Chen Xiaohong, Balmer Lois, Lin Kun, Cao Weijie, Huang Ziyu, Chen Xiang, Song Manshu, Chen Yongsong
Department of Endocrinology and Metabolism, The First Affiliated Hospital of Shantou University Medical College, Shantou, 515041 Guangdong China.
School of Medical and Health Sciences, Edith Cowan University, Joondalup, Perth, 6027 Australia.
EPMA J. 2025 Apr 15;16(2):419-435. doi: 10.1007/s13167-025-00410-x. eCollection 2025 Jun.
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).
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.
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 ( = 0.599, < 0.05), mild ( = 0.461, < 0.05), and severe ( = 0.666, < 0.01) IR groups. IgG N-glycosylation significantly influenced IR ( = 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.
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.
The online version contains supplementary material available at 10.1007/s13167-025-00410-x.
用于捕捉胰岛素抵抗(IR)中免疫代谢过程的可靠生物标志物仍然有限。IgG N-糖基化可调节免疫反应并反映代谢紊乱,但其在IR中的作用仍不清楚。本研究在预测、预防和个性化医学(PPPM/3PM)框架内探讨了其在早期检测、风险分层和靶向预防方面的潜力。
根据胰岛素抵抗的稳态模型评估(HOMA-IR),将313名参与者分为三组:胰岛素敏感组(HOMA-IR<2.69且无糖尿病,n = 75)、轻度IR组(HOMA-IR≥2.69且无糖尿病,n = 155)和重度IR组(HOMA-IR≥2.69且患有2型糖尿病,n = 83)。进行典型相关分析以探索IgG N-糖基化与以肿瘤坏死因子-α、白细胞介素-6、C反应蛋白和脂联素为指标的IR相关炎症之间的整体关系。进行中介分析以评估IgG N-聚糖对IR的影响。采用有序逻辑回归评估IgG N-聚糖与IR严重程度之间的关联,并使用受试者工作特征曲线评估判别能力。
随着IR严重程度增加,观察到促炎IgG N-糖型,其特征为唾液酸化和半乳糖基化减少,以及平分型N-乙酰葡糖胺增加。IgG N-糖基化与胰岛素敏感(r = 0.599,P<0.05)、轻度(r = 0.461,P<0.05)和重度(r = 0.666,P<0.01)IR组中的炎症标志物显著相关。IgG N-糖基化通过调节炎症部分显著影响IR(β = 0.406)。糖型FA2[6]G1(OR:0.86,95%CI:0.78 - 0.96)和A2G2S2(OR:0.88,95%CI:0.82 - 0.94)增加与较低的IR风险相关,胰岛素敏感、轻度和重度IR组的曲线下面积(AUC)分别为0.752、0.683和0.764。
IgG N-糖基化通过调节炎症反应促进IR。糖型FA2[6]G1和A2G2S2作为保护性生物标志物出现,在PPPM框架内通过一级预防策略为预测和预防IR提供了潜力。
在线版本包含可在10.1007/s13167-025-00410-x获取的补充材料。