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Cell-Free RNA Classifier Reveals Immune Signatures in Chronic Fatigue Syndrome

Analyses of over 700 transcripts with machine learning yielded about 77% classification accuracy, highlighting immune dysregulation through cell-specific signals.

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Chronic fatigue syndrome is akin to always feeling like your battery is low. (Image by Single Line on Shutterstock)

Overview

  • Sequencing of plasma-derived cell-free RNA from ME/CFS patients and sedentary controls revealed alterations in gene expression linked to the illness.
  • More than 700 transcripts showed significant differences between cases and controls, providing data for classifier development.
  • Machine-learning models built on these transcripts achieved roughly 77% accuracy in distinguishing ME/CFS cases from controls.
  • Cell-type deconvolution highlighted six altered immune cell signatures, with plasmacytoid dendritic cells most elevated.
  • Led by Anne Gardella with co-senior Iwijn De Vlaminck and Maureen Hanson, the NIH- and WE&ME Foundation-funded study awaits broader validation before clinical use.