Camu-camu decreases hepatic steatosis and liver injury markers in overweight, hypertriglyceridemic individuals: A randomized crossover trial
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD), recently referred to as “metabolic dysfunction-associated steatotic liver disease” (MASLD), affects 25% of the adult population with no effective drug treatments available. Previous animal studies reported that a polyphenol-rich extract from the Amazonian berry camu-camu (CC) prevented hepatic steatosis in a mouse model of diet-induced obesity. This study aims to determine the impact of CC on hepatic steatosis (primary outcome) and evaluate changes in metabolic and gut microbiota profiles (exploratory outcomes). A randomized, double-blind, placebo-controlled crossover trial is conducted on 30 adults with overweight and hypertriglyceridemia, who consume 1.5 g of CC capsules or placebo daily for 12 weeks. CC treatment decreases liver fat by 7.43%, while it increases by 8.42% during the placebo intervention, showing a significant difference of 15.85%. CC decreases plasma aspartate and alanine aminotransferases levels and promotes changes in gut microbiota composition. These findings support that polyphenol-rich prebiotic may reduce liver fat in adults with overweight, reducing the risk of developing NAFLD. • Camu-camu reduces hepatic steatosis in adults with overweight and hypertriglyceridemia • Camu-camu supplementation decreases plasma AST and ALT levels • Camu-camu supplementation alters gut microbiota composition Agrinier et al. demonstrate that camu-camu (CC) supplementation reduces liver fat and improves liver injury markers in individuals with overweight and hypertriglyceridemia. CC also alters gut microbiota composition, supporting its potential as a polyphenol-rich prebiotic supplement for reducing non-alcoholic fatty liver disease risk.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".