Increasing Dietary α-Linolenic Acid Enhances Tissue Levels of Long-Chain n-3 PUFA when Linoleic Acid Intake Is Low in Hamsters
Bibliographic record
Abstract
BACKGROUND/AIMS: We tested whether feeding hamsters diets varying in alpha-linolenic acid (ALA) content and low in linoleic acid (LA) could increase the tissue levels of eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), and docosahexaenoic acid (DHA) to the same extent as a fish oil-supplemented diet. METHODS: For 5 weeks, 60 hamsters were fed 1 of the following 5 diets containing 2% of total dietary energy (TE) as LA and either 0.5% (diet A), 1% (diets B and E), 2% (diet C), or 4% (diet D) ALA of TE, so that the ratio of LA/ALA was 4:1, 2:1, 1:1, or 1:2. Diet E was supplemented with fish oil at the level of 0.2% of total energy intake. At the end of the study, overnight-fasted hamsters were sacrificed, and blood and tissues were collected. RESULTS: Tissue levels of ALA, EPA, DPA, and DHA rose in proportion to the increase in the dietary ALA level (p < 0.01); however, the levels of DHA reached a plateau at ALA intakes above 1% (p < 0.01). These changes were accompanied by decreases in arachidonic acid with or without increases in LA levels (p < 0.01). Hamsters fed diet D had similar or higher EPA, DPA, and DHA tissue levels to those fed diet E (p < 0.01). CONCLUSIONS: In hamsters, diets containing 4% energy as ALA and 2% energy as LA can increase the tissue levels of EPA, DPA, and DHA to the same extent as feeding 0.2% energy as fish oil.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".