Analysis of Factors Influencing Vascular Calcification in Chronic Kidney Disease Patients Based on Oxidative Stress Response and Establishment of a Predictive Model
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Updated: Jun 5, 2026

Assessment of Vascular Function in Patients With Chronic Kidney Disease
Published on: June 16, 2014
1Department of Nephrology, Affiliated Hospital of Hebei University, Baoding, Hebei, 071000, China.
Introduction:
In this study, we compared the oxidative stress responses of patients with chronic kidney disease (CKD) and those with CKD-associated vascular calcification (CKD-VC), and established a risk predictive model for CKD-VC based on this comparison, providing a new approach for the clinical prevention and treatment of CKD-VC.
Methods:
We conducted several cross-sectional surveys involving 173 patients with chronic kidney disease (CKD) who were admitted between September 2023 and October 2024 admitted to Affiliated Hospital of Hebei University. Sixty-two patients had VC (CKD-VC group), while the 111 patients did not have VC (CKD group). The levels of superoxide dismutase (SOD), malondialdehyde (MDA), glutathione peroxidase (GSH-Px), and catalase (CAT) of the two groups were measured. Additionally, factors influencing CKD-VC were analyzed using Logistic regression, and a risk predictive model was established accordingly.
Results:
The levels of SOD and GSH-Px were lower in the CKD-VC group than the CKD group, whereas the CAT and MDA level was higher (P < .05). Logistic regression analysis indicated that SOD, GSH-Px, CAT, MDA, serum calcium, serum phosphorus, age, hypersensitive C-reactive protein (hs-CRP), albumin (ALB), and urine acid (UA) were all independent factors affecting CKD-VC (P < .05). The risk predictive model established based on these factors had high diagnostic sensitivity and specificity for CKD-VC, reaching 100.00% and 87.39%, respectively, with an area under the curve (AUC) of 0.980.
Conclusion:
This study established a risk predictive model for CKD-VC based on oxidative stress response, which has high diagnostic efficacy. This model provides a new approach for the clinical prevention and treatment of CKD-VC in the future, helping to more accurately identify high-risk patients and take corresponding intervention measures to effectively ensure patient safety.
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