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Kappa free light chains could predict early disease course in multiple sclerosis.

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BACKGROUND Cerebrospinal fluid (CSF) kappa free light chains (KFLC) have been suggested as quantitative alternative to oligoclonal bands (OB) in multiple sclerosis (MS) diagnosis. Despite OB have been associated to… Click to show full abstract

BACKGROUND Cerebrospinal fluid (CSF) kappa free light chains (KFLC) have been suggested as quantitative alternative to oligoclonal bands (OB) in multiple sclerosis (MS) diagnosis. Despite OB have been associated to poor disease prognosis, little is known on KFLC in predicting MS early progression. Our aim is to evaluate the prognostic value of KFLC in a cohort of Italian MS patients. METHODS 100 patients (64 females) underwent CSF analysis during their diagnostic MS work-up. We collected clinical/paraclinical features (gender, age at onset, clinical course, early MS treatments (within 1 year), gadolinium-enhancing (Gd+) lesions), calculated K index (ratio CSF-serum KFLC and albumin), and MS severity score (MSSS) at last follow up (minimum 1 year). Statistical analysis included Mann-Whitney descriptive analysis, Spearman correlation for independent samples, and linear regression for significant predictors. RESULTS K index resulted a significant predictor for disability over time being higher in patients who developed greater MSSS. Accordingly, K index was also significantly increased in patients undergoing early versus delayed treatment (N = 50/100, p = 0.046). A similar role in predicting MS disability was confirmed for age at onset. No other factors were retained in our regression model. Of note, K index was not associated to known MS prognostic markers such as gender, age at onset, and Gd+ lesions (N = 31/96). CONCLUSION Our study suggests KFLC as a CSF quantitative marker to predict early disability in MS (despite not being a substitute for OB).

Keywords: sclerosis; free light; multiple sclerosis; light chains; kappa free; predict early

Journal Title: Multiple sclerosis and related disorders
Year Published: 2019

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