Multimodal predictors of functional and cognitive 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 cohort of 88 people with multiple sclerosis, to test the predictive value of multi-omics, MRI, clinical examinations, self-reports on quality of life, demographics, and general health-related variables for future functional and cognitive disability. Progressive functional loss beyond an Expanded Disability Status Scale score≥4 was used to define a functional loss group. A cognitive decline group was defined by a ≥25% decrease from the maximum (cognitive) Paced Auditory Serial Addition Test score. We used a multiverse approach to identify which baseline variables were most predictive for functional and cognitive loss group memberships, independent of analysis bias.

We identified several factors predicting an increased risk of future functional loss (FLG) and cognitive decline groups (CDG) within the next 12 years from baseline: functional score (0-10, median Odds Ratio per baseline unit increase [mORFLG=2.15±0.51; mORCDG=2.46±1.60]), cognitive scores (1-60 [mORFLG=0.98±0.03; mORCDG=0.91±0.06]), the number of previous relapses [mORFLG=1.56±0.26; mORCDG=1.44±0.60], serum vitamin A levels (umol/l [mORFLG=0.92±0.06; mORCDG=0.33±0.36]), self-reported mental health (1-100 [mORFLG=0.96±0.02; mORCDG=0.91±0.09]) and physical functioning (1-100 [mORFLG=0.99±0.01; mORCDG=0.97±0.03]). Our results suggest that clinical assessment of physical function and cognition, self-reported mental health, and potentially vitamin A levels are the best predictors for risk-group stratifications of people with MS at baseline. While these findings are promising, we also want to underscore the observed analysis-choice induced variability which necessitates both an increase in transparency when reporting study findings as well as strategies which are robust to the many researcher degrees of freedom.