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During the congress, E-Posters will be accessible to all participants on the congress website 24/7, as well as in the E-poster stations in the congress center.
Preparing your E-Poster
Please review the E-Poster format requirements carefully when preparing your E-Poster. Should your E-Poster not meet the mentioned requirements, it may not be displayed as described above.
E-Poster Submission Deadline
Please prepare and upload your E-Poster no later than March 14, 2026 11.59PM CET. After this date, you will no longer be able to prepare and upload your E-poster and it will not be displayed and accessible on the congress website.
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Cardiovascular disease (CVD) is the most common complication inperitoneal dialysis(PD) patients. Abnormal glycation is related to the occurrence and development of CVD. This study aims to identify glycated peptides that could serve as markers for CVD in PD patients.
In the discovery phase, 48 PD patients who had experienced CVD in the past six months were enrolled, along with 48 matched PD patients without CVD. The serum samples were processed using selective enrichment of glycated peptides, label-free quantification and high-performance liquid chromatography-mass spectrometry.Sixteen machine learning models were applied to analyze the serum glycated proteomic data. Candidate biomarkers identified by the optimal model were then synthesized as heavy isotope-labeled standards, and a targeted absolute quantification method for these candidates was established. Finally, in the validation phase, an additional 16 pairs of PD patients were enrolled. Targeted quantification of the candidate glycated peptides in patient serum samples was performed using the synthesized standards to validate the diagnostic performance of the glycated peptide biomarkers for CVD.
1.A total of 2598±165 glycated peptides were identified in the CVD group, while 2498±141 glycated peptides were identified in the non-CVD group.
2.Compared with the non-CVD group, 237 glycated peptides were significantly up-regulated and 20 glycated peptides were significantly down-regulated in the CVD group.
3.Among the 16 machine learning models, the glmnet model demonstrated the best performance in distinguishing patients with and without CVD, achieving the highest area under the curve (AUC) value (0.92), accuracy (0.87), and F1-score (0.88).
4.Based on the results of the glmnet model and the selection criteria for target peptides, two glycated peptide biomarkers——K(g)QHLFVK and ELDRDTVFALVNYIFFK(g)GK were ultimately identified. These two glycated peptides belong to apolipoprotein B-100 and alpha-1-antitrypsin, respectively.
5.Targeted absolute quantification of the candidate glycated peptide biomarkers showed that the levels of both glycated peptides were significantly elevated in PD patients with CVD. The combined diagnostic performance of the two biomarkers yielded an area under the receiver operating characteristic curve as high as 0.977.
Significant differences exist in the serum glycated proteomics between PD patients with and without CVD, with most differentially expressed glycated peptides being up-regulated in patients with CVD. The serum levels of the glycated peptide biomarkers K(g)QHLFVK and ELDRDTVFALVNYIFFK(g)GK are significantly elevated in PD patients with CVD, and they exhibit excellent combined diagnostic efficacy for CVD. These two glycated peptides may serve as potential serum biomarkers for diagnosing CVD in PD patients.
