Ketogenic Diet Improves Core Symptoms of Autism in BTBR Mice
Bibliographic record
Abstract
Autism spectrum disorders share three core symptoms: impaired sociability, repetitive behaviors and communication deficits. Incidence is rising, and current treatments are inadequate. Seizures are a common comorbidity, and since the 1920's a high-fat, low-carbohydrate ketogenic diet has been used to treat epilepsy. Evidence suggests the ketogenic diet and analogous metabolic approaches may benefit diverse neurological disorders. Here we show that a ketogenic diet improves autistic behaviors in the BTBR mouse. Juvenile BTBR mice were fed standard or ketogenic diet for three weeks and tested for sociability, self-directed repetitive behavior, and communication. In separate experiments, spontaneous intrahippocampal EEGs and tests of seizure susceptibility (6 Hz corneal stimulation, flurothyl, SKF83822, pentylenetetrazole) were compared between BTBR and control (C57Bl/6) mice. Ketogenic diet-fed BTBR mice showed increased sociability in a three-chamber test, decreased self-directed repetitive behavior, and improved social communication of a food preference. Although seizures are a common comorbidity with autism, BTBR mice fed a standard diet exhibit neither spontaneous seizures nor abnormal EEG, and have increased seizure susceptibility in just one of four tests. Thus, behavioral improvements are dissociable from any antiseizure effect. Our results suggest that a ketogenic diet improves multiple autistic behaviors in the BTBR mouse model. Therefore, ketogenic diets or analogous metabolic strategies may offer novel opportunities to improve core behavioral symptoms of autism spectrum disorders.
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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 | 0.000 |
| 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".