with the risk of HFH and CV death (335 events over an average of 2.7 years of follow-up) in a HFpEF trial population. Proteins most strongly related to HFpEF outcomes included B2M, TIMP1, SERPINA4, and SVEP1 ( Figure ). Protein-outcome associations in patients with HFpEF did not markedly differ compared to HFrEF. Just 3 proteins (APOE, RTF1, and VWF) were differentially associated with outcomes between the HF subtypes (FDR <0.05). A proteomic risk score derived in HFpEF patients was not superior to a previous proteomic score derived in HFrEF nor to clinical risk factors, NT-proBNP, or high-sensitivity cardiac troponin. Conclusions: Numerous serum proteins linked to metabolic, coagulation, and extracellular matrix regulatory pathways were associated with worse HFpEF prognosis. SVEP1, a cell adhesion protein recently identified as a HFrEF prognosis biomarker, also strongly predicted risk in HFpEF. Our results demonstrate substantial similarities in serum proteomic risk markers across the EF spectrum.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".