The human milk microbiome varies by environmental factors and is associated with infant growth: findings from the IMiC Consortium
Bibliographic record
Abstract
Abstract Human milk (HM) is a complex ecological matrix that connects mothers and infants to the surrounding environment, and promotes infant growth and health. While certain components of HM are well studied, including its macronutrient content and immune properties, the microbial composition of HM (i.e. the microbiome) is poorly characterized and its impact on infant health phenotypes is largely unknown. We hypothesized that the HM microbiome varies by environmental factors and is associated with differences in growth outcomes among HM-fed infants in settings with elevated rates of undernutrition and growth faltering. We leveraged a large dataset of HM samples (N=451) collected from mothers living in rural Tanzania, rural Burkina Faso, and peri-urban Pakistan around 1 month postpartum as a part of the International Milk Composition (IMiC) Consortium. 16S rRNA bacterial gene sequencing revealed geographic and seasonal signatures of the HM microbiome. Machine learning models identified Corynebacterium as a key feature that predicted infant birth season and growth outcomes in each of the three populations, though individual predictive taxa within the genus differed across the models. This study highlights the evolutionary importance of the HM microbiome as a biological system that embeds local environments and is associated with growth phenotypes critical to infant health and survival.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".