Exploring the impacts of human breast milk functional lipidome on infant health and growth outcomes in early life using lipid bioinformatics
Bibliographic record
Abstract
Abstract Human breast milk lipidome is complex, and how changes in the functional lipid metabolism converge systematically to alter infants’ health outcomes is poorly understood. We used human breast milk and infant-mother dyads as a test system to demonstrate how the application of improved lipid bioinformatics can be effective in discerning systematic changes in functional lipid metabolism providing novel discoveries of how lactational programming in early life can influence infant health and growth outcomes. The study consisted of 40 mother-infant dyads where breast milk, maternal diet, infant anthropometrics [fat mass index (FMI), length z score, BMI z score, fat-free mass index (FFMI)], and infant atopic disease outcome (ear infection, cold, wheezing, diarrhea, and eczema) were collected at one and four months postpartum. Integrated Lipid Bioinformatics analyses were conducted using XLSTAT, Metaboanalyst 5.0. R software, Lipid Search, Xcalibur, and Cytoscape software. The results showed breast milk lipidome ordinated into distinct clusters based on maternal BMI status, and differences in developmental and atopic disease outcomes following redundancy analysis. Specifically, lipids from obese mothers clustered with FMI and eczema, while lipids from non-obese mothers clustered with FFM and wheezing. Receiver operating analysis was effective in identifying potential lipid biomarkers that were significantly associated with infant FMI, FFMI, and eczema during early life. Sphingolipid and glycerophospholipid pathways were significantly associated with the altered breast milk lipidome impacting infant development and atopic disease outcome during the first year of life. The findings following the advanced lipid bioinformatics suggest that the breastmilk functional lipid metabolism appears to play a key role in lipid-mediated lactational programming influencing development and atopic disease outcome, and present opportunities for potential dietary intervention in early life.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".