Cardiorenal ketone metabolism: a positron emission tomography study in healthy humans
Bibliographic record
Abstract
Ketones are alternative energy substrates for the heart and kidney but no studies have investigated their metabolism simultaneously in both organs in humans. The present double tracer positron emission tomography (PET) study evaluated the organ distribution and basal kinetic rates of the radiolabeled ketone, 11C-acetoacetate (11C-AcAc), in the heart and kidney compared to 11C-acetate (11C-Ac), which is a well-validated metabolic radiotracer. Both tracers were highly metabolized by the left ventricle and the renal cortex. In the heart, kinetic rates were similar for both tracers. But in the renal cortex, uptake of 11C-Ac was higher compared to 11C-AcAc, while the reverse was observed for the clearance. Interestingly, infusion of 11C-AcAc led to a significantly delayed release of radioactivity in the renal medulla and pelvis, a phenomenon not observed with 11C-Ac. This suggests an equilibrium of 11C-AcAc with the other ketone, 11C-D-beta-hydroxybutyrate, and a different clearance profile. Overall, this suggests that in the kidney, the absorption and metabolism of 11C-AcAc is different compared to 11C-Ac. This dual tracer PET protocol provides the opportunity to explore the relative importance of ketone metabolism in cardiac and renal diseases, and to improve our mechanistic understanding of new metabolic interventions targeting these two organs.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".