FIA-MS/MS-Based Targeted Metabolomics of Amino Acids and Acylcarnitines Uncovers Network-Level Metabolic Reprogramming in Chronic Kidney Disease.

Background/Objectives: Chronic kidney disease (CKD) is a major global public health problem, and conventional biomarkers such as serum creatinine primarily reflect excretory function with limited sensitivity for early detection and for predicting progression. Amino acids (AAs) and acylcarnitines (ACs) reflect metabolic processes related to nitrogen metabolism and mitochondrial fatty-acid oxidation that are influenced by renal function. This study aimed to characterize CKD-associated alterations in AAs and ACs profiles using an integrated analytical framework combining differential analysis, supervised multivariate modeling, differential network enrichment analysis (DNEA), and cross-compartment plasma-urine profiling, moving beyond individual metabolite associations toward a multi-level characterization of CKD-associated metabolic reprogramming. Methods: Plasma and urine samples from 78 patients with CKD and plasma samples from 70 healthy controls were analyzed using flow-injection tandem mass spectrometry (FIA-MS/MS). An integrated targeted metabolomics framework combining differential, multivariate and network-based analyses including DNEA was applied to plasma and paired urine samples to characterize systemic and urinary metabolic alterations in CKD, focusing on AAs and ACs. Results: CKD was characterized by significant elevated short-chain dicarboxylic acylcarnitines, increased methylhistidine (MetHis) and argininosuccinic acid (ASA), together with reduced tryptophan, serine, methionine, and tyrosine in plasma. These metabolites were consistently identified across different analyses and correlated with kidney function markers. DNEA revealed coherent network-level reorganization, with acylcarnitine pathways gaining connectivity and centrality while amino acid modules lost integration in CKD. Cross-compartment analysis identified both systemic and compartment-specific patterns of metabolite distribution. An exploratory clustering-guided biomarker panel combining MetHis, C3DC, and Trp achieved an area under the curve (AUC) of 0.881 for discriminating patients with CKD from controls. Moreover, C6, C8, and C10 remained significantly associated with CKD after additional adjustment for estimated glomerular filtration rate (eGFR). Conclusions: Targeted metabolomic profiling revealed a coordinated metabolic signature in CKD suggesting disturbances in pathways related to fatty-acid oxidation, nitrogen imbalance, and altered amino acid metabolism. Network-level analysis provided evidence of systemic metabolic reorganization beyond individual metabolite changes. As findings derive from an observational cohort with high comorbidity prevalence, the identified signatures should be considered CKD-associated rather than CKD-specific.