Timing and Predictive Value of Clinical Conditions Preceding Multiple Sclerosis in the UK Biobank

Objectives: Multiple sclerosis (MS) patients often experience a higher incidence of clinical conditions before diagnosis, suggesting a prodromal phase. However, their predictive value and temporal trajectories remain underexplored. We investigated these aspects using the large UK Biobank's population-based cohort, which provided clinical history through ICD-10 diagnosis codes. Methods: We assessed associations between 600 clinical conditions and MS risk in 477,421 individuals using Cox models adjusted for demographics, smoking, and MS polygenic risk score (MS-PRS). Statistically significant conditions were included in a LASSO Cox regression (five-fold cross-validation on 70% training set) to identify key predictors, with performance assessed by the C-index and age-dependent area under the curve (AUC) in the 30% test set. Lastly, temporal trajectories of MS-associated conditions were analyzed in MS cases. Results: We identified 192 conditions associated with MS, of which only ~20% were onset symptoms. Integrating these conditions into a predictive model already including demographics and smoking, improved the C-index from 0.65 to 0.71. Among the 30 model-selected best predictors, ~25% were prodromal conditions, including neuromuscular diseases, thromboembolism, and depression which typically occurred more than five years before MS diagnosis. Including MS-PRS further increased the C-index to 0.78, with an age-dependent AUC exceeding 0.80 in individuals over 50 years. Trajectory analysis highlighted migraine as a common early diagnosis, often followed by hypertension, depression, and dorsalgia. Interpretation: Our findings highlight early conditions and diagnostic trajectories of MS, supporting the existence of a prodromal phase. These insights could improve MS prediction and facilitate earlier detection, particularly for late-onset cases.