Resumen de NMR-based metabolomics for the identification of biomarkers of disease
Marina Dolores Botello Marabotto
The doctoral thesis titled "NMR-based metabolomics for the identification of biomarkers of disease" explores the potential of metabolomics through nuclear magnetic resonance (NMR) spectroscopy for the identification of disease biomarkers, enabling early and non-invasive diagnosis, as well as patient monitoring. The study focuses on four diseases: Alzheimer's, glaucoma, atherosclerosis and plaque vulnerability, and pulmonary fibrosis after COVID-19 pneumonia.
The introduction describes the metabolomic analysis process for biomarker identification and the main platforms and statistical tools used in NMR-based analysis. The study highlights the pathophysiological characteristics of the diseases studied, current diagnostic methods, and the need for new biomarkers.
The first chapter addresses the identification of biomarkers for Alzheimer's disease and the progression of mild cognitive impairment (MCI) to Alzheimer's using serum analysis. Classification models were developed to distinguish between Alzheimer's, MCI, and healthy controls. The research found that certain metabolites, such as lysine, pyruvate, and choline, show different concentrations depending on the evolution of MCI.
The second chapter studies metabolomic differences between MCI and controls using plasma analysis, combining NMR and lipid peroxidation markers detected by UPLC-MS/MS. The combination of both techniques improved biomarker identification, highlighting metabolites such as isoleucine, valine, and glutamate.
The third chapter analyzes tears from patients with primary open-angle glaucoma (POAG) to identify biomarkers in a minimally invasive medium. Metabolites such as taurine, glycine, and glucose were identified as potential biomarkers.
In the fourth chapter, atheroma plaques and serum from patients with carotid stenosis were studied to identify biomarkers of plaque vulnerability. In plaques, myo-inositol and glutamate were identified as potential biomarkers. In serum, threonine, histamine, and unsaturated fatty acids were highlighted.
The fifth chapter focuses on patients who developed pulmonary fibrosis after COVID-19 pneumonia, identifying serum biomarkers that can predict fibrosis, with glucose, valine, and fatty acids being prominent.
Finally, the general discussion and conclusions are presented, emphasizing the relevance of NMR-based metabolomics in identifying early and non-invasive biomarkers, potentially addressing a critical need in modern medicine.
