A polyphenol-rich cranberry extract protects from diet-induced obesity, insulin resistance and intestinal inflammation in association with increased <i>Akkermansia</i> spp. population in the gut microbiota of mice
Bibliographic record
Abstract
OBJECTIVE: The increasing prevalence of obesity and type 2 diabetes (T2D) demonstrates the failure of conventional treatments to curb these diseases. The gut microbiota has been put forward as a key player in the pathophysiology of diet-induced T2D. Importantly, cranberry (Vaccinium macrocarpon Aiton) is associated with a number of beneficial health effects. We aimed to investigate the metabolic impact of a cranberry extract (CE) on high fat/high sucrose (HFHS)-fed mice and to determine whether its consequent antidiabetic effects are related to modulations in the gut microbiota. DESIGN: C57BL/6J mice were fed either a chow or a HFHS diet. HFHS-fed mice were gavaged daily either with vehicle (water) or CE (200 mg/kg) for 8 weeks. The composition of the gut microbiota was assessed by analysing 16S rRNA gene sequences with 454 pyrosequencing. RESULTS: CE treatment was found to reduce HFHS-induced weight gain and visceral obesity. CE treatment also decreased liver weight and triglyceride accumulation in association with blunted hepatic oxidative stress and inflammation. CE administration improved insulin sensitivity, as revealed by improved insulin tolerance, lower homeostasis model assessment of insulin resistance and decreased glucose-induced hyperinsulinaemia during an oral glucose tolerance test. CE treatment was found to lower intestinal triglyceride content and to alleviate intestinal inflammation and oxidative stress. Interestingly, CE treatment markedly increased the proportion of the mucin-degrading bacterium Akkermansia in our metagenomic samples. CONCLUSIONS: CE exerts beneficial metabolic effects through improving HFHS diet-induced features of the metabolic syndrome, which is associated with a proportional increase in Akkermansia spp.
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) | 0.000 | 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".