Clinical metabolomics: analytical workflows, data interpretation, and translational considerations

초록

Clinical metabolomics has emerged as a powerful systems biology approach for characterizing metabolic alterations associated with human health and disease. By comprehensively profiling endogenous metabolites in clinical biospecimens, clinical metabolomics provides valuable insights into disease mechanisms, biomarker discovery, therapeutic monitoring, and personalized medicine. Recent advances in analytical technologies, including nuclear magnetic resonance (NMR) spectroscopy, gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and capillary electrophoresis-mass spectrometry (CE-MS), have substantially improved metabolite coverage, analytical sensitivity, and quantitative reliability. In parallel, developments in computational and bioinformatics tools have facilitated high-dimensional data interpretation and pathway-level biological understanding. Despite these advances, several analytical and translational challenges remain in clinical metabolomics, including biospecimen variability, lack of standardized sample preparation protocols, inter-laboratory reproducibility, batch effects, and difficulties in translating metabolomics-derived biomarkers into clinical practice. Therefore, robust workflows encompassing biospecimen handling, sample preparation, analytical validation, quality control, statistical analysis, and biological interpretation are essential for generating reliable and clinically meaningful metabolomics data. This review summarizes current analytical and computational workflows in clinical metabolomics, with particular emphasis on biospecimen handling, sample preparation strategies, analytical measurement platforms, data processing methodologies, and translational considerations. In addition, emerging trends including artificial intelligence-driven data analysis, multi-omics integration, and precision medicine applications are discussed. Collectively, this review highlights the critical role of standardized and translationally oriented workflows in advancing the clinical implementation of metabolomics.

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