Efficacy of Plant Sterol Enhanced Soy Beverage on Biomarkers of Cardiovascular Disease Risk in Humans
Bibliographic record
Abstract
It is increasingly clear that North Americans prefer dietary approaches to enhance health in favour of pharmaceutical approaches. Although the incorporation of phytosterols (PS) into various food matrices has been effective in lowering cholesterol, the lipid‐lowering potential of PS‐enriched soymilk has not been investigated. Therefore, the objective of this study was to examine the lipid‐lowering efficacy of a PS‐enriched soymilk beverage in comparison to a 1% dairy milk control. Twenty‐three hypercholesterolemic subjects consumed 3 tetrapacks per day of a PS‐enriched soy beverage (1.95g PS/d) or a 1% dairy milk control. The study was conducted as a 28 d controlled dietary intervention according to a two‐period cross‐over design. Total cholesterol was reduced by 12% ( P < 0.0001) in the PS‐enriched soymilk group (5.59±0.27 mmol/L) in comparison to the milk control (6.28±0.27 mmol/L). PS‐enriched soymilk consumption did not affect HDL cholesterol concentrations but did reduce ( P <0.0001) LDL cholesterol by 16% compared to the control group (3.60 vs. 4.20 mmol/L). Plasma triglyceride concentration was reduced ( P =0.02) by 9.4% in response to the PS‐enriched soy beverage in comparison to the milk control. We conclude that consumption of a PS‐enriched soy beverage is an effective dietary strategy to reduce circulating lipid levels in hypercholesterolemic individuals. Supported by WhiteWave Foods Company.
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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 | 0.000 |
| 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) | 0.001 | 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".