The Role of the Gut Microbiota in the Metabolism of Polyphenols as Characterized by Gnotobiotic Mice
Bibliographic record
Abstract
A growing body of experimental data suggests that microbes in the gut influence behavior and can alter brain physiology and neurochemistry. Although promising, researchers are only starting to understand the potential of the gut microbiota for use in neurological disease. Recent evidence demonstrated that gastrointestinal activities are linked to mood disorders such as anxiety, depression, and most recently, cognitive functions in age-related neurodegenerative disorders. Studies from our group and others are uncovering new evidence suggesting that the gut microbiota plays a crucial role in the metabolism and bioavailability of certain dietary compounds and synthetic drugs. Based on this evidence, this review article will discuss the implications of the gut microbiota in mechanisms of bioavailability and biotransformation with an emphasis on dietary polyphenol compounds. This will be followed by a survey of ongoing innovative research identifying the ability of individual gut bacteria to enhance the bioavailability of gut-derived, brain-penetrating, bioactive polyphenol metabolites that ultimately influence mechanisms associated with the promotion of resilience against psychological and cognitive impairment in response to stress. Lastly, current research initiatives aimed at promoting the generation of brain bioactive polyphenol metabolites by specialized gut microbes will be discussed, specifically the use of gnotobiotic mice to develop bioengineered second generation probiotics. We propose that leveraging the gut microbial ecosystem to generate brain targeted bioactive metabolites from dietary polyphenols can attenuate lifestyle risk factors and promote resilience against age-related cognitive decline.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".