1655-P: GLP-1R Is Required for Resveratrol in Exerting Its Metabolic Beneficial Effect in HFD-Challenged Male Mice
Bibliographic record
Abstract
The beneficial effects of dietary polyphenols including resveratrol have been elucidated during the past 2-3 decades, yet the mechanism underlying their metabolic functions remains elusive or even controversial. Recent investigations revealed that hepatic hormone FGF21 is the common target for both GLP-1RAs and dietary polyphenol intervention. Here we utilized wild type (WT) and GLP-1R-/- mice to access whether GLP-1R is required for resveratrol to exert its beneficial effects. In WT male mice with HFD challenge, concomitant resveratrol intervention (REV-I) for 12 weeks reduced body weight gain and improved glucose tolerance, while in male GLP-1R-/- mice, such metabolic effects were lost. In WT mouse liver and epididymal white adipose tissue (eWAT), REV-I ameliorated HFD-induced FGF21 resistant and stimulated a battery of genes that are involved in lipid homeostasis. In addition, HFD-induced alterations on expressions of a battery of adipose tissue specific genes including those encode for leptin and adiponectin were reversed by concomitant REV-I. REV-I was also shown to exert anti-inflammatory effects in the ileum of WT mice fed with HFD. Specifically, genes that encode for a battery of pro-inflammatory markers including IL-1β and IFN-γ were significantly elevated by HFD challenge, while REV-I attenuated their expression. HFD challenge also reduced IL-10 mRNA level in the WT mouse ileum, while REV-I restored the expression. Together, we bring a novel player GLP-1R to explore the mechanism underlying the metabolic functions of resveratrol. Collectively, we conclude that the metabolic beneficial effects of resveratrol on reducing body weight gain and improving glucose disposal require GLP-1R. Disclosure J.Feng: None. W.Shao: None. T.Jin: None. Funding Canadian Institutes of Health Research
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".