A susceptibility network analysis of disease trajectories leading to multiple sclerosis: a nationwide cohort study

BackgroundThe prodromal phase of multiple sclerosis (MS) is increasingly recognized, but most studies have focused on isolated symptoms or static comorbidity counts, leaving the evolving structure of pre-onset disease burden underexplored. ObjectiveTo characterize dynamic disease trajectories preceding MS onset through longitudinal network modeling. MethodsHealth data from 10,273 MS patients and 47,167 matched controls in Sweden were analyzed. Disease co-occurrence networks were constructed for three pre-onset windows (0-5, 5-10, 10-15 years), with comparisons of centrality, clustering, and path length. Rewiring scores captured structural shifts, while Markov clustering and trajectory mapping identified comorbidity communities. ResultsMS networks were denser, more clustered, and showed shorter path lengths than controls, reflecting higher systemic interconnectivity. Psychiatric and metabolic diagnoses, especially depression, anxiety, diabetes, and abdominal pain, were hubs that gained prominence over time. Distinct clusters, including neuropsychiatric-toxicological and immune-endocrine constellations, were observed only in MS. Rewiring analysis revealed significant topological shifts in key diagnoses, such as inflammatory CNS disorders and substance use, as onset approached. ConclusionsMS is preceded by dynamic reorganization of the comorbidity landscape, marked by increasing connectivity and rewired hubs. This framework highlights systemic disruption before diagnosis and provides a novel, network-based tool for studying prodromes in complex disorders.