Blood‐based inflammatory protein biomarker panel for the prediction of relapse and severity in patients with neuromyelitis optica spectrum disorder: A prospective cohort study
CNS Neuroscience & Therapeutics, 2024
Wei Q., Li J., Zhao C., Meng S., Liu N., Wu Z., Liu F., Cui L., Hu W., Zhao Y.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Immunological & Inflammatory Diseases | Patient Stratification | Plasma |  Olink Target 96 |
Abstract
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.
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 PD‐L1 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.