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Article

Olfactory-Related Quality of Life in Multiple Chemical Sensitivity: A Genetic-Acquired Factors Model

by
Alessandro Micarelli
1,2,*,†,
Andrea Cormano
3,†,
Daniela Caccamo
4 and
Marco Alessandrini
5
1
Institute of Mountain Emergency Medicine, EURAC Research, I-39100 Bolzano, Italy
2
ITER Center for Balance and Rehabilitation Research (ICBRR), 02032 Rome, Italy
3
Domus Medica, Bagnoli del Trigno, 86091 Isernia, Italy
4
Department of Biomedical Sciences, Dental Sciences and Morpho-functional Imaging, Polyclinic Hospital University, 98124 Messina, Italy
5
Department of Clinical Sciences and Translational Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Int. J. Mol. Sci. 2020, 21(1), 156; https://doi.org/10.3390/ijms21010156
Submission received: 5 December 2019 / Revised: 21 December 2019 / Accepted: 24 December 2019 / Published: 25 December 2019
(This article belongs to the Special Issue Environmental Sensitivity Illnesses: Mechanisms and Molecular Signatures)
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Abstract

:
Genetic polymorphisms as well as environmental exposures to chemical compounds, iatrogenic, psychological, and physical trauma may play a pathophysiological role in multiple chemical sensitivity (MCS) olfactory complaints, given that xenobiotic metabolism is influenced by sequence variations in genes of metabolizing enzymes. Thus, the aim of the present study was to depict—by means of multiple regression analysis—how different genetic conditions, grouped according to their function as well as clinical background and environmental exposure may interfere with those olfactory complaints referred by MCS patients. Therefore, MCS patients after gene polymorphism sequencing, the olfactory-related quality of life score—calculated by means of the Questionnaire of Olfactory Disorder in forty-six MCS patients—have been found to significantly rely on the phase I and II enzymes score and exposure to previous compounds and surgical treatments. The present work—implementing for the first time a genetic-acquired factors model on a regression analysis—further reinforces those theories, positing MCS as a complex, multifactorial, disease in which the genetic risk related to phase I and II enzymes involved in xenobiotic detoxification, olfactory, and neurodegenerative diseases play a necessary, but probably not sufficient role, along the pathophysiological route of the disease.

1. Introduction

Multiple chemical sensitivity (MCS) is a relatively common clinical diagnosis in Western populations [1]. The prevalence of self-reported chemical sensitivity symptoms in population-based studies ranges from 9 to 33% [2], whereas physician-diagnosed MCS or reports of disabling consequences in the form of social and occupational disruptions are much lower, ranging from 0.5 to 6.3% [2,3].
MCS patients usually react to a wide range of everyday chemical compounds such as petrol, perfume, or pesticides by complaining of a wide spectrum of symptoms ranging from headache, fatigue, respiratory symptoms, dizziness, nausea, and especially, disosmia [4,5,6]. The discussion on the definition and nomenclature reflects the fact that the etiology of MCS is still unclear and a matter of debate [4,6].
Most frequently discussed etiologies include neurogenic inflammation [7], classical conditioning [8], and biochemical disruptions [1,6,9], finally hypothesizing that environmental exposure to chemical compounds, iatrogenic, psychological, and physical trauma as well as genetic polymorphisms may play a pathophysiological, mutually fostering role in MCS given that xenobiotic metabolism is influenced by sequence variations in the genes of metabolizing enzymes [6,10]. This is particularly evident—beyond controversial literature among olfactory testing results [1,5,11,12,13,14,15]—when focusing on those daily activities negatively impacted by MCS olfactory disperception, which finally accounts for higher scores demonstrated by these patients along the Questionnaire of Olfactory Disorders (QOD), representing one of the most frequent clinical complaints referred by MCS patients [1,12,13]. Such debate on the mechanisms relating olfaction and pathophysiological processes at the central level in MCS reflects uncertainties among those phenomena regarding the pathways through which environmental compounds may elicit responses from sensorial organs to the central nervous system. In fact, if on one hand, small airborne molecules may interact with the olfactory epithelium [16], on the other hand, non-volatile substances such as steroids [17], peptides [18,19], and proteins [20] can be detected only by the vomeronasal organ (VNO) [21], a partially atrophic, even if functional in many subjects, organ that has been suspected to be the primary target of olfactory steroids [22]. In this light, the expression of sex hormone binding globulin, a transport protein for estradiol and testosterone, and vitamin D binding protein and receptor in the VNO has been recently observed [23,24,25]. Furthermore, biosynthesis and metabolism of these neuroactive steroids also occur within the brain [26,27] where they are involved in limbic functions [28,29]. These aspects have led to the proposal that a sensory–cognitive–physiologic pathway mediated by the VNO, or due to a direct effect for receptor co-localization in the brain, may also exist, thus representing an alternative candidate mechanism for the phenomenon of MCS beyond the olfactory-mediated responses to low levels of airborne chemicals [30].
This burden is of additional particular relevance in MCS cases where the absence of a dose–response relationship and of a characteristic symptom pattern may be identified, thus leading to no reliable physiological markers that can be used to separate sufferers from non-ill individuals nor to pathomechanisms that can be clearly advocated to explain the symptoms, especially those affecting the sense of smell and their daily consequences [31,32,33,34].
Consequently, MCS patients are offered insufficient healthcare solutions and experience being met with doubt or limited understanding of their condition by healthcare professionals, the social welfare system, and society in general [35,36,37]. It is therefore essential that a deeper knowledge about the pathogenic pathways leading to symptoms elicitation in MCS participants is being generated [38].
In this vision, many attempts—with different results—have been conducted in order to highlight the possible relationships between genetic polymorphisms and quality of life tests and to depict possible routes of pathophysiological models underpinning the development of MCS-related complaints. Among the vast literature regarding the gene polymorphisms involved in MCS, the overall approach is, at least, to define the individual genetic profiles of enzymes involved in body detoxification from xenobiotics such as phase I metabolism cytochrome P450 monoxygenases (CYP450), phase II metabolism glutathione-S-transferase (GST) and N-acetyl-transferase (NAT2), antioxidant defense, namely mitochondrial superoxide dismutase (SOD2), paraoxonase 1 and 2 (PON1, PON2), endothelial and inducible nitric oxide synthase (NOS2, NOS3) as well as folate cycle/methylation (MTHFR) [10,31,39,40,41,42,43,44,45,46]. The hypothesis underlying the above cited investigations was the fact that the extreme individual sensitivity in MCS patients could be related to the inherited impairment in xenobiotics/endobiotics metabolism and to a vicious cycle [47] including peroxynitrite overproduction and lipid peroxidation, possibly not coped by the impairment in SOD, catalase, glutathione peroxidase, and NOS enzyme activities [48].
Thus, given (i) the fact that the more endorsed models underpinning the MCS pathomechanisms rely on the reciprocal influences between environmental exposure, clinical background, and the genetic profile involved in xenobiotics metabolism, and ii) the lack of literature regarding the possible impact of this multifactorial model on olfactory behavior, the aim of the present study was to depict, by means of multiple regression analysis, how different genetic conditions grouped in light of their function as well as clinical background and environmental exposure may interfere with those olfactory complaints referred by MCS patients.

