A role for methanogens and methane in the regulation of <scp>GLP</scp>‐1
Bibliographic record
Abstract
INTRODUCTION: The gastrointestinal (GI) microbiome has emerged as a potential regulator of metabolism. However, the precise mechanisms of how microorganisms may influence physiology remain largely unknown. Interestingly, GI microorganisms, including methanogens, are localized within the same regions as the glucagon-like peptide-1 (GLP-1) secreting L cells. GLP-1 plays key roles appetite and glucose regulation. Furthermore, both methane and GLP-1 levels are altered in obese humans with metabolic disease. We predict that high-fat diet-induced obesity alters the abundance of GI methanogens and that methane may play a role in the GLP-1 secretory response from the L cell. METHODS: To demonstrate this, GLP-1 secretion response and faecal methanogens were examined in mice given a high-fat diet for 14 weeks. In addition, the direct effect of methane on GLP-1 secretion was assessed in two L-cell models (NCI-H716 and GLUTag). RESULTS: High-fat diet caused a significant increase in both GLP-1 secretion and faecal methanogen content. There was a direct correlation between GLP-1 secretion response and faecal methanogen levels. In L cells, methane stimulated GLP-1 secretion and enhanced intracellular cAMP content. CONCLUSION: These results indicate that alterations in the methanogen communities occurring in obesity may play a vital role in directly enhancing GLP-1 secretion, and that methane can directly stimulate the secretion of GLP-1.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".