Lowering dietary n-6 polyunsaturated fatty acids
Bibliographic record
Abstract
PURPOSE OF REVIEW: Arachidonic (ARA) and docosahexaenoic (DHA) acids are the most abundant polyunsaturated fatty acids (PUFA) in the brain, where they have many biological effects, including on inflammation, cell-signaling, appetite regulation, and blood flow. The Western diet contains a high ratio of n-6: n-3 PUFA. Although interest in lowering this ratio has largely focused on increasing intake of n-3 PUFA, few studies have examined lowering dietary n-6 PUFA. This review will evaluate the effect of lowering dietary n-6 PUFA on levels and metabolism of ARA and DHA in animal models and in humans, with a primary focus on the brain. RECENT FINDINGS: In animal models, lowering dietary ARA or linoleic acid generally lowers levels of brain ARA and raises DHA. Lowering dietary n-6 PUFA can also modulate the levels of ARA and DHA metabolizing enzymes, as well as their associated bioactive mediators. Human studies examining changes in plasma fatty acid composition following n-6 PUFA lowering demonstrate no changes in levels of ARA and DHA, though there is evidence of alterations in their respective bioactive mediators. SUMMARY: Lowering dietary n-6 PUFA, in animal models, can alter the levels and metabolism of ARA and DHA in the brain, but it remains to be determined whether these changes are clinically meaningful.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".