Development of the infant gut microbiome predicts temperament across the first year of life
Bibliographic record
Abstract
Abstract Perturbations to the gut microbiome are implicated in altered neurodevelopmental trajectories that may shape life span risk for emotion dysregulation and affective disorders. However, the sensitive periods during which the microbiome may influence neurodevelopment remain understudied. We investigated relationships between gut microbiome composition across infancy and temperament at 12 months of age. In 67 infants, we examined if gut microbiome composition assessed at 1–3 weeks, 2, 6, and 12 months of age was associated with temperament at age 12 months. Stool samples were sequenced using the 16S Illumina MiSeq platform. Temperament was assessed using the Infant Behavior Questionnaire-Revised (IBQ-R). Beta diversity at age 1–3 weeks was associated with surgency/extraversion at age 12 months.BifidobacteriumandLachnospiraceaeabundance at 1–3 weeks of age was positively associated with surgency/extraversion at age 12 months.Klebsiellaabundance at 1–3 weeks was negatively associated with surgency/extraversion at 12 months. Concurrent composition was associated with negative affectivity at 12 months, including a positive association withRuminococcus-1and a negative association withLactobacillus. Our findings support a relationship between gut microbiome composition and infant temperament. While exploratory due to the small sample size, these results point to early and late infancy as sensitive periods during which the gut microbiome may exert effects on neurodevelopment.
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 | 0.002 |
| 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 | 0.001 | 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".