Correlation between n‐3 fatty acid intakes estimated using a food frequency questionnaire and concentrations measured in plasma phospholipids
Bibliographic record
Abstract
Aim To verify whether intakes of n‐3 fatty acids (FA) estimated from a food frequency questionnaire (FFQ) correlate with n‐3 FA concentrations measured in plasma phospholipids (PL). Methods The study sample consisted of 100 men and 100 women. Dietary data were collected using a FFQ administered by a dietitian. Plasma PL FA profile was measured by gas chromatography. Results Docosahexaenoic acid (DHA) (men: r=0.52, p<0.0001; women: r=0.57, p<0.0001) and total n‐3 FA (men: r=0.47, p<0.0001; women: r=0.52, p<0.0001) intakes were positively correlated to their respective plasma PL FA concentrations in both sexes. In women only, eicosapentaenoic acid (EPA) (r=0.44, p<0.0001) and docosapentaenoic acid (DPA) (r=0.23, p=0.02) intakes correlated with plasma PL FA concentrations. The quintile assignment analysis highlights the relation between the n‐3 FA estimated versus measured in plasma PL. DHA is the FA that was classified the most successfully (72% of men and 62% of women assigned in the same or adjacent quintiles) and DPA is the FA that had the smallest level of agreement between the 2 methods (around 50% for both sexes). Conclusions Intakes of n‐3 FA estimated from a FFQ correlate with n‐3 FA concentrations measured in plasma PL. FFQ could be used as a simple, low cost tool in studies investigating effects of n‐3 FA intakes in some diseases. This study was supported by CIHR.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".