Lipid-lowering nutraceuticals in clinical practice: position paper from an International Lipid Expert Panel
Bibliographic record
Abstract
In recent years, there has been growing interest in the possible use of nutraceuticals to improve and optimize dyslipidemia control and therapy. Based on the data from available studies, nutraceuticals might help patients obtain theraputic lipid goals and reduce cardiovascular residual risk. Some nutraceuticals have essential lipid-lowering properties confirmed in studies; some might also have possible positive effects on nonlipid cardiovascular risk factors and have been shown to improve early markers of vascular health such as endothelial function and pulse wave velocity. However, the clinical evidence supporting the use of a single lipid-lowering nutraceutical or a combination of them is largely variable and, for many of the nutraceuticals, the evidence is very limited and, therefore, often debatable. The purpose of this position paper is to provide consensus-based recommendations for the optimal use of lipid-lowering nutraceuticals to manage dyslipidemia in patients who are still not on statin therapy, patients who are on statin or combination therapy but have not achieved lipid goals, and patients with statin intolerance. This statement is intended for physicians and other healthcare professionals engaged in the diagnosis and management of patients with lipid disorders, especially in the primary care setting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".