A proteo-genomic discovery approach to identify markers of early heart failure
Bibliographic record
Abstract
Abstract Background Heart failure is characterized by complex dysregulation across numerous molecular pathways, however, relevant biomarkers altered across the disease course remain lacking. We sought to identify plasma proteomic signatures that show disease associations across multiple forms of evidence using both genomic and prospective phenotypic data. Methods To identify markers altered early in heart failure, we conducted a multi-stage agnostic genomic and phenotypic analysis from the Prospective Urban Rural Epidemiology (PURE) study (n = 8,911; 55.2% female; mean age 53.4). Among 539 plasma proteins, we identified biomarkers associated with incident heart failure (226 incident HF events). We then correlated these 539 markers with heart failure genetic liability using a polygenic risk score for heart failure. Markers robustly associated with both genetic liability for heart failure and incident heart failure were subsequently evaluated for prognostic importance in individuals with heart failure using the Global Congestive Heart Failure (GCHF) registry (n=2,427; 37.1% Female Sex; Mean Age 62.3 years). Results 35 proteins were associated with incident heart failure and 24 were associated with genetic predisposition to heart failure. Of these, seven circulating proteins (BNP, MMP12, KIM1, CPM, HGF, IGFBP3, IL6) were strongly associated with both genetic predisposition to heart failure and incident heart failure (false discovery rate <0.05) in a free-living community cohort. Pathways implicated included immune/inflammatory markers, markers of hemostasis, markers of cell death, and markers of collagen/extracellular matrix homeostasis. Inclusion of these additional proteins enhances prediction of death and heart failure hospitalization compared to a baseline model that incorporates traditional heart failure risk severity markers and BNP (Likelihood Chi Square p-value = 1.33x10^-15). Continuous test set Net Reclassification Index at 1 year was 0.48 [95% Confidence Interval: 0.19-0.74], indicating significant improvement in risk stratification of adverse events in GCHF participants. Conclusions This study identified novel biomarkers with increased evidence of early dysregulation in heart failure and are also associated with clinically important outcomes. Further research is warranted to explore whether these identified proteins can alter clinical decision making, reflect ongoing disease activity, refine diagnostics, or serve as therapeutic targets after the clinical onset of heart failure.Study Outline Results of Discovery Analysis
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".