Cognitive function is associated with risk of conversion to secondary progressive multiple sclerosis: A cross-sectional study - Omid Mirmosayyeb; Ida Mohammadi; Shahryar Rajai Firouzabadi; Saeed Vaheb; Mohammad Yazdan Panah; Tom A. Fuchs; Bianca Weinstock-Guttman; Vahid Shaygannejad (PLoS ONE, 2026)
Background Predicting conversion from relapsing-remitting multiple sclerosis (RRMS) to secondary progressive multiple sclerosis (SPMS) is critical for managing people with multiple sclerosis (PwMS). Cognitive function has been linked to disease progression in RRMS, yet its association with conversion to SPMS remains underexplored. This study aimed to assess this relationship using the newly developed DAAE score to calculate the risk of conversion to SPMS. Methods The risk of conversion over five years was assessed using the DAAE score (0–12), and the PwMS were categorized into very-low-to-low and medium-to-high risk groups. Cognitive function was assessed using the Symbol Digit Modalities Test (SDMT), Brief Visuospatial Memory Test-Revised (BVMT-R), Paced Auditory Serial Addition Test (PASAT), and California Verbal Learning Test-Second Edition (CVLT-II). Pairwise correlations and hierarchical linear and logistic regression analyses were performed to examine the relationship between cognition and conversion risk. Results A total of 217 PwMS were included (very low to low = 185, medium to high = 32). The DAAE score was moderately correlated with SDMT and BVMT-R and weakly correlated with PASAT and CVLT-II. Multivariable linear regressions found SDMT (beta = −0.125, 95%CI: −0.155, −0.096, p-value < 0.001), BVMT-R (beta = −0.157, 95%CI: −0.200, −0.114, p-value < 0.001), PASAT (beta = −0.084, 95%CI: −0.111, −0.057, p-value < 0.001), and CVLT-II (beta = −0.088, 95%CI: −0.125, −0.050, p-value < 0.001) were independently associated with the risk of conversion. Among these, SDMT (beta = −0.093, 95% CI: −0.133 to −0.054, p-value < 0.001) was the most robust predictor of conversion risk. Conclusion In conclusion, low cognitive performance across the BICAMS battery and the PASAT, with the SDMT as the most robust predictor, is associated with an increased risk of conversion from RRMS to SPMS. By demonstrating this association through a machine-learning framework, the present study supports the integration of standardized neuropsychological assessments into routine clinical practice as tools for monitoring conversion risk in PwMS, beyond conventional clinical measures.