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Metabolomics and psychological features in fibromyalgia and electromagnetic sensitivity

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Abstract

Fibromyalgia (FM) as Fibromyalgia and Electromagnetic Sensitivity (IEI-EMF) are a chronic and systemic syndrome. The main symptom is represented by strong and widespread pain in the musculoskeletal system. The exact causes that lead to the development of FM and IEI-EMF are still unknown. Interestingly, the proximity to electrical and electromagnetic devices seems to trigger and/or amplify the symptoms. We investigated the blood plasma metabolome in IEI-EMF and healthy subjects using ¹H NMR spectroscopy coupled with multivariate statistical analysis. All the individuals were subjected to tests for the evaluation of psychological and physical features. No significant differences between IEI-EMF and controls relative to personality aspects, Locus of Control, and anxiety were found. Multivariate statistical analysis on the metabolites identified by NMR analysis allowed the identification of a distinct metabolic profile between IEI-EMF and healthy subjects. IEI-EMF were characterized by higher levels of glycine and pyroglutamate, and lower levels of 2-hydroxyisocaproate, choline, glutamine, and isoleucine compared to healthy subjects. These metabolites are involved in several metabolic pathways mainly related to oxidative stress defense, pain mechanisms, and muscle metabolism. The results here obtained highlight possible physiopathological mechanisms in IEI-EMF patients to be better defined.
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Metabolomics and psychological
features in bromyalgia
and electromagnetic sensitivity
Cristina Piras1*, Stella Conte2, Monica Pibiri1, Giacomo Rao3, Sandro Muntoni1,
Vera Piera Leoni1, Gabriele Finco4 & Luigi Atzori1
Fibromyalgia (FM) as Fibromyalgia and Electromagnetic Sensitivity (IEI-EMF) are a chronic and
systemic syndrome. The main symptom is represented by strong and widespread pain in the
musculoskeletal system. The exact causes that lead to the development of FM and IEI-EMF are
still unknown. Interestingly, the proximity to electrical and electromagnetic devices seems to
trigger and/or amplify the symptoms. We investigated the blood plasma metabolome in IEI-EMF
and healthy subjects using 1H NMR spectroscopy coupled with multivariate statistical analysis. All
the individuals were subjected to tests for the evaluation of psychological and physical features.
No signicant dierences between IEI-EMF and controls relative to personality aspects, Locus of
Control, and anxiety were found. Multivariate statistical analysis on the metabolites identied by
NMR analysis allowed the identication of a distinct metabolic prole between IEI-EMF and healthy
subjects. IEI-EMF were characterized by higher levels of glycine and pyroglutamate, and lower
levels of 2-hydroxyisocaproate, choline, glutamine, and isoleucine compared to healthy subjects.
These metabolites are involved in several metabolic pathways mainly related to oxidative stress
defense, pain mechanisms, and muscle metabolism. The results here obtained highlight possible
physiopathological mechanisms in IEI-EMF patients to be better dened.
Fibromyalgia (FM) is a complex multiorgan system disease with unknown etiology1. According to 2016 revisions
to the bromyalgia diagnostic criteria, bromyalgia may now be diagnosed in adults when all of the following
criteria are met: (1) generalized pain, symptoms have been present at a similar level for at least 3months, (2)
Widespread pain index (WPI) ≥ 7 and symptom severity scale (SSS) score 5 OR WPI of 4–6 and SSS score 9,
(3) a diagnosis of bromyalgia is valid irrespective of other diagnoses2. us, FM patients show a signicant
reduction in their quality of life and are frequently absent from their workplace. Several attempts have been
unsuccessfully made to identify reliable, sensitive, and measurable biomarker, so that, currently, the diagnosis is
mainly clinical1. However, some studies have shown a condition of oxidative stress, quantiable by measurement
of reactive oxygen species amount, as a relevant element in the pathogenesis of FM35.
Due to the increased electrosmog exposure, concerns about the likely harmful eects of the extremely low-
frequency magnetic, and both low and high electrical elds have increased over the last decades. Accordingly,
some people reported severe symptoms in the proximity of electrical devices operating at various frequency
ranges. is phenomenon is called idiopathic environmental intolerance attributed to electromagnetic eld
(EMF) which is associated with non-specic physical symptoms (NSPS) appearing when an electromagnetic
eld source is present and perceived by an individual6. is condition can have major implications with general
health status decline, increased distress, and health service use and impairments in occupational and social
functioning79. Like FM, IEI-EMF is a highly disabling pathology characterized by dermatological, neurologi-
cal, vegetative and cognitive symptoms. Among these, the prevailing are allergies, food and drugs intolerances,
stress, backache, abnormal fatigue, muscle tension, skin dryness, joint pain, headache, photosensitivity and
sleep disorder10, dizziness, diculties in concentration, memory problems, anxiety, respiratory problems (e.g.
diculties of breathing), gastrointestinal symptoms, eye and vision symptoms (e.g. double vision and blurred
vision), palpitations and so on11.
