Investigating the Contribution of Laboratory Parameters on Plasma Neurofilament Light Chain Levels in Multiple Sclerosis

Valerio Nicolella¹Monica Gelzo^2,3Carmela Polito⁴Giuseppina Affinito⁵Sveva Bagnasco^2,6Raffaella Addesso^2,3Gustavo Cernera^2,3Rosa Sirica⁴Evelina La Civita⁴Mariano Fiorenza⁴Federica Novarella¹Raffaele Palladino⁷Vincenzo Brescia Morra^1,8Giuseppe Castaldo^2,9Daniela Terracciano⁴Marcello Moccia^3,8*

• ¹Department of Neuroscience, Reproductive Science and Odontostomatology, Federico II University of Naples, Naples, Italy, Naples, Italy
• ²CEINGE Advanced Biotechnologies, University of Naples Federico II, Naples, Campania, Italy
• ³Department of Molecular Medicine and Medical Biotechnology, Federico II University of Naples, Italy, Naples, Italy
• ⁴Department of Translational Medical Sciences, Federico II University of Naples, Naples, Italy, Naples, Italy
• ⁵Department of Public Health, Federico II University of Naples, Naples, Italy, Naples, Italy
• ⁶Department of Precision Medicine in Medical, Surgical and Critical Care, University of Palermo, Italy, Naples, Italy
• ⁷Department of Public Health, School of Medicine and Surgery, University of Naples Federico II, Naples, Campania, Italy
• ⁸Multiple Sclerosis Unit, Policlinico Federico II University Hospital, Naples, Italy, Naples, Italy
• ⁹Department of Molecular Medicine and Medical Biotechnology, School of Medicine and Surgery, University of Naples Federico II, Naples, Campania, Italy

Objective: To investigate the associations between several laboratory parameters and plasma neurofilament light chain (pNfL) in individuals with multiple sclerosis (MS), as well as their additional contribution to the established relationships between pNfL, demographics, and MS disability. Methods: In this cross-sectional study, we included 638 people with MS (PwMS) and evaluated pNfL (using fully automated chemiluminescent enzyme immunoassay), along with demographic, clinical and laboratory variables. Laboratory variables were preliminary selected using univariate linear regression models and multicollinearity analysis. A multivariate linear regression model was then employed to determine independent predictors of pNfL levels. Finally, we used linear regression models to explore the clinical utility of adjusting pNfL level. Results: On the multivariate linear regression model, higher pNfL was associated with older age (Coeff=0.15; 95%CI=0.04, 0.26; p=0.007), presence of cardiovascular comorbidity (Coeff=3.67; 95%CI=0.82, 6.51; p=0.012), higher alkaline phosphatase (ALP) (Coeff=0.05; 95%CI=0.01, 0.09; p=0.19), higher lymphocytes’ fraction (Coeff=0.20; 95%CI=0.08,0.33; p=0.001), lower blood proteins (Coeff=-4.02; 95%CI=-6.09, -1.96; p <0.001), and lower haemoglobin (HB) (Coeff=-1.01; 95%CI=-1.73, -0.27; p=0.007). We confirmed known association between higher pNfL and worse MS-related disability (Coeff = 2.23;95%CI = 1.58, 2.87; <0.001), which did not significantly change after including selected laboratory variables (Coeff = 1.48; 95%CI = 0.72, 2.24; p <0.001). Conclusions: Although laboratory markers of lymphocyte depletion and metabolic/nutritional status are correlated with pNfL levels, they do not modify its relationship with MS disability.