Probiotic interventions promote metabolic health in high fat-fed hamsters in association with gut microbiota and endocannabinoidome alterations
Bibliographic record
Abstract
Probiotics represent a promising tool to improve metabolic health, including lipid profiles and cholesterol levels. Modulation of the gut microbiome and the endocannabinoidome - two interrelated systems involved in several metabolic processes influenced by probiotics - has been proposed as a potential mechanism of action. This study establishes the impact of probiotics on metabolic health, gut microbiota composition and endocannabinoidome mediators in an animal model of hypercholesterolaemia. Syrian hamsters were fed either a low-fat low-cholesterol or high-fat high-cholesterol (HFHC) diet to induce hypercholesterolaemia and gavaged for 6 weeks with either Lactobacillus acidophilus CL1285, Lactiplantibacillus plantarum CHOL-200 or a combination of the two. Globally, probiotic interventions ameliorated, at least partially, lipid metabolism in HFHC-fed hamsters. The interventions, especially those including L. acidophilus, modified the gut microbiota composition of the small intestine and caecum in ways suggesting reversal of HFHC-induced dysbiosis. Several associations were observed between changes in gut microbiota composition and endocannabinoidome mediators following probiotic interventions and both systems were also associated with improved metabolic health parameters. For instance, potential connexions between the Eubacteriaceae and Deferribacteraceae families, levels of 2‑palmitoylglycerol, 2‑oleoylglycerol, 2‑linoleoylglycerol or 2‑eicosapentaenoylglycerol and improved lipid profiles were found. Altogether, our results suggest a potential crosstalk between gut microbiota and the endocannabinoidome in driving metabolic benefits associated with probiotics, especially those including L. acidophilus, in an animal model of hypercholesterolaemia.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".