Novel food‐based delivery of microencapsulated omega‐3 fatty acids (μN3): Potential role in promoting cardiovascular health
Bibliographic record
Abstract
The role of N3 in supporting cardiovascular health has been well established yet, many Americans are resistant to consuming fish or fish‐oil supplements. We examined the use of a new food technology where μN3 is delivered in common foods such as bread, yogurt, milk and orange juice. In a randomized‐double blind, placebo‐controlled, pilot trial, we assigned 20 subjects (Age 2.5 ± 6.2 y; wt. 73.4 ± 5.1 kg) to receive a breakfast meal using μN3 foods (~500 kcals) delivering ~1,000 mg (450–550 mg EPA/DHA) or a matching placebo meal for 14d. Primary outcomes included changes in plasma [EPA] and [DHA]. Secondary outcomes examined triglycerides (TG). After analyzing our data using a 2x2 ANCOVA covaried for age and BMI and Tukey‐Kramer post‐hoc test. We observed that the μN3 group showed significant mean (SE) elevations in plasma DHA (91.18 ± 9.3 vs. 125.58 ±11.3 u mol/L; P<0.05) and reduced in TG (89.89 ± 12.8 vs. 80.78 ± 10.4 mg/dL; P<0.05). When expressed as Δ scores DHA and TG were different from placebo (P<0.05). No other changes were observed though [EPA] demonstrated a tendency to increase in the μN3 group (P=0.08). Interestingly, the change in TG was significantly correlated with the change in EPA (r 2 = 0.33; P<0.04). Lastly, participants reported no taste, odor or “burp‐back” associated with μN3 food ingestion. The use of μN3 foods should be further explored using longer treatment periods and more robust cardiovascular endpoints as a means of delivering N3 to populations resistant to fish or supplement ingestion. Supported by Ocean Nutrition Canada
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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 | 0.001 | 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".