Scientific Reports volume 12, Article number: 21923 (2022) Piras, C., Pibiri, M., Conte, S. et al. Metabolomics analysis of plasma samples of patients with fibromyalgia and electromagnetic sensitivity using GC–MS technique. Sci Rep 12, 21923 (2022). https://doi.org/10.1038/s41598-022-25588-2
Fibromyalgia (FM) is a chronic and systemic condition that causes widespread chronic pain, asthenia, and muscle stiffness, as well as in some cases depression, anxiety, and disorders of the autonomic system. The exact causes that lead to the development of FM are still unknown today. In a percentage of individuals, the symptoms of FM are often triggered and/or exacerbated by proximity to electrical and electromagnetic devices. Plasma metabolomic profile of 54 patients with fibromyalgia and self-reported electromagnetic sensitivity (IEI-EMF) were compared to 23 healthy subjects using gas chromatography-mass spectrometry (GC–MS) coupled with multivariate statistical analysis techniques. Before the GC–MS analysis the plasma samples were extracted with a modified Folch method and then derivatized with methoxamine hydrochloride in pyridine solution and N-trimethylsilyltrifuoroacetamide. The combined analysis allowed to identify a metabolomic profile able of distinguishing IEI-EMF patients and healthy subjects. IEI-EMF patients were therefore characterized by the alteration of 19 metabolites involved in different metabolic pathways such as energy metabolism, muscle, and pathways related to oxidative stress defense and chronic pain. The results obtained in this study complete the metabolomic “picture” previously investigated on the same cohort of IEI-EMF patients with 1H-NMR spectroscopy, placing a further piece for better understanding the pathophysiological mechanisms in patients with IEI-EMF.
Fibromyalgia (FM) is a condition characterized by a constellation of symptoms, including chronic pain, depression, anxiety, autonomic disturbance, fatigue, and memory and sleep dysfunction1. These numerous symptoms of functional and emotional origin seriously compromise the life quality of patients making the treatment harder and difficult2. At present, FM patients are diagnosed using the 2016 revised FM criteria3 based on the Fibromyalgia Research guideline4. According to these revised diagnostic criteria, fibromyalgia may be diagnosed in adults based on: (1) presence of generalized pain, defined as pain in at least 4 of 5 regions, (2) presence of symptoms of similar intensity level for at least 3 months, (3) widespread pain index (WPI) ≥ 7 and symptom severity scale (SSS) score ≥ 5 or WPI of 4–6 and SSS score ≥ 9; in addition, (4) a diagnosis of fibromyalgia does not exclude the presence of other clinical important illness3. However, the key causalfactors/mechanisms involved in FM development have not been identified yet5 and the lack of objective parameters to diagnose the pathology renders the discovery of more effective and safer diagnostic biomarkers urgently needed. Sometimes, FM is associated to electrosensitivity (EHS)6. EHS is described as a multi-organ adverse reaction to electromagnetic field (EMF), characterized by a wide range of unspecific symptoms. They can vary with intensity and duration and are experienced as a result of exposure in the workplace or home to EMF emitted by various sources, whether low or high frequency. Since the 60s, in countries of East Europe, there were reports of a new workplace disease defined as “microwave sickness”7: these cases involved thousands of workers in the manufacture, inspection, repair, and maintenance of microwave equipment such as radars and radio/TV stations. These reports have been extended to mobile phones in the last forty years. Researchers generally outline three characteristic syndromes: 1) neurological and/or asthenic: heaviness of head, fatigue, irritability, sleepiness, memory loss, and electroencephalography changes; 2) autonomic vascular changes: sweating, dermographism, blood pressure changes; 3) cardiac: heart pains and electrocardiography changes. Notably, workers exposed for periods above five years exhibited greater symptomatology. In addition, ceasing work was found to bring about a stabilization or improvement of symptoms7. The prevalence of EHS may continue to rise in the future, coinciding with the increasing exposure to local and global wireless networks. This could lead to an increased interest in the scientific community for the discovery of specific biomarkers. To better understand the pathogenesis of FM and EHS and to identify disease-specific biomarkers, we recently have compared the metabolomic profile between patients with FM and Idiopathic environmental intolerance attributed to electromagnetic field (IEI-EMF)8 and healthy subject (controls) by using 1H-NMR spectroscopy and multivariate statistical analysis. Self-reported IEI-EMF patients are subjects characterized by the amplification of fibromyalgia symptomatology in association with the proximity of EMF source exposure9.
Data obtained have shown a different plasma metabolomics profile between IEI-EMF and control subjects with the first being characterized by higher levels of metabolites mainly involved in oxidative stress defense, pain development and muscle metabolism. To better define and characterize the physiopathological mechanisms associated to the IEI-EMF, in the present study we have compared the plasma metabolomics profile of same IEI-EMF patients and control subjects studied by Piras et al.8 by using the mass spectrometry gas chromatography (GC–MS) analysis. GC–MS is a highly sensitivity technique that allows detection of many metabolites in complex biological samples. GC–MS and 1H-NMR provide complementary information about different metabolites, so that the integration of both techniques can be a major advantage to obtain a more holistic view of the metabolome10.
Patients had to fill in a questionnaire and the psychological tests according to the European EMF 2016 Guideline11.
In addition to the tests previously administered to IEI-EMF patients8, the PAI test allowed to evaluate further clinical and psychological characteristics such as: anxiety, depression, related anxiety disorders (like obsessive–compulsive disorders (OCD), phobias, etc.) and emotional instability. No differences between groups for clinical variables (e.g., depression, anxiety, anxiety correlates and emotional instability) were observed (p ≤ 0.05) and all subjects were located under the clinical cut-off (Supplementary Table S1 a) and b)).
Metabolomics: multivariate statistical analysis
Principal Component Analysis (PCA) was then carried out to visualize the global distribution of samples and to highlight possible outliers. None of the samples had to be removed as outliers. Additionally, the quality control (QC), featuring a mix of all samples, was positioned in the middle of the PCA scatter plot indicating a reliable performance and reproducible of the GC–MS analysis (Figure S1).
A supervised OPLS-DA analysis was subsequently conducted on the same dataset. OPLS-DA scores plot showed a clear separation based on the metabolomics profile between IEI-EMF subjects and controls (Fig. 1a). The optimum OPLS-DA model was established with two predictive components and one orthogonal component, with R2X(cum) of 0.456, R2Y(cum) of 0.825 and Q2 of 0.614. The validity of the OPLS-DA model was evaluated through a permutation test (Fig. 1b) using 500 cross validations.
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