Controversial Roles of Gut Microbiota-Derived Short-Chain Fatty Acids (SCFAs) on Pancreatic β-Cell Growth and Insulin Secretion
Bibliographic record
Abstract
In the past 15 years, gut microbiota emerged as a crucial player in health and disease. Enormous progress was made in the analysis of its composition, even in the discovery of novel species. It is time to go beyond mere microbiota-disease associations and, instead, provide more causal analyses. A key mechanism of metabolic regulation by the gut microbiota is through the production of short-chain fatty acids (SCFAs). Acting as supplemental nutrients and specific ligands of two G-protein-coupled receptors (GPCRs), they target the intestines, brain, liver, and adipose tissue, and they regulate appetite, energy expenditure, adiposity, and glucose production. With accumulating but sometimes conflicting research results, key questions emerged. Do SCFAs regulate pancreatic islets directly? What is the effect of β-cell-specific receptor deletions? What are the mechanisms used by SCFAs to regulate β-cell proliferation, survival, and secretion? The receptors FFA2/3 are normally expressed on pancreatic β-cells. Deficiency in FFA2 may have caused glucose intolerance and β-cell deficiency in mice. However, this was contrasted by a double-receptor knockout. Even more controversial are the effects of SCFAs on insulin secretion; there might be no direct effect at all. Unable to draw clear conclusions, this review reveals some of the recent controversies.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".