Multiplatform metabolome profiling to identify specific signatures and biomarkers in blood samplesuntargeted approach stars

Abstract

One of the significant challenges in identifying effective therapy in many chronic and neurodegenerative diseases is the need for reliable biomarkers. Thus, new point-of-care diagnostics tools are essential for unambiguously distinguishing diseased patients from healthy ones providing results in rapid time. In this doctoral thesis, an untargeted metabolomics approach based on high-throughput analytical techniques such as vibrational spectroscopy and liquid chromatography-mass spectrometry (LC-MS) was evaluated in different studies related to the field of health and disease. Thus, this doctoral thesis's main objective is to provide an objective diagnosis of disorders such as Parkinson’s, Alzheimer’s, Amyotrophic lateral sclerosis and Metabolic Syndrome. Different studies were performed to obtain a metabolic profile of healthy and diseased patients. Thus, to obtain specific metabolomic fingerprinting, multiple analytical and multivariate strategies were tested and combined to exploit their respective strengths and drawbacks. Therefore, distinct mid-infrared metabolic fingerprints in the abovementioned diseases were investigated for patient stratification and to guide an accurate and early differential diagnosis. In addition, UPLC-MS analysis successfully complemented vibrational spectroscopy, providing excellent patient discrimination based on specific blood biomarkers. The obtained results are auspicious, giving place to the new hypothesis about disease pathogenesis and possible involved metabolic pathways that should be validated by a further targeted and multidisciplinary approach.