The Prevalence of Small Intestinal Bacterial Overgrowth in Patients with Non-Alcoholic Liver Diseases: NAFLD, NASH, Fibrosis, Cirrhosis—A Systematic Review, Meta-Analysis and Meta-Regression
Bibliographic record
Abstract
Bacterial overgrowth in the small intestine (SIBO) is a pathological growth of the intestinal microbiota in the small intestine that causes clinical symptoms and can lead to digestive and absorption disorders. There is increasing evidence that people with NAFLD have a distinct gut microflora profile as well metabolome changes compared to people without NAFLD. Thorough analysis of observational and RCT studies in the current databases (EMBASE, Web of Science, PubMed, Cinahl, Clinical Trials) was conducted from 3 November 2021 to 21 June 2022. The following inclusion criteria were applied: confirmed NAFLD, NASH, LIVER FIBROSIS, CIRRHOSIS due to steatosis; diagnostic methods of liver diseases—biopsy, elastography, transabdominal ultrasound; nonalcoholic fatty liver disease activity score; confirmed SIBO; diagnostic methods of SIBO−breath tests (hydrogen test; methane test and mix test; duodenal and jejunal aspiration before any type of intervention; adults above 18yo; number of participants ≥20; full articles. We excluded review articles, populations with HBV/HCV infection and alcohol etiology and interventions that may affect NAFLD or SIBO treatment. The quality of each study methodology was classified by means of the Cochrane Collaboration’s tool (RCT) and Newcastle—Ottawa Quality Assessment Scale adapted for cross-sectional, cohort and case-control studies. The random effects meta-analysis of outcomes for which ≥2 studies contributed data was conducted. The I2 index to measure heterogeneity and the χ2 test of homogeneity (statistically significant heterogeneity p < 0.05) were applied. For categorical outcome, the pooled event rate (effect size) was calculated. This systematic review was reported according to PRISMA reporting guidelines. We initially identified 6643 studies, from which 18 studies were included in final meta-analysis. The total number of patients was 1263. Accepted SIBO diagnostic methods were both available breath tests (n-total = 15) and aspirate culture (n-total = 3). We found that among patients with non-alcoholic liver diseases, the random overall event rate of SIBO was 0.350 (95% CI, 0.244−0.472), p = 0.017. The subgroup analysis regarding a type of diagnosis revealed that the lowest ER was among patients who developed simultaneously NAFLD, NASH and fibrosis: 0.197 (95% CI, 0.054−0.510) as compared to other annotated subgroups. The highest prevalence of SIBO was observed in the NASH subgroup: 0.411 (95% CI, 0.219−0.634). There were no statistically significant differences in the prevalence of SIBO in different subgroups (p = 0.854). Statistically significant heterogeneity between studies was estimated (I2 = 86.17%, p = 0.00). Egger’s test did not indicate a publication bias (df = 16, p = 0.885). A meta-regression using a random-effects model revealed that higher percentage of males in the population with liver diseases is a predisposing factor toward SIBO (Q = 4.11, df = 1, p = 0.0426 with coefficient = 0.0195, SE = 0.0096, Z = 2.03). We showed that the prevalence of SIBO in patients with chronic non-alcoholic liver diseases can be as high as 35%, and it increases with the percentage of men in the population. The prevalence of SIBO does not differ significantly depending on the type of chronic liver disease. Despite the high heterogeneity and moderate and low quality of included studies, our meta-analysis suggests the existence of a problem of SIBO in the population of patients with non-alcoholic liver diseases, and the presence of SIBO, in turn, determines the therapeutic treatment of such type of patients, which indicates the need for further research in this area. The study protocol was registered with the international Prospective Register of Systematic Reviews (PROSPERO ID: CRD42022341473).
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.046 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".