Early-Life Microbiome and Neurodevelopmental Disorders: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This systematic review intends to find out how neurodevelopmental disorders, including Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD), are influenced by the gut microbiota throughout early childhood. The study looks at the variety and types of microbes that a child is exposed to, the particular microbiome profiles associated with neurodevelopmental outcomes, and the molecular processes that underlie these relationships. METHODS: We performed a thorough search of PubMed, Scopus, the WHO Global Health Library (GHL), and ISI Web of Science. After screening 2,744 original studies based on predetermined eligibility criteria, 19 studies were included. Microbial groupings, presence (high/low), and related neurodevelopmental disorders were among the primary areas of data extraction. The methodological quality of the studies was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). RESULTS: The investigated literature repeatedly showed a strong correlation between dysbiosis of the gut microbiota and neurodevelopmental disorders. Cases of ASD were associated with both a high number of Clostridium species and a low number of Bifidobacterium species. On the other hand, a Low number of E. coli and a high number of the class Clostridia, phylum Firmicute, genus Bifidobacterium, and Akkermansia, as well as the species Listeria monocytogenes, Toxoplasma gondii, Streptococcus mutans, and Mycobacterium tuberculosis have been linked to ADHD. The NOS evaluation showed variation in the quality of the methodology; some studies had high scores, suggesting sound technique, while other studies had lower scores, indicating serious methodological flaws. CONCLUSION: The results highlight the potential impact of the gut microbiome throughout early life on neurodevelopmental outcomes, indicating that microbial imbalances may play a role in the onset of disorders like ASD and ADHD. However, to improve the quality of data, larger-scale longitudinal studies would be required.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.023 |
| Bibliometrics | 0.010 | 0.011 |
| 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) | 0.004 | 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".