Metabolomics-Guided Biomarker Discovery for Early Detection of Metabolic and Neurodegenerative Disorders

Abstract

Metabolomics-guided biomarker discovery has been a new approach for the early detection of metabolic and neurodegenerative disorders by enabling comprehensive profiling of small-molecule metabolites in biofluids. High-throughput mass spectrometry and nuclear magnetic resonance techniques, integrated with advanced data analytics, facilitate the identification and profiling of small molecule metabolites in biological fluids. This clinical interpretation helps in bridging the gap between genotype, phenotype and the environment.  In the diagnosis of metabolic and neurodegenerative diseases, metabolomics helped in the early diagnosis and profiling of metabolite such as amino acids, lipids and organic acids with precision. By revealing the instabilities in energy metabolism, biological pathways and metabolism of biomolecule, improved therapeutic approaches are used. Despite of these advances’ certain challenges such as lack of standardized protocols and analytical techniques, validation in the integration of metabolomics data with other omics like proteomics and genomics, achieving reliability by performing large scale clinical trials exists. Thus, computational methods for multi omics and metabolomics data can be achieved by incorporating statistical, machine learning and network-based approaches. Computational tools improve metabolomics-based biomarker discovery. Advanced analytical techniques facilitate early diagnosis and enable personalized treatment strategies, thereby transforming predictive medicine and patient care.