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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".