Gut metagenome‐derived signature predicts hepatic decompensation and mortality in NAFLD‐related cirrhosis
Bibliographic record
Abstract
BACKGROUND: There are limited data on the diagnostic accuracy of gut microbial signatures for predicting hepatic decompensation in patients with cirrhosis. AIMS: To determine whether a stool metagenome-derived signature accurately detects hepatic decompensation and mortality risk in cirrhosis secondary to non-alcoholic fatty liver disease (NAFLD) METHODS: Shotgun metagenomic sequencing was performed on faecal samples collected at study entry from a prospective cohort of adults with NAFLD-related cirrhosis. A Random Forest machine learning algorithm was utilised to identify a metagenomic signature of decompensated cirrhosis (defined by ascites, hepatic encephalopathy or variceal haemorrhage) and subsequently validated in an external cohort. A Cox proportional hazards regression model was used to examine predictors of all-cause mortality. RESULTS: In all, 25 adults with NAFLD-related cirrhosis (training cohort) were included. Among the 16 participants with decompensated cirrhosis, 33% had ascites, 56% had hepatic encephalopathy and 22% had experienced a variceal haemorrhage (not mutually exclusive). We identified a stool metagenomic signature comprising 13 discriminatory species that reliably distinguished decompensated NAFLD-related cirrhosis (diagnostic accuracy, 0.97, 95% confidence interval [CI] 0.96-0.99). Diagnostic accuracy of the 13-species signature remained high after adjustment for lactulose (area under the curve [AUC] 0.99) and rifaximin use (AUC 0.93). The discriminative ability of 13-species metagenomic signature was robust in an independent test cohort (AUC 0.95, 95% CI 0.81-1.00). The 13-species metagenomic signature (hazard ratio [HR] 1.54, 95% CI 1.10-2.15, p = 0.01) was a stronger predictor of mortality than the Model for End-Stage Liver Disease score (HR 1.25, 95% CI 1.03-1.53, p = 0.03). CONCLUSIONS: This study provides evidence for a gut metagenome-derived signature with high diagnostic accuracy for hepatic decompensation that predicts risk of mortality in NAFLD-related cirrhosis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".