Functional contribution of the intestinal microbiome in autism spectrum disorder, attention deficit hyperactivity disorder, and Rett syndrome: a systematic review of pediatric and adult studies
Bibliographic record
Abstract
Introduction Critical phases of neurodevelopment and gut microbiota diversification occur in early life and both processes are impacted by genetic and environmental factors. Recent studies have shown the presence of gut microbiota alterations in neurodevelopmental disorders. Here we performed a systematic review of alterations of the intestinal microbiota composition and function in pediatric and adult patients affected by autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and Rett syndrome (RETT). Methods We searched selected keywords in the online databases of PubMed, Cochrane, and OVID (January 1980 to December 2021) with secondary review of references of eligible articles. Two reviewers independently performed critical appraisals on the included articles using the Critical Appraisal Skills Program for each study design. Results Our systematic review identified 18, 7, and 3 original articles describing intestinal microbiota profiles in ASD, ADHD, and RETT, respectively. Decreased Firmicutes and increased Bacteroidetes were observed in the gut microbiota of individuals affected by ASD and ADHD. Proinflammatory cytokines, short-chain fatty acids and neurotransmitter levels were altered in ASD and RETT. Constipation and visceral pain were related to changes in the gut microbiota in patients affected by ASD and RETT. Hyperactivity and impulsivity were negatively correlated with Faecalibacterium (phylum Firmicutes) and positively correlated with Bacteroides sp. (phylum Bacteroidetes) in ADHD subjects. Five studies explored microbiota-or diet-targeted interventions in ASD and ADHD. Probiotic treatments with Lactobacillus sp. and fecal microbiota transplantation from healthy donors reduced constipation and ameliorated ASD symptoms in affected children. Perinatal administration of Lactobacillus sp. prevented the onset of Asperger and ADHD symptoms in adolescence. Micronutrient supplementation improved disease symptomatology in ADHD without causing significant changes in microbiota communities’ composition. Discussion Several discrepancies were found among the included studies, primarily due to sample size, variations in dietary practices, and a high prevalence of functional gastrointestinal symptoms. Further studies employing longitudinal study designs, larger sample sizes and multi-omics technologies are warranted to identify the functional contribution of the intestinal microbiota in developmental trajectories of the human brain and neurobehavior. Systematic review registration https://clinicaltrials.gov/ , CRD42020158734.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".