Antibody response to microbiota antigens is associated with alcohol-related liver disease severity and predicts mortality
Bibliographic record
Abstract
BACKGROUND AND AIMS: Alcohol-related liver disease (ALD) is associated with dysbiosis and translocation of gut microbial components, stimulating the innate immunity. The potential role of adaptive immune responses to gut microbiota has been less studied. We hypothesized that microbiota-specific antibody responses could be associated with ALD severity and outcomes. APPROACH AND RESULTS: Two hundred and seven patients with ALD were recruited at a single site and classified as steato-fibrosis, compensated cirrhosis Child-Pugh A, decompensated cirrhosis Child-Pugh B or C without (CP B/C) and with severe alcohol-related hepatitis (sAH). Biophysical and functional characteristics of antibodies specific for 11 classes of microbiota antigens were determined. High-dimensional analyses were used to compare levels and characteristics of antibodies between groups and explore their association with clinical outcomes.ALD severity was associated with increased levels of microbiota-specific antibodies and binding to Fc-gamma-receptors (FcgR), with the strongest relationships observed for levels of Staphylococcus aureus IgA and Candida albicans IgG and for binding of Escherichia coli IgG to FcgRIIb. Additional associations were observed between ALD severity and microbiota-specific antibody effector functions, suggesting regulation of their functional potential. Among patients with CP B/C and sAH, microbiota-specific antibody profiles predicted the occurrence of septic shock and mortality at 90 days, independently of the MELD score. CONCLUSIONS: The antibody response to microbiota is associated with ALD severity and predicts the risk of severe outcomes. These results suggest a role for immune complexes in ALD pathogenesis and that antibody profiling has potential as a biomarker for clinical management of ALD patients.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".