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In immunoglobulin G (IgG), N-glycosylation plays a pivotal role in structure and function. It is often altered in different diseases, suggesting that it could be a promising health biomarker. Studies indicate that IgG glycosylation not only associates with various diseases but also has predictive capabilities. Additionally, changes in IgG glycosylation correlate with physiological and biochemical traits known to reflect overall health state. This study aimed to investigate the power of IgG glycans to predict physiological and biochemical parameters. We developed two models using IgG N-glycan data as an input: a regression model using elastic net and a machine learning model using deep learning. Data were obtained from the Korčula and Vis cohorts. The Korčula cohort data were used to train both models, while the Vis cohort was used exclusively for validation. Our results demonstrated that IgG glycome composition effectively predicts several biochemical and physiological parameters, especially those related to lipid and glucose metabolism and cardiovascular events. Both models performed similarly on the Korčula cohort; however, the deep learning model showed a higher potential for generalization when validated on the Vis cohort. This study reinforces the idea that IgG glycosylation reflects individuals' health state and brings us one step closer to implementing glycan-based diagnostics in personalized medicine. Additionally, it shows that the predictive power of IgG glycans can be used for imputing missing covariate data in deep learning frameworks. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular disease (CVD) causes premature mortality in rheumatoid arthritis (RA). Levels of soluble (s)RAGE change with aging, hypertension and hypercholesterolemia. We assessed whether sRAGE was associated with increased risk of CVD in RA patients.Methods: Serum sRAGE was measured in 184 female RA patients and analyzed with respect to CVD risk estimated by the Framingham algorithm (eCVR), metabolic profile and inflammation. Levels of sRAGE in 13 patients with known cardio-metabolic morbidity defined the cut-off for low sRAGE. Prospective 5-year follow-up of new CV and metabolic events was completed.Results: Low sRAGE was significantly associated with previous history and with new imminent cardiometabolic events in the prospective follow-up of RA patients. In both cases, low sRAGE reflected higher estimation of CVR in those patients. Low sRAGE was attributed to adverse metabolic parameters including high fasting plasma glucose and body fat content rather than inflammation. The association of sRAGE and poor metabolic profile was prominent in patients younger than 50 years.Conclusions: This study points at low sRAGE as a marker of metabolic failure developed during chronic inflammation. It highlights the importance for monitoring metabolic health in female RA patients for timely prevention of CVD.Trial registration:ClinicalTrials.gov with ID NCT03449589. Registered 28, February 2018.