Blood-Based Inflammatory Protein Biomarker Panel for the Prediction of Relapse and Severity in Patients with Neuromyelitis Optica Spectrum Disorder: A Prospective Cohort Study
Yinan Zhao1
1Department of Neurology, Xuanwu Hospital, National Center for Neurological Disorders, Capital Medical University, Beijing 100053, China.
Objective:
In this study, we aimed to use the proximity extension assay (PEA) in a prospective cohort to identify novel potential biomarkers and assess their predictive power. Moreover, we created two prognostic models by using a biomarker panel to increase prognostic accuracy and confirm the effectiveness of the model in a separate validation cohort. Thus, we obtained a more comprehensive overview of the acute-phase plasma proteins of NMOSD and developed a more precise plasma biomarker prediction model to reflect the immunological characteristics of NMOSD.
Background:
To date, most existing models for predicting neuromyelitis optica spectrum disorder (NMOSD) are based primarily on clinical characteristics. Blood-based NMOSD severity and prognostic predictive immune- and inflammation-related biomarkers are needed. We aimed to investigate the associations between plasma inflammatory biomarkers and relapse and attack severity in NMOSD.
Design/Methods:
This two-step, single-center prospective cohort study included discovery and validation cohorts. We quantified 92 plasma inflammatory proteins by using Olink's proximity extension assay and identified differentially expressed proteins in the relapse group (relapse within 1 year of follow-up) and severe attack group. To define a new molecular prognostic model, we calculated the risk score of each patient
based on the key protein signatures and validated the results in the validation cohort.
Results:
The relapse prediction model, including FGF-23,DNER, GDNF, and SLAMF1,predicted the 1-year relapse risk. The severe attack prediction model, including PDL1 and MCP-2,predicted the severe clinical attack risk. Both the relapse and severe attack prediction models demonstrated good discriminative ability and high accuracy in the validation cohort.
Conclusions:
Our discovered biomarker signature and prediction models may complement current clinical risk stratification approaches. These inflammatory biomarkers could contribute to the discovery of therapeutic interventions and prevent NMOSD progression.
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