Consumption of tall oil-derived phytosterols in a chocolate matrix significantly decreases plasma total and low-density lipoprotein-cholesterol levels
Bibliographic record
Abstract
In a randomized, double-blind, placebo-controlled trial we evaluated the effect of dietary chocolates enriched with a wood-based phytosterol-phytostanol mixture, containing 18 % (w/w) sitostanol, compared with placebo dietary chocolates in seventy subjects with primary hypercholesterolaemia (total cholesterol levels below 8 mmol/l). For 4 weeks, participants consumed three servings of the phytosterol-enriched chocolate/d that provided 1.8 g unesterified phytosterols/d or a placebo chocolate in conjunction with a low-fat, low-cholesterol diet. Plasma total and LDL-cholesterol levels were statistically significantly reduced by 6.4 % (-0.44 mmol/l) and 10.3 % (-0.49 mmol/l), respectively, after 4 weeks of phytosterol-enriched-chocolate treatment. Plasma HDL-cholesterol and triacylglycerol levels were not affected. Consumption of phytosterol-enriched chocolates significantly increased plasma lathosterol concentration (+20.7 %), reflecting an increased endogenous cholesterol synthesis in response to phytosterol-induced decreased intestinal cholesterol absorption. Furthermore, the chocolates enriched with phytosterols significantly increased both plasma sitosterol (+95.8 %) and campesterol (+64.1 %) levels, compared with the placebo chocolate group. However, the absolute values of plasma sitosterol and campesterol remained within the normal range, that is, below 10 mg/l. The chocolates with phytosterols were palatable and induced no clinical or biochemical side effects. These findings indicate that dietary chocolate enriched with tall oil-derived phytosterols (1.8 g/d) is effective in lowering blood total and LDL-cholesterol levels in subjects with mild hypercholesterolaemia and thus may be helpful in reducing the risk of CHD in these individuals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".