Pinto Bean Supplementation Modulates Gut Microbiota and Improves Markers of Gut Integrity in a Mouse Model of Estrogen Deficiency
Bibliographic record
Abstract
BACKGROUND: Emerging research suggests that changes in gut microbiota play a key role in menopause-related diseases by modulating gut health. OBJECTIVES: This study investigated the effects of pinto bean (PB) supplementation on gut integrity in an estrogen-deficient mouse model. METHODS: Sixty 3-mo-old female C57BL/6J mice were injected with either sesame oil (vehicle) or vinylcyclohexene diepoxide (VCD, 160 mg/kg) for 30 d to induce estrogen deficiency. Mice were then randomly assigned to 2 dietary groups (n = 15/group): control (AIN-93M) or AIN-93M + 10% (wt/wt) PB for 16 wk. Ovarian failure was confirmed by uterine weight and serum follicle-stimulating hormone (FSH). Gut health was assessed by measuring tight junction proteins, β-glucuronidase activity, short-chain fatty acids (SCFAs), and 16S microbiota composition. PB was evaluated for its estrogenic effects by molecular docking analysis of the identified polyphenols against estrogen receptor (ER)-α and ER-β. Data were analyzed by 2-way analysis of variance, with estrogen status (VCD) and diet as factors followed by post hoc tests when significant (P < 0.05) interaction effect was observed. RESULTS: =0.010) β-glucuronidase activity (∼25%). PB enriched some beneficial bacteria genera (i.e., Bifidobacterium, Bacteroides, Dubosiella, and Lactobacillus) and increased fecal acetic, propionic, n-butyric, and total SCFAs by 2-fold compared with those on the control diet. Molecular docking analysis identified sinapic and ferulic acid as phytoestrogens in PB with high binding affinity for ERs. CONCLUSIONS: PB supplementation improves gut microbial diversity and integrity in estrogen deficiency, offering potential benefits for menopause-related gut health.
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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 | 0.000 |
| 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) | 0.002 | 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".