Impact of maternal obesity and prebiotic supplementation on select maternal milk microRNA levels and correlation with offspring outcomes
Bibliographic record
Abstract
Breast milk composition varies with maternal factors including diet and confers health benefits to the neonate; however, the mechanisms mediating this protection remain incompletely understood. Our aim was to investigate the effects of supplementing a maternal high-fat/sucrose (HFS) diet with prebiotic oligofructose (OFS) on milk composition in rats and associations with offspring body composition and gut microbiota. Obese Sprague-Dawley dams consumed a control, HFS, HFS + OFS (10 % wt/wt) or HFS diet weight-matched to the HFS + OFS group (HFS-WM) during pregnancy and lactation. Pups were weaned onto a HFS diet on day 21. Milk was collected at weaning and analysed for protein, leptin and microRNA (miRNA) levels. Milk produced by HFS dams contained less protein than milk from lean controls which was normalised by OFS. Six miRNA (miR-222, miR-203a, miR-200a, miR-26a, miR-27a and miR-103) were differentially expressed in milk according to maternal diet. Milk leptin content was positively correlated with maternal body fat and faecal Enterobacteriaceae in male offspring at 24 weeks of age. Milk protein content was inversely associated with maternal body fat and body weight. miR-200a was positively associated with maternal body fat and Enterobacteriaceae in female offspring at 24 weeks of age. Correlations between milk protein and multiple milk miRNA and offspring body composition and gut microbiota differed by sex. Overall, our results suggest that obesogenic diets and prebiotic supplementation can alter the protein and miRNA levels in breast milk in rats and these milk components may explain, in part, the influence of these maternal diets on offspring body composition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".