While promising, additional research is required before this blood test can be adopted into routine clinical practice for screening purposes.

Researchers at the Medical University of Vienna have developed a highly accurate blood test that can predict the risk of developing multiple sclerosis (MS) years before symptoms appear, potentially enabling early intervention.

This innovative test measures autoantibodies targeting the Epstein-Barr virus (EBV), specifically against the EBNA-1 protein, which are linked to MS development.

The test detects autoantibodies that react with structures in the human brain and target a section of the EBV protein EBNA-1, which is associated with MS.

The study involved over 700 MS patients and more than 5,000 control subjects, finding that high levels of these antibodies significantly increase the risk of later MS diagnosis.

These antibodies can be detected within three years of EBV infection, often before any clinical symptoms of MS manifest, making early detection possible.

The presence of high antibody levels, especially in individuals with a history of infectious mononucleosis, can predict MS development years in advance, allowing for early diagnosis and treatment.

The findings, published in *Nature Communications* and led by prominent researchers, highlight the importance of early detection in managing MS.

This breakthrough addresses the longstanding challenge of early MS detection, which has been hindered by the disease's unpredictable progression and lack of early markers.

Since MS affects approximately 2.8 million people worldwide and is linked to immune reactions triggered by EBV infection, this test could be a game-changer in identifying at-risk populations.

The researchers recommend screening high-risk groups, such as those who have experienced infectious mononucleosis, although further studies are needed before clinical implementation.

Ultimately, this development could enable healthcare providers to take preemptive measures to delay or prevent the onset of MS, significantly impacting patient outcomes.