Author Identifier (ORCID)
Abstract
Background: IgG N-glycosylation regulates immune effector functions and inflammation, but disease-specific patterns in primary Sjögren’s disease (SjD), rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are insufficiently defined, particularly in SjD.
Objectives: To identify serum IgG N-glycan biomarkers that differentiate SjD, RA, and SLE and reflect disease activity in SjD.
Design: A clinical case-control study.
Methods: Serum IgG N-glycans from 88 SjD patients, 102 RA patients, 86 SLE patients and 91 healthy controls were profiled using ultra-performance liquid chromatography. Discriminative glycans were selected through machine learning-based feature selection and evaluated for their diagnostic performance using cross-validated logistic regression and receiver operating characteristic (ROC) analysis.
Results: All three diseases exhibited decreased galactosylation and increased bisecting N-acetylglucosamine (GlcNAc). SjD showed the lowest sialylation (19.92%) and the highest bisecting GlcNAc levels (19.36%). Shared features included elevated GP1 (FA1) and reduced GP9 (FA2[3]G1). Compared with RA, SjD was characterized by elevated GP3 (A2B, adjusted odds ratio [AOR] 4.27), GP8 (FA2[6]G1, AOR 3.53) and reduced GP23 (FA2G2S2, AOR 0.07). Elevated GP11 (FA2[3]BG1, AOR 11.99) distinguished SjD from SLE. These glycans outperformed conventional autoantibodies in ROC analysis. In SjD, higher disease activity was associated with reduced galactosylation, with GP5 (M5) positively (AOR 1.71) and GP14 (FA2G2) inversely (AOR 0.43) associated with activity. These glycans showed stronger correlations with disease activity than erythrocyte sedimentation rate or serum IgG levels (GP5: r = 0.462; GP14: r = −0.432), and the combined glycan model achieved an AUC of 0.758.
Conclusions: Distinct IgG N-glycan signatures differentiate SjD, RA, and SLE despite their shared clinical and immunologic characteristics and are associated with disease activity in SjD. These findings provide insights into IgG N-glycosylation in these autoimmune diseases and may inform future research into their potential utility as non-invasive biomarkers, particularly in SjD.
Keywords
biomarkers, IgG N-glycosylation, immunoglobulin G, machine learning, primary Sjögren’s disease, rheumatoid arthritis, systemic lupus erythematosus
Document Type
Journal Article
Date of Publication
1-1-2026
E-ISSN
17597218
ISSN
1759720X
Volume
18
Publication Title
Therapeutic Advances in Musculoskeletal Disease
Publisher
Sage
School
Nutrition and Health Innovation Research Institute / School of Medical and Health Sciences
Funding Information
This work was supported by grants from the Guangdong Basic and Applied Basic Research Foundation (No. 2024A1515012910), the Special Funds for Science and Technology of Guangdong Province (No. 210712096871576), the Science and Technology Planning Project of Shantou City (No. 2024-124), Shantou University Medical College Scientific Research Initiation Grant (No. 009-510858071) and the China-Australian Collaborative Grant (NSFC 81561128020 - NHMRC APP1112767). MS is supported by the Western Australian Future Health Research and Innovation Fund (Grant ID WANMA/Ideas2023-24/10).
Funding received from the National Health and Medical Research Council (NHMRC)
APP1112767
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Chen, Z., Xu, X., Song, M., Shen, F., Hou, H., Wang, W., & Lin, L. (2026). Serum IgG N-glycosylation profiles distinguish primary Sjögren’s disease, rheumatoid arthritis, and systemic lupus erythematosus and are associated with disease activity in Sjögren’s disease. Therapeutic Advances in Musculoskeletal Disease, 18. https://doi.org/10.1177/1759720X261488940