Human milk microbiome: associations with maternal diet and infant growth
Bibliographic record
Abstract
Introduction: Ingestion of human milk (HM) is identified as a significant factor associated with early infant gut microbial colonization, which has been associated with infant health and development. Maternal diet has been associated with the HM microbiome (HMM). However, a few studies have explored the associations among maternal diet, HMM, and infant growth during the first 6 months of lactation. Methods: = 35). Recruited mothers had vaginally delivered singleton births, had no subclinical mastitis or antibiotic treatments, and breastfed their infants. Data collected at both stages of lactation included two 24-h recalls, milk samples, and infant growth status indicators: head-circumference-for-age-z-score (HCAZ), length-for-age-z-score (LAZ), and weight-for-age-z-score (WAZ). Infants were divided into subgroups: normal weight (WAZ ≥ -1SD) and mildly underweight (WAZ < -1SD), non-stunted (LAZ ≥ -1.5SD) and mildly stunted (LAZ < -1.5SD), and normal head-circumference (HCAZ ≥ -1SD) and smaller head-circumference (HCAZ < -1SD). HMM was identified using 16S rRNA gene sequencing; amplicon analysis was performed with the high-resolution ANCHOR pipeline, and DESeq2 identified the differentially abundant (DA) HMM at the species-level between infant growth groups (FDR < 0.05) in both early and late lactation. Results: Using both cluster and univariate analyses, we identified (a) positive correlations between infant growth clusters and maternal dietary clusters, (b) both positive and negative associations among maternal macronutrient and micronutrient intakes with the HMM at the species level and (c) distinct correlations between HMM DA taxa with maternal nutrient intakes and infant z-scores that differed between breast-fed infants experiencing growth faltering and normal growth in early and late lactation. Conclusion: Collectively, these findings provide important evidence of the potential influence of maternal diet on the early-life growth of breastfed infants via modulation of the HMM.
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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 | 0.001 | 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) | 0.003 | 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".