The Ketogenic Diet Modifies Social and Metabolic Alterations Identified in the Prenatal Valproic Acid Model of Autism Spectrum Disorder
Bibliographic record
Abstract
Autism spectrum disorder (ASD) is a highly prevalent neurodevelopmental disorder characterized by abnormal social interactions, communication deficits and stereotyped or repetitive behaviors. Although the etiology of ASD remains elusive, converging lines of research indicate that mitochondrial dysfunction may play a substantive role in disease pathophysiology. Without an established causal link, the generation of therapeutic targets for ASD has been relatively unsuccessful and has focused solely on individual symptoms. The ketogenic diet (KD) is a high-fat low-carbohydrate diet that has previously been used for the treatment of intractable epilepsy and is known to enhance mitochondrial function. The purpose of this study was to determine if the KD could reverse the social deficits and mitochondrial dysfunction identified in the prenatal valproic acid (VPA) rodent model of ASD. Sprague-Dawley dams were administered VPA or saline on gestational day 12.5. The pups were treated with the KD or their standard diet (SD) for 10 days beginning on postnatal day 21 (PD21). On PD35 juvenile play behavior was tested with the play-fighting paradigm and rats were then sacrificed for mitochondrial bioenergetic analysis. The offspring exposed to VPA prenatally demonstrated a significant decrease in the number of play initiations/attacks and this was reversed with the KD. Prenatal VPA exposure also disrupted the pattern of play responses; VPA/SD animals used complete rotations more often than saline control animals. Treatment with the KD did not affect the number of complete rotations. In addition, while prenatal exposure to VPA altered mitochondrial respiration, the KD was able to restore aspects of bioenergetic dysfunction. As the KD was able to modify complex social behaviors and mitochondrial respiration, it may be a useful treatment option for ASD. Future studies will need to examine the effectiveness of the KD to reverse the two additional core deficits of ASD and to explore various treatment regimens to determine optimal treatment duration and formulation.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".