Treatment with camu camu ( <i>Myrciaria dubia</i> ) prevents obesity by altering the gut microbiota and increasing energy expenditure in diet-induced obese mice
Bibliographic record
Abstract
Objective The consumption of fruits is strongly associated with better health and higher bacterial diversity in the gut microbiota (GM). Camu camu ( Myrciaria dubia ) is an Amazonian fruit with a unique phytochemical profile, strong antioxidant potential and purported anti-inflammatory potential. Design By using metabolic tests coupled with 16S rRNA gene-based taxonomic profiling and faecal microbial transplantation (FMT), we have assessed the effect of a crude extract of camu camu (CC) on obesity and associated immunometabolic disorders in high fat/high sucrose (HFHS)-fed mice. Results Treatment of HFHS-fed mice with CC prevented weight gain, lowered fat accumulation and blunted metabolic inflammation and endotoxaemia. CC-treated mice displayed improved glucose tolerance and insulin sensitivity and were also fully protected against hepatic steatosis. These effects were linked to increased energy expenditure and upregulation of uncoupling protein 1 mRNA expression in the brown adipose tissue (BAT) of CC-treated mice, which strongly correlated with the mRNA expression of the membrane bile acid (BA) receptor TGR5. Moreover, CC-treated mice showed altered plasma BA pool size and composition and drastic changes in the GM (eg, bloom of Akkermansia muciniphila and a strong reduction of Lactobacillus ). Germ-free (GF) mice reconstituted with the GM of CC-treated mice gained less weight and displayed higher energy expenditure than GF-mice colonised with the FM of HFHS controls. Conclusion Our results show that CC prevents visceral and liver fat deposition through BAT activation and increased energy expenditure, a mechanism that is dependent on the GM and linked to major changes in the BA pool size and composition.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".