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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.
Please follow the instructions below to input your abstract title.
Abstract titles should be brief and reflect the content of the abstract.
Chronic kidney disease (CKD) is a global public health issue, with vascular calcification (VC) being an independent risk factor for poor prognosis in CKD patients. Therapeutic options for CKD-associated VC (CKD-VC) are limited, highlighting the need to explore therapeutic targets and underlying mechanisms. Our study investigates whether Epiphycan (EPYC), a protein in the extracellular matrix, contributes to VC and aims to establish a risk prediction model for CKD-VC, as well as to construct a nomogram.
Transcriptomic analysis was performed on vascular tissues from hemodialysis patients after arteriovenous fistula surgery, followed by immunohistochemistry and immunofluorescence staining to examine the relationship between EPYC and VC. A cross-sectional study was conducted in CKD patients. Pearson/Spearman correlation analysis assessed the association between EPYC and VC volume. Receiver operating characteristic (ROC) curve analysis evaluated the diagnostic performance of EPYC. Three prediction models were constructed: (1) predictive factors only, (2) EPYC only, and (3) EPYC combined with predictive factors. Model performance was assessed using the area under the curve (AUC) of the ROC curve. The variance inflation factor (VIF) was calculated to confirm the absence of significant multicollinearity among the independent variables in the optimal model. The optimal model was evaluated using the area under the curve (AUC) of the ROC curve, decision curve analysis (DCA), and so on. A clinical nomogram was constructed based on the optimal model.
Transcriptomic analysis showed significant upregulation of EPYC in CKD-VC patients (Log2 FC=5.74, p<0.01). Immunohistochemistry and immunofluorescence staining further confirmed high EPYC enrichment in CKD-VC vascular tissues. Clinical analysis showed: (1) significantly elevated serum EPYC levels in CKD-VC patients (p=0.014), positively correlated with VC volume (r=0.82, p<0.01); (2) ROC curve analysis showed an AUC of 0.719 for serum EPYC in diagnosing CKD-VC, with 73.8% sensitivity and 73.6% specificity. We found the model combining EPYC with predictive factors performed optimally, with an AUC of 0.904, 92.6% sensitivity, and 74.2% specificity. The calibration curves showed good fit (Hosmer-Lemeshow test: p=0.943 for training and p=0.238 for validation), with decision curve analysis demonstrating high net benefit within a 20-80% threshold range, indicating strong clinical utility. A nomogram was constructed based on the optimal model.
Our study confirms that EPYC is highly expressed in CKD-VC patients and closely related to VC volume, providing important insights for exploring therapeutic targets for CKD-VC. The nomogram based on the prediction model provides a visual and practical tool for auxiliary diagnosis of CKD-VC, with significant clinical application value.
