Insights into the Impact of Microbiota in the Treatment of NAFLD/NASH and Its Potential as a Biomarker for Prognosis and Diagnosis
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) is an increasing cause of chronic liver illness associated with obesity and metabolic disorders, such as hypertension, dyslipidemia, or type 2 diabetes mellitus. A more severe type of NAFLD, non-alcoholic steatohepatitis (NASH), is considered an ongoing global health threat and dramatically increases the risks of cirrhosis, liver failure, and hepatocellular carcinoma. Several reports have demonstrated that liver steatosis is associated with the elevation of certain clinical and biochemical markers but with low predictive potential. In addition, current imaging methods are inaccurate and inadequate for quantification of liver steatosis and do not distinguish clearly between the microvesicular and the macrovesicular types. On the other hand, an unhealthy status usually presents an altered gut microbiota, associated with the loss of its functions. Indeed, NAFLD pathophysiology has been linked to lower microbial diversity and a weakened intestinal barrier, exposing the host to bacterial components and stimulating pathways of immune defense and inflammation via toll-like receptor signaling. Moreover, this activation of inflammation in hepatocytes induces progression from simple steatosis to NASH. In the present review, we aim to: (a) summarize studies on both human and animals addressed to determine the impact of alterations in gut microbiota in NASH; (b) evaluate the potential role of such alterations as biomarkers for prognosis and diagnosis of this disorder; and (c) discuss the involvement of microbiota in the current treatment for NAFLD/NASH (i.e., bariatric surgery, physical exercise and lifestyle, diet, probiotics and prebiotics, and fecal microbiota transplantation).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".