Plant sterols/stanols as cholesterol lowering agents: a meta-analysis of randomized controlled trials
Bibliographic record
Abstract
BACKGROUND: Consumption of plant sterols has been reported to reduce low density lipoprotein (LDL) cholesterol concentrations by 5-15%. Factors that affect plant sterol efficacy are still to be determined. OBJECTIVES: To more precisely quantify the effect of plant sterol enriched products on LDL cholesterol concentrations than what is reported previously, and to identify and quantify the effects of subjects' characteristics, food carrier, frequency and time of intake on efficacy of plant sterols as cholesterol lowering agents. DESIGN: Fifty-nine eligible randomized clinical trials published from 1992 to 2006 were identified from five databases. Weighted mean effect sizes were calculated for net differences in LDL levels using a random effect model. RESULTS: Plant sterol containing products decreased LDL levels by 0.31 mmol/L (95% CI, -0.35 to -0.27, P= < 0.0001) compared with placebo. Between trial heterogeneity was evident (Chi-square test, P = <0.0001) indicating that the observed differences between trial results were unlikely to have been caused by chance. Reductions in LDL levels were greater in individuals with high baseline LDL levels compared with those with normal to borderline baseline LDL levels. Reductions in LDL were greater when plant sterols were incorporated into fat spreads, mayonnaise and salad dressing, milk and yoghurt comparing with other food products such as croissants and muffins, orange juice, non-fat beverages, cereal bars, and chocolate. Plant sterols consumed as a single morning dose did not have a significant effect on LDL cholesterol levels. CONCLUSION: Plant sterol containing products reduced LDL concentrations but the reduction was related to individuals' baseline LDL levels, food carrier, and frequency and time of intake.
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.047 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".