Circulating microRNA expression signatures accurately discriminate myalgic encephalomyelitis from fibromyalgia and comorbid conditions

Abstract

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), and fibromyalgia (FM) are two chronic complex diseases with overlapping symptoms affecting multiple systems and organs over time. Due to the absence of validated biomarkers and similarity in symptoms, both disorders are misdiagnosed, and the comorbidity of the two is often unrecognized. Our study aimed to investigate the expression profiles of 11 circulating miRNAs previously associated with ME/CFS pathogenesis in FM patients and individuals with a comorbid diagnosis of FM associated with ME/CFS (ME/CFS + FM), and matched sedentary healthy controls. Whether these 11 circulating miRNAs expression can differentiate between the two disorders was also examined. Our results highlight differential circulating miRNAs expression signatures between ME/CFS, FM and ME/CFS + FM, which also correlate to symptom severity between ME/CFS and ME/CFS + FM groups. We provided a prediction model, by using a machine-learning approach based on 11 circulating miRNAs levels, which can be used to discriminate between patients suffering from ME/CFS, FM and ME/CFS + FM. These 11 miRNAs are proposed as potential biomarkers for discriminating ME/CFS from FM. The results of this study demonstrate that ME/CFS and FM are two distinct illnesses, and we highlight the comorbidity between the two conditions. Proper diagnosis of patients suffering from ME/CFS, FM or ME/CFS + FM is crucial to elucidate the pathophysiology of both diseases, determine preventive measures, and establish more effective treatments.

HERV activation segregates ME/CFS from fibromyalgia and defines a novel nosological entity for patients fulfilling both clinical criteria

“…With no biomarkers available and unclear pathophysiology, ME/CFS and FM are often misdiagnosed, further complicating the identification of effective treatments. In this study, we tried to overcome patient heterogeneity by studying a very finely-phenotyped cohort diagnosed by a single expert clinician, representing one of the few studies ( 13 ) including samples of patients diagnosed with either ME/CFS, FM, or both (comorbid group) by two different clinical criteria (Canadian ( 4 ) and International Consensus ( 5 ) criteria, and the 1990 ( 7 ) and 2011 ( 3, 6 ) American College of Rheumatology (ACR) criteria, respectively).…”

“…Their frequent co-diagnosis drove the hypothesis of a "single syndrome" and promoted the search for common or differentiating factors (8)(9)(10). However, despite molecular support for ME/CFS and FM constituting different entities (11)(12)(13), their frequent joint appearance (over 50% comorbidity in females) (14) remains enigmatic.…”

Myalgic encephalomyelitis/chronic fatigue syndrome from current evidence to new diagnostic perspectives through skeletal muscle and metabolic disturbances

“…Recently, machine-learning approaches have been adopted to assist analysis of large data sets containing multiple parameters. 193,195,207,208 In addition to molecular markers, in vitro cell-based assays have been suggested as screening platforms. In one scenario, the cells are used as reporters to detect the existence of metabolic, cytokine, RNA, and other biomolecules in ME/CFS body fluids, based on the rich literature on the altered molecular profiles in these fluids.…”

“…Parallel to the effort to study single or a small number of potential biological markers, −omic approaches have been applied to analyze DNA, 95,191 RNA, 192 protein, 193–195 peptide, metabolic products, 158,196–200 microbiome, 134,189,201–204 and virome 192,205,206 in ME/CFS samples, aiming to understand disease pathology and mechanisms, and identify biomarker panels of diagnostic potential (Figure 4). Recently, machine‐learning approaches have been adopted to assist analysis of large data sets containing multiple parameters 193,195,207,208 . In addition to molecular markers, in vitro cell‐based assays have been suggested as screening platforms.…”

MicroRNA-Related Polymorphism and Their Association with Fibromyalgia

“…The following supporting information can be downloaded at: https://www. mdpi.com/article/10.3390/genes14071312/s1, Table S1: literature miRNAs [24][25][26][27][63][64][65][66][67][68]; Table S2: miRNASNP-v3 prediction; Table S3: miRDB target prediction.…”