Beta-Hydroxybutyrate Promotes Basal Insulin Secretion While Decreasing Glucagon Secretion in Mouse and Human Islets
Bibliographic record
Abstract
Dietary carbohydrates raise blood glucose levels, and limiting carbohydrate intake improves glycemia in patients with type 2 diabetes. Low carbohydrate intake (< 25 g) allows the body to utilize fat as its primary fuel. As a consequence of increased fatty acid oxidation, the liver produces ketones to serve as an alternative energy source. β-Hydroxybutyrate (βHB) is the most abundant ketone. While βHB has a wide range of functions outside of the pancreas, its direct effects on islet cell function remain understudied. We examined human islet secretory response to acute racemic βHB treatment and observed increased insulin secretion at a low glucose concentration of 3 mM. Because βHB is a chiral molecule, existing as both R and S forms, we further studied insulin and glucagon secretion following acute treatment with individual βHB enantiomers in human and C57BL/6J mouse islets. We found that acute treatment with R-βHB increased insulin secretion and decreased glucagon secretion at physiological glucose concentrations in both human and mouse islets. Proteomic analysis of human islets treated with R-βHB over 72 hours showed altered abundance of proteins that may promote islet cell health and survival. Collectively, our data show that physiological concentrations of βHB influence hormone secretion and signaling within pancreatic islets.
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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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".