Ketogenic diet modifies the gut microbiota in a murine model of autism spectrum disorder
Bibliographic record
Abstract
BACKGROUND: Gastrointestinal dysfunction and gut microbial composition disturbances have been widely reported in autism spectrum disorder (ASD). This study examines whether gut microbiome disturbances are present in the BTBR(T + tf/j) (BTBR) mouse model of ASD and if the ketogenic diet, a diet previously shown to elicit therapeutic benefit in this mouse model, is capable of altering the profile. FINDINGS: Juvenile male C57BL/6 (B6) and BTBR mice were fed a standard chow (CH, 13 % kcal fat) or ketogenic diet (KD, 75 % kcal fat) for 10-14 days. Following diets, fecal and cecal samples were collected for analysis. Main findings are as follows: (1) gut microbiota compositions of cecal and fecal samples were altered in BTBR compared to control mice, indicating that this model may be of utility in understanding gut-brain interactions in ASD; (2) KD consumption caused an anti-microbial-like effect by significantly decreasing total host bacterial abundance in cecal and fecal matter; (3) specific to BTBR animals, the KD counteracted the common ASD phenotype of a low Firmicutes to Bacteroidetes ratio in both sample types; and (4) the KD reversed elevated Akkermansia muciniphila content in the cecal and fecal matter of BTBR animals. CONCLUSIONS: Results indicate that consumption of a KD likely triggers reductions in total gut microbial counts and compositional remodeling in the BTBR mouse. These findings may explain, in part, the ability of a KD to mitigate some of the neurological symptoms associated with ASD in an animal model.
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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 | 0.000 |
| 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".