A ketogenic diet affects brain volume and metabolome in juvenile mice
Bibliographic record
Abstract
Ketogenic diet (KD) is a high-fat and low-carbohydrate therapy for medically intractable epilepsy, and its applications in other neurological conditions, including those occurring in children, have been increasingly tested. However, how KD affects childhood neurodevelopment, a highly sensitive and plastic process, is not clear. In this study, we explored structural, metabolic, and functional consequences of a brief treatment of a strict KD (weight ratio of fat to carbohydrate plus protein is approximately 6.3:1) in naive juvenile mice of different inbred strains, using a multidisciplinary approach. Systemic measurements using magnetic resonance imaging revealed that unexpectedly, the volumes of most brain structures in KD-fed mice were about 90% of those in mice of the same strain but fed a standard diet. The reductions in volumes were nonselective, including different regions throughout the brain, the ventricles, and the white matter. The relative volumes of different brain structures were unaltered. Additionally, as KD is a metabolism-based treatment, we performed untargeted metabolomic profiling to explore potential means by which KD affected brain growth and to identify metabolic changes in the brain. We found that brain metabolomic profile was significantly impacted by KD, through both distinct and common pathways in different mouse strains. To explore whether the volumetric and metabolic changes induced by this KD treatment were associated with functional consequences, we recorded spontaneous EEG to measure brain network activity. Results demonstrated limited alterations in EEG patterns in KD-fed animals. In addition, we observed that cortical levels of brain-derived neurotrophic factor (BDNF), a critical molecule in neurodevelopment, did not change in KD-fed animals. Together, these findings indicate that a strict KD could affect volumetric development and metabolic profile of the brain in inbred juvenile mice, while global network activities and BDNF signaling in the brain were mostly preserved. Whether the volumetric and metabolic changes are related to any core functional consequences during neurodevelopment and whether they are also observed in humans need to be further investigated. In addition, our results indicate that certain outcomes of KD are specific to the individual mouse strains tested, suggesting that the physiological profiles of individuals may need to be examined to maximize the clinical benefit of KD.
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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 | 0.001 |
| 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".