A polyphenol-rich cranberry extract reverses insulin resistance and hepatic steatosis independently of body weight loss
Bibliographic record
Abstract
OBJECTIVE: Previous studies have reported that polyphenol-rich extracts from various sources can prevent obesity and associated gastro-hepatic and metabolic disorders in diet-induced obese (DIO) mice. However, whether such extracts can reverse obesity-linked metabolic alterations remains unknown. In the present study, we aimed to investigate the potential of a polyphenol-rich extract from cranberry (CE) to reverse obesity and associated metabolic disorders in DIO-mice. METHODS: Mice were pre-fed either a Chow or a High Fat-High Sucrose (HFHS) diet for 13 weeks to induce obesity and then treated either with CE (200 mg/kg, Chow + CE, HFHS + CE) or vehicle (Chow, HFHS) for 8 additional weeks. RESULTS: CE did not reverse weight gain or fat mass accretion in Chow- or HFHS-fed mice. However, HFHS + CE fully reversed hepatic steatosis and this was linked to upregulation of genes involved in lipid catabolism (e.g., PPARα) and downregulation of several pro-inflammatory genes (eg, COX2, TNFα) in the liver. These findings were associated with improved glucose tolerance and normalization of insulin sensitivity in HFHS + CE mice. The gut microbiota of HFHS + CE mice was characterized by lower Firmicutes to Bacteroidetes ratio and a drastic expansion of Akkermansia muciniphila and, to a lesser extent, of Barnesiella spp, as compared to HFHS controls. CONCLUSIONS: Taken together, our findings demonstrate that CE, without impacting body weight or adiposity, can fully reverse HFHS diet-induced insulin resistance and hepatic steatosis while triggering A. muciniphila blooming in the gut microbiota, thus underscoring the gut-liver axis as a primary target of cranberry polyphenols.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".