Metabolic fate of glucose and candidate signaling and excess-fuel detoxification pathways in pancreatic β-cells
Bibliographic record
Abstract
Glucose metabolism promotes insulin secretion in β-cells via metabolic coupling factors that are incompletely defined. Moreover, chronically elevated glucose causes β-cell dysfunction, but little is known about how cells handle excess fuels to avoid toxicity. Here we sought to determine which among the candidate pathways and coupling factors best correlates with glucose-stimulated insulin secretion (GSIS), define the fate of glucose in the β-cell, and identify pathways possibly involved in excess-fuel detoxification. We exposed isolated rat islets for 1 h to increasing glucose concentrations and measured various pathways and metabolites. Glucose oxidation, oxygen consumption, and ATP production correlated well with GSIS and saturated at 16 mm glucose. However, glucose utilization, glycerol release, triglyceride and glycogen contents, free fatty acid (FFA) content and release, and cholesterol and cholesterol esters increased linearly up to 25 mm glucose. Besides being oxidized, glucose was mainly metabolized via glycerol production and release and lipid synthesis (particularly FFA, triglycerides, and cholesterol), whereas glycogen production was comparatively low. Using targeted metabolomics in INS-1(832/13) cells, we found that several metabolites correlated well with GSIS, in particular some Krebs cycle intermediates, malonyl-CoA, and lower ADP levels. Glucose dose-dependently increased the dihydroxyacetone phosphate/glycerol 3-phosphate ratio in INS-1(832/13) cells, indicating a more oxidized state of NAD in the cytosol upon glucose stimulation. Overall, the data support a role for accelerated oxidative mitochondrial metabolism, anaplerosis, and malonyl-CoA/lipid signaling in β-cell metabolic signaling and suggest that a decrease in ADP levels is important in GSIS. The results also suggest that excess-fuel detoxification pathways in β-cells possibly comprise glycerol and FFA formation and release extracellularly and the diversion of glucose carbons to triglycerides and cholesterol esters.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".