OPEN
Department of Biomedical Sciences, University of Cagliari, Cittadella Universitaria, Monserrato, CA,
Italy. Department of Education, Psychology and Philosophy, University of Cagliari, Cagliari, Italy. National
Institute for Occupational Accident Insurance (INAIL), Rome, Italy. Department of Medical Sciences, University of
Cagliari, Cagliari, Italy. *email: cristina.piras@unica.it
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A survey performed in 5 European countries (France, Germany, Italy, Portugal, and Spain) estimated 4.7%
of the prevalence of bromyalgia in the general population12. Health consequences can be serious for long-ill
patients who are oen forced to leave work and move from their homes.
Some studies focused on the probable causal link between health worsening and exposure to EMF. e trig-
ger of health problems may be the continuous exposure to dierent electrical devices and appliances such as
computers, general oce equipment, uorescent lights, household appliance, television etc. ese problems can
worsen with time as indicated by the relatively poor prognosis11. e dangerous eects related to EMF are well
described in the literature and sensitive subjects could be likely aected by low and high levels of EMF exposure.
Nevertheless, there is the assumption that only the acute exposure to EMF is dangerous to human health, whereas
the chronic one can be neglected. Many cases of childhood leukemia and tumors in adults due to occupational
and residential exposure to electric elds or EMF have been reported1319. Moreover, it has been associated with
increased incidence of spontaneous abortion20 and neurodegenerative diseases21, such as Parkinson and Alz-
heimer disease20 and lateral amyotrophic sclerosis22. Noteworthy, the increased risk was found at magnetic eld
levels comparable with the ones present in a residential situation (0.2–5.0 Mt)23. Furthermore, Gennaro etal.24
reported neoplastic and pre-neoplastic eects in rodents irradiated with EMF at levels corresponding to those
associated to human exposure, considering the dierent exposure conditions and the dierent lifespan between
humans and rodents (to 0.3µτ in residential exposure to power lines)25.
Some researchers interpret NSPS related to IEI-EMF as the outcome of anxiety, depression, somatization,
symptoms of exhaustion, and stress26. Other dened NSPS symptoms to be related to certain personality traits
and cognitive-emotional factors (e.g. somatizations tendency, somatosensory amplication, elevated risk percep-
tion, worries about the possibility of harmful features of modern life called modern health worries)9,27. us,
these symptoms are considered of psychogenic origin28 or associated with the so-called nocebo phenomenon6,7.
In the present study, we conducted a plasma metabolomics characterization of patients with IEI-EMF and
FM compared to a control group. Metabolomics is a powerful analytical tool used for the identication of low
molecular weight molecules, able to capture diseasespecic metabolic signatures as possible biomarkers. In
the last 10years, metabolomics has been applied widely and successfully in various elds of medicine for the
study and discrimination of various pathologies such as cardiovascular29,30 and neurodegenerative and psychi-
atric diseases31,32, as well as cancer33. To date, the pathogenesis of FM and specically of IEI-EMF is completely
unknown. In this study, we used metabolomics analysis as a tool to better understand the pathogenetic mecha-
nisms that support FM and identify disease-specic biomarkers34. Furthermore, to verify the psychogenic origin
of IEI-EMF, patients and control groups were tested for anxiety, locus of control, and personality.
Results
Multivariate analysis of variance: psychosocial descriptors. In accordance with the BFQ theory,
the dierences in personality, anxiety in state, and in trait locus of control were evaluated with multivariate
analysis of variance (MANOVA and ANOVA) to ascertain dierences between IEI-EMF subjects and controls.
MANOVA showed no signicant dierences between the two groups (F = 0.91; df = 7/49; p = 0.51). ANOVA
results with factor groups (IEI-EMF subjects and controls) and with dependent variable “Energy”, “Friendship,
“Conscientiousness, “Emotional stability”, “Openness”, STAI-Y (State), STAI-Y (Trait), and Locus of Control
showed no signicant dierences between IEI-EMF subjects and controls (p > 0.05) (Table1).
Metabolomics. Multivariate statistical analysis. 1H-NMR spectroscopy coupled with multivariate data
analysis was applied to investigate the metabolomics prole of plasma samples for both IEI-EMF subjects and
controls. 1H-NMR spectra of plasma samples from controls and IEI-EMF subjects are shown in Supplementary
Fig.S1. Each 1H-NMR spectrum can be divided into two main spectral zones: the region between 0.5–5.5ppm
characterized by a large number of partly overlapping peaks due to the aliphatic groups of free amino acids,
organic acids and sugars, and the region between 6.8–8.5ppm characterized by the signals from the aromatic
metabolites. e whole 1H-NMR dataset was subjected to multivariate statistical analysis. PCA analysis (data
not shown) was performed to evaluate the homogeneity of the samples in each group (IEI-EMF and controls)
and identify potential outliers (outside the 95% condence limit). Both IEI-EMF and control groups resulted in
Table 1. Psychological variables and FIQ (Fibromyalgia Impact Questionnaire) (means and standard
deviations).
