Identification of novel serum metabolic signatures to predict chronic kidney disease among Chinese elders using UPLC-Orbitrap-MS

Background: Chronic kidney disease (CKD) is a major public health concern. However, validated and broadly applicable biomarkers for early CKD diagnosis are currently not available. We aimed to identify serum metabolic signatures at an early stage of CKD to provide a reference for future investigations into the early diagnostic biomarkers. Methods: Serum metabolites were extracted from 65 renal dysfunction (RD) patients and 121 healthy controls (discovery cohort: 12 RD patients and 55 health participants; validation cohort: 53 RD patients and 66 health participants). Metabolite extracts were analyzed by ultraperformance liquid chromatography coupled with quadrupole -electrostatic field orbital trap mass spectrometry (UPLC-QE-Orbitrap MS) for untargeted metabolomics. Orthogonal partial least squares -discriminant analysis (OPLS-DA) was performed to detect different compounds between groups. Receiver operating characteristic (ROC) curve analysis was carried out to determine the diagnostic value of the validated differential metabolites between groups. We referred to the Kyoto Encyclopedia of Gene and Genomes (KEGG) to elucidate the metabolic pathways of the validated differential metabolites. Results: A total of 22 and 23 metabolites had significantly different abundances in the discovery and validation cohort, respectively. Six of them (creatinine, L-proline, citrulline, butyrylcarnitine, 1-methylhistidine, and valerylcarnitine) in the RD group was more abundant than that of the health group in both cohorts. The combination of the six validated differential metabolites were able to accurately detect RD (AUC 0.86). Three of the six metabolites are involved in the metabolism of arginine and proline. Conclusions: The present study highlights that creatinine, L-proline, citrulline, butyrylcarnitine, 1-methylhistidine, and valerylcarnitine are metabolite indicators with potential predictive value for CKD. (c) 2024 The Authors. Published by SERDI Publisher. Elsevier Masson SAS. This is an open access article under the CC BY -NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).