Untargeted metabolomic analysis identifies a specific metabolomic profile in patients with early chronic kidney disease

Background and Aims Chronic kidney disease (CKD) has become one of the most challenging diseases of the twenty-first century and is characterized by increased mortality and morbidity. The pathogenesis of CKD is heterogeneous and the evaluation of renal function is performed with biomarkers such as serum creatinine and blood urea, which have low specificity and sensitivity. Metabolomics, one of the omics sciences, has become of interest in the nephrology field of research. Metabolic pathways can impact both glomerular and tubular structures and can offer a better understanding of the pathogenic mechanisms of CKD. Also, metabolomics provides more sensitive biomarkers for an early detection of CKD. The aim of this study was to perform metabolomic profiling of serum and urine in CKD patients by means of untargeted metabolomics in relation with the glomerular filtration rate (eGFR) and to identify potential serum and urinary biomarkers of early CKD. Method In this cross-sectional study were included 99 patients with CKD, staged by eGFR in six subgroups, according to the KDIGO Guidelines and 20 healthy control subjects. Serum and urinary metabolomic profiling were performed by using ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry. Blood and urine samples were evaluated by multivariate analysis, followed by univariate analysis. First, the PLSDA score plot and VIP score were applied. By using the cross-validation algorithm for the first 4 molecules identified, a high accuracy, high R² values, and a significant Q2 value were obtained. Therefore, the model could be considered predictive. Biomarker analysis and prediction by Random Forest analysis was performed. The specificity and sensibility of the molecules identified as potential biomarkers were evaluated by applying the Receiver Operating Characteristic (ROC) curve and the area under the curve (AUC). Results Significant direct correlations with eGFR were demonstrated for serum levels of Oleoyl glycine (p < 0.05, Log2 = 0.773), alpha-lipoic acid (p < 0.05), Propylthiouracil (p< 0.05), and L-cysteine (p< 0.05, Log2 = 0.363). Interestingly, serum levels of 5-Hydroxyindoleacetic acid, Phenylalanine, Pyridoxamine, Cysteinylglycine, Propenoylcarnitine, and Uridine increased gradually from C group to subgroups G5. Also, 5-Hydroxyindoleacetic acid, Phenylalanine, Pyridoxamine, Cysteinylglycine, Propenoylcarnitine, Uridine, and All-trans retinoic acid levels correlated negatively with eGFR (p< 0.05). Moreover, in urine samples the majority of molecules were increased in group G4 and G5 versus G1-G3b and C groups, respectively. Urinary levels of Indoxyl sulfate, Glycylprolylarginine, and Butenoylcarnitine correlated negatively with eGFR (p < 0.05). Conclusion Amino acids, antioxidants, uremic toxins, and acylcarnitines are increased in all CKD stages. The impact of this metabolites on the glomerular and tubular structures, even in the early stages of CKD, can be explained by their dual serum and urinary variation. The study provided a particular metabolomic profile that can offer new biomarkers useful for the evaluation of early CKD and its progression, as well as potential therapeutic targets.