Combination of Phytosterols and Omega-3 Fatty Acids: A Potential Strategy to Promote Cardiovascular Health
Bibliographic record
Abstract
Phytosterols and omega-3 fatty acids (n-3) are natural food ingredients with potential cardiovascular benefits. Phytosterols inhibit cholesterol absorption, thereby reducing total cholesterol (TC) and LDL-cholesterol levels. Numerous clinical studies have shown that a daily intake of 1.5-2.0 g of phytosterols can result in a 10-15 % reduction in LDL levels, while consumption of n-3 is associated with a significant reduction in plasma triglyceride (TG) concentrations. Furthermore, n-3 may also beneficially modify a number of other risk factors of coronary heart disease (CHD). Thus, it is reasonable to suggest that combination of phytosterols and n-3 may further reduce cardiovascular risk factors. Esterification of phytosterols with non-n-3 fatty acids has substantially improved their incorporation into a variety of foods without affecting the efficacy of phytosterols. Therefore, it is assumed that esterification of phytosterols with n-3 may have advantages for both food industry and health. Evidence suggests that this combination is effective in reducing the levels of several cardiovascular risk factors including TC and TG concentrations, pro-aggregatory factors, arrhythmic eicosanoid and thromboxane A2 levels. In this mini-review, we have critically reviewed and summarized data from clinical and animal studies in which phytosterols and n-3, alone or in combination, were used. We have also provided information on structure-function relationship for these two natural compounds. Biological properties of several phytosterol derivatives including phytosterol-glucoside have been also discussed. Although the animal studies are supportive of this combination therapy, human studies are needed to address its long term effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".