What are biomarkers in ALS research?
September 16, 2026
What is a biomarker?
A biomarker is a measurable signal in the body that gives researchers clues about what’s happening inside. It might be a substance in the blood like cholesterol, or a physiological measure like blood pressure or your pulse. Doctors track these signals because they can indicate what is happening in the body and help diagnose a disease, track its progression, or measure the effectiveness of a treatment.
Types of biomarkers
- Diagnostic
- Supports diagnosis, differentiates from similar diseases, and enables earlier detection
- Prognostic
- Tracks disease progression, predicts future disease course, and can improve clinical trial design
- Therapeutic
- Measures treatment effects and can confirm biological response
Why do biomarkers matter in ALS?
ALS can look different from person to person. Some people’s symptoms progress quickly, others more slowly, and researchers still don’t fully understand why. This variation is why researchers need reliable ways to tell whether a change is caused by a potential treatment or by the disease’s own course.
Better biomarkers could help researchers:
- Help identify ALS earlier by ruling out conditions that can look similar
- Track how ALS is progressing over time
- Identify who may be more likely to benefit from a given treatment
- Determine whether an experimental therapy appears to be having its intended biological effect
What is neurofilament light chain (NfL)?
Neurofilament light chain, commonly abbreviated as NfL, is currently the most well-established biomarker in ALS research today and is a useful example of what a biomarker looks like in practice.
Neurofilaments are proteins that help give neurons their structure. When neurons are damaged and die, as in ALS, neurofilament proteins from those cells can eventually enter cerebrospinal fluid and blood, where their levels can be measured and changes tracked over time.
NfL isn’t specific to ALS or any other neurological disease, since levels can rise in other conditions that involve degeneration of neurons, which means it cannot be used on its own to diagnose a specific disease. NfL has shown greater value in helping researchers understand and predict ALS progression in the presence of other features of the disease. However, NfL alone is limited in the information it can provide, so scientists are actively searching for additional biomarkers to paint a clearer picture of ALS.
How could better biomarkers accelerate ALS research?
Identifying a potential biomarker is only the first step. Researchers need consistent, well-characterized samples (e.g., CSF or cerebrospinal fluid, which bathes the brain and spinal cord, blood, and urine) collected over time from many people with ALS as well as healthy individuals to understand what a biomarker can reliably tell them and how to use it in ALS research.
Better biomarkers could help researchers design more informative clinical trials, provide earlier insight into whether a treatment is having its intended biological effect, or help predict phenoconversion (when a pre-symptomatic carrier of an ALS gene mutation becomes symptomatic). A change in a biomarker does not necessarily mean a treatment will improve symptoms, but it can give researchers important information about how the treatment affects the disease’s underlying biology.
Biomarkers may also help identify which participants are most likely to respond to a specific treatment being tested, which could help shape who’s included in a trial and make that trial more efficient and informative.
Target ALS supports this work through research infrastructure such as the Longitudinal Biofluids Core, which collects biofluid samples (CSF, blood, and urine) from people with ALS over time, and the Target ALS Data Engine, which houses the clinical and genomic data associated with those samples. Together, they enable researchers to study potential biomarkers alongside data on how ALS progresses. In addition, Target ALS funds projects focused on developing new biomarkers through funding programs that support collaborative projects bringing together academic and industry scientists.
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