Addition of Prebiotics to the Ketogenic Diet Improves Metabolic Profile but Does Not Affect Seizures in a Rodent Model of Infantile Spasms Syndrome
Bibliographic record
Abstract
The ketogenic diet (KD) is an effective treatment for infantile spasms syndrome (IS). However, the KD has implications for somatic growth, development, and the gut microbiota. The impact of incorporating a prebiotic fiber (PRE, oligofructose-enriched inulin, 0.8 g/dL) into a KD diet on spasms, developmental milestones, fecal gut microbiota, metabolites, and hippocampal mitochondrial metabolism were examined. Following IS induction, animals were randomized to KD or KD + PRE diets. A third group without IS and suckled by dams was included as a normally developing reference group (R). PRE inclusion decreased ketones and increased circulating glucose levels but had no impact on spasms. In the liver, PRE increased triglyceride concentrations, decreased carnitine levels, and downregulated genes encoding enzymes responsible for ketogenesis. In the hippocampus, PRE increased glutathione levels but did not affect the maximal respiratory capacity of mitochondria. Analysis of the gut microbiota showed that KD + PRE increased microbial richness and the relative abundance of Bifidobacterium pseudolongum and Lactobacillus johnsonii. No differences in developmental milestones (i.e., surface righting, negative geotaxis, and open field behavior) were observed between KD and KD + PRE, except for ultrasonic vocalizations that were more frequent in KD + PRE. In summary, PRE did not impact spasms or developmental outcomes, but was effective in improving both metabolic parameters and gut microbiota diversity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".