IEI-EMF Controls F (df ) p
Energy-Extraversion 78.71 (± 11.28) 76.43 (± 8.93) 0.99 (1/49) 0.32
Friendship 84.90 (± 11.59) 86.30 (± 11.61) 0.03 (1/49) 0.85
Conscientiousness 81.10 (± 10.19) 81.85 (± 9.87) 0.69 (1/49) 0.41
Emotional Stability 67.72 (± 18.01) 73.28 (± 14.25) 0.48 (1/49) 0.49
Opennes 85.48 (± 14.26) 91.33 (± 10.15) 2.11 (1/49) 0.15
Stay-Y State 34.85 (± 14.26) 32.33 (± 11.04) 0.95 (1/49) 0.33
Stay-Y Traits 40.33 (± 12.33) 37.61 (± 11.04) 0.47 (1/49) 0.49
Locus of C ontrol 17.66 (± 13.55) 24.14 (± 17.30) 0.89 (1/49) 0.35
FIQ 66.23 (± 12.3) – –
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particularly consistent results and showed no outliers. To remove potential information not related to the disease
of interest and highlight possible metabolic dierences between IEI-EMF subjects and controls, and OPLS-DA
analysis was subsequently conducted on the same dataset. OPLS-DA scores plot (Fig.1a) showed good separa-
tion between IEI-EMF subjects and controls indicating dierences in the metabolomics prole between the two
groups.
e OPLS-DA model was established with one predictive and two orthogonal components and showed good
values of R2X, R2Y, and Q2 (Table2). e validity of the OPLS-DA model was evaluated through a permutation
test (Supplementary Fig.S2) using 500 times. e test results are reported in Table2 and indicate the statistical
validity of the OPLS-DA model. e S-line plot was used to identify the potential metabolites that contributed
to the plasma metabolome modication in IEI-EMF subjects compared to controls (Fig.1b). A p(corr) > 0.6 was
selected as a signicance level.
Figure1. (a) OPLS-DA scores plot of 1H-NMR spectra of plasma samples: Controls (full circle), IEI-EMF
subjects (open circle). (b) Color-coded coecient loadings plot of metabolomics prole between Controls
and IEI-EMF subjects. Peaks: 1 and 2, 2-hydroxyisocaproate and Isoleucine; 3, Lactate; 4 and 5, Homoserine
and Glutamine; 5 and 6, Glutamine and Pyroglutamate; 7, 8, 9 and 10, Glutamine, Glycine, Myo-inositol and
Choline; 10 and 11, Serine and Choline.
Table 2. Statistical parameters for OPLS-DA model. a e number of Predictive and Orthogonal components
used to create the statistical models. b,c R2X and R2Y indicated the cumulative explained fraction of the
variation of the X block and Y block for the extracted components. d Q2cum values indicated cumulative
predicted fraction of the variation of the Y block for the extracted components. *R2 and Q2 intercept values
are indicative of a valid model.
OPLS-DA modelusing the whole 1H-NMR metabolomics prole
ComponentsaR2XcumbR2YcumcQ2cumdR2 intercept Q2 intercept
IEI-EMF versus
controls 1P + 2O 0.639 0.699 0.557 0.239 −0.439
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Signicant metabolites identication. e metabolic prole of IEI-EMF was characterized by changes in dier-
ent spectral regions with overlapped signals due to dierent metabolites such as 2-hydroxyisocaproate, choline,
glucose, glutamine, glycine, homoserine, isoleucine, lactate, myo-inositol, pyroglutamate, serine, and taurine.
To evaluate the actual importance of the individual metabolites, the spectral regions highlighted in the S-line
plot were quantied by Chenomx NMR Suite 7.1. and subjected to Mann–Whitney U test to identify signicant
variations of their concentration in the two groups. e results of the univariate statistical analysis showed that
only 2-hydroxyisocaproate, choline, glutamine, glycine, isoleucine, and pyroglutamate changed signicantly in
IEI-EMF subjects compared to controls (with p-value < 0.05). e relative concentrations of these metabolites
in the two groups were compared using box-and-whisker plots. As shown in Fig.2, IEI-EMF subjects were
characterized by a higher level of glycine and pyroglutamate, and lower levels of 2-hydroxyisocaproate, cho-
line, glutamine, and isoleucine compared to controls. en, a new PCA model was constructed using only the
Figure2. Box-and-whisker plots showing progressive changes of the metabolites concentration on Controls
and IEI-EMF plasma samples. Statistical signicance was determined using the Mann–Whitney U test and a
p-value < 0.05 was considered statistically signicant. e Holm-Bonferroni adjustment was applied.
