Non-nutritive bioactive components in maternal milk and offspring development: a scoping review
Bibliographic record
Abstract
Lactation is a critical time in mammalian development, where maternal factors shape offspring outcomes. In this scoping review, we discuss current literature concerning maternal factors that influence lactation biology and highlight important associations between changes in milk composition and offspring outcomes. Specifically, we explore maternal nutritional, psychosocial, and environmental exposures that influence non-nutritive bioactive components in milk and their links to offspring growth, development, metabolic, and behavioral outcomes. A comprehensive literature search was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. Predetermined eligibility criteria were used to analyze 3,275 papers, and the final review included 40 primary research articles. Outcomes of this review identify maternal obesity to be a leading maternal factor influencing the non-nutritive bioactive composition of milk with notable links to offspring outcomes. Offspring growth and development are the most common modes of programming associated with changes in non-nutritive milk composition due to maternal factors in early life. In addition to discussing studies investigating these key associations, we also identify knowledge gaps in the current literature and suggest opportunities and considerations for future studies.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".