Artificial Intelligence Decodes Brain Elemental Signatures to Stratify Aging and Neurological Diseases

The elemental composition of brains changes progressively with age, yet these metallome alterations remain largely unexplored as diagnostic biomarkers in neurological disease. Here, we present a comprehensive analysis of 24 inorganic elements in paired cerebrospinal fluid and serum samples from 1,608 individuals spanning healthy aging through 14 neurological conditions, representing the largest systematically standardized cohort for neurological metallomics. Uniquely, our unselected, consecutively admitted clinical cohort captures the full heterogeneity of neurological presentations, overcoming the limitations of traditional case-control designs focused on isolated disease entities. Machine learning analysis reveals that aging is associated with distinct cerebrospinal fluid elemental signatures independent of peripheral blood changes, primarily reflecting blood-brain barrier permeability alterations that correlate with established albumin quotient measurements. We identify 2 predominant patterns of neurological elemental dysregulation: one mainly consistent with passive barrier-mediated leakage in inflammatory conditions, and another mainly indicative of disease-intrinsic perturbations of metal homeostasis in neurodegenerative disorders. Age-stratified analysis reveals that elemental signatures evolve differently across the lifespan for distinct pathological processes. The integration of elemental signatures with routine clinical parameters through ensemble learning approaches enhances diagnostic accuracy across all tested neurological categories, establishing metallomics as a complementary biomarker class that captures orthogonal pathophysiological information. These findings establish brain metallomics as an emerging field where artificial intelligence reveals complex multi-element interactions present in neurological aging, opening new avenues for precision medicine in age-related neurological disorders.