Prebiotic-like Effects of Proanthocyanidin-Rich Aronia Extract Supplementation on Gut Microbiota Composition and Function in the Twin-M-SHIME® Model
Bibliographic record
Abstract
Background: Phenolic compounds, particularly anthocyanins and proanthocyanidins (PACs), are poorly absorbed in the upper digestive tract and reach the colon largely intact, where they may influence gut microbiota (GM) composition and, in turn, impact host health. We hypothesized that a PAC-rich aronia extract would beneficially modulate the GM, promote the growth of health-associated bacteria, and enhance short-chain fatty acid (SCFA) production across different colon sections, with partial reversion effects after supplementation ends. Methods: The Twin-M-SHIME® system was used to simulate the digestion and colonic fermentation in two donors with contrasting microbiota profiles. The experimental design included four phases: stabilization (14 days), control (7 days), treatment with 500 mg/day PAC-rich aronia extract (21 days), and wash-out (10 days). SCFA production was monitored, and changes in microbiome composition were assessed using 16S rRNA gene sequencing. Results: PAC-rich aronia extract significantly modulated SCFA levels, increasing butyrate and reducing acetate, with some inter-donor variability. SCFA concentrations tended to return to baseline after the wash-out (WO) period. Metagenomic analysis revealed a decrease in Collinsella, Sutterella, Selenomonas, and Parabacteroides—genera linked to low-fiber diets and gut inflammation—while promoting Proteobacteria (e.g., Escherichia-Shigella, Klebsiella) and butyrate-associated Firmicutes such as Lactiplantibacillus. Although some microbial shifts partially reverted during the wash-out (e.g., Akkermansia, Bacteroides, and Bifidobacterium), other changes persisted. Conclusions: These findings suggest that PAC-rich aronia extract beneficially modulates GM and SCFA production, but continuous intake may be necessary to maintain these effects over time.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".