Metformin Boosts Intestinal Lipid Sensing via GIP to Suppress Feeding
Bibliographic record
Abstract
An acute increase of lipids in the upper small intestine (USI) of rodents and humans triggers lipid-sensing pathways to reduce food intake. However, USI lipid sensing does not reduce feeding in high-fat (HF) fed conditions, and the underlying mechanism remains elusive. Here, we report that HF feeding in male rats impaired USI lipid infusion to stimulate glucose-dependent insulinotropic polypeptide (GIP) secretion and decrease refeeding, and the defects of USI lipid sensing were restored by metformin. Next, we found that infusion of GIP receptor (GIPR) agonist in the nucleus of the solitary tract (NTS), but not mediobasal hypothalamus or area postrema, resulted in decreased refeeding in chow-fed rats. The anorectic effect of NTS GIPR agonist remained intact in HF rats and was inhibited by a genetic knockdown of GIPR. Finally, an inhibition of NTS GIPR also negated the ability of USI lipid sensing with metformin to decrease refeeding despite an increase in plasma GIP levels in HF rats. Thus, USI lipid sensing in HF rats is enhanced by metformin to trigger an endocrine GIP to NTS GIPR axis to reduce food intake, thereby unveiling small intestinal lipid-sensing pathways as potential targets to enhance GIP action and reduce weight in obesity. ARTICLE HIGHLIGHTS: High-fat (HF) feeding in rats impairs upper small intestine (USI) lipid sensing to increase plasma glucose-dependent insulinotropic polypeptide (GIP) levels and reduce feeding. Metformin enhances USI lipids to increase GIP and reduce feeding in HF-fed rats. GIP activates the GIP receptor (GIPR) in the nucleus of the solitary tract (NTS), which reduces food intake in HF-fed rats. GIPR in the NTS is required for small intestinal lipids with metformin to reduce feeding.
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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".