Human milk microbiome is shaped by breastfeeding practices
Bibliographic record
Abstract
There is evidence that breastfeeding practices may impact the milk microbiota diversity and differential abundance at the genera level; however, the possibility that distinct feeding practices, such as exclusive (EBF) and non-exclusive breastfeeding (non-EBF), might alter the milk microbiome at the species level has not been explored. This cross-sectional study analyzed the milk microbiome of 64 Mam-Mayan indigenous mothers from San Juan Ostuncalco in Guatemala. Two breastfeeding practices [exclusive (EBF) vs non-exclusive (non-EBF)] were analyzed at two stages of lactation [early (5–46 days post-partum) vs late (109–184 days post-partum)]. EBF was defined as offering only human milk and non-EBF was defined as feeding the infant herbal teas (agüitas) and/or complementary foods while continuing to breastfeed. Results identified four clusters with distinct microbial communities that segregated bacterial species by both breastfeeding practices and stage of lactation. Comparison among these clusters identified several notable patterns. First, during EBF, the microbiome differed by stage of lactation where there was a shift in differential abundance from Actinobacteria and Firmicutes in early to Bacteroidetes and Proteobacteria species in late lactation. Second, a similar comparison between non-EBF mothers by stage of lactation also identified a higher differential abundance of Actinobacteria and Firmicutes species in early lactation, but only Proteobacteria and not Bacteroidetes in late lactation, indicating a further shift in the milk microbial ecosystem with fewer oral bacteria present in late lactation. Third, comparisons between EBF and non-EBF mothers at both early and late lactation showed that mothers who exclusively breastfed had more differentially abundant species in early (11 vs 1) and late (13 vs 2) lactation. Fourth, EBF at early and late lactation had more commensal and lactic acid bacteria, including Lactobacillus gasseri, Granulicatella elegans, Streptococcus mitis, and Streptococcus parasanguinis, compared to those who did not exclusively breastfeed. Collectively, these results show that EBF has more differentially abundant bacteria, including commensal and lactic acid bacteria, and that the addition of agüitas (herbal teas) and/or complementary foods modify the milk microbiome composition by reducing the oral bacteria and introducing more environmentally sourced bacteria to the ecosystem.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".