The Interaction between Peanut Skin Proanthocyanidins and the Gut Microbiota Regulates Glucose and Lipid Metabolism in Obese Mice
Bibliographic record
Abstract
This study aimed to reveal the critical role of the interaction between procyanidins and the gut microbiota in regulating obesity. Changes in the body mass, body fat, and homeostatic model assessment of insulin resistance index (HOMA-IR) of high-fat diet-induced obese mice were analyzed after administration with different doses of peanut skin procyanidins (PSP), and gut microbiota diversity and fecal metabolomics were examined by 16S rRNA gene sequencing and high performance liquid chromatography-mass spectrometry (HPLC-MS). The results showed that PSP alleviated obesity, insulin resistance, and liver ectopic fat accumulation in high-fat diet mice. PSP decreased the ratio of Firmicutes to Bacteroidetes, significantly up-regulating the abundance of Akkermansia, Bacteroides, Alistipes, and Parabacteroides, and down-regulating the abundance of Blautia. PSP inhibited the accumulation of oxidative lipids such as 9-hydroxyoctadecadienoic acid (9-HODE), 9,10-dihydroxy-12Z,15Z-octadecadienoic acid (9,10-DHOME), 9,10-dihydroxy-12Z-octadecenoic acid (9,10-DiHODE), and 9,10,13-trihydroxy-11-octadecenoic acid (9,10,13-TriHOME) in the feces of obese mice, and up-regulated the levels of bile acids including muricholic acid, 12-ketolithocholic acid, and deoxycholic acid. Moreover, the proanthocyanidins A1 and A2 in PSP, as well as PSP-derived metabolites including 4-hydroxy-5-phenylvaleric acid, 5-(3’,4’-dihydroxy)-γ-valerolactone, and dehydroxylated C-ring opened catechin, were positively correlated with gut microbial genera such as Alistipes, Parabacteroides, and Akkermansia, which in turn influenced the levels of fecal metabolites, including urobilinogen, stearic acid, lysophosphatidylcholine (16:0), 9,10-DiHODE, and 9,10-DHOME. The interaction between PSP-derived microbial metabolites and endogenous metabolites altered gut microbiota balance, ultimately affecting the body mass and insulin resistance of mice.
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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) | 0.000 | 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".