S2581 A Systematic Review of the Role of Immunoglobulin G (IgG) in Primary Biliary Cholangitis With or Without Autoimmune Hepatitis and PBC-AIH Overlap Syndrome

Introduction: Immunoglobulin G (IgG) is a key humoral immune component frequently elevated in primary biliary cholangitis (PBC) and autoimmune hepatitis (AIH). Its role extends beyond immune surveillance, influencing fibrosis, disease progression, and therapeutic response. While IgG elevation is a hallmark of AIH, recent evidence underscores its clinical relevance in subsets of PBC patients—particularly those with interface hepatitis, overlap features, or poor biochemical response to ursodeoxycholic acid (UDCA). This systematic review aims to clarify the diagnostic, prognostic, and therapeutic implications of IgG in PBC with or without AIH, and PBC-AIH overlap syndromes. Methods: We conducted a comprehensive literature search in PubMed, Scopus, and Google Scholar using terms related to IgG and PBC with or without AIH, and autoimmune overlap syndromes. Experimental and observational studies evaluating associations between serum or tissue IgG levels and clinical, biochemical, immunological, or histological outcomes were included. Findings were synthesized with emphasis on risk stratification, glycosylation profiles, response to treatment, and integration into predictive models. Results: Eighteen studies were included. Elevated baseline IgG predicted both fibrosis regression with immunosuppression and poorer outcomes with UDCA monotherapy. IgG normalization during treatment correlated with improved prognosis. Tissue IgG/IgM ratios aided phenotypic classification, and altered IgG glycosylation patterns were observed in PBC patients with different autoantibody profiles. IgG-AMA titers also tracked biochemical response. Composite scores incorporating IgG demonstrated strong predictive power. Conclusion: IgG represents a multifaceted biomarker in autoimmune liver disease. Its elevation offers diagnostic clarity, therapeutic monitoring utility, and prognostic insight. Standardization of thresholds and biomarker integration into clinical algorithms are needed to optimize patient care.