Bioequivalence of docosahexaenoic acid intake to a novel estimate of the dietary alpha-linolenic acid requirement in growing rats using non-esterified oxylipins and fatty acids
Bibliographic record
Abstract
Alpha-linolenic acid (ALA) is an essential n-3 fatty acid that can be converted to docosahexaenoic acid (DHA), which by itself can also meet the dietary n-3 fatty acid requirement. However, the amount of dietary DHA that is bioequivalent to ALA remains unknown. The study objective was to estimate the DHA dose that is equivalent to a recently proposed ALA requirement assessment, using non-esterified oxylipins and fatty acids. 168 male and female Sprague-Dawley rats received graded doses of dietary ALA (0.10 to 2.0g/100g diet) or DHA (0.07 to 1.3g/100g diet). All diets contained 2g of linoleic acid/100g diet and were based on the AIN93G. Non-esterified fatty acids and oxylipins were analyzed in serum, liver, heart, and brain by HPLC-MS/MS. Using piecewise regression, breakpoints were calculated for ALA diets using DHA/arachidonic acid (ARA) and hydroxy-DHA/hydroxy-ARA oxylipins (DHA OH /ARA OH ). Breakpoints in serum and liver were highest and deemed most suitable to determine the dietary ALA requirement, with no differences between sexes or whether the DHA/ARA or DHA OH /ARA OH ratio was used. These breakpoints indicated an estimated average dietary ALA requirement of 0.55g ALA/100g diet (1.32% energy) 95% CI [0.42, 0.69], which is higher than what is provided by the AIN93G diet. DHA was ∼4-6 (mean ± SE: 4.74 ± 0.22) times more effective than ALA, with 0.12g DHA/100g diet (0.28% energy) being equivalent to the 0.55g ALA/100g diet that meets the ALA requirement. Hence, dietary DHA is ∼5 times more effective than ALA in meeting the proposed dietary ALA requirement in the growing rat.
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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 | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".