Encapsulated phytosterol ester ingestion positively alters lipid profiles in hypercholesterolemic adults
Bibliographic record
Abstract
The National Cholesterol Education Program (ATP III) endorses the ingestion of food‐based phytosterol esters as part of a therapeutic lifestyle aimed at lowering cholesterol; however, the efficacy of encapsulated administration (E‐Phyt) is largely unexplored. We examined the effect of 2.6 g/d of E‐Phyt on lipid indices in hypercholesterolemic adults (LDL > 3.3 mmol/L; 20–70 y) randomized into treatment (E‐Phyt; n = 25) or placebo (PLA; n = 29) groups and instructed to ingest their respective supplements with meals for 12W. Fasting blood samples were collected at 0W and 12W and analyzed for TC, LDL‐C, HDL‐C, and LDL particle size and electrophoretic characteristics. We used a 2 X 2 ANOVA with Tukey‐Kramer post‐hoc analysis to detect treatment differences. Mean (± SD) baseline TC and LDL‐C were 6.29 ± 0.7 and 4.27 ± 0.7 mmol/L for E‐Phyt and 6.00 ± 0.7 and 4.00 ± 0.8 for PLA, respectively. Changes in TC and LDL‐C were greater in E‐Phyt (−0.23 ± 0.4 and −0.22 ± 0.5 mmol/L, respectively) than PLA (0.13 ± 0.5 and 0.16 ± 0.5; P < 0.05). Within group reductions were also significant for TC and LDL‐C with E‐Phyt (P<0.05). We did not see a treatment effect for HDL‐C or LDL particle size nor electrophoretic characteristics. E‐Phyt appears to modulate some lipid biomarkers associated with CHD in hypercholesterolemic adults. Future research should focus on the optimal timing and dose of E‐Phyt administration. Funded by Cargill Industries.
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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".