Decoding the molecular and cellular aspects of multiple sclerosis: From diagnosis to therapeutic strategies

Multiple sclerosis (MS) is a multifaceted, chronic neuroinflammatory condition of the central nervous system (CNS) marked by demyelination, gliosis, and axonal degradation. Despite the comprehensive investigation, the exact etiology remains unclear, requiring complex interactions among genetic, epigenetic, and environmental variables. This review highlights the pressing necessity to integrate emerging evidence connecting genetic predisposition, specifically HLA-DRB1*15:01 and over 200 non-HLA variants, with alterable environmental factors, including Epstein-Barr virus (EBV) infection, vitamin D deficiency, smoking, obesity, and heavy metal exposure. The study underscores the distinct gender-specific and geographical patterns of disease frequency and elucidates the immunological changes during pregnancy and adolescence that influence disease progression. The article methodologically synthesizes data from genome-wide association studies, experimental autoimmune encephalomyelitis models, and epidemiological cohorts, integrating these with technological advancements including neuroimaging, biosensor development, and AI-driven analytics. It examines the pathophysiological consequences of heavy metals, highlighting their involvement in mitochondrial dysfunction, oxidative stress, and neuroinflammation. The paper delineates the transformational potential of nanotherapeutics, extracellular vesicles, and artificial intelligence in improving early diagnostics, medication administration, and tailored treatment approaches. The review presents MS as a disease continuum, advocating for a paradigm shift in its classification and management. Additionally, this review offers a comprehensive viewpoint by integrating immunogenetics with digital medicine and novel medicines, potentially informing future diagnostic and therapeutic advancements. The study emphasizes the imperative of amalgamating genetic, environmental, and computational knowledge to enhance precision therapy in MS.