Exploring autoantibodies as predictors of severe fibrosis or cirrhosis in metabolic dysfunction associated with steatotic liver disease
Bibliographic record
Abstract
Background: Metabolic dysfunction associated steatotic liver disease (MASLD) and metabolic dysfunction associated steatohepatitis (MASH) are rapidly growing public health concerns. Identifying predictive markers for advanced liver disease in MASLD patients is crucial for early intervention. This study investigates the association between autoantibody positivity and risk for severe fibrosis or cirrhosis across various subgroups. Methods: We conducted a retrospective study of adult patients diagnosed with MASLD between 1994 and 2019. Autoantibody status (anti-nuclear and anti-smooth muscle antibodies) was assessed using laboratory studies. Hepatic fibrosis or cirrhosis was determined histologically or through accepted non-invasive measures. Logistic regression analyses were employed to evaluate the association between autoantibody positivity and severe fibrosis or cirrhosis. Patients with comorbid viral and alcohol liver disease were assessed separately. Results: Among 2,749 MASLD patients, 1,425 (51.8%) were male and 1,324 (48.2%) were female, with a mean age of 58.7 years. A total of 541 (19.7%) patients tested positive for autoantibodies. Autoantibody positivity was associated with a higher risk of severe fibrosis or cirrhosis in MASLD patients (odds ratio 1.28, 95% CI [1.0-1.6]). This association persisted across various subgroups, including those with concurrent hepatitis B and C virus infections. In contrast, in alcohol liver disease, autoantibody-positive patients exhibited a lower risk. Conclusion: Autoantibody positivity emerges as a potential predictive marker for advanced liver disease in MASLD patients, facilitating risk stratification and tailored interventions. This study highlights the clinical relevance of autoantibodies in MASLD and underscores the need for prospective validation and mechanistic investigations to refine risk assessment and management strategies.
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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 | 0.001 |
| 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".