Blood Tests Show Promise for Early ALS Detection Years Before Symptoms Appear
Key Insights
Researchers have developed blood tests that can detect ALS (search)-related molecular signatures up to a decade before symptoms appear, potentially revolutionizing early diagnosis of the devastating neurodegenerative disease.
Two breakthrough studies demonstrate different approaches: one using proteomics analysis achieving over 98% accuracy in distinguishing ALS (search) patients from healthy individuals, and another utilizing cell-free DNA fragments with machine learning models.
The advances could enable earlier clinical trial enrollment, faster diagnosis, and better disease monitoring for ALS (search) patients, who currently face diagnostic delays of months to over a year.
Researchers have achieved significant breakthroughs in developing blood tests that can detect amyotrophic lateral sclerosis (search) (ALS (search)) years before symptoms appear, potentially transforming diagnosis and treatment of the devastating neurodegenerative disease. Two separate studies published in 2025 demonstrate promising approaches using different molecular signatures in blood samples.
Proteomics-Based Detection Shows Remarkable Accuracy
A study published in Nature Medicine in August 2025 revealed that signals associated with ALS (search) can be detected in blood samples as much as a decade before patients notice symptoms. The research, led by an international team including scientists from the Uniformed Services University of the Health Sciences (USU), used advanced proteomics technology to measure thousands of proteins simultaneously.
Dr. Clifton L. Dalgard, professor in USU's Department of Anatomy, Physiology and Genetics and director of The American Genome Center (search) (TAGC), served as co-senior author on the study. "By applying next-generation sequencing and multi-dimensional data analysis, we were able to support this global collaboration and help uncover findings that could transform how ALS (search) is diagnosed," Dalgard said.
The research team identified a distinct molecular "signature" in people who would later develop ALS (search). Using machine learning, they built a predictive model that distinguished ALS patients from healthy individuals and from people with other neurological conditions with over 98 percent accuracy.
Cell-Free DNA Approach Offers Alternative Detection Method
A separate study published in Genome Medicine by UCLA Health (search) researchers presents another promising approach using cell-free DNA fragments released into the blood from dying cells. This research, led by Dr. Christa Caggiano, represents the first study to test cell-free DNA as a potential ALS (search) biomarker.
"There is an urgent need for a biomarker in ALS (search) to diagnose patients more quickly, support clinical trials and monitor disease progression," said Caggiano, a postdoctoral fellow at UCLA Health (search)'s Neurology Department. "Our study presents cell-free DNA, combined with a machine learning model, as a promising candidate to fill this gap."
The UCLA team tested cell-free DNA because it releases from dying cells in different body tissues affected by the disease and carries distinct signatures caused by DNA methylation patterns. The test was able to significantly discriminate between ALS (search) patients and healthy participants and provided insights into disease severity.
Addressing Critical Diagnostic Challenges
ALS (search), commonly known as Lou Gehrig's disease (search), gradually destroys nerve cells (search) that control movement, leading to muscle weakness, paralysis, and death usually within two to five years of diagnosis. The disease typically affects patients aged 50-70, though earlier diagnosis in younger patients can provide higher life expectancy.
Currently, neurologists lack a standalone method to detect ALS (search) before symptoms appear. Most patients wait months, sometimes more than a year, before receiving a diagnosis, which delays treatment and prevents many from joining clinical trials in time.
Broader Clinical Implications
The UCLA study revealed an additional advantage: the cell-free DNA test examines signals from multiple tissue types, not just nerve cells (search). "The test was able to pick up signals from dying muscle tissue and inflammation, suggesting that ALS (search) may also affect muscle tissues and immune cells," according to the research findings.
Caggiano noted that their model "could not only distinguish ALS (search) patients from healthy individuals but also from those with other neurological conditions, which is a challenge for current ALS biomarkers."
Future Clinical Applications
The discoveries offer multiple potential benefits for patients and researchers. "For patients and families, the promise of earlier detection means hope for a beneficial outcome and empowers future planning," Dalgard explained. "If clinicians can identify ALS (search) long before symptoms appear, they may one day intervene earlier, slow disease progression, and extend lives."
The same proteomics approach may also provide valuable insights into other neurological conditions, such as Parkinson's disease (search), expanding its impact beyond ALS (search). However, both research teams acknowledge that larger studies with more diverse participants are needed before these tests can be implemented in clinical settings.
UCLA Health (search) is currently conducting a larger trial in conjunction with other research institutions to validate their biomarker test, while the international proteomics study continues to be recognized as a potential game-changer in neuroscience and medicine.