Establishment of the gut microbiome in preterm infants is associated with exposure to stress-inducing medical interventions
Bibliographic record
Abstract
The gut microbiome of preterm infants follows distinct maturational trajectories relative to that of term infants, largely determined by gestational age at birth. While the development of the bacterial microbiome in preterm infants has been characterized in the context of known microbiome-modifying factors, the impact of early-life stress experienced in-hospital remains underexplored, despite having well-established associations with developmental outcomes. In this study, we conducted a longitudinal analysis of bacterial and fungal gut microbiome maturation over the first 8 weeks of life in 105 preterm infants born across all gestational ages. We identified distinct maturational patterns between the bacterial and fungal gut microbiome, where the bacterial microbiome followed predictable successional patterns that converged across prematurity categories and was strongly influenced by probiotic supplementation. In contrast, the fungal microbiome exhibited greater sparsity and stochastic compositional shifts. Frequent exposure to stress-inducing medical procedures and invasive ventilation during hospitalization were associated with greater colonization by opportunistic microbes (i.e., Staphylococcus, Klebsiella, and Candida ), suggesting a link between stress-inducing procedures and microbiome composition. Meanwhile, urine cortisol levels exhibited only weak associations with microbiome diversity, emphasizing the need for improved understandings of the influence of gut-brain communication mechanisms on the early-life microbiome and health outcomes in clinical cohorts. These findings reveal a potential link between procedural-based stress experienced during initial hospitalization and gut microbiome establishment, highlighting the need to mitigate early-life stressors to support gut microbial resilience and long-term health outcomes in children born preterm.
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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 | 0.004 |
| 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) | 0.001 | 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".