The therapeutic potential of ketones in cardiometabolic disease: impact on heart and skeletal muscle
Bibliographic record
Abstract
β-Hydroxybutyrate (βOHB) is the major ketone in the body, and it is recognized as a metabolic energy source and an important signaling molecule. While ketone oxidation is essential in the brain during prolonged fasting/starvation, other organs such as skeletal muscle and the heart also use ketones as metabolic substrates. Additionally, βOHB-mediated molecular signaling events occur in heart and skeletal muscle cells, and via metabolism and/or signaling, ketones may contribute to optimal skeletal muscle health and cardiac function. Of importance, when the use of ketones for ATP production and/or as signaling molecules becomes disturbed in the presence of underlying obesity, type 2 diabetes, and/or cardiovascular diseases, these changes may contribute to cardiometabolic disease. As a result of these disturbances in cardiometabolic disease, multiple approaches have been used to elevate circulating ketones with the goal of optimizing either ketone metabolism or ketone-mediated signaling. These approaches have produced significant improvements in heart and skeletal muscle during cardiometabolic disease with a wide range of benefits that include improved metabolism, weight loss, better glycemic control, improved cardiac and vascular function, as well as reduced inflammation and oxidative stress. Herein, we present the evidence that indicates that ketone therapy could be used as an approach to help treat cardiometabolic diseases by targeting cardiac and skeletal muscles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".