Integrated Analysis of Human Milk Microbiota With Oligosaccharides and Fatty Acids in the CHILD Cohort
Bibliographic record
Abstract
Background: Human milk contains many bioactive components that are typically studied in isolation, including bacteria. We performed an integrated analysis of human milk oligosaccharides and fatty acids to explore their associations with milk microbiota. Methods: We studied a sub-sample of 393 mothers in the CHILD birth cohort. Milk was collected at 3-4 months postpartum. Microbiota was analysed by 16S rRNA gene V4 sequencing. Oligosaccharides and fatty acids were analysed by rapid high-throughput high performance and gas liquid chromatography, respectively. Dimension reduction was performed with principal component analysis for oligosaccharides and fatty acids. Centre log-ratio transformation was applied to all three components. Associations between components were assessed using Spearman rank correlation, network visualization, multivariable linear regression, redundancy analysis, and structural equation modelling. P-values were adjusted for multiple comparisons. Key covariates were considered, including fucosyltransferase-2 (FUT2) secretor status of mother and infant, method of feeding (direct breastfeeding or pumped breast milk), and maternal fish oil supplement use. Results: Overall, correlations were strongest between milk components of the same type. For example, FUT2-dependent HMOs were positively correlated with each other, and Staphylococcus was negatively correlated with other core taxa. Some associations were also observed between components of different types. Using redundancy analysis and structural equation modelling, the overall milk fatty acid profile was significantly associated with milk microbiota composition. In addition, some individual fatty acids (22:6n3 (docosahexaenoic acid), 22:5n3, 20:5n3, 17:0, 18:0) and oligosaccharides (fucosyl-lacto-N-hexaose, lacto-N-hexaose, lacto-N-fucopentaose I) were associated with microbiota α diversity, while others (C18:0, 3’-sialyllactose, disialyl-lacto-N-tetraose) were associated with overall microbiota composition. Only a few significant associations between individual HMOs and microbiota were observed; notably, among mothers using breast pumps, Bifidobacterium prevalence was associated with lower abundances of disialyl-lacto-N-hexaose. Additionally, among non-secretor mothers, Staphylococcus was positively correlated with sialylated HMOs. Conclusion: Using multiple approaches to integrate and analyse milk microbiota, oligosaccharides, and fatty acids, we observed some associations between different milk components and microbiota, some of which were modified by secretor status and/or breastfeeding practices. Additional research is needed to further validate and mechanistically characterize these associations and determine their relevance to infant gut and respiratory microbiota development and 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".