Equivalency of EPA and DHA Bioavailability When Comparing Microalgal and Fish Oil Supplementation
Bibliographic record
Abstract
be higher than the amount required to maximize tissue DHA levels.Further, we also observed that dietary ALA could reduce both arachidonic acid (ARA) and its oxylipins.However, only DHA levels have classically been used to estimate the ALA requirement.Since oxylipins mediate many fatty acid functions, this study was designed to examine whether both DHA and ARA oxylipins could be used to more accurately determine the dietary ALA requirement.Methods: Rats and mice were provided a range of 0.1-2.5 g ALA and a consistent level of 2g of linoleic acid per 100g diet.Non-esterified oxylipins in serum, liver, kidney and brain homogenates underwent solid phase extraction and were analyzed by HPLC-MS/MS.Results: In response to increasing levels of dietary ALA, DHA oxylipins initially increased rapidly and then plateaued whereas ARA oxylipins tended to decrease and plateau.Thus, the ratio of DHA/ARA oxylipins synthesized via the major common biosynthetic pathway (lipoxygenase) was calculated and the breakpoint of the transition from increase to plateau was estimated by piecewise regression.In serum, liver and kidney the estimated breakpoint indicated an average ALA requirement of ~0.7g/ 100g diet (1.7%energy), which would result in a recommended dietary allowance of ~1.0g/100g diet (2.4%energy).In contrast, in the brain this oxylipin ratio was constant across all levels of dietary ALA, indicating that the breakpoint occurred at less than 0.1g of ALA/100g of diet, and suggesting that the brain has priority for ALA metabolism.Conclusions: The combination of DHA and ARA oxylipins can be used to estimate the dietary ALA requirement and indicates that the requirement is higher than previously estimated using DHA alone.Further, this ratio could be used as a biomarker of w-3 fatty acid status to address questions such as the bioequivalence of w-3 fatty acids, or the optimal dietary w-6/w-3fatty acid ratio.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".