Dietary supplements could prevent cardiometabolic syndrome: Are they safe and reliable enough for disease prevention and health promotion?
Bibliographic record
Abstract
Dietary supplements (DS) and their purchase is often based on a consumer's personal choice and advertisements. The associated DS regulations, particularly in manufacturing and marketing, are far more flexible and permissive than that of the well-regulated prescription pharmaceuticals. However, the adverse health effects associated with the inadvertent use of mega-doses of DS are not well understood. The demand for DS, nutraceuticals, and herbal remedies has experienced an upswing during the past two to three decades, and global product sales have thrived. More so, the prevention of cardiometabolic syndrome (CMS) and related disorders like diabetes mellitus, obesity, hypertension, and serum lipid abnormalities, as well as of other noncommunicable diseases (NCDs), is of highest health care priority globally, since these disorders impose very high economic burdens on health care systems and society. In this review, we argue why DS could prevent cardiometabolic syndrome, by providing the potential benefits and risks associated with them, especially self-medication considering their intake by the public at large. Good manufacturing practices and quality control are absolutely necessary for the manufacture of DS products, and proper labeling is needed regarding the optimal dose schedules of various DS and bioactive ingredients. Specific examples are used to underscore the indications and dosage recommendations made for the marketing and promotion of fish oil, coenzyme Q10, and Mg-containing products for the prevention of cardiometabolic syndrome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".