Genetics

This blog post examines a 2024 iScience study investigating how blood-based metabolomic and transcriptomic signatures may distinguish relapsing-remitting multiple sclerosis (RRMS) from secondary progressive multiple sclerosis (SPMS). By combining nuclear magnetic resonance metabolomics, whole-blood RNA sequencing, machine-learning classification, and gene–metabolite network analysis, the researchers identified coordinated alterations in lipid metabolism, cellular respiration, glycolysis, gluconeogenesis, and ketogenesis associated with progressive disease. The findings suggest that integrated molecular signatures in blood could provide candidate biomarkers for disease stratification while also offering insight into the metabolic stress and biological pathways linked to MS progression. However, further validation in larger and longitudinal patient cohorts is required before such signatures can be considered for routine clinical application.