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Classification of Illness

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...

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Protein-Based Classifier to Predict Conversion from Clinically Isolated Syndrome to Multiple Sclerosis.

Eva Borràs¹, Ester Cantó², Meena Choi³

¹From the ‡Proteomics Unit, Centre de Regulació Genòmica (CRG), Dr. Aiguader 88, 08003 Barcelona, Spain; §Universitat Pompeu Fabra (UPF), Dr. Aiguader 88, 08003 Barcelona, Spain;

Molecular & Cellular Proteomics : MCP

|November 11, 2015

Summary

A new diagnostic test using targeted mass spectrometry can predict multiple sclerosis (MS) development from clinically isolated syndrome (CIS). This test identifies patients at high risk, enabling earlier treatment to prevent MS progression and brain damage.

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Area of Science:

Neurology
Biochemistry
Immunology

Background:

Multiple sclerosis (MS) is a central nervous system inflammatory, demyelinating, and neurodegenerative disease.
Clinically isolated syndrome (CIS) is the initial neurological event for most MS patients, but conversion to MS is uncertain.
Current diagnostic methods lack definitive tests to predict MS development from CIS.

Purpose of the Study:

To develop a sensitive and specific diagnostic molecular classifier for predicting MS development from CIS.
To differentiate between CIS patients with high and low risk of converting to clinically defined MS.
To aid in early diagnosis and treatment initiation for MS.

Main Methods:

Utilized targeted mass spectrometry for protein abundance analysis in cerebrospinal fluid.
Identified and quantified specific protein biomarkers, including chitinase 3-like 1 and ala-β-his-dipeptidase.
Developed a statistical model to calculate the probability of conversion from CIS to MS.

Main Results:

Established a diagnostic molecular classifier with high sensitivity and specificity.
The classifier accurately differentiates CIS patients based on their risk of developing MS.
The model provides a precise probability of conversion to clinically defined MS using protein biomarkers.

Conclusions:

The developed classifier offers a reliable method for early MS risk assessment in CIS patients.
Early identification of high-risk individuals allows for timely therapeutic intervention.
This approach has the potential to significantly improve MS management by preventing irreversible brain damage and slowing disease progression.