What's Normal? Microbiomes in Human Milk and Infant Feces Are Related to Each Other but Vary Geographically: The INSPIRE Study
Bibliographic record
Abstract
Background Microbial communities in human milk and those in feces from breastfed infants vary within and across populations. However, few researchers have conducted cross-cultural comparisons between populations, and little is known about whether certain “core” taxa occur normally within or between populations and whether variation in milk microbiome is related to variation in infant fecal microbiome. The purpose of this study was to describe microbiomes of milk produced by relatively healthy women living at diverse international sites and compare these to the fecal microbiomes of their relatively healthy infants. Methods We analyzed milk (n = 394) and infant feces (n = 377) collected from mother/infant dyads living in 11 international sites (2 each in Ethiopia, The Gambia, and the US; 1 each in Ghana, Kenya, Peru, Spain, and Sweden). The V1-V3 region of the bacterial 16S rRNA gene was sequenced to characterize and compare microbial communities within and among cohorts. Results Core genera in feces were Streptococcus, Escherichia/Shigella, and Veillonella, and in milk were Streptococcus and Staphylococcus, although substantial variability existed within and across cohorts. For instance, relative abundance of Lactobacillus was highest in feces from rural Ethiopia and The Gambia, and lowest in feces from Peru, Spain, Sweden, and the US; Rhizobium was relatively more abundant in milk produced by women in rural Ethiopia than all other cohorts. Bacterial diversity also varied among cohorts. For example, Shannon diversity was higher in feces from Kenya than Ghana and US-California, and higher in rural Ethiopian than Ghana, Peru, Spain, Sweden, and US-California. There were limited associations between individual genera in milk and feces, but community-level analyses suggest strong, positive associations between the complex communities in these sample types. Conclusions Our data provide additional evidence of within- and among-population differences in milk and infant fecal bacterial community membership and diversity and support for a relationship between the bacterial communities in milk and those of the recipient infant’s feces. Additional research is needed to understand environmental, behavioral, and genetic factors driving this variation and association, as well as its significance for acute and chronic maternal and infant health.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".