Altered gut microbiota and short chain fatty acids in Chinese children with autism spectrum disorder
Bibliographic record
Abstract
Autism spectrum disorder (ASD) is a neurodevelopmental disorder that is characterized by impairments in social interactions and communication, restricted interests and repetitive behaviors. Several studies report a high prevalence of gastrointestinal (GI) symptoms in autistic individuals. Cumulative evidence reveals that the gut microbiota and its metabolites (especially short-chain fatty acids, SCFAs) play an important role in GI disorders and the pathogenesis of ASD. However, the composition of the gut microbiota and its association with fecal SCFAs and GI symptoms of autistic children remain largely unknown. In the present study, we sequenced the bacterial 16S rRNA gene, detected fecal SCFAs, assessed GI symptoms and analyzed the relationship between the gut microbiome and fecal SCFAs in autistic and neurotypical individuals. The results showed that the compositions of the gut microbiota and SCFAs were altered in ASD individuals. We found lower levels of fecal acetic acid and butyrate and a higher level of fecal valeric acid in ASD subjects. We identified decreased abundances of key butyrate-producing taxa (Ruminococcaceae, Eubacterium, Lachnospiraceae and Erysipelotrichaceae) and an increased abundance of valeric acid associated bacteria (Acidobacteria) among autistic individuals. Constipation was the only GI disorder in ASD children in the present study. We also found enriched Fusobacterium, Barnesiella, Coprobacter and valeric acid-associated bacteria (Actinomycetaceae) and reduced butyrate-producing taxa in constipated autistic subjects. It is suggested that the gut microbiota contributes to fecal SCFAs and constipation in autism. Modulating the gut microbiota, especially butyrate-producing bacteria, could be a promising strategy in the search for alternatives for the treatment of autism spectrum disorder.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".