1638-P: Metformin Enhances Upper Small Intestinal Lipid Sensing to Regulate Food Intake Independent of GDF15 Release
Bibliographic record
Abstract
Metformin, a first-line therapy for obesity-associated diabetes, lowers feeding and weight by increasing plasma growth differentiation factor 15 (GDF15) levels, but whether GDF15-independent mechanisms are involved is unknown. The upper small intestine (USI) detects lipids and triggers a gut-brain axis to lower food intake in chow but not high fat (HF)-fed conditions, while metformin enhances USI glucose sensing to lower glucose levels in HF rats. Here, we assessed whether metformin enhances lipid sensing in the USI of HF rats to lower food intake. We performed refeeding studies and measured plasma GDF15 levels after infusing 10% Intralipid into the USI of male rats. Infusion of Intralipid vs. saline into the USI suppressed food intake (lipid: 31±1 vs. sal: 43±2 kcal; p<0.01; n=18, 15) and increased plasma GDF15 levels (lipid: 113±9 vs. sal: 70±6 pg/ml; p<0.01; n=4, 5) up to 6h after refeeding in chow rats. In HF rats, both the effects of USI Intralipid vs. saline on feeding (lipid: 42±3 vs. sal: 41±1 kcal; n=10, 11) and plasma GDF15 levels (lipid: 68±6 vs. sal: 73±5 pg/ml; n=5, 3) were consistently abolished. Interestingly, metformin (50 mg/kg) administered to the USI of HF rats 1d prior to the refeeding studies enhanced USI Intralipid vs. saline infusion to suppress food intake (met-lipid: 35±2 vs. met-sal: 45±3 kcal; p<0.01; n=24,17), but notably did not increase plasma GDF15 levels (met-lipid: 69±3 vs. met-sal: 76±13 pg/ml; p>0.05; n=6, 5). Thus, for the first time we found that metformin enhances lipid sensing in the USI to lower food intake independent of GDF15 release. We propose that metformin lowers feeding in GDF15-dependent and -independent pathways. Disclosure R. Kuah: None. K. Bruce: None. S. Zhang: None. R.J.W. Li: None. D.R. Barros: None. T.K. Lam: None. Funding Canadian Institutes of Health Research (PJT-183901)
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".