Beyond cholesterol-lowering effects of plant sterols: clinical and experimental evidence of anti-inflammatory properties
Bibliographic record
Abstract
Inflammation is a strong risk factor for cardiovascular disease. Dietary plant sterols are known to reduce plasma cholesterol levels and thereby reduce cardiovascular risk. Recent observations from animal and human studies have demonstrated anti-inflammatory effects of phytosterols. For example, several animal and human studies report reductions in the levels of proinflammatory cytokines, including C-reactive protein, after consumption of dietary plant sterols. Although the cholesterol-lowering effects of phytosterols in humans are well documented, studies on the effects of phytosterols on inflammatory markers have produced inconsistent results. This review summarizes and discusses findings from recent animal and human studies with regard to the potential anti-inflammatory effects of dietary phytosterols. Findings on the effects of plant sterols on inflammation remain limited and confounding. Future research using better-designed and well-controlled laboratory studies and clinical trials are needed to fully understand the mechanisms through which phytosterols influence inflammation. Additional well-designed placebo-controlled studies are needed to better understand how and to what extent dietary plant sterols may modify the immune system and the production of inflammatory markers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".