Milk mineral composition is strongly associated with the human milk microbiome
Bibliographic record
Abstract
Introduction Associations between maternal mineral intake, human milk mineral concentrations, and their interactions with the milk microbiota remain understudied, especially in low- and middle-income countries. To understand potential interactions and gain insight into milk composition dynamics, we explored associations of milk mineral concentrations with maternal mineral intakes and the human milk microbiome in an indigenous Guatemalan community. Methods In this cross-sectional study, milk samples were collected from 77 Mam-Mayan mothers and classified into early and established lactation. Concentrations of 9 milk minerals were analyzed, and maternal dietary intake was obtained from two 24-h recalls. Microbiome diversity was assessed by 16S rRNA gene sequencing (V5–V6 region). DESeq2 was used for differential abundance analysis. PCA and Spearman’s rank correlation explored relationships among milk minerals, maternal mineral intake, and differentially abundant microbial taxa; results with FDR-adjusted p -values < 0.1 were retained. Results Our multifactorial analysis revealed strong associations between milk minerals and the milk microbiome and weak associations with maternal intake. Several maternal intakes (Ca, Se, K, Fe, Mn) and milk mineral concentrations (Ca, Se, K, Mg, Na) were below reference values. In early lactation, milk Fe, Mn, Se, and Cu correlated with differentially abundant taxa, while in established lactation, Fe, Mn, Se, Ca, and Na were correlated. Fe and Mn accounted for 64% of bacterial associations in early lactation and 75% in established lactation. These minerals were correlated with Pseudomonadota (early), Actinomycetota (established), and Bacillota (both), but all species were unique to each stage. Conclusion Our findings reveal a complex interplay between milk minerals and the microbiome. Iron, manganese, and selenium were consistently associated with milk bacteria across lactation stages. These correlations may reflect microbial responses to mineral availability. Further longitudinal studies with larger samples are needed to clarify how this interaction influences mineral bioavailability and infant growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".