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identied signicant metabolites and the result of the analysis is shown in Fig.3. Figure3 shows the projection
of the samples on the plane formed by the rst two PCs that explain 62.2% of the total variance and good separa-
tion between IEI-EMF and controls was observed, indicating how the identied metabolites play an important
role in the separation of the two groups.
e pathways analysis, performed using all the signicantly dierent metabolites resulting from the multivari-
ate analysis, underlined Aminoacyl-tRNA biosynthesis, Glutathione metabolism, Purine metabolism, Nitrogen
metabolism, Glycine, Serine and reonine metabolism, Cyanoaminoacid metabolism, D-Glutamine and D-glu-
tamate metabolism and Alanine, aspartate and glutamate metabolism as the most important networks (Fig.4).
Discussion
e IEI-EMF is a disorder potentially due to exposure to magnetic elds produced by electrical devices of com-
mon daily use. As the pathogenesis of IEI-EMF is currently controversial and little is known, to better characterize
the mechanisms associated with its onset here we investigated the blood plasma metabolome of IEI-EMF patients
and healthy subjects using 1H-NMR spectroscopy coupled with multivariate statistical analysis. To better under-
stand the metabolomics results, it was of fundamental importance to completely exclude that the symptomatology
described by IEI-AMF patients was dependent on psychological disorders. us, patient psychological charac-
teristics were evaluated through the administration of three tests, named Big Five Questionnaire, Locus of Control
Test and Stai-Y test. Data obtained, showed no signicant psychological dierences between IEI-EMF patients
and controls. It is important to emphasize that IEI-EMF patients did not take specic pharmacological treatments
for the reduction of pain and other comorbidities that accompany their daily lives. So, this feature likely has led
them to have a better compensatory cognitive strategy to control anxiety and, by using some precautions (such
as avoiding long exposures to EMF and using specic shielding clothing), a better adaptation to the environ-
ment. Opposite, and with the caveat that are preliminary data, the IEI-EMF patients showed alterations of the
metabolomics prole markedly distinguished from the controls, suggesting that the symptomatology of IEI-EMF
patients can be of pathological and not psychological nature. e separation between the two groups (IEI-EMF
subjects and controls) was mainly due to a particular set of metabolites. ese metabolites are involved in several
pathways, such as glutathione metabolism, purine metabolism, nitrogen metabolism, and, in general, the amino
acid metabolism. In particular, the IEI-EMF subjects showed signicantly higher levels of glycine and pyroglu-
tamate and lower levels of 2-hydroxyisocaproate, choline, glutamine, and isoleucine compared to controls. e
pain is a predominant symptom in IEI-EMF subjects, and in general in those aected by bromyalgia. Indeed, in
95% of cases, constant and diuse pain is mainly localized in the limbs and torso35,36. Some Magnetic Resonance
Imaging studies conducted to evaluate brain metabolism in patients with bromyalgia were able to highlight
the activation of the same brain areas triggered by painful stimuli (called "pain matrix") through the analysis of
specic molecules such as N acetylaspartate, creatine, choline, lactate, myoinositol, glutamine and glutamate3739.
en, the primary hypothesis on the pathogenesis of bromyalgia highlights the role of the central nervous
system in the amplication of pain perception and the development of other co-morbid symptoms, such as
sleep-related problems, fatigue, emotional distress, and cognitive diculties.
Figure3. PCA scores plot built with only identied signicant metabolites: Controls (full circle), IEI-EMF
subject (open circle).
