Multimodal predictors of disability progression and processing speed decline in relapsing–remitting multiple sclerosis

The underlying mechanisms for neurodegeneration in multiple sclerosis are complex and incompletely understood. Multivariate and multimodal investigations integrating demographic, clinical, multi-omics, and neuroimaging data provide opportunities for nuanced analyses, aimed to define disease progression markers. We used data from a 12-year longitudinal multicenter cohort of 88 people with multiple sclerosis, to test the predictive value of multi-omics, T₁-weighted MRI (lesion count and volume, lesion-filled brain-predicted age), clinical examinations, self-reports on quality of life, demographics, and general health-related variables for future functional and cognitive disability. Systematic increases in Expanded Disability Status Scale (EDSS) scores were used to stratify a progressive disability group (PDG) from relatively stabile disability. A processing speed decline group (PSDG) was defined by a ≥ 20% decrease of Paced Auditory Serial Addition Test score from previous timepoints. We used a multiverse approach to identify which baseline variables were most predictive for PDG and PSDG memberships, considering multiple analysis paths. Future disability (median area under the curve: mAUC = 0.83 ± 0.04, median Brier score: mBS = 0.16 ± 0.02) and the loss of processing speed (mAUC = 0.89 ± 0.05, mBS = 0.10 ± 0.03) could be successfully classified across models. Varibles significantly (median p-values < 0.05) predicting stable disability included receiving disease modifying treatment at 12-year follow-up (median Odds Ratio: mOR_PDG = 7.44 ± 4.07, p_median = 0.013, proportion of the OR's directionality: PORSD = 100%), lower baseline EDSS for each 1-unit (mOR_PDG = 0.25 ± 0.11, p_median = 0.013, PORSD = 100%), and counter-intuitively every year increase in baseline age (mOR_PDG = 1.12 ± 0.04, p_median = 0.020, PORSD = 100%), and lower vitamin A per 1 umol/L (mOR_PDG = 0.10 ± 0.05, p_median = 0.016, PORSD = 99.7%) and D levels per 1 nmol/L (mOR_PDG = 0.95 ± 0.02, p_median = 0.025, PORSD = 100%). Variables significantly predicting stable processing speed were receiving disease modifying treatment at 12-year follow-up (mOR_PSDG = 0.10 ± 0.08, p_median = 0.013, PORSD = 100%) and baseline PASAT score (mOR_PSDG = 0.86 ± 0.03, p_median = 0.005, PORSD = 99.73%). These findings were supported by an additional simulation study. Concordant with the literature, disease modifying treatments influence disability progression, as well as a higher EDSS and PASAT scores at measurement start. Experimental and counterintuitive findings on vitamin A and D levels require further validation. The large variability across models suggests a strong influence of analytic flexibility, such as the selection of covariates.