Gut metabolites and inflammation factors in non-alcoholic fatty liver disease: A systematic review and meta-analysis
Bibliographic record
Abstract
The interaction of gut microbiota, related metabolites and inflammation factors with nonalcoholic fatty liver disease (NAFLD) remains unclearly defined. The aim of this systematic review and meta-analysis was to synthesize previous study findings to better understand this interaction. Relevant research articles published not later than September, 2019 were searched in the following databases: Web of Science, PubMed, Embase, and Cochrane Library. The search strategy and inclusion criteria for this study yielded a total of 47 studies, of which only 11 were eligible for meta-analysis. The narrative analysis of these articles found that there is interplay between the key gut microbiota, related metabolites and inflammation factors, which modulate the development and progression of NAFLD. In addition, the results of meta-analysis showed that probiotic supplementation significantly decreased tumor necrosis factor-α (TNF-α) in NAFLD patients (standardized mean difference (SMD) = -0.52, confidence interval (CI): -0.86 to -0.18, and p = 0.003) and C-reactive protein (CRP) (SMD = -0.62, CI: -0.80 to -0.43, and p < 0.001). However, whether therapies can target TNF-α and CRP in order treat NAFLD still needs further investigation. Therefore, these results suggest that the interaction of the key gut microbiota, related metabolites and inflammation factors with NAFLD may provide a novel therapeutic target for the clinical and pharmacological treatment of NAFLD.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.043 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".