Alterations of Gut Microbiota in Patients With Irritable Bowel Syndrome Based on 16S rRNA-Targeted Sequencing: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Alterations of gut microbiota have been thought to be associated with irritable bowel syndrome (IBS). Many studies have reported significant alterations of gut microbiota in patients with IBS based on 16S ribosomal RNA-targeted sequencing. However, results from these studies are inconsistent or even contradictory. We performed a systematic review to explore the alterations of gut microbiota in patients with IBS compared with healthy controls (HCs). METHODS: The databases PubMed, Cochrane Library, Web of Science, and Embase were searched for studies published until February 28, 2018, for case-control studies detecting gut microbiota in patients with IBS. Methodological quality was assessed using the Newcastle-Ottawa Scale. The α-diversity and alterations of gut microbiota in patients with IBS compared with HCs were analyzed. RESULTS: Sixteen articles involving 777 patients with IBS and 461 HCs were included. Quality assessment scores of the studies ranged from 5 to 7. For most studies, patients with IBS had a lower α-diversity than HCs in both fecal and mucosal samples. Relatively consistent changes in fecal microbiota for patients with IBS included increased Firmicutes, decreased Bacteroidetes, and increased Firmicutes:Bacteroidetes ratio at the phylum level, as well as increased Clostridia and Clostridiales, decreased Bacteroidia and Bacteroidales at lower taxonomic levels. Results for mucosal microbiota were inconsistent. CONCLUSIONS: Alterations of gut microbiota exist in patients with IBS and have significant association with the development of IBS. Further studies are needed to draw conclusions about gut microbiota changes in patients with IBS. TRANSLATIONAL IMPACT: This knowledge might improve the understanding of microbial signatures in patients with IBS and would guide future therapeutic strategies.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".