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High NfL Levels Predict Greater MS Disability Within 2 Years

— Temporal relationship is a critical insight, says Ahmed Abdelhak, MD

MedpageToday

Multiple sclerosis patients whose blood tests revealed elevated levels of neurofilament light chain (NfL), a biomarker of axonal damage, had worsening disability 1 to 2 years later, according to a study presented at the joint meeting of the European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS) and the Americas Committee for Treatment and Research in Multiple Sclerosis (ACTRIMS).

Study author Ahmed Abdelhak, MD, a neurologist and clinical instructor at the University of California San Francisco, describes the study design and the clinical implications of the results.

Following is a transcript of his remarks:

Both cohorts have together more than 13,000 visits where we have NfL values. We use those NfL values to try to find if there is a temporal association -- an association between time of NfL elevation and the occurrence of disease progression.

So, we classify the patient the way we see them clinically. When I see a patient and I find their disease has worsened, we look backward and see, okay, what happened one visit before, what happened two visits before? And we had very interesting findings in that we found the NfL elevation to occur before we diagnosed the progression based on our EDSS [Expanded Disability Scale Status] score, and not actually when we see the patients.

So, if you see those subjects or those participants had disease progression associated with a relapse, this NfL elevation was around 1 year before that. And if you look at the subjects who have NfL elevation and who have disease progression without a clinical relapse, the elevation was even more delayed -- between 1 to 2 years before.

And if you try to then use this NfL elevation to predict the disease progression, we have a very clear finding that high NfL values were associated with a disease progression event associated with a relapse within a year, or independently of clinical relapse within 1 to 2 years.

So, the findings from these cohorts are very relevant for both the applications of NfL to measure progression and also for our understanding of progression itself. So, if you want to utilize NfL to predict the disease progression, you have to be very careful about when you are measuring the NfL and at which time point you're trying to predict the progression.

And on the other hand, it tells us when we see a patient and we diagnose progression, that pathology inside the brain that is associated with this progression happens around 1 to 2 years before. When we see the patient, there's almost nothing more we can do, but we should really, in all our clinical trials, aim to prevent the occurrence of this progression over this prolonged period of time.

I think the fact that we are realizing that when we see the patients having progression, the damage in the brain happened before that, is a critical insight for clinical studies. So, if I want to design a clinical study and I want to use that to prevent progression or you want to use my intervention to prevent progression, if an event would happen in the first 6 months, there is no way my medication could impact this event, because the CNS [central nervous system] damage happened 1 to 2 years before. So, we really need to be sure that our studies are designed to cover at least 2 years of time period to be sure we impact this window where the damage is happening.

And the same in clinical care: If we are using one of our medications with the particular aim that we want to prevent an ongoing progression in our patient, we can't evaluate if this medication will work or not within the first year; it probably needs to be longer before we decide. That might be challenging, and it might create other challenges in the clinical setting and clinical trials, but we definitely don't want to miss this time window where the active damage in the brain is happening.

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    Greg Laub is the Senior Director of Video and currently leads the video and podcast production teams.