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e metabolomics analysis here reported, have shown lower choline levels in the plasma of IEI-EMF subjects
compared to controls. Choline is a marker of phospholipid metabolism and participates in cellular membrane
turnover and osmotic regulation in glial cells. Previously, Fayed etal. have found40,41 a lower concentration of
choline in hippocampal and posterior cingulate cortex areas in FM patients compared to healthy subjects. Fur-
thermore, some authors reported that the change in choline levels provides a sensitive indication of altered brain
metabolic activity42,43. e decrease in choline concentration is associated with osmolar changes in the brain,
in particular, it seems to work as a compensatory response to the increased intracellular osmolarity caused by
the accumulation of glutamine in astrocytes. Based on this, the decline in choline concentration observed in
our IEI-EMF patients could be explained as a marker of altered cerebral activity which could be responsible for
the cognitive symptoms (such as confusion, forgetfulness, anxiety) appearing in these subjects in presence of
EMF source6. Compared to controls, IEI-EMF patients were also found to have higher levels of glycine. Glycine
is an inhibitory transmitter in the spinal cord and a positive modulator of the N-methyl--aspartate receptor
(NMDAr), which is thought to be involved in nervous system reorganization and chronic pain in bromyalgia
patients44,45. e NMDAr shows increased activity in bromyalgia and several studies have analyzed receptor
modulation as a target for therapeutic intervention46. When is in its inactive state, the NMDAr is bound to
extracellular magnesium and zinc and prevents the ow of cations through the synaptic channel. Receptor acti-
vation occurs aer binding to glutamate and glycine amino acids at dierent receptor sites. It results in calcium
inux, which triggers neuronal excitation and intracellular signaling cascades involved in synaptic plasticity
processes4648. e malfunctioning of the spinal inhibitory input on the central pain circuits shows a crucial role
in the facilitation and maintenance of chronic pain. Some research groups have shown that glycine-mediated
synaptic inhibitory neurotransmission in the spinal cord dorsal horn suppresses pain. Furthermore, inhibition
of glycine reuptake and positive allosteric modulation of the glycine receptor has been shown to increase spinal
glycinergic tone and improve pain behaviors in various rodent models of acute, inammatory, and neuropathic
pain47,49,50. e metabolomics analysis of IEI-EMF patients compared to controls has also shown low levels of iso-
leucine, glutamine, and end-products of leucine metabolism. Isoleucine and leucine, are branched-chain amino
acids (BCAAs) and are involved in stress, energy, and muscle metabolism. Resting muscle metabolizes BCAAs
and transamination amino acids to produce ATP through the tricarboxylic acid cycle (Supplementary Fig.S3).
Several authors5153 have suggested that an alteration in energy metabolism may be present in some of the
muscle bers of bromyalgia patients and that this may determine many of their symptoms, including generalized
pain. It has been hypothesized that a reduction in plasma and urine concentrations of BCAAs, such as isoleucine,
leucine e valine could be associated with potential muscle depletion. Accordingly, many studies have shown that
BCAAs supplementation may decrease muscle catabolism, reducing central fatigue, through increased competi-
tion for the cerebral uptake mechanism of tryptophan5456. e decreased levels of BCCAs may aect the body
glutamate-glutamine pool leaving the tissues more vulnerable to oxidative stress. Indeed, compared to controls,
Figure4. Summary of pathway analysis of IEI-EMF group compared to Controls. Plot wasobtained by using
MetaboAnalyst 4.0. “X axis” represents the impact of the identied metabolites on the indicated pathway. “Y
axis” indicates the extent to which the designated pathway is enriched in the identied metabolites. Circle
colors indicate pathway enrichment signicance. Circle size indicates pathway impact. 1, Glycine, serine and
reonine metabolism; 2, Aminoacyl-tRNA biosynthesis; 3, Nitrogen metabolism; 4, Glutathione metabolism;
5, Purine metabolism; 6, Cyanoaminoacid metabolism; 7, Alanine, aspartate and glutamate metabolism and 8,
-glutamine and -glutamate metabolism.
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the IEI-EMF patients showed a lower concentration of glutamine. Glutamine has numerous functions includ-
ing glutathione production57, muscle protein synthesis58, gut health, maintenance of acid–base balance in the
kidney59, and removal of toxic ammonia from the tissues. Glutathione is one of the most important antioxidant
molecules, and a limited supply of glutamine in the body could cause low levels of glutathione determining oxida-
tive stress-associated damage. us, in a study conducted on patients suering from chronic fatigue60, glutamine
supplementation has been suggested as a mean to support glutathione pathway deregulation. An alteration of
the glutathione pathway is consistent with the increased concentration of glycine and pyroglutamate observed
in IEI-EMF patients. e dipeptide γ-glutamylcysteine, formed in the rst step of glutathione synthesis, can
be the substrate for two dierent enzymes: γ-glutamylcyclotransferase (which produces pyroglutamate) and
GSH-synthetase (which uses glycine to produce glutathione)61. Several studies have shown that oxidative stress
due, for instance, to metal and / or drug toxicity, may lead to glutathione depletion with consequent accumula-
tion of pyroglutamate in the urine62, considered a specic marker for glutathione depletion63. Finally, in IEI-
EMF patients were found lower 2-hydroxyisocaproate levels compared to controls. e 2-hydroxyisocaproate
derives from leucine metabolism by transamination in human tissues, such as muscle and connective tissue.
It can be considered as an anti-catabolic substance and some studies have shown a decreased muscle protein
degradation associated with its intravenous infusion64,65. Moreover, it is a potent inhibitor of branched-chain
α-ketoacid dehydrogenase kinase, which may lead to increased catabolism of BCAAs. e catabolism in the
muscle is associated with the breakdown of muscle proteins and delayed-onset muscle soreness. Several studies
indicate that the administration of BCAAs, in particular leucine, and their transaminated metabolites, such as
Table 3. Demographic characteristics of the population (means and standard deviations).
