Targeting Myocardial Substrate Metabolism in Heart Failure: Potential for New Therapies
Bibliographic record
Abstract
The incidence and prevalence of heart failure have increased significantly over the past few decades. Available data suggest that patients with heart failure independent of the aetiology have viable but dysfunctional myocardium that is potentially salvageable. Although a great deal of research effort has focused on characterizing the molecular basis of heart failure, cardiac metabolism in this disorder remains an understudied discipline. It is known that many aspects of cardiomyocyte energetics are altered in heart failure. These include a shift from fatty acid to glucose as a preferred substrate and a decline in the levels of ATP. Despite these demonstrated changes, there are currently no approved drugs that target metabolic enzymes or proteins in heart failure. This is partly due to our limited knowledge of the mechanisms and pathways that regulate cardiac metabolism. Better characterization of these pathways may potentially lead to new therapies for heart failure. Targeting myocardial energetics in the viable and potentially salvageable tissue may be particularly effective in the treatment of heart failure. Here, we will review metabolic changes that occur in fatty acid and glucose metabolism and AMP-activated kinase in heart failure. We propose that cardiac energetics should be considered as a potential target for therapy in heart failure and more research should be done in this area.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".