Fermentable carbohydrate stimulates FFAR2-dependent colonic PYY cell expansion to increase satiety
Bibliographic record
Abstract
Dietary supplementation with fermentable carbohydrate protects against body weight gain. Fermentation by the resident gut microbiota produces short-chain fatty acids, which act at free fatty acid receptor 2 (FFAR2). Our aim was to test the hypothesis that FFAR2 is important in regulating the beneficial effects of fermentable carbohydrate on body weight and to understand the role of gut hormones PYY and GLP-1. Wild-type or Ffar2−/− mice were fed an inulin supplemented or control diet. Mice were metabolically characterized and gut hormone concentrations, enteroendocrine cell density measurements were carried out. Intestinal organoids and colonic cultures were utilized to substantiate the in vivo findings. We provide new mechanistic insight into how fermentable carbohydrate regulates metabolism. Using mice that lack FFAR2, we demonstrate that the fermentable carbohydrate inulin acts via this receptor to drive an 87% increase in the density of cells that produce the appetite-suppressing hormone peptide YY (PYY), reduce food intake, and prevent diet-induced obesity. Our results demonstrate that FFAR2 is predominantly involved in regulating the effects of fermentable carbohydrate on metabolism and does so, in part, by enhancing PYY cell density and release. This highlights the potential for targeting enteroendocrine cell differentiation to treat obesity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".