IEI-EMF Controls
Age 47.46 (± 11.28) 45.86 (± 10.43)
Schoolarity 13.90 (± 4.23) 15.23 (± 3.71)
Main food intolerance of IEI-EMF (number of patients)
Foods containing nickel 6
Foods containing lactose 14 4
Foods containing gluten 15
Fruits 8 –
Solanaceous 11 –
e most frequent symptoms of IEI-EMF (number of patients)
Burning or pain of hands 16
Burning or pain of legs 12
Burning or pain of shoulder 14
Burning or pain of foot 14
Laryngitis 10 –
Pharyngitis 13 –
Tinnitus 13 –
Dizziness 14 –
Photosensitivity 15 –
Diculty in breathing 12
Tachycardia 11 –
Chills 15 –
Digestive slowness 22
Abdominal swelling 23
Irritable bowel 13 –
Heartburn 16 –
Bloating 14 –
Cystitis 10 –
Electromagnetic exposure at home and work
Main road 30 23
Nuclear site (less than 10km) 13 3
Steel Industries 17 10
Mobile towers (less than 1km) 31 14
Power lines 26 11
Dumps (less than 5km) 19 12
Industries of Chemicals (less than 5km) 21 8
Airport (less than 5km) 14 7
Renery (less than 5km) 17 5
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2-hydroxyisocaproate, can eectively relieve muscle pain symptoms and protect muscle from catabolism. In this
view, the lower levels of 2-hydroxyisocaproate observed in our IEI-EMF patients could be responsible of the
muscle weakness, which manifests as abnormal fatigue and muscle tension.
Conclusion
To the best of our knowledge, this is the rst study dealing with FM and electromagnetic sensitivity together
(IEI-EMF). We could not nd any study focusing on the relationship between IEI-EMF and metabolomics. is
aspect is the main element of the novelty of this current study.
e results obtained from our metabolomics study demonstrate how IEI-EMF patients are characterized by a
signicantly dierent metabolomics prole compared to control subjects. e most signicantly altered pathways
appear the ones correlated to oxidative stress defense and pain control. e results show no signicant dierences
between IEI-EMF patients and controls for personality aspects, Locus of Control, and anxiety. e cohort of IEI-
EMF patients studied is not fully representative of the FM population, as the patients were "highly motivated".
e almost total overlap between healthy controls and IEI-EMF patients for psychological characteristics may
be due to increased resilience, hope, and optimism in subjects with chronic invalidating, but non-fatal disease.
ey tend to hope that a better solution can be found for their pathology. Although preliminary and with some
limits, our data, indicate the presence of metabolic changes suggesting a possible physiopathological mecha-
nism. A limitation of our study is the population under investigation including only bromyalgic patients with
electromagnetic sensitivity. We could not nd patients with IEI-EMF without FM. We started from the general
assumption that, while all the IEI-EMF are also FM, not all FM are IEI-EMF as well. We agree that further studies
to better dene the role of FM vs IEI-EMF are needed. So, in our study, it is not possible to dierentiate eects
which are FM- or IEI-EMF-related. Some of the dierences observed may not be associated with IEI-EMF but
to FM too. In a future study, another group of patients with the only FM would be evaluated. Validation of the
present results may lead to new biomarkers discovery and therapeutic approaches.
Materials and methods
Characteristics of the study population. e study was carried out on 54 subjects: 31 aected by FM
and electromagnetic sensitivity, IEI-EMF (30 females and 1 male), and 23 controls (21 females and 2 males). e
demographic characteristics of the population under study are shown in Table3.
e participants lled in a questionnaire about their dietary habits to identify intolerance (or food avoidance).
Most IEI-EMF subjects (73%) showed intolerances particularly for milk and derivatives, wheat, and Solanaceae
(Table3).
e patients needed a previous diagnosis of FM to take part in this current study. Furthermore, according
to the American College of Rheumatology (ACR)66,67 they were required to ll in a questionnaire about their
symptomatology to ascertain the presence of FM. e questionnaire for symptoms was scored as 0 = no prob-
lem; 1 = slight problem (intermittent); 2 = moderate: considerable problem (oen detected); 3 = severe problem
(pervasive, continuous, aecting the quality of life).
Furthermore, the major inclusion criteria used to identify individuals with IEI-EMF were68: (1) attribution
of NSPS to either various or specic source of EMF; (2) self-reported IEI-EMF; (3) experience of symptoms
during or soon (from 20min to 24h) aer the presence or use of an EMF exposure source; (4) high score on
a symptom scale (corresponding to pervasive or considerable) in the presence of a EMF exposure source; (5)
limitation in the daily functioning of the individual due to the EMF-related health eects. e most frequent
symptoms are reported in Table3.
