Ketone therapy for heart failure: current evidence for clinical use
Bibliographic record
Abstract
During conditions that result in depleted circulating glucose levels, ketone bodies synthesized in the liver are necessary fuel substrates for the brain. In other organs, such as the heart, the reliance on ketones for generating energy in the absence of glucose is less important as the heart can utilize alternative fuel sources, such as fatty acids. However, during pathophysiological conditions, such as heart failure, cardiac defects in metabolic processes that normally allow for sufficient energy production from fatty acids and carbohydrates contribute to a decline in contractile function. As such, it has been proposed that the failing heart relies more on ketone bodies as an energy source than previously appreciated. Furthermore, it has been shown that ketone bodies function as signaling molecules that can suppress systemic and cardiac inflammation. Thus, it is possible that intentionally elevating circulating ketones may be beneficial as an adjunct treatment for heart failure. Although many approaches can be used for 'ketone therapy', each of these has their own advantages and disadvantages in the treatment of heart failure. Thus, we summarize current preclinical and clinical studies involving various types of ketone therapy in cardiac disease and discuss the advantages and disadvantages of each modality as possible treatments for heart failure.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".