Quantitative Data-Independent Acquisition Mass Spectrometry Proteomics and Weighted Correlation Network Analysis of Plasma Samples for the Discovery of Chronic Kidney Disease-Specific Atherosclerosis Risk Factors.

Abstract

Chronic kidney disease (CKD) accelerates atherosclerosis. The mechanism of CKD-related atherosclerosis is complex, and CKD-specific risk factors may contribute to this process in addition to traditional risk factors such as hypertension, diabetes, and hypercholesterolemia. In the present study, to discover CKD-specific atherosclerosis risk factors, a total of 62 patients with different stages of kidney function were enrolled. All patients underwent coronary angiographies and the severity of coronary atherosclerosis was defined by the SYNTAX score. Patients were divided into different groups according to their kidney function levels and coronary atherosclerosis severity. Data-independent acquisition mass spectrometry was used to identify differentially expressed proteins (DEPs) in the plasma samples, and weighted correlation network analysis (WGCNA) was employed to identify significant protein modules and hub proteins related to CKD-specific atherosclerosis. The results showed that 10 DEPs associated with atherosclerosis were found in the comparative groups with modest and severe CKD. Through WGCNA, 1768 proteins were identified and 8 protein modules were established. Enrichment analyses of protein modules revealed functional clusters mainly associated with inflammation and the complement and coagulation cascade as atherosclerosis developed under CKD conditions. The results may help to better understand the mechanisms of CKD-related atherosclerosis and guide future research on developing treatments for CKD-related atherosclerosis.