Roux-en-Y gastric bypass and sleeve gastrectomy induce substantial and persistent changes in microbial communities and metabolic pathways
Bibliographic record
Abstract
Bariatric surgery induces significant microbial and metabolomic changes, however, links between microbial and metabolic pathways have not been fully elucidated. The objective of this study was to conduct a comprehensive investigation of the microbial, metabolomic, and inflammatory changes that occur following Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG). A prospective clinical trial was conducted with participants undergoing RYGB, SG, and non-operative controls (CTRL). Clinical parameters, blood samples, and fecal samples were collected pre-intervention and at 3 and 9 months. A multi-omics approach was used to perform integrated microbial-metabolomic analysis to identify functional pathways in which weight loss and metabolic changes occur after surgery. RYGB led to profound microbial changes over time that included reductions in alpha-diversity, increased Proteobacteria and Verrucomicrobiota, decreased Firmicutes, and numerous changes at the genera level. These changes were associated with a reduction in inflammation and significant weight loss. A reduction in Romboutsia genera correlated strongly with weight loss and integrated microbial-metabolomic analysis revealed the importance of Romboutsia. Its obliteration correlated with improved weight loss and insulin resistance, possibly through decreases in glycerophospholipids. In contrast, SG was associated with no changes in alpha-diversity, and only a small number of changes in microbial genera. A cluster of Firmicutes genera including Butyriciccocus, Eubacterium ventriosum, and Monoglobus was decreased, which correlated with decreased weight, insulin resistance, and systemic inflammation. This work represents comprehensive analyses of microbial-metabolomic changes that occur following bariatric surgery and identifies several pathways that are associated with beneficial metabolic effects of surgery.
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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 | 0.000 |
| 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".