Fatty acids of plasma phospholipids and erythrocytes are reliable biomarkers of n‐3 polyunsaturated fatty acid supplementation
Bibliographic record
Abstract
The availability of reliable biomarkers to assess the efficacy of dietary interventions is a prerequisite of meaningful clinical studies with n‐3 polyunsaturated fatty acids (PUFA). Objective To determine the suitability of using either plasma phopholipids (PL) or erythrocytes fatty acids (FA) composition as indicators of n‐3 PUFA status. Methods We measured FA % in erythrocytes and plasma PL obtained from 28 men supplemented daily with 3g of n‐3 PUFA (1.9 g eicosapentaenoic acid (EPA) and 1.1 g docosahexaenoic acid (DHA)) during 6 weeks. Results After supplementation, EPA, DHA and EPA+DHA in plasma PL or erythrocytes increased by 334%, 43%, 174% and 195%, 16%, 29%, respectively (p<0.05 versus baseline for all analyses). EPA in plasma PL was correlated to EPA in erythrocytes at baseline (r=0.77, p<0.01) and after supplementation (r=0.63, p<0.01). In addition, a correlation (r=0.47, p=0.02) was seen between the combined EPA+DHA in plasma PL and erythrocytes after supplementation. Conclusion Even tough differences do exist in the FA composition of plasma PL and erythrocytes, both biomarkers significantly increased with n‐3 PUFA supplementation over 6 weeks suggesting that they are both reliable biomarkers of n‐3 PUFA status after supplementation. Funding provided by a CIHR Operating Grant.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".