Carotenoids and fatty liver disease: Current knowledge and research gaps
Bibliographic record
Abstract
Carotenoids form an important part of the human diet, consumption of which has been associated with many health benefits. With the growing global burden of liver disease, increasing attention has been paid on the possible beneficial role that carotenoids may play in the liver. This review focuses on carotenoid actions in non-alcoholic fatty liver disease (NAFLD), and alcoholic liver disease (ALD). Indeed, many human studies have suggested an association between decreased circulating levels of carotenoids and increased incidence of NAFLD and ALD. The literature describing supplementation of individual carotenoids in rodent models of NAFLD and ALD is reviewed, with particular attention paid to β-carotene and lycopene, but also including β-cryptoxanthin, lutein, zeaxanthin, and astaxanthin. The effect of beta-carotene oxygenase 1 and 2 knock-out mice on hepatic lipid metabolism is also discussed. In general, there is evidence to suggest that carotenoids have beneficial effects in animal models of both NAFLD and ALD. Mechanistically, these benefits may occur via three possible modes of action: 1) improved hepatic antioxidative status broadly attributed to carotenoids in general, 2) the generation of vitamin A from β-carotene and β-cryptoxanthin, leading to improved hepatic retinoid signaling, and 3) the generation of apocarotenoid metabolites from β-carotene and lycopene, that may regulate hepatic signaling pathways. Gaps in our knowledge regarding carotenoid mechanisms of action in the liver are highlighted throughout, and the review ends by emphasizing the importance of dose effects, mode of delivery, and mechanism of action as important areas for further study. This article is part of a Special Issue entitled Carotenoids recent advances in cell and molecular biology edited by Johannes von Lintig and Loredana Quadro.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".