Feeding Expressed Breast Milk Alters the Microbial Network of Breast Milk and Increases Breast Milk Microbiome Diversity over Time
Bibliographic record
Abstract
Breastfeeding supplies nutrition, immunity, and hormonal cues to infants. Feeding expressed breast milk may result in de-phased milk production and feeding times, which distort the real-time circadian cues carried by breast milk. We hypothesized that providing expressed breast milk alters the microbiotas of both breast milk and the infant's gut. To test this hypothesis, we analyzed the microbiota of serial breast milk and infant fecal samples obtained from 14 mother-infant dyads who were lactating, half of which were providing expressed breast milk. Infant fecal microbiota showed lower α-diversity than breast milk microbiota. Bacterial amplicon sequence variant sharing occurred between breast milk and infant feces with no feeding group differences. However, the age-dependent gain in breast milk α-diversity was only significant in the expressed breast milk group and not in the direct breastfeeding group, suggesting that decreased contact with the infant's mouth influences the milk microbiota. Trending lower connectivity was also noted with breast milk microbes in the direct breastfeeding group, consistent with regular perturbations of the developing baby's oral microbiota by latching on the breast. The results of this preliminary study urge further research to independently confirm the effects of providing expressed breast milk and their health significance.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".