Application of NMR-based metabolomics on biomedical research

Nuclear magnetic resonance (NMR)-based metabolomics is an emerging and robust -omic science that deals with the systematic identification, characterization, and quantification of the complete set of metabolites that are present in biological specimens (i.e. cells, tissues, biofluids, food-derived matrices, etc.). The metabolome – the object investigated by metabolomics – can be described as a highly complex and organized biochemical network in which metabolites, lipids, and lipoproteins, thanks to their fluctuations in terms of concentration and thanks to their interconnections, are directly responsible for the emerging phenotype of the organism. The metabolome represents also a dynamic and evolving entity arising from the interaction between genome, transcriptome, and proteome, under the combined influence of several endogenous and exogenous stimuli. Considering this dynamic behavior of the metabolome, metabolomic data can be easily correlated with the phenotype and act as a direct signature of biochemical activity, since metabolites play a central role in disease development, cellular signaling, and physiological control. In this light, the very high reproducibility, the minimal requirement in sample preparation, and the possibility to simultaneously detect all metabolites presenting active nuclei (at least above the detection limit) make NMR-based metabolomics one of the most powerful and versatile techniques for the analysis of any type of biological sample, providing a global snapshot of the complex metabolic, biological and biophysical processes that occur in a specific organism at the time of sampling. In this scenario, the methodological thesis here presented aims to apply and demonstrate the potential of the untargeted metabolomics approach, particularly in the biomedical field, covering various topics, obtaining new insights on different biological and physiological conditions, shedding light on the dimorphic mechanisms of aging, determining how an improving human well-being treatment (i.e. probiotics) could affect the metabotypes, characterizing the metabolomic and lipoproteomic profiles associated with the inherited blood types (ABO and Rh systems), characterising the metabolic components of two diseases, acute ischemic stroke and colorectal cancer, providing prognostic and diagnostic biomarkers of these specific pathologies. This thesis also proposes a study in which a robust statistical approach, based on the construction of linear regression Random Forest models, is developed to calculate several chemical parameters and sensory profiles of olive oil using 1H-NMR spectra. In summary, the results here presented suggest that untargeted NMR-based metabolomics, in combination with biochemical, analytical chemistry, bioinformatics tools, and robust statistical analysis, is a useful and reasonable candidate for increasing knowledge in various research fields, especially focusing on biomedical research.