Cluster-specific associations between the gut microbiota and behavioral outcomes in preschool-aged children
Bibliographic record
Abstract
BACKGROUND: The gut microbiota is recognized as a regulator of brain development and behavioral outcomes during childhood. Nonetheless, associations between the gut microbiota and behavior are often inconsistent among studies in humans, perhaps because many host-microbe relationships vary widely between individuals. This study aims to stratify children based on their gut microbiota composition (i.e., clusters) and to identify novel gut microbiome cluster-specific associations between the stool metabolomic pathways and child behavioral outcomes. METHODS: Stool samples were collected from a community sample of 248 typically developing children (3-5 years). The gut microbiota was analyzed using 16S sequencing while LC-MS/MS was used for untargeted metabolomics. Parent-reported behavioral outcomes (i.e., Adaptive Skills, Internalizing, Externalizing, Behavioral Symptoms, Developmental Social Disorders) were assessed using the Behavior Assessment System for Children (BASC-2). Children were grouped based on their gut microbiota composition using the Dirichlet multinomial method, after which differences in the metabolome and behavioral outcomes were investigated. RESULTS: Four different gut microbiota clusters were identified, where the cluster enriched in both Bacteroides and Bifidobacterium (Ba2) had the most distinct stool metabolome. The cluster characterized by high Bifidobacterium abundance (Bif), as well as cluster Ba2, were associated with lower Adaptive Skill scores and its subcomponent Social Skills. Cluster Ba2 also had significantly lower stool histidine to urocanate turnover, which in turn was associated with lower Social Skill scores in a cluster-dependent manner. Finally, cluster Ba2 had increased levels of compounds involved in Galactose metabolism (i.e., stachyose, raffinose, alpha-D-glucose), where alpha-D-glucose was associated with the Adaptive Skill subcomponent Daily Living scores (i.e., ability to perform basic everyday tasks) in a cluster-dependent manner. CONCLUSIONS: These data show novel associations between the gut microbiota, its metabolites, and behavioral outcomes in typically developing preschool-aged children. Our results support the concept that cluster-based groupings could be used to develop more personalized interventions to support child behavioral outcomes. Video Abstract.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".