Functional foods for the prevention and treatment of cardiovascular diseases: cholesterol and beyond
Bibliographic record
Abstract
Cardiovascular disease (CVD) is a major cause of death and disability in many developed countries. The purpose of this literature review is to establish a recommendation for the intake of functional food ingredients in a healthy diet--such as plant sterols (PSs) in low-fat and functional matrices, fatty acid composition and other nutrients of tree nuts and flavonoids in dark chocolate--for the prevention and treatment of CVD. These three specific functional foods are explored in this review, since there is a higher potential for their increased consumption by the population to prevent CVD. First, PS have been added to various nontraditional matrices, such as low-fat products and functional oils, which have shown cholesterol-lowering effects in most clinical trials. Secondly, a growing number of clinical studies indicate that the beneficial effect of tree nuts may not only be due to their fatty acid composition but to other key nutrients, which may provide supplementary health benefits, such as endothelial cell function, as well as decreasing total cholesterol and low-density lipoprotein cholesterol (LDL-C). Lastly, flavonoids in dark chocolate may protect LDL-C particles from undergoing oxidative modification. However, some gaps in our knowledge need to be filled before firm recommendations can be made for habitual dark chocolate consumption. Overall, these functional foods should be considered as an addition to current lipid-lowering recommendations for improving CVD risk.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".