University of Miami Researchers Identify Blood Panel to Predict ALS Symptoms

For families carrying gene variants linked to amyotrophic lateral sclerosis, the wait for symptoms is one of the most agonizing aspects of the condition. While inherited genetics dictate that some high-risk individuals develop the disease relatively young, others experience onset later in life or never show symptoms at all. Physicians have long lacked a reliable method to forecast who will progress from genetic risk to clinical disease—a transition known as phenoconversion—creating a major barrier for clinical trials and early intervention.

Two Decades of Tracking Familial ALS Through the Pre-fALS Study

That clinical blind spot is beginning to clear thanks to a new international analysis led by scientists at the University of Miami Miller School of Medicine. The long-running project has tracked individuals with elevated genetic susceptibility to ALS as well as frontotemporal dementia, utilizing routine neurological exams, cerebrospinal fluid collection, and blood draws.

This longitudinal approach previously helped researchers identify neurofilament light chain, a protein that leaks into blood and spinal fluid when nerve fibers are damaged. That biomarker laid the groundwork for the pivotal phase 3 ATLAS trial, which tests whether the drug tofersen—commercially known as Qalsody—can delay or prevent symptom onset in unaffected carriers of a mutated SOD1 gene. Yet clinicians have continued to search for broader biomarker signatures to map the earliest biological shifts across a more diverse at-risk population.

How the 19-Protein Blood Panel Outperformed NEFL

To uncover those earlier warning signs, researchers examined 516 blood plasma samples from 137 participants across four distinct categories. The cohort included individuals who eventually developed ALS, those already diagnosed, asymptomatic gene carriers, and healthy controls without ALS-related variants. Using high-throughput proteomics platforms, the team measured more than 5,400 proteins in each sample to track how molecular levels changed years before clinical symptoms emerged.

The analysis revealed 92 proteins that altered in the blood prior to symptom onset. Through machine learning models, the researchers distilled these findings down to a specific 19-protein panel. While NEFL remains a powerful individual predictor—surging sharply in the final months before diagnosis—the 19-protein panel outperformed NEFL alone across most tested time frames, forecasting phenoconversion in windows ranging from six months to five years.

Prediction Window	19-Protein Panel Accuracy	NEFL Alone Accuracy
1 Year Out	0.889	0.775
5 Years Out	0.802	0.712

When tested against validation datasets, including information from the UK Biobank, the predictive model demonstrated consistent performance. The panel estimated the time remaining until symptom onset with an average error of approximately 18 months, offering a substantially tighter window for clinicians and trial organizers.

Shifting Focus From Nerves to Muscle Biology

A striking takeaway from the proteomics data is that many of the proteins in the 19-member panel are not traditional markers of nerve degeneration. Instead, a significant share relates directly to muscle structure, maintenance, and breakdown. Proteins associated with muscle wasting began rising in the bloodstream years before neurological symptoms appeared, hinting that muscular changes may initiate much earlier in the disease timeline than previously recognized.

Dr. Benatar noted that the findings create new opportunities for prevention trials by allowing researchers to enroll appropriate participants at the optimal time. Investigators emphasize, however, that the blood test is not yet ready for routine clinical deployment. Further validation across larger, more diverse populations is required, alongside adaptation to simpler laboratory platforms, before physicians can routinely answer when genetic carriers are likely to fall ill.