Gut microbiota metabolites, secretory immunoglobulin A and Bayley-III cognitive scores in children from the CHILD Cohort Study
Bibliographic record
Abstract
Background: Dysbiosis of the gut microbiota has been demonstrated in neurodevelopmental disorders but the underlying mechanisms that may explain these associations are poorly understood. Gut secretory immunoglobulin A (SIgA) binds pathogenic microbes, preventing mucosal penetration. Gut microbes also influence SIgA production and its binding characteristics through short-chain fatty acid (SCFA) metabolites, allowing them to regulate the immune response. Serum IgA deficiency has been noted in children with autism spectrum disorders (ASD). In this study, we aimed to determine whether SIgA level in infancy is associated with gut microbiota taxonomy and metabolites, and neurodevelopmental outcomes in preschool children. Methods: For a subsample of 178 children from the Canadian CHILD Cohort Study, gut microbiota of fecal samples collected at 3-4 months and 12 months was profiled using 16S rRNA sequencing. Gut bacterial metabolites levels and SIgA level were measured by nuclear magnetic resonance (NMR) based metabolomics and SIgA enzyme-linked immunosorbent assay at 3-4 months, respectively. Bayley-III Scale of Infant Development was assessed at 12 and 24 months. We evaluated direct relationships in multiple linear regression models and putative causal relationships in statistical mediation models. Results: Propionate and butyrate levels at 3-4 months were associated with decreased Bayley cognitive score at 24 months (p-values: 0.01 and 0.02, respectively) in adjusted multiple linear regression models, but when we investigated an indirect relationship mediated by decreased SIgA level at 3-4 months, it did not reach statistical significance (p-values: 0.18 and 0.20, respectively). Lactate level at 3-4 months was associated with increased Bayley cognitive score at 24 months in adjusted multiple linear regression models (p-value: 0.01), but the statistical model mediated by increased SIgA level at 3-4 months did not reach statistical significance neither (p-value: 0.20). Conclusions: Our study contributes to growing evidence that neurodevelopment is influenced by the infant gut microbiota and that it might involve SIgA level, but larger studies are 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".