The ketogenic diet raises brain oxygen levels, attenuates postictal hypoxia, and protects against learning impairments
Bibliographic record
Abstract
OBJECTIVES: A prolonged vasoconstriction/hypoperfusion/hypoxic event follows self-terminating focal seizures. The ketogenic diet (KD) has demonstrated efficacy as a metabolic treatment for intractable epilepsy and other disorders but its effect on local brain oxygen levels is completely unknown. This study investigated the effects of the KD on tissue oxygenation in the hippocampus before and after electrically elicited (kindled) seizures and whether it could protect against a seizure-induced learning impairment. We also examined the effects of the ketone β-hydroxybutyrate (BHB) as a potential underlying mechanism. METHODS: Male and female rats were given access to one of three diet protocols 2 weeks prior to the initiation of seizures: KD, caloric restricted standard chow, and ad libitum standard chow. Dorsal hippocampal oxygen levels were measured prior to initiation of diets as well as before and after a 10-day kindling paradigm. Male rats were then tested on a novel object recognition task to assess postictal learning impairments. In a separate cohort, BHB was administered 30 min prior to seizure elicitation to determine whether it influenced oxygen dynamics. RESULTS: The KD increased dorsal hippocampal oxygen levels, ameliorated postictal hypoxia, and prevented postictal learning impairments. Acute BHB administration did not alter oxygen levels before or after seizures. INTERPRETATION: The ketogenic diet raised brain oxygen levels and attenuated severe postictal hypoxia likely through a mechanism independent of ketosis and shows promise as a non-pharmacological treatment to prevent the postictal state.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".