Impact of maternal dietary n-3 and n-6 fatty acids on milk medium-chain fatty acids and the implications for neonatal liver metabolism
Bibliographic record
Abstract
Levels of n-6, n-3, and medium-chain fatty acids (MCFA) in milk are highly variable. Higher carbohydrate intakes are associated with increased mammary gland MCFA synthesis, but the role of unsaturated fatty acids for milk MCFA secretion is unclear. This study addressed whether n-6 and n-3 fatty acids, which are known to inhibit hepatic fatty acid synthesis, influence MCFA in rat and human milk and the implications of varying MCFA, n-6, and n-3 fatty acids in rat milk for metabolic regulation in the neonatal liver. Rats were fed a low-fat diet or one of six higher-fat diets, varying in 16:0, 18:1n-9, 18:2n-6, 18:3n-3, and long-chain (LC) n-3 fatty acids. Higher maternal dietary 18:2n-6 or 18:3n-3 did not influence milk MCFA, but lower maternal plasma triglycerides, due to either a low-fat or a high-fat high-LC n-3 diet led to higher milk MCFA. MCFA levels were inversely associated with 18:1n-9, 18:2n-6, and 18:3n-3 in human milk, likely reflecting the association between dietary total fat and unsaturated fatty acids. High LC n-3 fatty acid in rat milk was associated with lower hepatic Pklr, Acly, Fasn, and Scd1 and higher Hmgcs2 in the milk-fed rat neonate, with no effect of milk 18:1n-9, 18:2n-6, or MCFA. These studies show that the dietary fatty acid composition does not impact MCFA secretion in milk, but the fatty acid composition of milk, particularly the LC n-3 fatty acid, is relevant to hepatic metabolic regulation in the milk-fed neonate.
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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".