Targeted discovery proteomics to identify clinical phenotypes in heart failure with preserved ejection fraction: a proteomics substudy of VITALITY-HFpEF
Bibliographic record
Abstract
Abstract Background Heart failure with preserved ejection fraction (HFpEF) is a heterogenous syndrome that may emerge from overlapping systemic processes associated with several medical co-morbidities, often within an inflammatory milieu. Identification of unique proteins associated with distinct phenotypes may yield insight into novel therapeutics. Purpose Determine if unique clusters of circulating proteins are associated with specific clinical characteristics in patients with HFpEF. Methods A targeted discovery proteomics approach with 358 unique proteins associated with cardiovascular disease and inflammation (Olink) was used at baseline in VITALITY-HFpEF among 789 participants with documented left ventricular EF ≥45% and recent decompensation (<6 mos). Proteins were clustered applying the weighted correlation network analysis (WCNA). The associations of the clinical characteristics and frailty and clusters were estimated with linear regression adjusted for age and eGFR. Frailty was characterized as normal, pre-frail, and frail using the Fried criteria. KCCQ was the primary and 6-minute walk distance (6MWD) the secondary endpoint of VITALITY-HFpEF. Results Four unique clusters were identified containing 24, 66, 197, and 81 proteins, respectively. Figure 1 shows the adjusted association of the 4 protein clusters, shown with their hub proteins, with the clinical characteristics. The color (red: positive, green: negative relationship) and intensity indicate the magnitude of the standardized difference (relative to the variation [i.e., T-value]); p-value shown in boxes. Cluster 3, with tumor necrosis factor receptor 1 as a hub protein that mediates apoptosis and inflammation, was associated with greater frailty and physical limitation along with shorter 6MWD. In contrast, cluster 4, with protein C as a hub protein that regulates anticoagulation and exerts a protective function on endothelial cells, is associated with less frailty and younger age, and more frequently male sex. Cluster 2 was associated with only younger age and cluster 1 with no clinical characteristics. Conclusions Proteomics appear to identify specific clinical phenotypes associated with HFpEF. Further exploration of this approach may provide insight into the diverse pathophysiology characterizing this disorder and a more targeted approach to therapy. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): VITALITY-HFpEF was funded by Merck Sharp & Dohme Corp., a subsidiary of Merck & Co., Inc., Kenilworth, NJ, USA and Bayer AG, Wuppertal, Germany.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".