Olive oil containing olive oil fatty acid esters of plant sterols and dietary diacylglycerol reduces low-density lipoprotein cholesterol and decreases the tendency for peroxidation in hypercholesterolaemic subjects
Bibliographic record
Abstract
Plant sterols (PS) and MUFA are well-documented cholesterol lowering agents. We aimed to determine the effect of PS esterified to olive oil fatty acids (PS-OO) on blood lipid profile and lipid peroxidation in hypercholesterolaemic subjects. Twenty-one moderately overweight, hypercholesterolaemic subjects consumed three consecutive treatment diets, each lasting 28 d and separated by 4-week washout periods, using a randomized crossover design. Diets contained 30 % energy as fat, 70 % of which was provided by olive oil (OO), and differed only in the treatment oils: OO, PS esterified to sunflower oil fatty acids (PS-SO), and PS-OO. Both PS-SO and PS-OO treatments provided 1.7 g PS /d. PS-OO and PS-SO consumption resulted in a decrease (P = 0.0483) in LDL-cholesterol (LDL-C) concentrations compared with the OO diet. Although total cholesterol and apo B-100 levels were not significantly affected, PS-SO and, to some extent, PS-OO reduced the total:HDL-cholesterol (HDL-C) ratio (P = 0.0142) and the apo B-100:apo A-I ratio (P = 0.0168) compared with the OO diet. There were no differences across diets in lipoprotein(a) (Lp(a)) and lipid peroxidation levels. However, following consumption of OO and PS-SO, Lp(a) concentrations increased (P = 0.0050 and 0.0421, respectively), while PS-OO treatment did not affect Lp(a) levels. Furthermore, there was a decrease (P = 0.0097) in lipid peroxidation levels with PS-OO treatment during the supplementation phase. Our results suggest that supplementing an OO-rich diet with PS-OO favourably alters the plasma lipid profile and may decrease the susceptibility of LDL-C to lipid peroxidation in hypercholesterolaemic subjects.
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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) | 0.000 | 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".