Faecal proteomics links neutrophil degranulation with mortality in patients with alcohol-associated hepatitis
Bibliographic record
Abstract
OBJECTIVE: Patients with alcohol-associated hepatitis (AH) have a high mortality. Alcohol exacerbates liver damage by inducing gut dysbiosis, bacterial translocation and inflammation, which is characterised by increased numbers of circulating and hepatic neutrophils. DESIGN: In this study, we performed tandem mass tag (TMT) proteomics to analyse proteins in the faeces of controls (n=19), patients with alcohol-use disorder (AUD; n=20) and AH (n=80) from a multicentre cohort (InTeam). To identify protein groups that are disproportionately represented, we conducted over-representation analysis using Reactome pathway analysis and Gene Ontology to determine the proteins with the most significant impact. A faecal biomarker and its prognostic effect were validated by ELISA in faecal samples from patients with AH (n=70), who were recruited in a second and independent multicentre cohort (AlcHepNet). RESULT: Faecal proteomic profiles were overall significantly different between controls, patients with AUD and AH (principal component analysis p=0.001, dissimilarity index calculated by the method of Bray-Curtis). Proteins that showed notable differences across all three groups and displayed a progressive increase in accordance with the severity of alcohol-associated liver disease were predominantly those located in neutrophil granules. Over-representation and Reactome analyses confirmed that differentially regulated proteins are part of granules in neutrophils and the neutrophil degranulation pathway. Myeloperoxidase (MPO), the marker protein of neutrophil granules, correlates with disease severity and predicts 60-day mortality. Using an independent validation cohort, we confirmed that faecal MPO levels can predict short-term survival at 60 days. CONCLUSIONS: We found an increased abundance of faecal proteins linked to neutrophil degranulation in patients with AH, which is predictive of short-term survival and could serve as a prognostic non-invasive marker.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".