Diet high in omega‐3 fatty acids alters the fatty acid composition of bioactive lipids: a lipidomic approach
Bibliographic record
Abstract
Omega (n)‐3polyunsaturated fatty acids (PUFA) are converted to bioactive lipid components that are important mediators in metabolic and physiological pathways; however, which bioactive compounds are metabolically active, and their mechanisms of action are still not clear. We investigated using lipidomics techniques, the effects of diets high in n‐3 PUFA on the fatty acid composition of various bioactive lipids in plasma and liver. Female C57BL/6 mice were fed semi‐purified diets (20% w/w fat) containing varying amounts of n‐3 PUFA before mating, during pregnancy, and until weaning. Male offspring (n=6) were continued on their mothers’ diets for four months. Hepatic and plasma lipids were extracted and tandem electrospray ionization mass spectrometry methods were used to measure the fatty acid compositions. There was a higher concentration of eicosapentaenoic acid containing phosphatidylcholine (PC) and lysophosphatidylcholine (LPC) (p < 0.05) in the high n‐3 PUFA group compared to the low n‐3 PUFA group. Plasma and liver from the high n‐3 PUFA group also had higher concentration of free n‐3 PUFA (p < 0.05). Our findings reveal for the first time that diet high in n‐3 PUFA caused enrichment of n‐3 PUFA in PC, LPC and free fatty acids in the offspring. PC and LPC are important bioactive lipids, thus altering their fatty acyl composition will likely have important metabolic and physiological roles. Supported by NSERC
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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 | 0.001 |
| 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".