On Sept. 22nd the Agency for Healthcare Research and Quality (AHRQ) and their Evidence-Based Practice Centers published the draft systematic evidence review on the Diagnosis and Treatment of ME/CFS for comment. This report will be used for the Pathway to Prevention Workshop for ME/CFS to be held on December 9 & 10, 2014. (Read our full response to this draft report HERE.) One of the recommendations of the review is the need to test ME/CFS diagnostic criteria in other populations with diseases similar to ME/CFS where diagnostic uncertainty exists. This is necessary because ME/CFS is defined by symptoms that are common in many other medical and psychiatric diseases. Comparing ME/CFS to similar disorders helps determine effective diagnostic criteria to more specifically identify those who have ME/CFS.
Once ME/CFS is diagnosed there are many differences among patients; this is called heterogeneity and is common in most chronic diseases. Objective biological measures – known as biomarkers – can be helpful for delineating this heterogeneity and identifying ME/CFS subtypes. Importantly, biomarkers intended to be diagnostic for ME/CFS should be compared to diseases similar to ME/CFS to ensure the accuracy of the biomarker.
While there is more work to be done, in this month’s Research Digest we review three different studies that look at diseases that are common among patients labeled as ME/CFS and to identify more specific biomarkers for ME/CFS.
There have been studies published over the years that have looked at whether ME/CFS patients defined by the 1994 Fukuda criteria have other medical and psychiatric diseases that more accurately explain their symptoms. A study published in the 2013 Journal of Psychosomatic Medicine found that undiagnosed and comorbid disorders were common in people with a presumed diagnosis of ME/CFS.(1) The investigators set up an integrated diagnostic pathway designed to detect known medical and psychiatric diseases that may otherwise go undiagnosed. There were 377 patients with a presumed ME/CFS diagnosed referred to the study. Of these 279 were eligible for the study. An unequivocal ME/CFS diagnosis was given to 65 patients. Another 59 patients had ME/CFS together with a comorbid disorder that did not exclude the ME/CFS diagnosis. The remaining patients had sleep disorders, medical diseases or psychiatric diagnoses that excluded an ME/CFS diagnosis. This study highlights the importance of and need for diagnostic criteria that accurately detects ME/CFS and distinguishes it from other disorders. This will help target treatments appropriately and avoid diagnostic labels that are potentially harmful.
Several studies have used blood gene expression in an attempt to identify biomarkers that delineate ME/CFS subtypes. Jonathan Kerr has spearheaded many of these studies and in September published a paper in the Journal of Clinical Pathology titled, “Use of single-nucleotide polymorphisms (SNPs) to distinguish gene expression subtypes of chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME)”.(2) Kerr had previously identified 8 ME/CFS subtypes with different gene expression profiles (measuring the message RNA produced from genes.) In this study, he used the DNA sequence information of these genes to determine if it could be used to identify the same 8 ME/CFS subtypes and distinguish from people with depression and healthy controls. Kerr wanted to use the DNA genetic sequence rather than the message RNA because message RNA deteriorates quickly, making it challenging to use as a diagnostic biomarker. Kerr found that only some of the 8 ME/CFS subtypes were identified using the DNA genetic sequence data but that this method was insufficient to reproducibly differentiate subtypes. There are several reasons why this method did not delineate ME/CFS subtypes including small sample size and sample heterogeneity. Nonetheless, these results help inform future studies using genomic technologies to develop objective biomarkers for ME/CFS.
Ekua Brenu and a team from Australia published an interesting paper about the potential for a particular type of biomarker in PLOS ONE this September titled, “High-throughput sequencing of plasma microRNA in Chronic Fatigue Syndrome/Myalgic Encephalomyelitis”.(3) What makes this study interesting is the use of plasma – the clear liquid component of blood that is relatively easy and noninvasive to collect to detect microRNA. Unlike message RNA (discussed in the above study) microRNA are a more readily measured because they are short and can evade destruction, making it intriguing for use as a biomarker. MicroRNAs use their short sequence structure to regulate gene expression (they do not code for proteins as message RNAs do.) Brenu and team identified 19 microRNAs that were differentially expressed in the plasma of ME/CFS patients compared to controls. They confirmed significant up-regulation (increased expression) of three of these microRNAs. More ME/CFS patient samples need to be tested – as do diseases with similar symptoms – to determine the diagnostic utility of these plasma biomarkers for ME/CFS.
(1) Mariman A, Delesie L, Tobback E, Hanoulle I, Sermijn E, Vermeir P, Pevernagie D, Vogelaers D. Undiagnosed and comorbid disorders in patients with presumed chronic fatigue syndrome. J Psychosom Res. 2013 Nov;75(5):491-6.
(2) Shimosako N, Kerr JR. Use of single-nucleotide polymorphisms (SNPs) to distinguish gene expression subtypes of chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME). J Clin Pathol. 2014 Sep 19.
(3) Brenu EW, Ashton KJ, Batovska J, Staines DR, Marshall-Gradisnik SM. High-Throughput Sequencing of Plasma MicroRNA in Chronic Fatigue Syndrome/Myalgic Encephalomyelitis. PLoS One. 2014 Sep 19;9(9):e102783.Tags: Biomarker discovery, biomarkers, Blood-based Biomarkers, CFS research, diagnostic criteria, ME/CFS Research, research digest, Research funding October 24, 2014