A meta-analysis of changes in gut microbiota structure following bariatric surgery
Bibliographic record
Abstract
BACKGROUND: Bariatric surgery is a common intervention for obesity, yet its impact on gut microbiota remains unclear. OBJECTIVE: This systematic review and meta-analysis evaluated changes in gut microbiota composition before and after bariatric surgery. METHODS: We searched PubMed, Embase, Web of Science, and Cochrane Library up to October 2024 for randomized controlled trials (RCTs) and observational studies reporting pre- and post-surgery gut microbiota composition. Two reviewers independently screened studies, extracted data, and assessed bias using the Cochrane risk of bias tool and the Newcastle-Ottawa Scale (NOS). Primary outcomes included alpha diversity changes (Chao and Shannon indices), while secondary outcomes focused on relative abundance changes at phylum, family, and genus levels. Data were pooled using random-effects models. RESULTS: Among 3670 screened articles, 45 were included, with 30 achieving NOS scores ≥7 and one trial having some concerns in the risk of bias assessment. Post-surgery, alpha diversity significantly increased but with high heterogeneity (Chao index: SMD 0.50, 95% CI 0.01-0.99, P = 0.046, I 2 = 87.3%; Shannon index: SMD 0.37, 95% CI 0.04-0.70, P = 0.028, I 2 = 90.2%). Meta-regression identified age and geographic region as heterogeneity sources. Both RYGB and LSG surgery increased the abundance of Akkermansia, Bacteroides, Streptococcus , and Veillonella , but the abundance of Bifidobacterium and Lactobacillus was reduced after LSG surgery. CONCLUSION: Bariatric surgery significantly increases gut microbiota alpha diversity, with notable genus-level changes that indicate probiotic supplementation may be beneficial post-LSG. Owing to the high heterogeneity in taxonomic findings, further studies are needed to robustly establish the causal effects of specific surgical procedures on individual taxa.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.075 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".