Dietary flaxseed oil and high‐oleic canola oil modulate plasma fatty acid composition and conversion of 13C‐α‐linolenic acid to long‐chain fatty acids
Bibliographic record
Abstract
The objective was to analyze the effects of diets enriched in flaxseed oil (FXCO) or high-oleic canola oil (HOCO) versus a Western fat (WF) blend on plasma fatty acid composition and conversion of 13C-α-linolenic acid (ALA) to long-chain n-3 fatty acids. Using a randomized, controlled, crossover trial, 18 hyperlipidemic subjects consumed 3 isoenergetic diets for 28 d enriched in FXCO (20.6 g/d ALA), HOCO (2.4 g/d ALA), or WF (1.3 g/d ALA). On day 27, blood was sampled at t = 0, 24, and 48 h after subjects consumed 45 mg uniformly labelled 13C-ALA. FXCO diet increased plasma ALA ~5-fold (4.69 ± 0.26% total fatty acids; P<0.001), eicosapentaenoic acid (EPA) ~2-fold (1.76 ± 0.17%; P<0.001), and docosapentaenoic acid (DPA) by ~50% (0.76 ± 0.04%; P<0.001), with no change in docosahexaenoic acid (DHA) compared to HOCO and WF diets. At 24 h the amount of administered 13C-ALA recovered as plasma 13C-EPA and 13C-DPA was 0.86 ± 0.10 mg and 0.13 ± 0.02 mg after FXCO diet, which was lower (P=0.004 and P=0.011) than that of 1.33 ± 0.22 mg and 0.23 ± 0.04 mg after HOCO diet and 1.15 ± 0.16 mg and 0.24 ± 0.05 mg after WF diet, respectively. No change in 13C-DHA was observed between diets. In conclusion, although a high intake of ALA in FXCO diet increased plasma levels of n-3 fatty acids, such increased levels are not a result of higher conversion efficiency, particularly for DHA. Supported by Flax Canada 2015, Canola Council of Canada, and ARDI. Grant Funding Source: Flax Canada 2015, Canola Council of Canada, and ARDI
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".