Serum and Cerebrospinal Fluid Cytokine Biomarkers for Diagnosis of Multiple Sclerosis
Ekaterina Martynova1, Mehendi Goyal1,2, Shikhar Johri3
1Institute of Fundamental Medicine and Biology, Kazan Federal University, Kazan, Tatarstan 420008, Russia.
Background:
Multiple sclerosis (MS) is a chronic debilitating disorder characterized by persisting damage to the brain caused by autoreactive leukocytes. Leukocyte activation is regulated by cytokines, which are readily detected in MS serum and cerebrospinal fluid (CSF).
Objective:
Serum and CSF levels of forty-five cytokines were analyzed to identify MS diagnostic markers.
Methods:
Cytokines were analyzed using multiplex immunoassay. ANOVA-based feature and Pearson correlation coefficient scores were calculated to select the features which were used as input by machine learning models, to predict and classify MS.
Results:
Twenty-two and twenty cytokines were altered in CSF and serum, respectively. The MS diagnosis accuracy was ≥92% when any randomly selected five of these biomarkers were used. Interestingly, the highest accuracy (99%) of MS diagnosis was demonstrated when CCL27, IFN-γ, and IL-4 were part of the five selected cytokines, suggesting their important role in MS pathogenesis. Also, these binary classifier models had the accuracy in the range of 70-78% (serum) and 60-69% (CSF) to discriminate between the progressive (primary and secondary progressive) and relapsing-remitting forms of MS.
Conclusion:
We identified the set of cytokines from the serum and CSF that could be used for the MS diagnosis and classification.
Insights
Researchers identified specific cytokines in cerebrospinal fluid (CSF) and serum that can accurately diagnose multiple sclerosis (MS). These biomarkers also help classify different MS disease types.
Area of Science:
- Neuroimmunology
- Biomarker Discovery
Background:
- Multiple sclerosis (MS) is a chronic neurological disorder involving brain damage from autoreactive leukocytes.
- Cytokines, key regulators of leukocyte activation, are found in MS patient serum and cerebrospinal fluid (CSF).
Purpose of the Study:
- To identify diagnostic markers for multiple sclerosis (MS) by analyzing serum and CSF cytokine levels.
- To evaluate the potential of identified cytokines in classifying different forms of MS.
Main Methods:
- Multiplex immunoassay was used to analyze 45 cytokines in serum and CSF.
- Machine learning models were trained using ANOVA-based feature selection and Pearson correlation coefficients to predict and classify MS.
Main Results:
- Significant alterations in 22 CSF cytokines and 20 serum cytokines were observed in MS patients.
- High diagnostic accuracy (≥92%) for MS was achieved using combinations of five selected cytokine biomarkers.
- Specific cytokines like CCL27, IFN-γ, and IL-4 were highlighted for their potential role in MS pathogenesis, achieving 99% diagnostic accuracy.
- Models differentiated between progressive and relapsing-remitting MS with 70-78% accuracy using serum markers and 60-69% using CSF markers.
Conclusions:
- A panel of serum and CSF cytokines has been identified as potential biomarkers for MS diagnosis.
- These cytokine biomarkers can also aid in the classification of multiple sclerosis subtypes.
