Nutraceutical approaches to non-alcoholic fatty liver disease (NAFLD): A position paper from the International Lipid Expert Panel (ILEP)
Bibliographic record
Abstract
Non-Alcoholic Fatty Liver Disease (NAFLD) is a common condition affecting around 10-25% of the general adult population, 15% of children, and even > 50% of individuals who have type 2 diabetes mellitus. It is a major cause of liver-related morbidity, and cardiovascular (CV) mortality is a common cause of death. In addition to being the initial step of irreversible alterations of the liver parenchyma causing cirrhosis, about 1/6 of those who develop NASH are at risk also developing CV disease (CVD). More recently the acronym MAFLD (Metabolic Associated Fatty Liver Disease) has been preferred by many European and US specialists, providing a clearer message on the metabolic etiology of the disease. The suggestions for the management of NAFLD are like those recommended by guidelines for CVD prevention. In this context, the general approach is to prescribe physical activity and dietary changes the effect weight loss. Lifestyle change in the NAFLD patient has been supplemented in some by the use of nutraceuticals, but the evidence based for these remains uncertain. The aim of this Position Paper was to summarize the clinical evidence relating to the effect of nutraceuticals on NAFLD-related parameters. Our reading of the data is that whilst many nutraceuticals have been studied in relation to NAFLD, none have sufficient evidence to recommend their routine use; robust trials are required to appropriately address efficacy and safety.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".