Publications & Posters

Longitudinal Biomarkers In Amyotrophic Lateral Sclerosis


Huang F, Zhu Y, Hsiao-Nakamoto J, Tang X, Dugas JC, Moscovitch-Lopatin M, Glass JD, Brown RH, Jr., Ladha SS, Lacomis D, Harris JM, Scearce-Levie K, Ho C, Bowser R and Berry JD

 Ann Clin Transl Neurol. 2020. 




To investigate neurodegenerative and inflammatory biomarkers in people with amyotrophic lateral sclerosis (PALS), evaluate their predictive value for ALS progression rates, and assess their utility as pharmacodynamic biomarkers for monitoring treatment effects.


De‐identified, longitudinal plasma, and cerebrospinal fluid (CSF) samples from PALS ( = 108; 85 with samples from ≥2 visits) and controls without neurological disease ( = 41) were obtained from the Northeast ALS Consortium (NEALS) Biofluid Repository. Seventeen of 108 PALS had familial ALS, of whom 10 had C9orf72 mutations. Additional healthy control CSF samples ( = 35) were obtained from multiple sources. We stratified PALS into fast‐ and slow‐progression subgroups using the ALS Functional Rating Scale‐Revised change rate. We compared cytokines/chemokines and neurofilament (NF) levels between PALS and controls, among progression subgroups, and in those with C9orf72 mutations.


We found significant elevations of cytokines, including MCP‐1, IL‐18, and neurofilaments (NFs), indicators of neurodegeneration, in PALS versus controls. Among PALS, these cytokines and NFs were significantly higher in fast‐progression and C9orf72 mutation subgroups versus slow progressors. Analyte levels were generally stable over time, a key feature for monitoring treatment effects. We demonstrated that CSF/plasma neurofilament light chain (NFL) levels may predict disease progression, and stratification by NFL levels can enrich for more homogeneous patient groups.


Longitudinal stability of cytokines and NFs in PALS support their use for monitoring responses to immunomodulatory and neuroprotective treatments. NFs also have prognostic value for fast‐progression patients and may be used to select similar patient subsets in clinical trials.