Holistic Voltammetric Profiles as a Scalable Strategy for Metabolic Fingerprinting

Abstract Metabolic fingerprinting is a powerful approach for phenotypic characterization but typically relies on costly analytical platforms such as NMR spectroscopy and mass spectrometry. Here, we demonstrate that differential pulse voltammetry (DPV) data acquired directly from intact urine samples can be exploited as holistic metabolic fingerprints. Coupling voltammetric data with chemometric and machine-learning methodologies enabled the robust discrimination of individual metabolic phenotypes and gender, achieving cross-validated classification performances with AUC values up to 0.93. Importantly, DPV measurements exhibited reproducibility below 5%, confirming that the recorded fingerprints primarily reflect biological variations. Comparative analyses with 1H NMR metabolomics data further supported the biological relevance of the voltammetric fingerprints. Collectively, these findings demonstrate the potential of holistic voltammetric profiles as a rapid, low-cost, and scalable approach for metabolic fingerprinting, providing a foundation for further investigation in metabolomics, phenotyping, and future point-of-care applications.