Purple grumixama anthocyanins (<i>Eugenia brasiliensis</i>Lam.) attenuate obesity and insulin resistance in high-fat diet mice
Bibliographic record
Abstract
Some polyphenols have been reported to modulate the expression of several genes related to lipid metabolism and insulin signaling, ameliorating metabolic disorders. We investigated the potential for the polyphenols of two varieties of grumixama, the purple fruit rich in anthocyanins and the yellow fruit, both also rich in ellagitannins, to attenuate obesity-associated metabolic disorders. Mice were fed a high fat and high sucrose diet, supplemented daily with yellow and purple extracts (200 mg per kg of body weight) for eight weeks. Purple grumixama supplementation was found to decrease body weight gain, improve insulin sensitivity and glucose-induced hyperinsulinemia, and reduce hepatic triglyceride accumulation. A decrease in intrahepatic lipids in mice treated with the purple grumixama extract was associated with lipid metabolism modulation by the PPAR signaling pathway. LPL, ApoE, and LDLr were found to be down-regulated, while Acox1 and ApoB were found to be upregulated. Some of these genes were also modulated by the yellow extract. In addition, both extracts decreased oGTT and plasma LPS. The results were associated with the presence of phenolic acids and urolithins. In conclusion, most likely the anthocyanins from the purple grumixama phenolic extract is responsible for reducing obesity and insulin resistance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".