Does the ketogenic diet improve neurological disorders by influencing gut microbiota? A systematic review
Bibliographic record
Abstract
BACKGROUND: The aim of this systematic review is to evaluate the changes in gut microbiota (GM) induced by the Ketogenic Diets (KD) as a potential underlying mechanism in the improvement of neurological diseases. METHODS: A comprehensive search was conducted on three electronic databases, including PubMed/Medline, Web of Science, and Scopus until December 2022. The inclusion criteria were studies that described any changes in GM after consuming KD in neurological patients. Full text of studies such as clinical trials and cohorts were added. The quality assessment of cohort studies was conducted using the Newcastle-Ottawa Quality Assessment Scale and for the clinical trials using the Cochrane Collaboration tool. The search, screening, and data extraction were performed by two researchers independently. RESULTS: Thirteen studies examining the effects of the KD on the GM in neurological patients were included. Studies have shown that KD improves clinical outcomes by reducing disease severity and recurrence rates. An increase in Proteobacteria phylum, Escherichia, Bacteroides, Prevotella, Faecalibacterium, Lachnospira, Agaricus, and Mrakia genera and a reduction in Firmicutes, and Actinobacteria phyla, Eubacterium, Cronobacter, Saccharomyces, Claviceps, Akkermansia and Dialister genera were reported after KD. Studies showed a reduction in concentrations of fecal short-chain fatty acids and branched-chain fatty acids and an increase in beta Hydroxybutyrate, trimethylamine N-oxide, and N-acetylserotonin levels after KD. CONCLUSION: The KD prescribed in neurological patients has effectively altered the GM composition and GM-derived metabolites.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".