Natural health products for treatment of metabolism dysfunction-associated steatotic liver disease
Bibliographic record
Abstract
Metabolism dysfunction-associated steatotic liver disease affects approximately 30% of the world’s population, yet there is only one approved treatment option applicable to more advanced disease. Many individuals consume natural health products for general health and a variety of medical conditions but none are recommended for metabolism dysfunction-associated steatotic liver disease in current European or American guidelines. Nevertheless, human trials indicate that some of these products may be efficacious for treatment of metabolism dysfunction-associated steatotic liver disease and these are supported by mechanistic studies using animal models. This narrative review aims to highlight recent research in human and animal trials on selected natural health products. So far, neither probiotics nor omega-3 polyunsaturated fatty acids have produced convincing, consistent benefits in human randomized controlled trials although studies in mouse models suggest that they have actions can lead to reduction of hepatic steatosis or other markers, such as liver enzymes. Two of the many polyphenols that have been studied were also reviewed here. Trials with resveratrol in humans have not yielded significant results whereas curcumin, the active ingredient in turmeric, appeared to consistently lower steatosis or liver enzymes. Both compounds reduced steatosis in rodent models of MASLD, involving a variety of mechanisms including anti-oxidant, anti-inflammatory and metabolic effects. More, better-designed and powered human trials are required to provide convincing evidence of efficacy of most natural health products.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".