Brain: Analysis of cerebrospinal fluid helps differential diagnosis of neurological diseases

While conventional cerebrospinal fluid analysis can diagnose nervous system diseases, but it is mainly used to distinguish infection resistance, self- immune inflammatory and degenerative diseases of the central nervous system
.

Since the pathophysiological changes of the central nervous system are reflected in the cerebrospinal fluid, its analysis combined with radiology, (neuro)physiology and neuropsychological examinations is helpful for the diagnosis of neurological diseases

Diagnosis of infection immunity

Catharina C Gross et al.
investigated neuroinflammation, degenerative and vascular diseases in patients with different cellular parameters in peripheral blood and cerebrospinal fluid.
These parameters can improve the classification of neurological diseases and provide insights into common and different pathophysiological processes
.

Catharina C Gross et al.
investigated neuroinflammation, degenerative and vascular diseases in patients with different cellular parameters in peripheral blood and cerebrospinal fluid.
These parameters can improve the classification of neurological diseases and provide insights into common and different pathophysiological processes
.

Blood vessel

Research methods and summary

In order to study whether the characteristics of multi-dimensional cellular blood and cerebrospinal fluid can support the diagnosis of clinically similar neurological diseases, Catharina C Gross et al.
analyzed 546 patients with autoimmune neuroinflammation, degeneration or vascular disease
.

By combining feature selection with dimensionality reduction and machine learning methods, the pan-disease parameters that change in all autoimmune neuroinflammatory central nervous system diseases are determined, and they are compared with other neurological diseases and subdifferentiated central nervous system mutations.

Identify pan-disease parameters that characterize CNS neuroinflammation

Identify pan-disease parameters that characterize CNS neuroinflammation

The figure above shows the classification of autoimmune diseases with different characteristics of neuroinflammatory diseases
.

(A) The prediction accuracy and AUC of plasmacytogenesis and intrathecal IgG synthesis determined by logistic regression are used to distinguish early RRM, including radiation or clinical isolated syndrome (RIS/CIS) and NMOSD (yellow triangle), Susac synthesis Sign (SuS, pink triangle) and AIE (blue triangle)

The figure above describes the factors in the evolution and activity of RRMS disease
.

The heat map shows the relative change, the dot map shows the pan-autoimmunity and internal autoimmunity between patients with non-inflammatory diseases and the radiation or clinical isolation syndrome (RIS/CIS, yellow triangle, n=26) or RRMS patients in the discovery cohort Classification of changes in folding (early, light red triangles, n=125) and disease manifestations after 36 months (>36 M, dark red triangles, n=45)

The study proved the role of multidimensional cerebrospinal fluid analysis in the classification of neurological diseases and endophenotypes within the spectrum of diseases (such as multiple sclerosis), and has an impact on individualized treatment
.

Future research includes more detailed cells (B/T cell subpopulations) and additional solubility parameters such as interferon-γ, tumor necrosis factor-α, amyloid-β, neurofilament light chain, CXCL13 and proliferation-inducing ligand Body, analyzed by the most advanced deep learning technology, may further improve the differential diagnosis

This study proves the role of multi-dimensional cerebrospinal fluid analysis in the classification of neurological diseases and endophenotypes in the disease spectrum (such as multiple sclerosis), and has an impact on individualized treatment .

Overall, the study found that the comprehensive analysis of blood and cerebrospinal fluid parameters improves the differential diagnosis of neurological diseases, thereby facilitating early treatment decisions

Classification of neurological diseases using multi-dimensional CSF analysis, Brain, Volume 144, Issue 9, September 2021, Pages 2625 –2634, https://doi.

Classification of neurological diseases using multi-dimensional CSF analysis, Brain, Volume 144, Issue 9, September 2021, Pages 2625 –2634, https://doi.
org/10.
1093/brain/awab147 Classification of neurological diseases using multi-dimensional CSF analysis , Brain, Volume 144, Issue.
9, September 2021, Pages 2625 -2634, https://doi.
org/10.
1093/brain/awab147 in this message