The Interplay between the Gut and Ketogenic Diets in Health and Disease
Bibliographic record
Abstract
The gut plays a central role in translating dietary signals into systemic health effects, making it a key mediator of the ketogenic diet (KD), a high fat, low carbohydrate regimen. This review synthesizes current knowledge on the interaction between the KD and the gut, emphasizing gut-mediated mechanisms as an interface between dietary interventions and systemic health outcomes, spanning gastrointestinal to neurological health. Topics address gut physiology (gut digestion and absorption, epithelial nutrient sensing, gut motility), intestinal immunity (covering innate, adaptive, and antiviral responses), and extracellular to intracellular processes (i.e. mitochondrial function, stem cell fate, and intestinal circadian rhythm). Special focus is given to the gut microbiome, including both bacterial and fungal communities and how the KD modulates them in conditions such as epilepsy, obesity, traumatic brain injury, and multiple sclerosis. Innovative methods for tailoring the KD, including the use of alternative formulations, ketone esters, and microbiome-focused interventions such as prebiotics and probiotics are examined. Strategies to maximize the diet's benefits while reducing potential side effects are considered. Together, these insights herein offer a comprehensive framework for understanding the interactions between the KD and the gut, a prerequisite for optimizing the overall health benefits of metabolism-based treatments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.003 | 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".