A nanoelectronics-blood-based diagnostic biomarker for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)

The research aimed to stimulate post-exertional malaise at a cellular level used a high-salt environment to simulate hypersmotic stress white blood cells from patients who met both the Canadian Consensus Criteria for ME/CFS and the more common CDC chronic fatigue syndrome criteria; the test correctly identified all patients with ME/CFS and all healthy controls. Isolated PBMCs from patient's blood were incubated in their own plasma. Stressing the PBMCs should cause excessive consumption of the high-energy metaboliate ATP. The study investigators stated that this type of salt stress has previously been used in this way in a number of human, animal and other studies.

Funding
Part supported by the National Institutes of Health, grant P01 HG000205, and the Open Medicine Foundation.

Abstract
There is not currently a well-established, if any, biological test to diagnose myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The molecular aberrations observed in numerous studies of ME/CFS blood cells offer the opportunity to develop a diagnostic assay from blood samples. Here we developed a nanoelectronics assay designed as an ultrasensitive assay capable of directly measuring biomolecular interactions in real time, at low cost, and in a multiplex format. To pursue the goal of developing a reliable biomarker for ME/CFS and to demonstrate the utility of our platform for point-of-care diagnostics, we validated the array by testing patients with moderate to severe ME/CFS patients and healthy controls. The ME/CFS samples’ response to the hyperosmotic stressor observed as a unique characteristic of the impedance pattern and dramatically different from the response observed among the control samples. We believe the observed robust impedance modulation difference of the samples in response to hyperosmotic stress can potentially provide us with a unique indicator of ME/CFS. Moreover, using supervised machine learning algorithms, we developed a classifier for ME/CFS patients capable of identifying new patients, required for a robust diagnostic tool.

Criticism

 * Only patients with ME/CFS and healthy controls were compared. The test was not tried for patients with other fatiguing or neurological illness.


 * Small sample size

Investigators

 * Rahim Esfandyarpour
 * Biochemistry research engineer at Stanford, and assistant professor in Electrical Engineering and Computer Science, University of California, Irvinehttp://engineering.uci.edu/users/rahim-esfandyarpour


 * Alex Arron Kashi
 * Stanford Genome Technology Center, Stanford University https://med.stanford.edu/profiles/browse?org=school-of-medicine/biochemistry/stanford-genome-technology-center


 * Moshen Nemat-Gorgani https://www.omf.ngo/2018/08/15/mohsen-nemat-gorgani/
 * Stanford Genome Technology Center and Department of Biochemistry, School of Medicine, Stanford University


 * Julie Wilhelmy
 * Department of Biochemistry, School of Medicine, Stanford University https://www.omf.ngo/2018/07/25/julie-wilhelmy/


 * Ron W. Davis
 * Stanford Genome Technology Center and Department of Biochemistry, School of Medicine, Stanford University, and Director of the Scientific Advisory Board of the Open Medicine Foundation

Citation
Esfandyarpour, R., Kashi, A., Nemat-Gorgani, M., Wilhelmy, J., and Davis, R. W. "A Nanoelectronics-blood-based Diagnostic Biomarker for Myalgic Encephalomyelitis/chronic Fatigue Syndrome (ME/CFS)." Proceedings of the National Academy of Sciences, April 29, 2019, 201901274. doi:10.1073/pnas.1901274116.