NIH-funded researchers have identified a 19-protein blood biomarker panel that may help predict when amyotrophic lateral sclerosis symptoms will emerge, based on the University of Miami-led Pre-fALS study. The findings could help researchers select participants and time preventative ALS trials before substantial motor neuron damage develops.
Biological changes associated with amyotrophic lateral sclerosis can begin before clinical symptoms become apparent. Researchers examined thousands of proteins in blood samples from people at elevated genetic risk, seeking a measurable signature that could indicate when symptoms are likely to emerge.
Researchers Identify Predictive ALS Protein Changes
Researchers analysing the NIH-funded Pre-symptomatic Familial ALS study found that levels of certain blood proteins changed months to several years before participants developed symptoms. The University of Miami team said the changes may provide advance warning of clinically manifest ALS or frontotemporal dementia among genetically predisposed participants.
The analysis included plasma samples from 137 participants, 33 of whom later demonstrated clinical manifestations of ALS or frontotemporal dementia. Using the Olink proteomic platform, the researchers measured more than 5,000 proteins and identified 92 whose levels differed before symptoms or signs emerged.
| Indicator | Study Result | Context |
|---|---|---|
| Participants analysed | 137 | The NIH-funded Pre-fALS study followed people at significantly elevated genetic risk who had not developed clinically manifest disease. |
| Clinical manifestations | 33 participants | The University of Miami team reported that these participants later showed manifestations of ALS or frontotemporal dementia. |
| Proteins measured | More than 5,000 | Researchers used high-throughput Olink analysis to measure protein levels in plasma samples. |
| Proteins initially identified | 92 | The study found that their levels differed before participants developed clinical symptoms or signs. |
| Final predictive panel | 19 proteins | Machine-learning analysis selected the combination that produced the strongest predictive performance across several time periods. |
Study Findings and Participant Data
The researchers used the protein measurements to estimate when participants were approaching clinically manifest disease. Senior author Michael Benatar said the model predicted symptom onset with an average error of approximately 18 months.
The prediction period covered time horizons ranging from six months to five years. In practice, that degree of precision could help clinical trial teams identify people who may be approaching onset and determine when preventative treatment should be evaluated.
The study did not establish a diagnostic test for routine clinical use. Instead, it produced a research model intended to estimate timing among people already known to carry ALS-associated genetic variants.
Long-Term Pre-fALS Research Supports Earlier Prediction
The Pre-fALS study has followed people at elevated inherited risk of ALS for nearly 20 years, collecting clinical information and biological samples before and after clinically manifest disease develops. Benatar and University of Miami researcher Joanne Wuu lead the programme.
Earlier work involving 10 Pre-fALS participants found that neurofilament light chain increased in blood during the months before symptom onset. Neurofilament light chain is a structural protein in neurons and is used in neurological research as an indicator of nerve-cell injury.
The latest analysis expands that earlier finding into a 19-protein panel designed to improve estimates of when onset may occur. Neurofilament light chain remained within the final panel, but the additional proteins improved predictive performance when analysed together.
Machine Learning Produces a 19-Protein Panel
The research team used machine-learning methods to test different protein combinations across prediction periods extending from six months to five years. The authors selected 19 proteins, including neurofilament light chain, as the combination that maximised accuracy.
This could narrow a previously uncertain period for people carrying ALS-associated variants and provide a more practical basis for preventative trial enrolment
| Potential Use | Current Limitation |
|---|---|
| Estimate when symptoms may emerge | The average timing error remained approximately 18 months. |
| Help select participants for preventative trials | The panel has not been validated for routine clinical screening. |
| Identify participants approaching clinically manifest disease | The model cannot guarantee that symptoms will emerge on the estimated schedule. |
| Support treatment-timing decisions | The biomarker study does not show that early treatment prevents ALS. |
| Extend earlier neurofilament findings | Further prospective validation is required before broader clinical use. |
UK Biobank Data Supports Broader Relevance
Researchers also applied the predictive model to data from the UK Biobank and reported similar results outside the specialised Pre-fALS cohort. The UK Biobank includes health and biological information from a broader population than the group carrying known familial ALS variants.
The comparison supports the possibility that the protein signature may have wider relevance. However, differences between the datasets and the absence of prospective clinical validation mean the panel is not ready for population-wide screening.
The UK Biobank findings therefore provide external support rather than confirmation of a general clinical test. The model’s immediate application remains within research involving ALS risk and preventative trial design.
Biomarkers Could Guide Preventative ALS Trials
The research comes as gene-targeting treatments are being studied earlier in the ALS disease process. Amy Bany Adams, acting director of NIH’s National Institute of Neurological Disorders and Stroke, said reliable biofluid signatures are urgently needed to identify near-term onset among people carrying ALS risk genes.
Tofersen is approved for symptomatic ALS associated with certain SOD1 mutations and is being evaluated as a preventative treatment through the ATLAS clinical trial. Benatar designed ATLAS with Biogen to test whether beginning treatment before symptoms appear could avert or delay clinically manifest ALS.
Timing is important because investigators need to identify participants who are sufficiently close to likely onset for preventative treatment to be meaningfully assessed. The biomarker panel could support those enrolment decisions, although the present study did not demonstrate that tofersen or another therapy can prevent ALS.
The distinction remains central: the protein model estimates likely onset, while ATLAS tests whether a treatment strategy can alter the course of disease.
Stakeholder Comments
“If someone carrying an ALS-associated genetic variant had asked me in the past when they would become symptomatic, I would have struggled to provide a reasonable estimate,”
said senior author Michael Benatar, M.D., Ph.D., a professor of neurology and public health sciences at the University of Miami.
“These biomarkers give us a far better idea of the timing, allowing us to estimate the time to symptom onset with an average error of about 18 months. That’s something we can work with.”
Amy Bany Adams, Ph.D., acting director of NIH’s National Institute of Neurological Disorders and Stroke (NINDS), said:
“With preventative gene-targeting treatments now becoming available, there is a particularly urgent need for reliable biofluid-based signatures that indicate near-term onset in individuals that carry ALS risk genes.”
Adams linked the findings to the development of gene-targeting treatments, noting the need for biological signatures that indicate near-term onset. Her comments reflected the practical challenge of beginning treatment before symptoms while avoiding intervention many years too early.
The NIH-funded research identified a 19-protein blood panel that estimated ALS symptom onset with an average error of approximately 18 months. The findings could improve participant selection and treatment timing in preventative trials, but the model requires further validation before routine clinical use.
The study advances efforts to measure presymptomatic ALS risk while maintaining a clear distinction between predicting disease onset and demonstrating that early treatment can prevent it.
Sources: National Institute of Neurological Disorders and Stroke, Pre-fALS Study Record, Neurofilament Light Chain Research, UK Biobank.
Prepared by Ivan Alexander Golden, Founder of THX News, an independent news organization delivering timely insights from global official sources.
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