Evaluation of policosanols as functional foods for cholesterol‐lowering
Bibliographic record
Abstract
Studies conducted in Cuba have reported substantial reductions in plasma lipids using Cuban sugar cane policosanol mixtures. However, external research groups using different mixtures have failed to reproduce the efficacy of policosanols observed in the earlier studies. Objectives Evaluate the effect of the authentic sugar cane policosanols (SCP) on plasma lipids in healthy hypercholesterolemic volunteers. Design and Methods The effect of policosanols on lipid levels in humans was nested in a randomized, double‐blind crossover design. 21 volunteers were administered 10 mg of SCP per day or a placebo incorporated in margarine as an afternoon snack, for a period of 28 days. Subjects were asked to maintain their habitual diet and physical activity, and body weights were monitored daily throughout the study period. Blood was collected at the start and end of the feeding period and lipid levels were measured using enzymatic kits. Results No significant difference was observed between treatment and control groups in plasma total, LDL‐, HDL‐cholesterol and triglyceride levels. Body weights did not vary significantly throughout the trial, and did not impact plasma lipid values. Conclusion The present study is the first to be conducted in North America using the authentic mixture of policosanols in order to examine their effect on lipid levels. Our results show no beneficial effect of policosanols on lipid parameters in hypercholesterolemic individuals and question the clinical usefulness of policosanol mixtures as cholesterol‐lowering natural health products. Funded by AFMnet of NCE.
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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".