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BLOOD COUNT-DERIVED INFLAMMATORY BIOMARKERS AND NEUROCOGNITIVE PERFORMANCE IN RELAPSING REMITTING MULTIPLE SCLEROSIS: A PROSPECTIVE PILOT STUDY
Andros Vazquez Monteroand 3 co-authors
National Autonomous University of Mexico
FENS Forum 2026 (2026)
Barcelona, Spain
Board PS03-08AM-008
Presentation
Date TBA
Board: PS03-08AM-008
Event Information
Poster Board
PS03-08AM-008
Poster
View posterAbstract
Relapsing–remitting multiple sclerosis (RRMS) is a chronic neuroinflammatory disease characterized by immune dysregulation and neurological impairment. Peripheral inflammatory indices derived from routine blood counts have emerged as accessible biomarkers, yet their clinical relevance in RRMS remains incompletely defined. We evaluated blood count–derived inflammatory indices and their association with systemic inflammation and neurocognitive and functional performance in RRMS patients before and after methylprednisolone treatment.
This exploratory pilot study included RRMS patients during relapse (T0) and after treatment (T1), as well as age- and sex-matched healthy controls. Inflammatory indices (NLR, MLR, PLR, SIRI, SII, AISI, CLR and CAR) and serum C-reactive protein (CRP) were assessed. Disability and cognition were evaluated using the EDSS scale and the MoCA test. Group comparisons, correlation analyses, receiver operating characteristic (ROC) curves, t-distributed stochastic neighbor embedding (t-SNE), Random Forest classification and Boolean modeling were performed.
At baseline, RRMS patients showed significantly elevated NLR, MLR, SIRI, SII, AISI, CLR and CAR compared with controls. Several indices remained abnormal after treatment, indicating persistent systemic inflammation. SII correlated with CRP and EDSS after treatment. ROC analysis identified MLR, SIRI and AISI as the most accurate discriminators of RRMS (AUC 0.83–0.92). Random Forest analysis highlighted SIRI and AISI as the most influential predictors. Boolean modeling yielded a simple rule (SIRI > 0.62) achieving 92% accuracy.
Peripheral inflammatory indices, particularly SIRI, MLR and AISI, are altered during RRMS relapse and retain discriminatory value after corticosteroid therapy. These low-cost biomarkers may support clinical monitoring and future risk stratification in RRMS.
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