Blood Test Breakthrough Offers Hope for Chronic Fatigue Syndrome Diagnosis

When cells die, they release RNA into the bloodstream, creating a record of cellular activity. This RNA reveals changes in gene expression, cellular signaling, and tissue injury – offering a window into biological processes.

Cornell researchers have developed machine-learning models to analyze this cell-free RNA and pinpoint key biomarkers for myalgic encephalomyelitis, more commonly known as chronic fatigue syndrome (ME/CFS). This approach could pave the way for a diagnostic test for this debilitating disease, which is often difficult to confirm due to overlapping symptoms with other illnesses.

The findings were published August 11 in Proceedings of the National Academy of Sciences. Anne Gardella, a doctoral student in biochemistry, molecular, and cell biology in the De Vlaminck Lab, is the lead author.

This project was a collaboration between the labs of co-senior authors Iwijn De Vlaminck, associate professor of biomedical engineering in Cornell Engineering, and Maureen Hanson, Liberty Hyde Bailey Professor in the Department of Molecular Biology and Genetics in the College of Agriculture and Life Sciences.

“By reading the molecular fingerprints that cells leave behind in blood, we’ve taken a concrete step toward a test for ME/CFS,” De Vlaminck said. “This study shows that a simple blood draw can provide clues about the disease’s biology.”

De Vlaminck’s lab previously used this cell-free RNA technique to identify Kawasaki disease and multisystem inflammatory syndrome in children (MIS-C) – conditions that are also challenging to diagnose.