Plasma n‐3 fatty acids are inversely associated with n‐6 linoleic acid in men consuming high or low n‐6 linoleic acid and constant n‐3 linolenic acid
Bibliographic record
Abstract
The n‐3 fatty acids (FA) linolenic acid (ALA), eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) are associated with decreased risk of cardiovascular and inflammatory diseases. High intakes of linoleic acid (LA) may affect conversion of ALA to EPA and DHA, thus compromising the benefit of n‐3 FA. However, limited data are available on the effect of LA intakes on n‐3 FA status, at constant LNA, in humans. We determined whether lowering LA intake by substituting low for high LA oils with constant LNA in oil blends with a LA:ALA ratio of 2:1 or 8:1 increases plasma phospholipid (PL) EPA or DHA, and decreases the ARA/EPA ratio. Adult men, n=24 consumed fats, oils and baked foods with high or low LA, 4 wks/diet, to replace these foods in their diet in a random cross over design. Fish and seafood were eliminated from 2 wk before and throughout the study. Plasma PL FA, triglyceride (TG), total, HDL, and LDL cholesterol were measured at Wk 0, 4 and 8. The plasma PL LA, and ARA/EPA ratio was significantly higher, and EPA was significantly lower during the high compared to low LA diet period. Plasma PL LA was significantly inversely related to EPA (P<0.001) across all subjects and time points. Total, HDL and LDL cholesterol were not different between the high and low LA diets, but plasma TG was inversely related to PL LA. These studies show that lowering dietary LA improves the ARA/EPA ratio, which may be important in inflammatory diseases.
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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 | 0.001 |
| 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".