Consumption of Ahiflower Oil Is Safe and Increases Tissue EPA Levels Compared to Flaxseed Oil – Results of a Phase I Clinical Trial
Bibliographic record
Abstract
Ahiflower oil is a dietary oil rich in stearidonic acid (SDA, 18:4 n‐3) extracted from Buglossoides arvensis seeds. We report herein a randomized, double blind, placebo‐controlled Phase I Clinical Trial. This is the first study of this novel dietary oil in humans. Healthy subjects (n=40) supplemented their diets (28 days, 9.8 g/d) with Ahiflower oil (46% α‐linolenic acid (ALA), 20% SDA) or with flaxseed oil (59% ALA). Safety parameters were blood and urine chemistries, blood lipid profiles, hepatic and renal function tests and hematology. No clinically‐significant safety‐related values were measured in either group. Fasting plasma, RBC, polymorphonuclear (PMN) and mononuclear cells (MC) were collected at baseline, 14 and 28 days and fatty acids were measured by gas chromatography. Linear mixed models (repeated measures design) probed for differences in time and time x treatment interaction using gender, age and BMI as covariates. Amongst significant tissue fatty acid changes, EPA (20:5 n‐3) as % of total fatty acids increased from baseline to day 28 in both the Ahiflower (plasma, RBC, PMN and MC, respectively: 0.46 vs 1.34, 0.44 vs 0.74, 0.15 vs 0.24 and 0.20 vs 0.48; all p<0.05) and flaxseed (0.43 vs 0.76, 0.42 vs 0.55, 0.16 vs 0.19, 0.20 vs 0.27; all p<0.05) groups. Notably, EPA accrual in plasma and cells was greater in the Ahiflower group (time x treatment interactions, p<0.05) and the change in plasma EPA at day 28 was 2.5‐times greater in the Ahiflower group. In conclusion, Ahiflower oil is a safe and sustainable seed oil for enrichment of tissues with n‐3 PUFA and is more effective than flaxseed oil. (Funded by: CIHR, Atlantic Innovation Fund, Technology Crops Intl)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".