Blueberry proanthocyanidins and anthocyanins improve metabolic health through a gut microbiota-dependent mechanism in diet-induced obese mice
Bibliographic record
Abstract
Blueberry consumption can prevent obesity-linked metabolic diseases, and it has been proposed that the polyphenol content of blueberries may contribute to these effects. Polyphenols have been shown to favorably impact metabolic health, but the role of specific polyphenol classes and whether the gut microbiota is linked to these effects remain unclear. We aimed to evaluate the impact of whole blueberry powder and blueberry polyphenols on the development of obesity and insulin resistance and to determine the potential role of gut microbes in these effects by using fecal microbiota transplantation (FMT). Sixty-eight C57BL/6 male mice were assigned to one of the following diets for 12 wk: balanced diet (Chow); high-fat, high-sucrose diet (HFHS); or HFHS supplemented with whole blueberry powder (BB), anthocyanidin (ANT)-rich extract, or proanthocyanidin (PAC)-rich extract. After 8 wk, mice were housed in metabolic cages, and an oral glucose tolerance test (OGTT) was performed. Sixty germ-free mice fed HFHS diet received FMT from one of the above groups biweekly for 8 wk, followed by an OGTT. PAC-treated mice were leaner than HFHS controls although they had the same energy intake and were more physically active. This observation was reproduced in germ-free mice receiving FMT from PAC-treated mice. PAC- and ANT-treated mice showed improved insulin responses during OGTT, and this finding was also reproduced in germ-free mice following FMT. These results show that blueberry PAC and ANT polyphenols can reduce diet-induced body weight and improve insulin sensitivity and that at least part of these beneficial effects are explained by modulation of the gut microbiota.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".