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Chronic kidney disease (CKD) has emerged as a major public health problem worldwide. It frequently progresses to end-stage renal disease, which is related to very high cost and mortality. Novel biomarkers can provide insight into the novel mechanism, facilitate early detection, and monitor progression of CKD and its response to therapeutic interventions. To identify potential biomarkers, we applied an UPLC-HDMS together with univariate and multivariate statistical analyses using plasma samples from patients with CKD of diverse etiologies (100 sera in discovery set and 120 sera in validation set) and two different rat models of CKD. Using comprehensive screening and validation workflow, we identified a panel of seven metabolites that were shared by all patients and animals regardless of the underlying cause of CKD. These included ricinoleic acid, stearic acid, cytosine, LPA(16:0), LPA(18:2), 3-methylhistidine, and argininic acid. The combination of these seven biomarkers enabled the discrimination of patients with CKD from healthy subjects with a sensitivity of 83.3% and a specificity of 96.7%. In addition, these biomarkers accurately reflected improvements in renal function in response to the therapeutic interventions. Our results indicated that the identified biomarkers may improve the diagnosis of CKD and provide a novel tool for monitoring of the progression of disease and response to treatment in CKD patients.

Metabolomic Signatures of Chronic Kidney Disease of Diverse Etiologies in the Rats and Humans