Quantification of polyreactive immunoglobulin G facilitates the diagnosis of autoimmune hepatitis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Detection of autoantibodies is a mainstay of diagnosing autoimmune hepatitis (AIH). However, conventional autoantibodies for the workup of AIH lack either sensitivity or specificity, leading to substantial diagnostic uncertainty. We aimed to identify more accurate serological markers of AIH with a protein macroarray. APPROACH AND RESULTS: During the search for more-precise autoantibodies to distinguish AIH from non-AIH liver diseases (non-AIH-LD), IgG antibodies with binding capacities to many human and foreign proteins were identified with a protein macroarray and confirmed with solid-phase ELISAs in AIH patients. Subsequently, polyreactive IgG (pIgG) was exemplarily quantified by reactivity against human huntingtin-interacting protein 1-related protein in bovine serum albumin blocked ELISA (HIP1R/BSA). The diagnostic fidelity of HIP1R/BSA binding pIgG to diagnose AIH was assessed in a retrospective training, a retrospective multicenter validation, and a prospective validation cohort in cryoconserved samples from 1,568 adults from 10 centers from eight countries. Reactivity against HIP1R/BSA had a 25% and 14% higher specificity to diagnose AIH than conventional antinuclear and antismooth muscle antibodies, a significantly higher sensitivity than liver kidney microsomal antibodies and antisoluble liver antigen/liver pancreas antigen, and a 12%-20% higher accuracy than conventional autoantibodies. Importantly, HIP1R/BSA reactivity was present in up to 88% of patients with seronegative AIH and in up to 71% of AIH patients with normal IgG levels. Under therapy, pIgG returns to background levels of non-AIH-LD. CONCLUSIONS: pIgG could be used as a promising marker to improve the diagnostic workup of liver diseases with a higher specificity for AIH compared to conventional autoantibodies and a utility in autoantibody-negative AIH. Likewise, pIgG could be a major source of assay interference in untreated AIH.
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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".