The infant microbiota hopscotches between community states toward maturation—longitudinal stool parameters and microbiota development in a cohort of European toddlers
Bibliographic record
Abstract
The development of the gut microbiome is critical during early life and is associated with infant health. To test whether this development is deterministic and how it is influenced by factors such as diet and mode of birth, we studied microbiota profiles and fecal parameters of 540 European infants, fed a synbiotic or control infant formula during their first year of life, up to 36 months of age. The diversity of the microbiota gradually increased until 36 months, at which point it resembled adult community states, indicating that microbiota maturation had occurred. However, distinct gut microbiota community states were observed that differed at each stage of maturation. The distribution of infants within the communities even at 36 months was significantly influenced by early life events, with a higher prevalence of infants born by cesarean section having the immature M36-C1 community state at 36 months. The microbial community state at one time point was not predictive of the next; instead, we observed hopscotching of the infant microbiota between different community states. This work provides new longitudinal data on the infant gut microbiome in relation to diet, suggesting that ecosystem development is not deterministic, but that early life events influence the community state of an individual's gut microbiota beyond infancy.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".