Estrogen increases the conversion of α‐linolenic acid to docosahexaenoic acid in ovariectomized mice
Bibliographic record
Abstract
n‐3 polyunsaturated fatty acids (PUFA), namely eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) have beneficial health effects. The conversion of α‐linolenic acid (ALA) to EPA and DHA is low, especially in males compared to females and varies in the literature. Estrogen (E2) is suggested to affect ALA conversion but few studies have confirmed the E2 effect. Our objective was to determine the independent effect of E2 on n‐3 PUFA levels in serum and various tissues in female ovariectomized mice fed diets with low and high ALA. After 7 weeks exposure to an E2 pellet, mice were randomized to a corn oil‐based basal diet or an ALA‐rich flaxseed diet (FS) with or without removal of the E2 pellet for 4 weeks. E2 responsiveness was indicated by the 269% higher (P<0.001) uterus weight in E2‐ treated mice compared to those with E2 pellet removed. FS‐fed mice had higher proportions of ALA, EPA and DHA in total lipids extracted from serum, liver and brain. E2‐treated mice had higher serum (15.1%; P<0.001) and liver (18.2%; P<0.001) DHA but not brain DHA. EPA levels were not affected by E2. There was a diet × E2 interaction for ALA in the liver where the FS+E2 group had lower ALA compared to FS‐E2. In conclusion, E2 results in higher serum and liver DHA, does not affect brain DHA and does not affect EPA in any of the tissues measured. This finding may help explain the variability in ALA effect in studies involving males and females. Grant Funding Source : NSERC
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".