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Record W2423451854 · doi:10.1097/mol.0000000000000255

Lowering dietary n-6 polyunsaturated fatty acids

2015· article· en· W2423451854 on OpenAlexaff
Shoug Alashmali, Kathryn E. Hopperton, Richard P. Bazinet

Bibliographic record

VenueCurrent Opinion in Lipidology · 2015
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPolyunsaturated fatty acidDocosahexaenoic acidArachidonic acidLinoleic acidMetabolismEndocrinologyChemistryInternal medicineBiologyFood scienceBiochemistryFatty acidEnzymeMedicine

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.739
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.175
GPT teacher head0.416
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2015
Admission routes1
Has abstractyes

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