Volatilomic analysis of blood and urine by headspace solid-phase microextraction gas chromatography/mass spectrometry for complementary assessment of chronic kidney disease.

Chronic Kidney Disease (CKD) is a progressive disorder requiring strategies for early detection and disease stratification. In this context, volatilomics represents a promising analytical approach for investigating metabolic alterations through the profiling of volatile organic compounds (VOCs) in biological matrices. In this study, headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC/MS) was applied to characterize VOC profiles in blood and urine samples collected from healthy controls and CKD patients at different disease stages. The analytical workflow combined headspace extraction, SPME-based pre-concentration, non-target GC/MS analysis, matrix-matched semi-quantification, and chemometric data analysis to evaluate matrix-specific VOC signatures associated with renal dysfunction. Blood and urine VOC datasets were analysed in parallel to compare their discriminatory information and complementary contribution to CKD-related volatilomic characterization. Univariate analysis revealed VOCs significantly differing among study groups (p < 0.05), while multivariate Partial Least Squares-Discriminant Analysis (PLS-DA) demonstrated clear separation of the investigated groups according to disease status. Comparative analysis indicated that blood- and urine-derived VOC patterns provide matrix-dependent and complementary information. Correlation analysis showed significant associations between selected VOCs and conventional clinical parameters, including hemoglobin, serum creatinine, urea, and estimated glomerular filtration rate (eGFR). Regression models based on VOC profiles indicated potential for estimating key indicators of renal function. Overall, these findings demonstrate the applicability of HS-SPME-GC/MS-based volatilomic profiling to complex biological samples and support blood and urine VOC signatures as complementary analytical markers of renal dysfunction. The parallel evaluation of biological matrices supports method-driven biomarker discovery in clinical and biological chemistry.