Comparison with healthy controls: meta-analysis of changes in intestinal flora in Chinese patients with irritable bowel syndrome
Bibliographic record
Abstract
Background The characteristics of the gut microbiota in Chinese patients with irritable bowel syndrome (IBS) and their association with the disease remain unclear. Although current studies have revealed some changes in the gut microbiota of IBS patients, these changes vary across different studies and no consistent conclusions have been reached. Additionally, geographical differences may lead to unique microbial characteristics in IBS patients, which could serve as potential diagnostic tools or therapeutic targets. We hypothesize that the gut microbiota of Chinese IBS patients exhibits unique compositional and functional characteristics that differ significantly from those of healthy individuals. Aim To investigate differential characteristics of gut microbiota in Chinese patients with IBS compared to healthy controls (HC). Methods We conducted a comprehensive search in PubMed, Web of Science, Embase, and China National Knowledge Infrastructure (CNKI) databases from their inception to May 2025. The search strategy was built around key concepts of IBS, gut microbiota, and China, combining subject headings (e.g., MeSH terms) and free-text keywords. The search was performed in line with the PRISMA guidelines. Studies were included if they were case-control studies comparing gut microbiota between IBS patients and all participants were Chinese, IBS diagnosis adhered to internationally recognized Rome criteria, HCs were free from gastrointestinal diseases, and sufficient data were provided for meta-analysis. Exclusion criteria encompassed non-case-control designs, studies involving non-Chinese populations or mixed control groups, unavailable or insufficient data, and studies of low methodological quality (NOS score < 5). Study selection was carried out by two researchers independently. Data were extracted and quality assessed using the Newcastle-Ottawa Scale (NOS). The quality assessment was independently conducted by two researchers, and any discrepancies were resolved with the assistance of a third researcher. Data were analyzed using RevMan 5.4 software, with standardized mean differences (SMD) and 95% confidence intervals (CIs) calculated. The I2 statistic was used to assess heterogeneity among studies. When I2 > 50%, indicating significant heterogeneity, the random effects model was used for calculations. Otherwise, the fixed effects model was applied. Results This meta-analysis of 12 studies (766 IBS patients, 351 HC) revealed that Chinese IBS patients exhibited significantly higher abundance of Enterobacter (SMD = 1.55; 95% CI=[0.90,2.19]; P < 0.00001) and Enterococcus (SMD = 0.69; 95% CI=[0.25, 1.13]; P = 0.002), but lower Lactobacillus (SMD=-1.30; 95% CI=[-1.71, -0.89]; P < 0.00001) and Bifidobacterium (SMD=-1.56; 95% CI=[-2.04, -1.09]; P < 0.00001) versus HC, with no Bacteroides difference. In IBS-D Patients, Enterobacter (SMD = 1.47; 95% CI=[0.75, 2.19]; P < 0.0001) and Enterococcus (SMD = 0.53; 95% CI=[0.06, 1.01]; P = 0.03) remained elevated, while Lactobacillus (SMD=-1.39; 95% CI=[-1.76,-1.02];P < 0.00001), Bifidobacterium (SMD=-1.66; 95% CI=[-2.15,-1.17];P < 0.00001), and Bacteroides (SMD=-0.72; 95% CI=[-1.05, -0.39]; CI=[-1.05,-0.39];P < 0.0001) decreased significantly. IBS-C patients showed no significant differences in these genera. Conclusion This meta-analysis reveals gut microbiota imbalances in Chinese IBS patients: significantly increased Enterobacter and Enterococcus with decreased Lactobacillus and Bifidobacterium overall, and reduced Bacteroides specifically in IBS-D.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".