The immunogenetics of primary biliary cirrhosis: A comprehensive review
Bibliographic record
Abstract
Primary biliary cirrhosis (PBC), a classic autoimmune liver disease, is characterised by a progressive T cell predominant lymphocytic cholangitis, and a serologic pattern of reactivity in the form of specific anti-mitochondrial antibodies (AMA). CD4+ T cells are particularly implicated by PBC's cytokine signature, the presence of CD4+ T cells specific to mitochondrial auto-antigens, the expression of MHC II on injured biliary epithelial cells, and PBC's coincidence with other similar T cell mediated autoimmune conditions. CD4+ T cells are also central to current animal models of PBC, and their transfer typically also transfers disease. The importance of genetic risk to developing PBC is evidenced by a much higher concordance rate in monozygotic than dizygotic twins, increased AMA rates in asymptomatic relatives, and disproportionate rates of disease in siblings of PBC patients, PBC family members and certain genetically defined populations. Recently, high-throughput genetic studies have greatly expanded our understanding of the gene variants underpinning risk for PBC development, so linking genetics and immunology. Here we summarize genetic association data that has emerged from large scale genome-wide association studies and discuss the evidence for the potential functional significance of the individual genes and pathways identified; we particularly highlight associations in the IL-12-STAT4-Th1 pathway. HLA associations and epigenetic effects are specifically considered and individual variants are linked to clinical phenotypes where data exist. We also consider why there is a gap between calculated genetic risk and clinical data: so-called missing heritability, and how immunogenetic observations are being translated to novel therapies. Ultimately whilst genetic risk factors will only account for a proportion of disease risk, ongoing efforts to refine associations and understand biologic links to disease pathways are hoped to drive more rational therapy for 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".