Multiple sclerosis (MS) is a chronic autoimmune disorder of the central nervous system characterized by substantial clinical and pathological heterogeneity, leading to highly variable prognoses and outcomes. Over the last decade, the therapeutic landscape has been transformed by the development of disease-modifying therapies (DMTs) with distinct mechanisms of action, administration routes, and safety profiles. However, identifying biomarkers that clarify the mechanisms of inflammation and neurodegeneration, and that predict response and safety to DMTs, remains a major challenge.

Within this framework, omics technologies have emerged as powerful tools to address this complexity, providing insights into disease mechanisms and enabling more precise patient stratification. Integrating proteomics, metabolomics, and lipidomics with advanced machine learning and artificial intelligence allows robust data analysis, biomarker discovery, and individualized predictions.

The objective of this Research Topic is to consolidate and expand recent advances in proteomics, metabolomics, and lipidomics that are transforming our understanding of multiple sclerosis (MS). Although remarkable progress has been made in deciphering the molecular basis of inflammation and neurodegeneration, the mechanisms that drive disease heterogeneity, therapeutic response, and progression remain incompletely understood.

Compared to genomics and transcriptomics, which have been extensively applied for a longer time in MS research, proteomic, metabolomic, and lipidomic approaches are more recent and less frequently integrated with artificial intelligence (AI). Nonetheless, these emerging omics hold great potential to provide complementary insights into disease mechanisms and therapeutic outcomes.

This Research Topic aims to highlight how multi-omics approaches, particularly when combined with AI and machine learning, can unravel complex biological networks, identify reliable biomarkers, and generate predictive models of disease evolution and treatment response. By integrating large-scale datasets, we seek to promote discoveries that refine patient stratification, support personalized therapeutic strategies, and accelerate the translation of precision medicine into clinical practice.

We welcome a wide range of contributions, including original research, reviews, systematic reviews, mini reviews, perspectives, and clinical studies, that demonstrate how multi-omics data and AI can deepen mechanistic understanding, guide biomarker development, and advance therapeutic innovation in MS:

• identify and validate biomarkers of disease activity, progression, and treatment response,

• elucidate inflammatory and neurodegenerative mechanisms,

• develop predictive models for personalized medicine,

• integrate datasets across omics layers and biological compartments (CSF, blood, PBMCs, microbiome),

• explore translational applications in clinical settings.

Together, these contributions will showcase how multi-omics and AI can accelerate precision medicine and innovation in MS research.

Topic Editor Hernan Inojosa declares speaker honoraria from Roche and financial support for research activities from Merck, Neuraxpharm, Novartis, Teva, Biogen, and Alexion.

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Emerging Omics and Artificial Intelligence Approaches to Transform Multiple Sclerosis Research