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Dietary α‐Linolenic Acid (ALA) Rich Flax Oil Elevates Renal and Hepatic Docsosahexaenoic Acid (DHA) Derived Bioactive Lipids

2016· article· en· W3192126687 on OpenAlexafffundabout
Jessay G. Devassy, Tamio Yamaguchi, Naser Ibrahim, Melissa Gabbs, Tanja Winter, Amir Ravandi, Harold M. Aukema

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldNursing
TopicFatty Acid Research and Health
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
FundersCanadian Institutes of Health Research
KeywordsDocosahexaenoic acidEicosapentaenoic acidOxylipinPolyunsaturated fatty acidFood scienceChemistryWeanlingalpha-Linolenic acidFatty acidPhospholipidArachidonic acidBiochemistryLinseed oilBiologyEndocrinology

Abstract

fetched live from OpenAlex

Oxylipins are bioactive lipids that are oxygenated metabolites of polyunsaturated fatty acids, and are the main mediators of their effects in the body. These bioactive molecules are involved in numerous essential physiologic processes. For example, oxylipins derived from docosahexaenoic acid (DHA), such as hydroxy DHA (HDoHE) and protectins, have anti‐proliferative, anti‐inflammatory, anti‐hyperalgesic and pro‐resolving activities. However, conclusions regarding the effects of dietary lipids are often based on their effects on tissue fatty acid composition data. Such data indicate that α‐linolenic acid (ALA) is not efficiently converted to DHA. Since tissue fatty acid composition may not accurately reflect oxylipin levels, the objective of the current studies was to determine whether dietary ALA could influence DHA derived oxylipin levels. In the first study, normal and Han:SPRD‐ Cy weanling male rats (with cystic kidney disease) were given either the AIN93G diet with soy oil as the lipid source or an experimental diet in which ALA rich flax oil replaced 80% of the soy oil. After 8 weeks of dietary treatment, renal phospholipid fatty acids were analyzed by GLC, and select oxylipins by HPLC‐MS/MS. In diseased compared to normal kidneys, DHA and DHA derived 4‐HDoHE levels were 74% lower when rats were given the soy oil diet. As expected, providing flax oil to diseased rats resulted in higher ALA and eicosapentaenoic acid (EPA), but did not alter DHA levels. Similarly, ALA and EPA derived oxylipins were elevated with dietary flax oil, but in contrast to the lack of effect on DHA, the level of 4‐HDoHE was restored to normal levels. This demonstrated that dietary ALA can increase the level of a DHA derived oxylipin. Therefore, in the second study, soy and flax oil diets identical to those in the first study were provided to two models that develop cystic liver disease: PCK rats and Mx1Cre + Pkd1 flox/flox (Pkd1) mice. In this study, a high throughput HPLC‐MS/MS based targeted lipidomic approach was used to simultaneously quantify a large number of oxylipins from the liver tissues. Providing flax oil compared to soy oil resulted in higher levels of hepatic ALA and EPA derived oxylipins in both models, and n‐6 fatty acid derived oxylipins were generally reduced. With respect to DHA derived oxylipins, in PCK rat livers, 7‐, 8‐, 10‐, 11‐, 13‐, 14‐, 16‐, and 17‐ HDoHE were elevated (by 28 – 79 %) and in Pkd1 mouse livers, 10‐, 16‐, 17‐, 13‐, 19‐ and 20‐HDoHE were elevated (by 33 – 70%) with dietary flax oil. In conclusion, although the conversion of ALA to DHA may not appear to be efficient, dietary ALA does increase DHA derived oxylipins. This demonstrates that dietary ALA can be converted to DHA in amounts sufficient to increase DHA derived bioactive lipids and may mediate physiological effects via this conversion. The implications of these findings for dietary ALA and DHA recommendations remain to be elucidated. Support or Funding Information Canadian Institutes of Health Research

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.284
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2016
Admission routes3
Has abstractyes

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