Berberine ameliorates high-fat diet-induced metabolic disorders through promoting gut Akkermansia and modulating bile acid metabolism
Bibliographic record
Abstract
BACKGROUND: Coptidis Rhizoma, the rhizome of Coptis chinensis Franch., has long been employed in the treatment of diabetes. Its active component, berberine, has been utilized in clinical practice; however, the underlying mechanisms of its protective effects remain to be fully elucidated. METHODS: Metabolomics and lipidomics analyzed plasma metabolite and lipid changes in mice fed a high-fat diet and treated with 25 mg/kg/day berberine for three months. Metagenomics and microbiota transplantation identified gut microbiota responding to berberine. Co-administration of berberine and Akkermansia was studied for metabolic effects, analyzing plasma and fecal metabolomics. RESULTS: Berberine reduced triglycerides and cholesterol, showing metabolic protective effects. Metagenomics identified Akkermansia as key to berberine's benefits, validated by microbiota transplantation. Berberine enhanced Akkermansia growth, preserving intestinal mucus and tight junctions. It promotes the conversion of cholesterol to bile acids by inhibiting adenosine 5 '-monophosphate -activated protein kinase (AMPK), which promotes the expression of cholesterol 7-alpha hydroxylase (CYP7A1). Co-administration of berberine and Akkermansia amplified these effects. Potential metabolites, including linoleic acid and N-acetylputrescine, contributed to the observed benefits. CONCLUSION: Berberine, through Akkermansia, maintains intestinal integrity and reduces cholesterol, highlighting its potential as a therapeutic agent for metabolic disorders. Combining berberine with Akkermansia enhances its efficacy against hyperlipidemia.
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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 | 0.000 | 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) | 0.002 | 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".