Are functional foods redefining nutritional requirements?
Bibliographic record
Abstract
Functional foods are increasing in popularity owing to their ability to confer health and physiological benefits. Nevertheless, the notion that functional foods improve health when providing nutrients at levels above and beyond existing recommended intakes is inconsistent with the definition of requirement. This disparity highlights the need for an alternative definition of nutrient requirement. The present objective is to examine distinctions between optimization of health, as defined by what we currently deem as required intakes, versus adding physiological benefit using bioactive agents found in functional foods. Presently, requirement is defined as the lowest amount of intake of a nutrient that will maintain a defined level of nourishment for a specific indicator of adequacy. In contrast, functional foods are described as ingredients that are not necessary for body function, yet provide added physiological benefit that confer better overall health. Plant sterols are one example of such an ingredient. Plant sterols lower plasma cholesterol concentrations, and may thus be considered essential nutrients in physiological situations where circulating cholesterol concentrations are high. Similarly, intakes of omega-3 fats beyond existing requirement may confer additional health benefits such as hypolipidemic and anti-diabetic effects. These examples underscore the inconsistencies between what is defined as a nutrient requirement versus what is identified as a health benefit of a functional food. Such discrepancies emphasize the need for a more all-encompassing definition of a nutrient requirement; that is, one that moves beyond the prevention of overt deficiency to encompass improved health and disease risk reduction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".