The ME Association End of Week Research Round-up and ME/CFS Research Index| 31 January 2020


Charlotte Stephens, Research Correspondent, ME Association.

We show below two new research studies and abstracts published on ME/CFS in the final week of January, together with simple summaries that we hope you might find helpful.

The Index of Published ME/CFS Research

The research published in the month of January has now been included in the central Index of Published ME/CFS Research. This is a convenient way to locate and read all the most recent and past studies by subject matter and author with links to PubMed or the relevant journal.

The Index is free to download, comes with an interactive contents table and carries an A-Z list of all the most important studies (and selected key documents, articles and parliamentary debates etc.).

You can also find the index in the Research section of the website together with a list of Research Summaries from the ME Association that provide lay explanations of the more important and interesting work that has been published to date.

ME/CFS Research Published 24th – 31st January 2020

This week, two new research studies have been published:

  1. A brain imaging study from America has identified a pattern of activated neurological regions that differentiate CFS patients from sedentary controls, using functional magnetic resonance imaging (fMRI). The researchers scanned the brains of 38 CFS patients and 31 healthy controls before and after exercise, while undergoing a memory test. Using the results, they created a predictive model which was able to differentiate CFS from controls at 80% accuracy on Day 1 of the test. They concluded “This pattern (of activated neurological regions) provides a first step toward developing fMRI as a diagnostic biomarker.”
  2. Researchers from China established a mouse model of Fibromyalgia by injecting them with acid-saline (to mimic lactic acid build up in muscles- which causes fatigue and pain). They then successfully reduced the fibromyalgia pain using electroacupuncture (passing electrical currents through needles placed in acupuncture points). They also reduced fibromyalgia pain by injecting a drug called ‘APETx2’, which has analgesic (pain-killing) effects. It works by blocking ASIC3, an acid-sensing ion channel that sends pain signals to the brain via the central nervous system.

ME/CFS Research References and Abstracts

1. Provenzano D et al. (2020)
A Machine Learning Approach to the Differentiation of Functional Magnetic Resonance Imaging Data of Chronic Fatigue Syndrome (CFS) From a Sedentary Control.
Frontiers in Computer Neuroscience 14: 2.

Abstract
Chronic Fatigue Syndrome (CFS) is a debilitating condition estimated to impact at least 1 million individuals in the United States, however there persists controversy about its existence. Machine learning algorithms have become a powerful methodology for evaluating multi-regional areas of fMRI activation that can classify disease phenotype from sedentary control. Uncovering objective biomarkers such as an fMRI pattern is important for lending credibility to diagnosis of CFS.

fMRI scans were evaluated for 69 patients (38 CFS and 31 Control) taken before (Day 1) and after (Day 2) a submaximal exercise test while undergoing the n-back memory paradigm. A predictive model was created by grouping fMRI voxels into the Automated Anatomical Labeling (AAL) atlas, splitting the data into a training and testing dataset, and feeding these inputs into a logistic regression to evaluate differences between CFS and control. Model results were cross-validated 10 times to ensure accuracy.

Model results were able to differentiate CFS from sedentary controls at a 80% accuracy on Day 1 and 76% accuracy on Day 2 (Table 3). Recursive features selection identified 29 ROI’s that significantly distinguished CFS from control on Day 1 and 28 ROI’s on Day 2 with 10 regions of overlap shared with Day 1 (Figure 3). These 10 shared regions included the putamen, inferior frontal gyrus, orbital (F3O), supramarginal gyrus (SMG), temporal pole; superior temporal gyrus (T1P) and caudate ROIs.

This study was able to uncover a pattern of activated neurological regions that differentiated CFS from Control. This pattern provides a first step toward developing fMRI as a diagnostic biomarker and suggests this methodology could be emulated for other disorders. We concluded that a logistic regression model performed on fMRI data significantly differentiated CFS from Control.

2. Yen LT et al. (2020)
Preventing the induction of acid saline-induced fibromyalgia pain in mice by electroacupuncture or APETx2 injection.
Acupuncture in Medicine [Epub ahead of print].

Abstract
Background: Fibromyalgia (FM) is a syndrome involving chronic pain, fatigue, sleep difficulties, morning stiffness and muscle cramping lasting longer than 3 months. The epidemiological prevalence is approximately 3-5% in women and increases with age. Antagonism of acid-sensing ion channel 3 (ASIC3) reportedly attenuates acid saline-induced FM pain in mice.

Aims: Whether pre-treatment with electroacupuncture (EA) or APETx2 can attenuate mechanical hyperalgesia in this murine model remains unknown.

Methods: Accordingly, we examined the analgesic effect of EA in a murine model of FM pain induced by dual injections of acid saline and investigated whether EA or APETx2 can attenuate FM pain via the ASIC3 channel.

Results: EA significantly reduced mechanical hyperalgesia in this model. ASIC3 antagonism, induced by injecting APETx2, also significantly reduced mechanical hyperalgesia. The expression of ASIC3 in the dorsal root ganglia, spinal cord and thalamus was increased after FM model induction. Over-expression of these nociceptive channels was attenuated by pre-treatment with EA or an ASIC3 antagonist.

Conclusion: Our data reveal that both EA and ASIC3 blockade significantly reduce FM pain in mice via the ASIC3, Nav1.7 and Nav1.8 signalling pathways. Moreover, our findings support the potential clinical use of EA for the treatment of FM pain.

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