Phytosterols in low- and nonfat beverages as part of a controlled diet fail to lower plasma lipid levels
Bibliographic record
Abstract
Dietary phytosterols have been shown to reduce plasma cholesterol concentrations when consumed in different food matrices, but their effectiveness in nonfat or low-fat beverages has not been established. The objective of this study was to examine whether phytosterols alter plasma lipid levels when incorporated into nonfat or low-fat beverages. Fifteen moderately hypercholesterolemic men and women consumed three precisely controlled diets for periods of 21 days each in random order. Diets contained either a nonfat placebo beverage (NF), a beverage that is nonfat with added phytosterols (NFPS), or a beverage that is low in fat with added phytosterols (LFPS). Total cholesterol concentrations were not different between groups at endpoint, decreasing (P < 0.05) equally by 8.5%, 11.6%, and 10.1% with NF, NFPS, and LFPS consumption, respectively. There was no effect of dietary treatment on LDL cholesterol concentrations, which decreased over time (P < 0.05) by 5%, 10.4%, and 8.5% with NF, NFPS, and LFPS, respectively. HDL cholesterol and triacylglycerol concentrations were unaffected by the diets. Provision of phytosterols as part of nonfat and low-fat beverages did not exert any greater hypocholesterolemic effect than a nonfat placebo beverage. These results show that intake of phytosterols in a low-fat beverage format is not efficacious for lipid level modification.
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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".