Dietary Fat, Ketosis, and Seizure Resistance in Rats on the Ketogenic Diet
Bibliographic record
Abstract
PURPOSE: Fat is the major component of the ketogenic diet (KD), yet no studies have examined whether the type of fat used in the diet can be optimized to provide additional benefits. The purpose of the present experiments was to compare the efficiency of different fats in inducing ketosis and affording seizure resistance. METHODS: The effects of KDs that incorporate lard, butter, medium-chain triglycerides (MCT), or flaxseed oil or a mixture of the latter three fats were examined in rats fed KD for up to 98 days. The maximal electroshock (MES) or pentylenetetrazole (PTZ) threshold tests were used to assess seizure susceptibility in two separate experiments. RESULTS: The rank order of induced ketosis was MCT > mixture > or = flaxseed oil > or = lard = butter > or = control. MES failed to reveal anticonvulsant effects, but the PTZ test indicated that up to 50% of rats fed the KD were seizure protected (p < 0.05). The measures of seizure protection, seizure incidence and score, did not correlate, however, with the level of ketosis in the range of 0. 7-5.2 mmol/L for beta-hydroxybutyrate. In the long-term study, flaxseed oil KD maintained stable ketosis throughout 98 days, whereas ketones declined with lard and butter KD to the control level. CONCLUSIONS: Seizure protection with the versions of the KD did not improve with the higher level of ketosis. The focus of the KD improvement, therefore, is not the achievement of higher ketosis per se but rather designing a diet that provides steady ketosis, exploits advantages of certain fats for neurological development or seizure protection via a nonketogenic mechanism, and is nutritionally balanced.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".