Evaluation of classical and novel autoantibodies for the diagnosis of Primary Biliary Cholangitis-Autoimmune Hepatitis Overlap Syndrome (PBC-AIH OS)
Bibliographic record
Abstract
BACKGROUND AND AIMS: Up to 20% of Primary Biliary Cholangitis (PBC) patients are estimated to have features that overlap with Autoimmune Hepatitis (AIH). Patients with PBC-AIH overlap syndrome (PBC-AIH OS) have been reported to exhibit suboptimal responses to ursodeoxycholic acid therapy, and are more likely to progress to cirrhosis. Anti-double stranded DNA (anti-dsDNA) and anti-p53 have been previously suggested to be potential autoantibodies for identifying patients with PBC-AIH OS. In our well defined PBC patient cohorts, a comprehensive assessment of various classical and novel autoantibodies was evaluated for their utility in identifying PBC-AIH OS patients. METHODS: PBC-AIH OS was classified according to the Paris criteria and PBC as per the European Association for the Study of the Liver guidelines. Biobanked serum samples from 197 patients at the University of Calgary Liver Unit and the University of Alberta were analyzed for classical and novel autoantibodies. Anti-dsDNA was measured by the Crithidia luciliae immunofluorescence (CLIFT) assay (1:20 dilution) and chemiluminescence (CIA: QUANTA Flash®, Inova Diagnostics, San Diego). Anti-p53, anti-Ro52/TRIM21, anti-YB 1, anti-GW182, anti-Ge-1, and anti-Ago 2 were measured by either an addressable laser bead immunoassay (ALBIA) or line immunoassay (LIA). Autoantibodies against MIT3, gp210, sp100, LKM1, SLA, and the novel autoantibodies Hexokinase-1 (HK-1), and Kelch like protein 12 (KLHL-12) were measured using QUANTA Lite® ELISA assays. We applied non-parametric methods to compare the biomarkers frequencies between study groups. We used multivariate adjusted models and AUROC to compare the diagnostic accuracy of the different autoantibodies alone or in combination with serum biochemistry. RESULTS: 16 out of 197 PBC patients (8.1%) were classified as PBC-AIH OS. Compared to PBC patients, PBC-AIH OS patients were similar in age (median: 59 vs. 63, P = 0.21) and female predominance (94% vs. 89%, P = 1.00). Anti-dsDNA-by CLIFT (37.5% in PBC-AIH OS vs 9.9% in PBC alone, P <0.01) was the only autoantibody associated with PBC-AIH OS; a finding consistent with previous reports. Significant elevation in serum ALT (62 IU/L in PBC-AIH OS vs 37 IU/L in PBC alone, P < 0.01), and serum IgG (17.6 g/L in OS vs 12.1 g/L in PBC alone, P <0.01) were observed in patients with PBC-AIH OS receiving medical/immunosuppressive therapy. In a multivariate model, positive anti-dsDNA by CLIFT, ALT and IgG were significant predictors of PBC-AIH OS with an area under the receiver operator curve (AUROC) value of 0.84. CONCLUSIONS: Consistent with previous findings, the presence of anti-dsDNA by CLIFT is associated with PBC-AIH OS. Contrary to previous reports, anti-p53 was not associated with PBC-AIH OS. Our comprehensive evaluation of various classical and novel autoantibody biomarkers including Ro52/TRIM21, anti-p53, anti-KLHL-12 and anti-HK-1 were not significantly associated with PBC-AIH OS. Our findings highlight the ongoing need for the research and development of new autoantibody biomarkers to aid in the diagnosis of PBC-AIH OS.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".