Author Identifier
Lois Balmer: https://orcid.org/0000-0001-5618-0555
Manshu Song: https://orcid.org/0000-0003-1433-7192
Document Type
Journal Article
Publication Title
EPMA Journal
Publisher
Springer
School
School of Medical and Health Sciences
RAS ID
78843
Funders
Special Fund Project for Science and Technology of Guangdong Province (200113155895097) / China-Australian Collaborative Grant (NSFC 81561128020-NHMRC) / National Natural Science Foundation of China (NSFC 81773527) / Special Fund Project for Science and Technology Innovating Strategy Program for Municipal and County Science and Technology Innovation (STKJ2023007) / Western Australian Future Health Research and Innovation Fund (WANMA/Ideas2023-24/10)
Grant Number
NHMRC Number : APP1112767
Abstract
Background: Reliable biomarkers capturing immunometabolic processes in insulin resistance (IR) remain limited. IgG N-glycosylation modulates immune responses and reflects metabolic disorders, yet its role in IR remains unclear. This study investigated its potential for early detection, risk stratification, and targeted prevention within the framework of predictive, preventive, and personalised medicine (PPPM/3PM). Methods: A total of 313 participants were categorized into three groups based on the homeostatic model assessment for insulin resistance (HOMA-IR): insulin-sensitive (HOMA-IR < 2.69 without diabetes, n = 75), mild IR (HOMA-IR ≥ 2.69 without diabetes, n = 155), and severe IR (HOMA-IR ≥ 2.69 with type 2 diabetes, n = 83). Canonical correlation analysis was conducted to explore the overall relationship between IgG N-glycosylation and IR-related inflammation, indicated by tumour necrosis factor-α, interleukin- 6, C-reactive protein, and adiponectin. Mediation analysis was performed to evaluate the effect of IgG N-glycans on IR. Ordinal logistic regression was used to assess the association between IgG N-glycans and IR severity, with discriminative power evaluated using receiver operating characteristic curves. Results: Pro-inflammatory IgG N-glycoforms, characterized by reduced sialylation and galactosylation, along with increased bisecting N-acetylglucosamine, were observed as IR severity increased. IgG N-glycosylation significantly correlated with inflammatory markers in the insulin-sensitive (r = 0.599, p < 0.05), mild (r = 0.461, p < 0.05), and severe (r = 0.666, p < 0.01) IR groups. IgG N-glycosylation significantly influenced IR (β = 0.406) partially via modulation of inflammation. Increased glycoforms FA2[6]G1 (OR: 0.86, 95% CI: 0.78–0.96) and A2G2S2 (OR: 0.88, 95% CI: 0.82–0.94) were associated with a lower IR risk, with respective area under the curves (AUCs) of 0.752, 0.683, and 0.764 for the insulin sensitive, mild, and severe IR groups. Conclusions: IgG N-glycosylation contributes to IR by modulating inflammatory responses. Glycoforms FA2[6]G1 and A2G2S2 emerge as protective biomarkers, offering potential for predicting and preventing IR through primary prevention strategies within the PPPM framework.
DOI
10.1007/s13167-025-00410-x
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Chen, X., Balmer, L., Lin, K., Cao, W., Huang, Z., Chen, X., ... & Chen, Y. (2025). IgG N-glycosylation contributes to different severities of insulin resistance: Implications for 3P medical approaches. EPMA Journal. Advance online publication. https://doi.org/10.1007/s13167-025-00410-x