Fecal transplant from resveratrol-fed donors improves glycaemia and cardiovascular features of the metabolic syndrome in mice
Bibliographic record
Abstract
Oral administration of resveratrol attenuates several symptoms associated with the metabolic syndrome, such as impaired glucose homeostasis and hypertension. Recent work has shown that resveratrol can improve glucose homeostasis in obesity via changes in the gut microbiota. Studies involving fecal microbiome transplants (FMTs) suggest that either live gut microbiota or bacterial-derived metabolites from resveratrol ingestion are responsible for producing the observed benefits in recipients. Herein, we show that obese mice receiving FMTs from healthy resveratrol-fed mice have improved glucose homeostasis within 11 days of the first transplant, and that resveratrol-FMTs is more efficacious than oral supplementation of resveratrol for the same duration. The effects of FMTs from resveratrol-fed mice are also associated with decreased inflammation in the colon of obese recipient mice. Furthermore, we show that sterile fecal filtrates from resveratrol-fed mice are sufficient to improve glucose homeostasis in obese mice, demonstrating that nonliving bacterial, metabolites, or other components within the feces of resveratrol-fed mice are sufficient to reduce intestinal inflammation. These postbiotics may be an integral mechanism by which resveratrol improves hyperglycemia in obesity. Resveratrol-FMTs also reduced the systolic blood pressure of hypertensive mice within 2 wk of the first transplant, indicating that the beneficial effects of resveratrol-FMTs may also assist with improving cardiovascular conditions associated with the metabolic syndrome.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".