Impact of Cyanidin-3-Glucoside on Gut Microbiota and Relationship with Metabolism and Inflammation in High Fat-High Sucrose Diet-Induced Insulin Resistant Mice
Bibliographic record
Abstract
The present study assessed the effects of freeze-dried cyanidin-3-glucoside (C3G), an anthocyanin enriched in dark-red berries, compared to Saskatoon berry powder (SBp) on metabolism, inflammatory markers and gut microbiota in high fat-high sucrose (HFHS) diet-induced insulin-resistant mice. Male C57 BL/6J mice received control, HFHS, HFHS + SBp (8.0 g/kg/day) or HFHS + C3G (7.2 mg/kg/day, equivalent C3G in SBp) diet for 11 weeks. The HFHS diet significantly increased plasma levels of glucose, cholesterol, triglycerides, insulin resistance and inflammatory markers. The HFHS + SBp diet increased the Bacteroidetes/Firmicutes (B/F) ratio and relative abundance of Muriculaceae family bacteria in mouse feces detected using 16S rRNA gene sequencing. The HFHS + SBp or HFHS + C3G diet attenuated glucose, lipids, insulin resistance and inflammatory markers, and increased the B/F ratio and Muriculaceae relative abundance compared to the HFHS diet alone. The relative abundances of Muriculaceae negatively correlated with body weight, glucose, lipids, insulin resistance and inflammatory mediators. Functional predication analysis suggested that the HFHS diet upregulated gut bacteria genes involved in inflammation, and downregulated bacteria involved in metabolism. C3G and SBp partially neutralized HFHS diet-induced alterations of gut bacteria. The results suggest that C3G is a potential prebiotic, mitigating HFHS diet-induced disorders in metabolism, inflammation and gut dysbiosis, and that C3G contributes to the metabolic beneficial effects of SBp.
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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 | 0.000 | 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".