The Prevalence of Autoantibodies and Autoimmune Hepatitis in Patients with Nonalcoholic Fatty Liver Disease
Bibliographic record
Abstract
OBJECTIVES: Autoantibodies may exist in serum of patients with nonalcoholic fatty liver disease (NAFLD) although their prevalence and clinical significance is uncertain. We aimed at examining the prevalence of autoantibodies and autoimmune hepatitis among patients with NAFLD. METHODS: Prevalence of antinuclear antibodies (ANA), anti-smooth muscle antibodies (anti-SMA), and autoimmune hepatitis (AIH) based on the International AIH Group were determined in 225 patients with liver biopsy-proven NAFLD. RESULTS: Fifty-one patients [23% (95% CI 17.3-28.2%)] had autoantibodies in serum which is significantly higher than the prevalence in the general population. ANA were present in 46 patients (20%), SMA in 6 (3%), and both antibodies in one patient. Positive autoantibodies were associated with higher fibrosis stage (p= 0.03), higher inflammatory grade (p= 0.02), and higher levels of gammaglobulin (p= 0.01). Prior to liver biopsy, 45 of the 51 (88%) patients with positive autoantibodies fulfilled diagnostic criteria for 'probable' or 'definite' AIH. After liver biopsy, however, only four patients fulfilled diagnostic criteria. CONCLUSIONS: Routinely measured autoantibodies are present in one quarter of patients with NAFLD and are associated with more severe histological damage. Liver biopsy is required to rule out AIH in most NAFLD patients with positive autoantibodies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".