Biomarker-Based Predictive Models for Prognosis in Amyotrophic Lateral Sclerosis.
IMPORTANCE Although median survival in amyotrophic lateral sclerosis (ALS) is 2 to 4 years, survival ranges from months to decades, creating prognostic uncertainty. Strategies to predict prognosis would benefit clinical management and outcomes assessments of clinical trials. OBJECTIVE To identify biomarkers in plasma and cerebrospinal fluid (CSF) of patients with ALS that can predict prognosis. DESIGN, PARTICIPANTS, AND SETTING We conducted a retrospective study of plasma (n?=?29) and CSF (n?=?33) biomarkers identified in samples collected between March 16, 2005, and August 22, 2007, from patients with ALS at an academic tertiary care center. Participants included patients who were undergoing diagnostic evaluation in the neurology outpatient clinic and were eventually identified as having definite, probable, laboratory-supported probable, or possible ALS as defined by revised El-Escorial criteria. All were white and none had a family history of ALS. Clinical information extended from initial presentation to death. Genotyping for hemochromatosis (HFE) gene status was performed. Multiplex and immunoassay analysis of plasma and CSF was used to measure levels of 35 biomarkers. Statistical modeling was used to identify biomarker panels that could predict total disease duration. MAIN OUTCOMES AND MEASURES Total disease duration, defined as the time from symptom onset to death, was the main outcome. The hypothesis being tested was formulated after data collection. RESULTS Multivariable models for total disease duration using biomarkers from plasma, CSF, and plasma and CSF combined incorporated 7, 6, and 6 biomarkers to achieve goodness-of-fit R2 values of 0.769, 0.617, and 0.962, respectively. After classification into prognostic categories, actual and predicted values achieved moderate to good agreement, with Cohen ? values of 0.526, 0.515, and 0.930 for plasma, CSF, and plasma and CSF combined models, respectively. Inflammatory biomarkers, including select interleukins, growth factors such as granulocyte colony-stimulating factor, and l-ferritin, had predictive value. CONCLUSIONS AND RELEVANCE This study provides proof-of-concept for a novel multivariable modeling strategy to predict ALS prognosis. These results support unbiased biomarker discovery efforts in larger patient cohorts with detailed longitudinal follow-up.