Phytosterols and human lipid metabolism: efficacy, safety, and novel foods
Bibliographic record
Abstract
Plant sterols have been known for several decades to cause reductions in plasma cholesterol concentrations. These plant materials have been granted a conditional health claim in the United States regarding their effects in the prevention of cardiovascular disease and are being sold in functional foods in several countries in Europe as well as in the United States and Australia. It is generally suggested that daily consumption of approximately 2 g of plant sterols can lower cholesterol concentrations as part of a dietary prevention strategy. However, phytosterols have been added and tested for their cholesterol-lowering effects mainly in spreads. Consumption of these high-fat foods seemingly flies in the face of current recommendations for the promotion of heart health, which suggest lowering total fat and energy intake to maintain weight. Hence, new food formulations are being evaluated using phytosterols incorporated into low-fat and reduced-fat food items. The purpose of this review is to examine the cholesterol-lowering efficacy of plant sterols, focusing on novel food applications, their mechanism of action, and safety. These novel food formulations include new solubilization processes that lead to improved uses for plant sterols, as well as new foods into which phytosterols have been incorporated, such as breads, cereals, and beef. Such new foods and formulations should pave the way for greater use of phytosterols in heart health promotion, increasing the longer-term potential for the creation of innovative functional foods containing plant sterols and their derivatives.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".