Validation of fatty acid intakes estimated by a food frequency questionnaire using erythrocyte fatty acid profiling in the Montreal Heart Institute Biobank
Bibliographic record
Abstract
BACKGROUND: To improve the prevention, treatment and risk prediction of cardiovascular diseases, genetic markers and gene-diet interactions are currently being investigated. The Montreal Heart Institute (MHI) Biobank is suitable for such studies because of its large sample size (currently, n = 17 000), the availability of biospecimens, and the collection of data on dietary intakes of saturated (SFAs) and n-3 and n-6 polyunsaturated (PUFAs) fatty acids estimated from a 14-item food frequency questionnaire (FFQ). We tested the validity of the FFQ by correlating dietary intakes of these fatty acids with their red blood cell (RBC) content in MHI Biobank participants. METHODS: Seventy-five men and 75 women were selected from the Biobank. We successfully obtained RBC fatty acids for 142 subjects using gas chromatography coupled to mass spectrometry. Spearman correlation coefficients were used to test whether SFA scores and daily intakes (g day(-1)) of n-3 and n-6 PUFAs correlate with their RBC content. RESULTS: Based on covariate-adjusted analyses, intakes of n-3 PUFAs from vegetable sources were significantly correlated with RBC α-linolenic acid levels (ρ = 0.23, P = 0.007), whereas n-3 PUFA intakes from marine sources correlated significantly with RBC eicosapentaenoic acid (ρ = 0.29, P = 0.0008) and docosahexaenoic acid (ρ = 0.41, P = 9.2 × 10(-7)) levels. Intakes of n-6 PUFAs from vegetable sources correlated with RBC linoleic acid (ρ = 0.18, P = 0.04). SFA scores were not correlated with RBC total SFAs. CONCLUSIONS: The MHI Biobank 14-item FFQ can appropriately estimate daily intakes of n-3 PUFAs from vegetable and marine sources, as well as vegetable n-6 PUFAs, which enables the possibility of using these data in future studies.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".