Efficacy, side effects, adherence, affordability, and procurement of dietary supplements for treating hypercholesterolemia: a narrative review
Bibliographic record
Abstract
INTRODUCTION: Statins are effective in reducing high cholesterol levels; however, due to associated side effects, many patients actively seek alternative medications. This review evaluates the efficacy, side effects, patient adherence, cost-effectiveness, and accessibility of dietary supplements (DS) as a treatment option for hypercholesterolemia. METHODS: This narrative review compares red yeast rice (RYR), flaxseed, artichokes, bergamot, Ayurvedic mixtures (with garlic as a prominent ingredient), and statins for treating hypercholesterolemia. We searched PubMed, Scopus, and Cochrane databases for studies published between 2012 and 2024 using "hypercholesterolemia" in combination with a dietary supplement (red yeast rice, flaxseed, artichokes, garlic, or bergamot). The selected articles were published until 28th January 2024 with no language restrictions. RESULTS: Study results suggest that alternative treatments using dietary supplements such as flaxseed, bergamot, or red yeast rice may effectively reduce cholesterol levels, with the specific value varying based on the study. CONCLUSION: The following natural ingredients - red yeast rice, artichoke extract, bergamot, garlic, and flaxseed - have been specifically selected for their cholesterol-lowering properties. Based on consistent usage, except for aged garlic extract, these ingredients appear to have a beneficial impact on cholesterol levels. (1) It is advisable to conduct a comprehensive cohort study to assess the efficacy of relevant dietary supplements, particularly red yeast rice, bergamot, and flaxseed, in treating hypercholesterolemia. This is important due to the varying effectiveness of alternative treatments. However, there are lingering concerns regarding the lack of supervision and quality control that require attention. (2) Further research into the specific molecular composition and the underlying mechanisms by which it reduces cholesterol levels is warranted.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".