Gut feelings begin in childhood: how the gut metagenome links to early environment, caregiving, and behavior
Bibliographic record
Abstract
ABSTRACT Psychosocial environments impact normative behavioral development in children, increasing the risk of problem behaviors and psychiatric disorders across the lifespan. Converging evidence demonstrates early normative development is affected by the gut microbiome, which itself can be altered by early psychosocial environments. Nevertheless, these relationships are poorly understood in childhood, particularly beyond peri- and postnatal microbial colonization. To determine the gut microbiome’s role in the associations between childhood adversity and behavioral development, we conducted a metagenomic investigation among cross-sectional sample of early school-aged children with a range of adverse experiences and caregiver stressors and relationships. Our results indicate that the taxonomic and functional composition of the gut microbiome links to behavioral dysregulation during a critical period of child development. Furthermore, our analysis reveals that both socioeconomic risk exposure and child behaviors associate with the relative abundances of specific taxa (e.g., Bacteroides and Bifidobacterium species) as well as functional modules encoded in their genomes (e.g., monoamine metabolism) that have been linked to cognition and health. We also identified heretofore novel linkages between gut microbiota, their functions, and behavior. These findings hold important translational implications for developmental psychology and microbiome sciences alike, as they suggest that caregiver behavior might mitigate the impact of socioeconomic risk on the microbiome and modify the relationship between subclinical symptoms of behavioral dysregulation and the gut microbiome in early school-aged children.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".