Glycosylation Gap in Patients with Diabetes with Chronic Kidney Disease and Healthy Participants: A Comparative Study

The Correlation Between Glycation Gap and Renal Complications in Patients with Type 2 Diabetes Mellitus

Purpose

The aim of this study was to investigate the correlations between the glycation gap (GG) and renal complications such as urinary albumin-creatinine ratio (UACR) and estimated glomerular filtration rate (eGFR) in type 2 diabetes mellitus patients.

Materials and Methods

A cross-sectional study was conducted on 104 individuals (52 males and 52 females), aged 36-93 years old. Fasting blood glucose (FBG), HbA1c, and serum fructosamine were measured simultaneously. GG was calculated as the difference between the measured and fructosamine-based predicted HbA1c levels (FHbA1c).

Results

There was a moderately positive correlation between HbA1c and fructosamine concentration (r = 0.488; p < 0.001). GG was positively correlated with UACR (r = 0.3275; p = 0.0007), negatively correlated with eGFR (r = -0.3400; p = 0.0004). HbA1c was positively correlated with UACR (r = 0.2437; p = 0.0127) but not correlated with eGFR (r = -0.444; p = 0.6542). Fructosamine has a positive correlation with eGFR (r = 0.2426; p = 0.0131) but not with UACR (r = -0.1021; p = 0.3025).

Conclusion

GG was positively correlated with UACR and inversely correlated with eGFR in type 2 Diabetes mellitus patients. This suggests that GG is a valuable index for predicting kidney complications due to diabetes.

Positive-unlabelled learning of glycosylation sites in the human proteome

BACKGROUND

As an important type of post-translational modification (PTM), protein glycosylation plays a crucial role in protein stability and protein function. The abundance and ubiquity of protein glycosylation across three domains of life involving Eukarya, Bacteria and Archaea demonstrate its roles in regulating a variety of signalling and metabolic pathways. Mutations on and in the proximity of glycosylation sites are highly associated with human diseases. Accordingly, accurate prediction of glycosylation can complement laboratory-based methods and greatly benefit experimental efforts for characterization and understanding of functional roles of glycosylation. For this purpose, a number of supervised-learning approaches have been proposed to identify glycosylation sites, demonstrating a promising predictive performance. To train a conventional supervised-learning model, both reliable positive and negative samples are required. However, in practice, a large portion of negative samples (i.e. non-glycosylation sites) are mislabelled due to the limitation of current experimental technologies. Moreover, supervised algorithms often fail to take advantage of large volumes of unlabelled data, which can aid in model learning in conjunction with positive samples (i.e. experimentally verified glycosylation sites).

RESULTS

In this study, we propose a positive unlabelled (PU) learning-based method, PA2DE (V2.0), based on the AlphaMax algorithm for protein glycosylation site prediction. The predictive performance of this proposed method was evaluated by a range of glycosylation data collected over a ten-year period based on an interval of three years. Experiments using both benchmarking and independent tests show that our method outperformed the representative supervised-learning algorithms (including support vector machines and random forests) and one-class learners, as well as currently available prediction methods in terms of F1 score, accuracy and AUC measures. In addition, we developed an online web server as an implementation of the optimized model (available at http://glycomine.erc.monash.edu/Lab/GlycoMine_PU/ ) to facilitate community-wide efforts for accurate prediction of protein glycosylation sites.

CONCLUSION

The proposed PU learning approach achieved a competitive predictive performance compared with currently available methods. This PU learning schema may also be effectively employed and applied to address the prediction problems of other important types of protein PTM site and functional sites.