Abstract 16904: Are All Omega-3s Created Equal? Comparison of the Bioavailability and Efficacy of Four Omega-3 Preparations
Bibliographic record
Abstract
There are many omega-3 products available in the prescription and non-prescription marketplace. Neither health care professionals nor consumers may be aware of the potential influence of the manufacturing processes and source(s) of the starting material. Manufacturers of the various omega-3 preparations imply that their product is superior to others, based on its structural characteristics, whether phospholipid, triglyceride or ethyl ester. The objective of this study was to compare the ability of four (4) different omega-3 preparations to raise whole blood fatty acid levels. This was a Prospective, Open-label, Blinded Endpoint (PROBE), four period, cross-over study. Thirty-five (35) healthy subjects were randomized to consume one of four test articles for four weeks, followed by a four-week washout, and this process was repeated until all products had been consumed by all subjects. The test article doses were based on their label specifications. The four products and daily doses were: Triglyceride fish oil: EPA of 650 mg, DHA of 450 mg Ethyl Ester fish oil: EPA of 756 mg, DHA of 228 mg Phospholipid krill oil: EPA of 150 mg, DHA of 90 mg Triglyceride salmon oil: EPA of 180 mg, DHA of 220 mg Blood samples before and after supplementation were quantified for whole blood fatty acids by gas chromatography flame ionization. Bioavailability was assessed for each product individually, based on its change from baseline. Percentage change from baseline was then compared across all four products. TG fish oil was statistically superior to all of its comparators in the bioavailability of EPA+DHA, and of DHA alone. Its EPA percentage increase was more than four times that of PL krill oil and TG salmon oil. Only the two TG products significantly increased whole blood DHA levels. For investigators and clinicians, the type and dose of omega-3s may be clinically important when assessing potential clinical impact.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".