The role of maternal diet on offspring gut microbiota development: A review
Bibliographic record
Abstract
In offspring, an adequate maternal diet is important for neurodevelopment. One mechanism by which maternal diet impacts neurodevelopment is through its dynamic role in the development of the gut microbiota. Communication between the gut, and its associated microbiota, and the brain is facilitated by the vagus nerve, in addition to other routes. Currently, the mechanisms through which maternal diet impacts offspring microbiota development are not well-defined. Therefore, this review aims to investigate the relationship between maternal diet during pregnancy and offspring microbiota development and its impact on neurodevelopment. Both human and animal model studies were reviewed to understand the impact of maternal diet on offspring microbiota development and potential consequences on neurodevelopment. In the period after birth, as reported in both human and model system studies, maternal diet impacts offspring bacterial colonization (e.g., decreased presence of Lactobacillus reuteri as a result of a high-fat maternal diet). It remains unknown whether these changes persist into adulthood and whether they impact vulnerability to disease. Therefore, further long-term studies are required in both human and model systems to study these changes. Our survey of the literature indicates that maternal diet influences early postnatal microbiota development, which in turn, may serve as a mechanism through which maternal diet impacts neurodevelopment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".