Balancing the Oral–Gut–Brain Axis with Diet
Bibliographic record
Abstract
Background: The oral microbiota is the second largest microbial community in humans. It contributes considerably to microbial diversity and health effects, much like the gut microbiota. Despite physical and chemical barriers separating the oral cavity from the gastrointestinal tract, bidirectional microbial transmission occurs between the two regions, influencing overall host health. Method: This review explores the intricate interplay of the oral–gut–brain axis, highlighting the pivotal role of the oral microbiota in systemic health and ageing, and how it can be influenced by diet. Results: Recent research suggests a relationship between oral diseases, such as periodontitis, and gastrointestinal problems, highlighting the broader significance of the oral–gut axis in systemic diseases, as well as the oral–gut–brain axis in neurological disorders and mental health. Diet influences microbial diversity in the oral cavity and the gut. While certain diets/dietary components improve both gut and oral health, others, such as fermentable carbohydrates, can promote oral pathogens while boosting gut health. Conclusions: Understanding these dynamics is key for promoting a healthy oral–gut–brain axis through dietary interventions that support microbial diversity and mitigate age-related health risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".