Glucagon Receptor Antagonist for Heart Failure With Preserved Ejection Fraction
Bibliographic record
Abstract
BACKGROUND: Heart failure with preserved ejection fraction (HFpEF) is an emerging major unmet need and one of the most significant clinic challenges in cardiology. The pathogenesis of HFpEF is associated with multiple risk factors. Hypertension and metabolic disorders associated with obesity are the 2 most prominent comorbidities observed in patients with HFpEF. Although hypertension-induced mechanical overload has long been recognized as a potent contributor to heart failure with reduced ejection fraction, the synergistic interaction between mechanical overload and metabolic disorders in the pathogenesis of HFpEF remains poorly characterized. METHOD: /J (ob/ob) mice, followed by single-nuclei RNA-seq and targeted manipulation of a top-ranked signaling pathway differentially affected in the 2 experimental cohorts. RESULTS: In contrast to the post-transverse aortic constriction C57Bl/6J lean mice, which developed pathological features of heart failure with reduced ejection fraction over time, the post-transverse aortic constriction ob/ob mice showed no significant changes in ejection fraction but developed characteristic pathological features of HFpEF, including diastolic dysfunction, worsened cardiac hypertrophy, and pathological remodeling, along with further deterioration of exercise intolerance. Single-nuclei RNA-seq analysis revealed significant transcriptome reprogramming in the cardiomyocytes stressed by both pressure overload and obesity/diabetes, markedly distinct from the cardiomyocytes singularly stressed by pressure overload or obesity/diabetes. Furthermore, glucagon signaling was identified as the top-ranked signaling pathway affected in the cardiomyocytes associated with HFpEF. Treatment with a glucagon receptor antagonist significantly ameliorated the progression of HFpEF-related pathological features in 2 independent preclinical models. Importantly, cardiomyocyte-specific genetic deletion of the glucagon receptor also significantly improved cardiac function in response to pressure overload and metabolic stress. CONCLUSIONS: These findings identify glucagon receptor signaling in cardiomyocytes as a critical determinant of HFpEF progression and provide proof-of-concept support for glucagon receptor antagonism as a potential therapy for the disease.
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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) | 0.000 | 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) | 0.004 | 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".