Natriuretic Peptide Clearance Receptor (NPR-C) Pathway as a Novel Therapeutic Target in Obesity-Related Heart Failure With Preserved Ejection Fraction (HFpEF)
Bibliographic record
Abstract
Heart failure (HF) with preserved ejection fraction (HFpEF) is a major public health problem with cases projected to double over the next two decades. There are currently no US Food and Drug Administration-approved therapies for the health-related outcomes of HFpEF. However, considering the high prevalence of this heterogeneous syndrome, a directed therapy for HFpEF is one the greatest unmet needs in cardiovascular medicine. Additionally, there is currently a lack of mechanistic understanding about the pathobiology of HFpEF. The phenotyping of HFpEF patients into pathobiological homogenous groups may not only be the first step in understanding the molecular mechanism but may also enable the development of novel targeted therapies. As obesity is one of the most common comorbidities found in HFpEF patients and is associated with many cardiovascular effects, it is a viable candidate for phenotyping. Large outcome trials and registries reveal that being obese is one of the strongest independent risk factors for developing HFpEF and that this excess risk may not be explained by traditional cardiovascular risk factors. Recently, there has been increased interest in the intertissue communication between adipose tissue and the heart. Evidence suggests that the natriuretic peptide clearance receptor (NPR-C) pathway may play a role in the development and pathobiology of obesity-related HFpEF. Therefore, therapeutic manipulations of the NPR-C pathway may represent a new pharmacological strategy in the context of underlying molecular mechanisms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".