A Blood Test to Tell the Difference Between Types of Multiple Sclerosis?

Multiple sclerosis (MS) is a complex immune-mediated neurodegenerative disease that affects the brain and spinal cord. It's like the body's immune system gets confused and starts attacking its own nerve fibers. There are different kinds of MS, but the two main ones are relapsing-remitting MS (RRMS) and secondary progressive MS (SPMS).

* RRMS is characterized by periods of flare-ups (relapses) followed by periods of recovery (remission).

* SPMS often develops after RRMS. It's marked by a gradual worsening of symptoms and disability, regardless of whether relapses happen or not.

The tricky part is that the transition from RRMS to SPMS can be really hard to pinpoint. It's often a gradual process, and the usual clinical signs don't always show up right away. As a result, diagnosing SPMS can take a long time, sometimes years, and it's often done "after the fact," when a person's disability has already gotten worse. This creates a problem because treatments are different for these types of MS. So, there is a big need for tests that can accurately distinguish between RRMS and SPMS earlier in the disease process.

Metabolomics to the Rescue?
This is where metabolomics comes in. Think of it as a way of studying all the small molecules (metabolites) in a biological sample, such as blood. These metabolites are like the end products of all the processes going on in our bodies, and they can give us a peek into what’s happening at a cellular level.

Previous studies have shown that there are indeed differences in blood metabolites between people with MS and those without the disease. But, importantly, there are also differences in blood metabolites between people with RRMS and SPMS. This raises the possibility of using a metabolomics test to differentiate between the two MS types.

The Study
In a recent study, researchers took blood samples from 31 people with RRMS and 28 people with SPMS. They used a method called nuclear magnetic resonance (NMR) to analyze the metabolites in the serum (the liquid part of the blood). The researchers then developed a statistical model (OPLS-DA) that could distinguish between RRMS and SPMS based on these metabolite profiles.

The optimized protocol for blood sample collection involved allowing blood to stand for 30 minutes before being centrifuged for 10 minutes at room temperature to separate out the serum. The serum was then stored at -80°C until NMR sample preparation. This is a common method used in research settings.

Key Findings
* Accurate Test: The model was able to accurately differentiate between RRMS and SPMS with a 91% accuracy rate when using samples collected under optimized conditions.

* Discriminatory Metabolites: The study identified seven metabolites that were particularly important in distinguishing between the two types of MS. These included:

* Lower in SPMS compared to RRMS: Lipoproteins, choline, and 3-hydroxybutyrate.

* Higher in SPMS compared to RRMS: Glucose and N-acetylated glycoproteins/glycolipids.

* Real-World Application: The researchers wanted to know how well their test would hold up in a "real-world" clinical setting, where blood samples aren't always processed immediately. So, they tested samples that had experienced variations in handling such as a freeze-thaw cycle and delayed processing times. They found that even with these variations, the test could still distinguish between RRMS and SPMS with accuracy ranging from 85.5% to 88%. The most discriminatory metabolites were still able to distinguish between RRMS and SPMS despite the variations in sample handling. This is good news for practical application of the test.

* Sub-optimal sample collection for model development: The study showed that using samples collected with sub-optimal protocols for model development resulted in reduced predictive accuracies. The OPLS-DA models developed using samples within the freeze–thaw, 120 min and 240 min protocols resulted in predictive accuracies of 89.6%, 81.7% and 84.9%, respectively.

* Long-term storage of samples: The study found that OPLS-DA models built using samples stored for between 1 and 10 years resulted in a predictive accuracy of only 58%. When only samples stored for ≤ 5 years were used, the accuracy improved to 66%.

Why is this important?
This study is important for several reasons:

* It confirms that a metabolomics-based blood test has the potential to accurately distinguish between RRMS and SPMS.

* It shows that the test is robust enough to handle some variations in sample collection, making it more feasible for use in real-world clinics.

* It identifies key metabolites that may play a role in the progression of MS, which could lead to new treatment strategies.

What's Next?
The researchers acknowledge that more work needs to be done, including testing the model on a larger group of patients, and using serial samples of patients transitioning from RRMS to SPMS. However, this study represents an important step towards having a reliable blood test that can help diagnose SPMS earlier and monitor the disease progression. This could make a big difference in how MS is managed, by ensuring patients get the right treatment at the right time.

Disclaimer: This blog post is based on the provided research article and is intended for informational purposes only. It is not intended to provide medical advice. Please consult with a healthcare professional for any health concerns.

References:
Yeo, T., Sealey, M., Zhou, Y. et al. A blood-based metabolomics test to distinguish relapsing–remitting and secondary progressive multiple sclerosis: addressing practical considerations for clinical application. Sci Rep 10, 12381 (2020).