Human gut microbiota after bariatric surgery alters intestinal morphology and glucose absorption in mice independently of obesity
Bibliographic record
Abstract
Objective Bariatric surgery is an effective treatment for type 2 diabetes (T2D) that changes gut microbial composition. We determined whether the gut microbiota in humans after restrictive or malabsorptive bariatric surgery was sufficient to lower blood glucose. Design Women with obesity and T2D had biliopancreatic diversion with duodenal switch (BPD-DS) or laparoscopic sleeve gastrectomy (LSG). Faecal samples from the same patient before and after each surgery were used to colonise rodents, and determinants of blood glucose control were assessed. Results Glucose tolerance was improved in germ-free mice orally colonised for 7 weeks with human microbiota after either BPD-DS or LSG, whereas food intake, fat mass, insulin resistance, secretion and clearance were unchanged. Mice colonised with microbiota post-BPD-DS had lower villus height/width and crypt depth in the distal jejunum and lower intestinal glucose absorption. Inhibition of sodium-glucose cotransporter (Sglt)1 abrogated microbiota-transmissible improvements in blood glucose control in mice. In specific pathogen-free (SPF) rats, intrajejunal colonisation for 4 weeks with microbiota post-BPD-DS was sufficient to improve blood glucose control, which was negated after intrajejunal Sglt-1 inhibition. Higher Parabacteroides and lower Blautia coincided with improvements in blood glucose control after colonisation with human bacteria post-BPD-DS and LSG. Conclusion Exposure of rodents to human gut microbiota after restrictive or malabsorptive bariatric surgery improves glycaemic control. The gut microbiota after bariatric surgery is a standalone factor that alters upper gut intestinal morphology and lowers Sglt1-mediated intestinal glucose absorption, which improves blood glucose control independently from changes in obesity, insulin or insulin resistance.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".