Combined analysis of cytokines and neurofilaments improves differentiation and prognostication in ALS

Abstract

Abstract Background Amyotrophic lateral sclerosis (ALS) is a heterogeneous neurodegenerative syndrome hallmarked by adult-onset degeneration of upper and lower motor neurons and their associated tracts. ALS may be difficult to differentiate from some other neurological disorders termed ALS mimics and to estimate prognosis. Methods We retrospectively measured the concentrations of neurofilaments (NFs) and cytokines in cerebrospinal fluid (CSF) and plasma in ALS patients (n = 234) and ALS mimics (n = 44), to assess their association to diagnosis and prognosis in ALS. Two in-house developed enzyme-linked immunosorbent assays were used to measure NF light chain (NFL) and phosphorylated NF heavy chain (pNFH) in CSF. Single-molecule array was used to measure NFL in plasma and cytokines in CSF and plasma. Results Significantly higher concentrations of CSF tumor necrosis factor (TNF)-α and lower concentrations of plasma interleukin (IL)-1β were found in bulbar-onset ALS compared with ALS mimics. Plasma IL-6, plasma IL-17A and plasma TNF-α concentrations in ALS patients were significantly negatively correlated with the ALS functional rating scale revised and significantly positively correlated with King’s ALS clinical staging. Furthermore, plasma IL-6 and plasma TNF-α concentrations were significantly negatively correlated with the disease duration and significantly positively correlated with the disease progression rate in ALS patients. In both CSF and plasma in ALS patients, TNF-α was significantly positively correlated with NFL. ALS patients having plasma NFL or plasma IL-6 concentrations equal to or higher than the upper quartile (≥ Q3) showed a shorter disease duration compared with ALS patients having plasma NFL or plasma IL-6 equal to or lower than the median (≤ Q2). Conclusions Combined analysis of inflammatory and neurodegenerative biomarkers may be useful in differentiation between ALS and ALS-mimics. Such analysis may also provide useful prognostic information and facilitate stratification of less heterogeneous groups in clinical drug trials.