Exogenous ketones exert antiseizure effects and modulate the gut microbiome and mycobiome in a clinically relevant murine model of epilepsy
Bibliographic record
Abstract
OBJECTIVE: Despite growing interest in the potential use of exogenous ketones for the treatment of epilepsy, their impact on seizures and the gut microbiome and mycobiome remain unclear. METHODS: Here, we examined the effects of both oral gavage and subcutaneous (SC) injection of a ketone ester (KE) in spontaneously epileptic Kcna1-null (KO) mice that model seminal aspects of human temporal lobe epilepsy. Electroencephalographic recordings and biochemical analyses were performed in KE-treated KO mice. Fecal microbial and fungal communities were profiled to determine whether the antiseizure activity of KE involves changes in the gut microbiome. RESULTS: We found that exogenous KE administration by SC injection was more effective than oral gavage in terms of rendering antiseizure effects while generating similar degrees of ketonemia. However, reductions in mean daily seizure counts were accompanied by overall alterations in the fecal bacterial microbiome. Either oral or SC injection imposed a greater impact on the microbiome in male than female mice. In males, oral KE decreased Bacteroidota phylum and genera of Ligilactobacillus and Muribaculaceae, whereas SC injection decreased Bacteroides, Lactobacillus, and Lachnospiraceae. The fecal mycobiome was affected by KE injection to a greater degree than by oral gavage, and more in females than in males, as reflected by an increase in Ascomycota and Saccharomyces. Correlation analysis between microbiome and seizure counts revealed that in mice receiving KE injection, the seizure count was positively correlated with an amplicon sequencing variant of Lactobacillus (Spearman rho = .64, p = .03) and tended toward a negative correlation with Saccharomyces (Spearman rho = -.57, p = .057). SIGNIFICANCE: Our findings demonstrate that exogenous ketone administration alone can induce antiseizure effects equally via different routes of administration, and that they induce differential shifts in both the bacterial microbiome and mycobiome.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".