The genetic factors involved in functional food efficacy on cardiovascular disease etiology
Bibliographic record
Abstract
While the impacts of modifiable and non-modifiable risk factors on chronic diseases such as cardiovascular disease (CVD) are widely established, the interactions between such coexisting risk factors and their subsequent effects on the promotion or suppression of CVD are less known. As part of the diet, functional foods are considered a modifiable factor that influence health beyond their basic nutritional value. The relationship between these functional foods and the underlying genome, along with their joint implication in health and disease, forms the focus of the emerging field of nutrigenomics. Reviewed in this paper are some prominent gene-diet interactions demonstrated in CVD etiology. Specifically, the interaction between foods such as phytosterols and isoflavones with genetic factors of the consuming population are examined in relation to CVD. By determining how nutritional intake affects genetics and vice versa, we create the potential to offer improved dietary guidelines to certain individuals, subgroups, or populations in order to maximize health benefits of specific diets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".