2194 Targeted metabolomics analysis identifies a specific metabolomic profile in patients with early chronic kidney disease

• Published in: Nephrology, dialysis, transplantation Vol. 39; no. Supplement_1
• Main Authors: Glavan, Mihaela, Socaciu, Carmen, Socaciu, Andreea, Gadalean, Florica, Vlad, Adrian, Muntean, Danina, Bob, Flaviu, Suteanu-Simulescu, Anca, Balint, Lavinia, Ienciu, Silvia, Iancu, Lavinia, Petrica, Ligia
• Format: Journal Article
• Language: English
• Published: 23.05.2024
• ISSN: 0931-0509; 1460-2385
• DOI: 10.1093/ndt/gfae069.464

Abstract Background and Aims The complex interconnections between various metabolites and the kidney play a major role in the pathogenesis of chronic kidney disease (CKD). Hence, by the assessment of the relationship between metabolites and the kidney, more accurate biomarkers could be discovered to prevent and detect CKD at an early stage. The primary goal of this study was to find new serum and urine biomarkers for the diagnosis of early CKD by conducting a metabolomic profiling through targeted metabolomics of both blood and urine in patients with CKD. Method This cross-sectional study comprised 80 patients with CKD who were categorized into six subgroups (G1-G6) based on their eGFR according to the KDIGO Guidelines. Additionally, 20 healthy individuals were included as healthy control participants (group C). Ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry was used to perform serum and urine metabolomic profiling. The 6 metabolites evaluated by this study were selected based on the results of a previous untargeted analysis research. Both blood and urine samples underwent multivariate analysis, which was subsequently followed by univariate analysis and quantitative analysis. Initially, the PLSDA score plot and VIP score were utilized. The application of the cross-validation procedure to the initial set of 6 molecules provided outstanding results, including high accuracy, high R² values, and a significant Q2 value. Hence, the model might be deemed as predictive. Random Forest analysis was utilized to conduct biomarker analysis and prediction. The specificity and sensitivity of the compounds identified as possible biomarkers were assessed using the Receiver Operating Characteristic (ROC) curve and the area under the curve (AUC). Results There were strong positive correlations between eGFR and serum levels of Arginine (p < 0.05) and L-Acetylcarnitine (p < 0.05). Of note, the amounts of L-Methionine, L-Phenylalanine, Kynurenic acid, and Indoxyl sulfate in serum showed a progressive increase from group C to subgroups G1-G5. Furthermore, L-Methionine, L-Phenylalanine, Kynurenic acid, and Indoxyl sulfate exhibited a negative correlation with eGFR (p < 0.05). In addition, there was a positive correlation between the levels of urine Arginine, L-Methionine, and L-Phenylalanine and eGFR (p < 0.05). Urine levels of Kynurenic acid, Indoxyl sulfate, and L-Acetylcarnitine were higher in groups G4 and G5, as compared to groups G1-G3b and C, respectively. The urinary concentrations of Indoxyl sulfate, Kynurenic acid, Indoxyl sulfate, and L-Acetylcarnitine exhibited a negative correlation with eGFR (p < 0.05). Conclusion The current study shows that serum and urinary metabolites levels in patients with CKD display an alteration in production, secretion, and excretion in all CKD stages. There was demonstrated a distinct metabolomic profile in the course of CKD which points to novel biomarkers useful for the diagnosis and progression evaluation of CKD from its early stages.