Lipid-lowering nutraceuticals update on scientific evidence
Bibliographic record
Abstract
: Cardiovascular diseases (CVDs) are the main cause of mortality worldwide. Risk factors of CVD can be classified into modifiable (smoking, hypertension, diabetes, hypercholesterolemia) through lifestyle changes or taking drug therapy and not modifiable (age, ethnicity, sex and family history). Elevated total cholesterol (TC) and low-density lipoprotein-cholesterol (LDL-C) levels have a lead role in the development of coronary heart disease (CHD), while high levels of high-density lipoprotein-cholesterol (HDL-C) seem to have a protective role.The current treatment for dyslipidemia consists of lifestyle modification or drug therapy even if not pharmacological treatment should be always considered in addition to lipid-lowering medications.The use of lipid-lowering nutraceuticals alone or in association with drug therapy may be considered when the atherogenic cholesterol goal was not achieved.These substances can be classified according to their mechanisms of action into natural inhibitors of intestinal cholesterol absorption, inhibitors of hepatic cholesterol synthesis and enhancers of the excretion of LDL-C. Nevertheless, many of them are characterized by mixed or unclear mechanisms of action.The use of these nutraceuticals is suggested in individuals with borderline lipid profile levels or with drug intolerance, but cannot replace standard lipid-lowering treatment in patients at high, or very high CVD risk.Nutraceuticals can also have vascular effects, including improvement in endothelial dysfunction and arterial stiffness, as well as antioxidative properties. Moreover, epidemiological and clinical studies reported that in patients intolerant of statins, many nutraceuticals with demonstrated hypolipidemic effect are well tolerated.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".