Exploring the effect of prenatal maternal stress on the microbiomes of mothers and infants: A systematic review
Bibliographic record
Abstract
Prenatal maternal stress (PNMS)-characterized by exposure to stress, anxiety, depression, or intimate partner violence-has been linked to biological alterations in infants, including disruptions to their intestinal microbiota, which have long-term implications for children's developmental outcomes. Significant research has been done examining the effects of PNMS on the microbiome in animals, but less is known about these effects in human research. The current systematic review aimed to synthesize current findings on the association between PNMS and mother and infant microbiomes. Medline, Embase, PsycInfo, Web of Science, and Eric databases were searched through to February 2022. A total of eight studies (n = 2219 infants, 2202 mothers) were included in the qualitative synthesis. Findings provided promising evidence of the role that PNMS plays in altering the microbial composition, diversity, and gut immunity in mothers and infants. Notably, majority of included studies found that higher PNMS was linked to increases in genera from the phylum Proteobacteria. The factors influencing these effects are explored including nutrition, birth mode, and parenting behaviors. Potential interventions to mitigate the adverse effects of PNMS are discussed, along with recommendations for future studies with longitudinal designs to better understand the appropriate type and timing of interventions needed to promote "healthy" maternal and infant microbial functioning.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".