All participants were asked to complete a preliminary questionnaire focused on personal characteristics as
well as their personal medical and psychological history. Furthermore, they were all asked to ll in a question-
naire to assess their electromagnetic exposure at home and work and another questionnaire to assess their
chemical exposure at home and work currently or in the past (Table3). e details of the questionnaire about
electromagnetic exposure and chemical exposure are reported in Supporting Information.
e analysis of the questionnaires allowed us to classify the triggering events into ve groups approximately:
(1) chemical exposure; (2) electromagnetic exposure; (3) biological exposure (viruses, bacteria, fungi, mold,
etc.); (4) high fever; (5) psychological trauma. Many patients have undergone several convergent triggering
events in the same year. Conversely, subjects included in the healthy group did not present physical pathologies.
No participants took drugs 30days before blood sampling. Someone consumed supplements and cannabi-
noids. e exclusion criteria for both patients and controls were: the presence of systemic diseases such as
hypo- or hyperthyroidism; rheumatoid arthritis, vasculitis, diabetes mellitus, heart disorders, history of acute
or chronic infections, cerebrovascular diseases, alcohol abuse, depressive disorders, psychiatric pathologies and
abnormalities in routinary biochemical analyses of blood and urine.
e institutional ethics committee (University of Cagliari, Italy) approved the study and written informed
consent was obtained from all participating subjects and that was conducted in accordance with the Declaration
of Helsinki.
Psychological analysis. Evaluation of psychological characteristics. All subjects were administered three
tests to assess psychological characteristics: Big Five Questionnaire69, Locus of Control Test70 and Stai-Y test71.
All tests are self-report questionnaires and that can be administered in individual format. e details of the tests
are reported in Supporting Information.
Multivariate analysis of variance. A MANOVA was performed to ascertain dierences between IEI-EMF sub-
ject and controls in personality according to Big Five Questionnaire theory (BFQ), State–Trait Anxiety Inven-
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tory (STAI) and Locus of Control. Factors analyzed were: groups (2 levels: patients and controls) and dependent
variables: 5 for BFQ (Energy, Friendship, Conscientiousness, Emotion Stability, and Openness); 2 for Stay-Y
(Stai-Y Trait and Stai – Y State) and Locus of Control.
Aer MANOVA, eight 1-way ANOVAs were performed to test dierences between IEI-EMF subjects and
controls for each dependent variable72.
1H-NMR analysis. Sample collection and preparation for 1H-NMR experiments. Blood samples were col-
lected in heparinized tubes, immediately centrifuged at 4000rpm for 15min, and about 800 L stored at 80°C
until metabolomics analysis. e extraction of water-soluble metabolites from plasma samples was performed
based on the Folch, Lees, and Sloane-Stanley procedure73 and has been already described in previous papers
published31. 400L of plasma were dissolved in 1.2mL of a chloroform/methanol mixture (1:1, v/v) and 175µL
of H2O. e solution was centrifuged at 4500rpm and 4°C for 30min and 1mL of hydrophilic phase, con-
taining the low molecular weight water-soluble components, was separated from the lipophilic one, dried using
a speed vacuum concentrator (Eppendorf, Hamburg, Germany) and then stored at 80°C. Dried hydrophilic
plasma extracts were re-dissolved in 690 L of potassium phosphate buer in D2O (100mM, pH 7.4) and 10L
of TSP (sodium 3-trimethylsilyl-propionate-2,2,3,3,-d4) as chemical shi reference (δ 0.0) (98 atom % D, Sigma-
Aldrich, Milan). An aliquot of 650L was analyzed by 1H-NMR.
1H-NMR spectroscopic analysis. 1H-NMR measurements of plasma samples were carried out using a
Varian UNITY INOVA 500 spectrometer operating at 499.839MHz for proton and equipped with a 5mm dou-
ble resonance probe (Agilent Technologies, CA, USA). 1H-NMR spectra were acquired at 300K with a spectral
width of 6000Hz, a 90° pulse, an acquisition time of 2s, a relaxation delay of 2s. For each sample, 256 free
induction decays were collected into 64K data points74. e residual water signal was suppressed by applying a
presaturation technique with low power radiofrequency irradiation for 2s.
Aer Fourier transformation with 0.3Hz line broadening and a zero-lling to 64K, 1H-NMR spectra were
manually phased and baseline corrected using ACDLab Processor Academic Edition (Advanced Chemistry
Development, 12.01, 2010). Spectral chemical shi referencing on the TSP CH3 signal at 0.00ppm was performed
on all spectra. Metabolites were identied based on literature information and by using a dedicated library, such
as the Human Metabolome Database (HMDB, https ://www.hmdb.ca) and the 500MHz library from Chenomx
NMR suite 7.1 (Chenomx Inc., Edmonton, Alberta, Canada)75. Chenomx NMR Suite is an integrated set of tools
for identifying and quantifying metabolites in NMR spectra. It is equipped with reference libraries that contain
numerous pH-sensitive compound models that are identical to the spectra of pure compounds obtained under
similar experimental conditions. Essentially, a Lorentzian peak shape model of each reference compound is
generated from the database information and superimposed upon the actual spectrum. e linear combination
of all modeled metabolites gives rise to the total spectral t, which can be evaluated with a summation line.
