Role of Presurgical Gut Microbial Diversity in Roux-en-Y Gastric Bypass Weight-Loss Response: A Cohort Study
Bibliographic record
Abstract
INTRODUCTION: Roux-en-Y gastric bypass (RYGB) substantially alters the gut microbial composition which could be associated with the metabolic improvements seen after surgery. Few studies have been conducted in Latin American populations, such as Mexico, where obesity prevalence is above 30% in the adult population. Thus, the aim of this study was to characterize the changes in the gut microbiota structure in a Mexican cohort before and after RYGB and to explore whether surgery-related changes in the microbial community were associated with weight loss. METHODS: Biological samples from patients who underwent RYGB were examined before and 12 months after surgery. Fecal microbiota characterization was performed through 16S rRNA sequencing. RESULTS: Twenty patients who underwent RYGB showed a median excess weight loss of 66.8% 12 months after surgery. Surgery increased alpha diversity estimates (Chao, Shannon index, and observed operational taxonomic units, p < 0.05) and significantly altered gut microbiota composition. Abundance of four genera was significantly increased after surgery: Oscillospira, Veillonella, Streptococcus, and an unclassified genus from Enterobacteriaceae family (PFDR < 0.1). The change in Veillonella abundance was associated with lower excess weight loss (rho = -0.446, p = 0.063) and its abundance post-surgery with a greater BMI (rho = 0.732, p = 5.4 × 10-4). In subjects without type 2 diabetes, lower bacterial richness and diversity before surgery were associated with a greater Veillonella increase after surgery (p < 0.05), suggesting that a lower bacterial richness before surgery could favor the bloom of certain oral-derived bacteria that could negatively impact weight loss. CONCLUSION: Presurgical microbiota profile may favor certain bacterial changes associated with less successful results.
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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 | 0.001 | 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) | 0.000 | 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".