The multifaceted roles of ketones in physiology
Bibliographic record
Abstract
The production of ketones, referred to as ketogenesis, plays an essential role in maintaining energy homeostasis during prolonged fasting/starvation, which primarily stems from its ability to serve as a fuel source to support neuronal ATP production, thereby limiting muscle wasting. Over the years, the field has come to appreciate that ketones are much more than just a fuel source supporting neuronal metabolism, as many other oxidative organs, such as the heart and skeletal muscle, are capable of metabolizing ketones. Furthermore, ketones appear to be an important fuel source for exercising muscle. Beyond supporting ATP production, it is also becoming widely recognized that ketones are powerful signalling molecules, as they serve as ligands for G-protein coupled receptors and can even modify gene expression via regulating DNA post-translational modifications. As they play a key role in supporting whole-body physiology, it is not surprising that perturbations in ketone metabolism can contribute to various pathologies, particularly in relation to cardiometabolic diseases. Some of the strongest evidence supporting the aforementioned statement is seen for both heart failure and type 2 diabetes. Accordingly, we will review herein the multifaceted roles of ketones in supporting whole-body physiology, while interrogating the evidence to suggest whether modifying ketone metabolism may have a therapeutic role in the management of heart failure and type 2 diabetes.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".