Do Intakes of 250–500mg/d of EPA and DHA Increase Blood Biomarkers?
Bibliographic record
Abstract
Intakes of 250–500 mg/d of eicosapentaenoic acid (EPA, 20:5n‐3) and docosahexaenoic acid (DHA, 22:6n‐3) have been proposed for health benefits. There is little data examining the response of omega‐3 blood biomarkers to these dietary intakes as most intervention studies use higher doses of EPA+DHA. Presently, we examine the blood biomarker response of men (n=11) and women (n=10) after the consumption of 250mg/d of EPA+DHA and 500mg/d EPA+DHA for 4 week periods. Erythrocytes and plasma were collected at baseline, week 4 and week 8 and fingertip prick whole blood was collected weekly. Intake of EPA+DHA at baseline was 72±51mg/d as determined by food frequency questionnaire validated for n‐3 HUFA (highly unsaturated fatty acids, ≥20 carbons, ≥3 carbon‐carbon double bonds). Baseline %n‐3 HUFA in total HUFA was 24.0±2.0 and 22.3±3.3 in venous erythrocytes and plasma, respectively. Initial results show the %n‐3 HUFA in total HUFA in fingertip prick whole blood increased from 22.1±2.1% at baseline to 24.3±1.6% at week 1 and to 25.2±1.9% at week 2 (p<0.01). These results will indicate if 250–500mg/d of EPA+DHA can increase blood levels of EPA+DHA to levels associated with the prevention of sudden cardiac death. Supported by a doctoral research award (ACP) from the Canadian Institutes of Health Research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".