Diagnosing ALS earlier through blood biomarkers
Summary
Using machine-learning models, scientists have identified a potential way to diagnose amyotrophic lateral sclerosis (ALS) earlier from a blood sample with more than 90% accuracy.
At a glance
Investigators found more than 2500 unique genes that express differently in ALS when compared with controls.
They input the data into a machine learning model, XGBoost, which they trained to predict whether ALS was present.
After narrowing panels down to contain between 27 and 46 genes, the model predicted ALS with up to 91% accuracy.
The models, which analyse blood for biomarkers through gene expression with RNA sequencing to detect ALS, also have the potential to predict disease severity and how long a person might live with the neurodegenerative condition, it is claimed.
Co-senior author behind the work, Eva L Feldman said: “Our findings present an incredible opportunity to potentially diagnose ALS earlier, which opens up doors to treatments and clinical trials for which people otherwise may not be eligible due to advanced disease.”
Patients with ALS typically survive between two and four years after they’re diagnosed. However, ALS is difficult for physicians to identify, especially early in the disease. Many early symptoms overlap with other more common neurological problems.
As a result, it can take over a year to get an official diagnosis, and patients may undergo unnecessary tests and procedures.
Instead of identifying a single biomarker measure of ALS, Michigan Medicine researchers developed a gene classifier that detected several future disease biomarkers to expedite diagnosis.
This tool, called a gene expression biomarker panel, is commonly used in oncology to diagnose breast cancer and classify tumour subtypes.
Chrispin Mukuwa
Software Developer
Fullstack software developer
