# Serum IgG N-glycans act as serum biomarkers for differentiation of cold and heat pattern in rheumatoid arthritis

## TL;DR

Researchers found specific sugar molecules in blood that can help diagnose rheumatoid arthritis and distinguish between cold and heat patterns in Traditional Chinese Medicine.

## Contribution

Identified novel IgG N-glycan biomarkers for RA diagnosis and TCM syndrome differentiation.

## Key findings

- Three acidic N-glycans effectively distinguished RA patients from healthy controls with high accuracy.
- The model showed strong discrimination between RA heat pattern and healthy controls (AUC 0.99).
- A logistic regression model was developed for predicting RA diagnosis and TCM syndrome patterns.

## Abstract

To develop and validate a panel of serum IgG N-glycan biomarkers for both the diagnosis of rheumatoid arthritis (RA) and the differentiation of Traditional Chinese Medicine (TCM) syndromes in RA patients.

We conducted a case–control study involving 105 patients meeting the 2010 American College of Rheumatology/European Alliance against Rheumatism RA classification criteria and 79 healthy controls. RA patients were classified according to TCM principles into cold and heat patterns. Serum IgG was enriched using titanium dioxide-porous graphitic carbon (TiO2-PGC) wafers and analyzed by high-performance liquid chromatography. IgG N-glycans were quantified using multiple reaction monitoring. Potential N-glycan biomarkers for RA diagnosis and TCM syndrome differentiation were identified and validated using multivariate data analysis.

Orthogonal partial least squares discriminant analysis (OPLS-DA) identified 57 N-glycans (variable importance in projection > 1) that differentiated between RA cold pattern, heat pattern, and healthy controls. Through random forest machine learning and Kruskal–Wallis testing, we identified three acidic N-glycans (5_4_0_1-a, 5_4_0_2-a, and 5_4_0_2-b) as potential diagnostic biomarkers. In the training set, receiver operating characteristic analysis demonstrated that this three-N-glycan panel effectively distinguished RA patients from healthy controls (AUC 0.90), with particularly strong discrimination between RA heat pattern and healthy controls (AUC 0.99) and between RA cold pattern and healthy controls (AUC 0.84). The robust predictive performance was further validated in an independent test set. Additionally, we developed a logistic regression model for future clinical application in predicting both RA diagnosis and its heat/cold syndrome patterns.

This glycomics-based approach identified and validated novel N-glycan biomarkers associated with both RA diagnosis and TCM syndrome differentiation. The combination of these N-glycan biomarkers and our diagnostic model offers a promising strategy for integrating modern diagnostic techniques with TCM classification in RA management.

The online version contains supplementary material available at 10.1186/s13020-025-01246-3.