Assessing the role of rare pathogenic variants in heart failure progression by exome sequencing in 8,089 patients
Bibliographic record
Abstract
Abstract Most therapeutic development is targeted at slowing disease progression, often long after the initiating events of disease incidence. Heart failure is a chronic, life-threatening disease and the most common reason for hospital admission in people over 65 years of age. Genetic factors that influence heart failure progression have not yet been identified. We performed an exome-wide association study in 8,089 patients with heart failure across two clinical trials, CHARM and CORONA, and one population-based cohort, the UK Biobank. We assessed the genetic determinants of the outcomes ‘time to cardiovascular death’ and ‘time to cardiovascular death and/or hospitalisation’, identifying seven independent exome-wide-significant associated genes, FAM221A , CUTC , IFIT5 , STIMATE , TAS2R20 , CALB2 and BLK . Leveraging public genomic data resources, transcriptomic and pathway analyses, as well as a machine-learning approach, we annotated and prioritised the identified genes for further target validation experiments. Together, these findings advance our understanding of the molecular underpinnings of heart failure progression and reveal putative new candidate therapeutic targets.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".