NMR data preprocessing and multivariate statistical analysis. e ACD Labs intelligent bucketing
method was used for spectral integration between 0.80 and 8.50ppm74. A 0.04ppm bucket width was dened
with an allowed 50% looseness, resulting in buckets that ranged between 0.02 and 0.06ppm in width. e degree
of looseness allows the bucket width to vary over a particular value from the set bucket value. e intelligent
bucket method contains an algorithm, which identies local minima in the spectra and sets the buckets accord-
ingly. In this manner, a peak is integrated into one bucket, although it may be dierently shied in the spectra
because of the pH eect, for instance. e spectral region between 4.70 and 5.20ppm was excluded from the
analysis to remove the eect of variations in the presaturation of the residual water resonance. e spectral data
set was normalized to the total area to minimize the eects of variable concentration among dierent samples
and imported into the SIMCA soware (Version 15.0, Sartorius Stedim Biotech, Umea, Sweden). e variables
(spectral data) were mean Pareto scaled. Pareto scaling, i.e. each variable is divided by the square root of the
standard deviation, gives greater weight to the NMR data variables with less intensity but is not as extreme as
using unscaled data76.
Dierent procedures for multivariate statistical analyses of NMR data were used: Principal component analy-
sis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA).
A PCA was performed in the spectral data set to evaluate the homogeneity of the samples (controls and IEI-
EMF) and identify any possible trends and/or outliers between the samples77. OPLS-DA was used to reduce model
complexity and to better highlight samples discrimination. OPLS-DA a supervised classication technique and
maximizes the covariance between the measured data of the X-variable (peak intensities in NMR spectra) and
the response of the Y-variable (class assignment) within the groups. e goodness of the model was evaluated
using a sevenfold cross-validation and “permutation test” (500 times). e permutation test was calculated by
randomizing the Y-matrix (class assignment or continuous variables) while the X-matrix (peak intensity in NMR
spectra) was kept constant. e permutation plot then displays the correlation coecient between the original
y-variable and the permuted y-variable on the x-axis versus the cumulative R2 and Q2 on the y-axis and draws
the regression line. e intercept is a measure of the overt, Q2Y intercept value less than 0.05 is indicative of
a valid model. e estimated predictive power of the models was expressed by R2Y and Q2Y, which represent
the fraction of the variation of Y-variable and the predicted fraction of the variation of Y-variable, respectively.
A good prediction model is achieved when Q2 > 0.5. To highlight potential metabolites that mainly contributed
to group separation, an S-line plot for the OPLS-DA model was created. e S-line is a customized S-plot for
NMR spectroscopy data and combines the covariance (peak height) and correlation (color code) for the model
variables displaying both in a single graph. In particular, red signals in the spectra corresponded to metabolites
with greater contribution to the separation between the groups than blue signals, while the observed phase of
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the resonance signals on the predictive component reects the decrease or increase (negative or positive peaks)
of metabolite level in the groups. e p(ctr) is the centered loading vector of the rst principal component.
Univariate statistical analysis for 1H-NMR data. e statistical signicance of the dierences in
metabolite concentrations, quantied by using Chenomx NMR suite 7.1, was calculated by using using the
Mann–Whitney U test and a p-value < 0.05 was considered statistically signicant. e Holm-Bonferroni adjust-
ment was subsequently applied to the obtained p-values to acquire the level of signicance for multiple testing78.
Pathways analysis. e identied metabolites and their average relative intensities were analyzed using
the pathway topology search tool in MetaboAnalyst program79. e global test and relative-betweenness central-
ity were selected for pathway enrichment analysis and the pathway topology analysis, respectively. e relative-
betweenness centrality estimates the number of shortest paths going through the node.
Received: 2 May 2020; Accepted: 30 October 2020
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Author contributions
L.A., S.C. and C.P. conceived the study, directed the project, and designed the experiments. S.C. and G.R. obtained
the samples and clinical details. C.P. and V.P.L. performed metabolomics experiments and data analysis. C.P.,
S.C., and M.P. wrote the rst dra of the manuscript, and C.P., L.A., S.C., M.P., S.M., and G.F. contributed to the
nal version. C.P., L.A., S.C., M.P., S.M., and G.F. critically reviewed the data and the manuscript. All the authors
have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Competing interests
e authors declare no competing interests.
Additional information
Supplementary information is available for this paper at https ://doi.org/10.1038/s4159 8-020-76876 -8.
Correspondence and requests for materials should be addressed to C.P.
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