Direct Determinations of the Fatty Acid Composition of Daily Dietary Intakes Incorporating Nutraceuticals and Functional Food Strategies to Increase n-3 Highly Unsaturated Fatty Acids
Bibliographic record
Abstract
OBJECTIVE: North American diets are low in eicosapentaenoic acid (20:5n-3, EPA) and docosahexaenoic acid (22:6n-3, DHA). This investigation aims to assess the ability to increase EPA and DHA in the Canadian diet using traditional whole food, functional food or nutraceutical strategies. METHODS: A typical Canadian diet (TC) was compared to four diets enriched with EPA and DHA but with similar caloric and macronutrient composition: a nutraceutical fish oil capsule diet (FO), an EPA + DHA-enriched functional foods diet (ED), a traditional whole foods (fish) diet (TW) and a comprehensive diet combining fish with functional foods (FF) containing EPA + DHA and alpha-linolenic acid. Direct biochemical quantitations were performed for energy, protein, carbohydrate (proximate analysis) and fat (gas chromatography). Costs of each diet and EPA + DHA source were assessed. RESULTS: The FO (1.03 +/- 0.01 g EPA + DHA), ED (0.59 +/- 0.02 g), TW (3.23 +/- 0.09 g) and FF (3.15 +/- 0.06 g) diets provided significantly higher amounts of EPA + DHA compared to the TC diet (0.08 +/- 0.01 g). Using the TC diet as a baseline, the daily cost increase for each revised diet was $0.53 (FO), $0.82 (TW), $0.93 (ED) and $1.62 (FF). The cost per gram of EPA + DHA was lowest for fish oil nutraceuticals ($0.53/g), followed by fish ( approximately $1.05/g). CONCLUSIONS: The EPA and DHA content of daily diets can be increased significantly and cost effectively using nutraceuticals, functional foods and whole foods. Several North American EPA + DHA recommendations for healthy individuals can be met using these strategies and American Heart Association recommendations for secondary coronary heart disease prevention can be met via traditional whole food, nutraceutical or combination approaches.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".