2. Results

Forty-nine consecutive MCS patients were enrolled. Among them, one was using antidepressant drugs, one reported a history of alcohol abuse, one of diabetes, and were therefore excluded. Therefore, 46 MCS patients (27 women and 19 men; mean age 47.2 ± 10 years) met the eligible criteria and were included in the study. Table 1 depicts the clinical–anamnestic data of the MCS population in the study and the number and percentage of patients for each class of enzymes and for the other factors computed in the regression model as well as their score average.
The analysis of polymorphisms in genes coding for enzymes involved in xenobiotic metabolism pathways showed that the frequencies of CYP2C9*2 and *3 polymorphisms, in heterozygous state and double heterozygous state, were significantly higher in MCS patients compared with those in the Caucasian general population available from the literature (Table 2). Individuals bearing either the *1/*2 or the *1/*3 genotype were classified as CYP2C9 poor metabolizers, while those with the *2/*3 diplotype were considered very poor metabolizers. Although a higher frequency was observed for CYP2C19 *1/*2 and *1/*17 genotypes, associated with the phenotype poor metabolizer (PM), no significant differences resulted when comparing MCS patients with the Caucasian general population, likely due to the small number of recruited subjects.
The following CYP2D6 alleles were included in this study with functional status as assigned: normal enzyme activity (functional) *1, *2, *2xN, *2A; reduced enzyme activity: *10, *41; null enzyme activity: *4, *6 [49]. The frequencies of CYP2D6 mutated alleles were similar to those described in the Caucasian general population [50]. Individual phenotypes were inferred from participants on the basis of the commonly assigned function for allele combinations, as follows: functional/functional (extensive metabolizer, EM), functional/reduced (EM), functional/null (EM), reduced/reduced (intermediate metabolizer, IM), reduced/null (IM), null/null (poor metabolizer, PM). The frequencies of the called phenotypes were: PM = 6.5%, IM = 6.5%, EM = 80.5%, and UM = 6.5%. The proportion of these phenotypes was within the range estimated in a general Caucasian population by Crews et al. [50]. The majority of the EM phenotype group possessed the CYP2D6 *1/*1 genotype (28.3%), followed by people having the CYP2D6 *2/*2, and *2A/*2A. Significant differences were found only after comparison of the IM MCS patients with IM in the Caucasian general population (Table 2).
A higher prevalence was found for mutated alleles of GST isoforms, namely GSTP1 313G, GSTM1 Del, and GSTT1 Del as well as for the variant UGT1A1*28 in MCS patients compared with the Caucasian general population. However, these differences did not reach statistical significance, or only in some cases tended to statistical significance, likely due to the small number of recruited subjects.
As a whole, these findings indicate that MCS patients have an impaired metabolism of xenobiotics, both in phase I and phase of body detoxification, and confirm previously reported observations [40,43,44,51,52].
When compared with the Caucasian general population, MCS patients presented with significantly higher frequencies of polymorphisms in genes coding for antioxidant enzymes, namely SOD2 A16V, CAT -C262T, and PON1 L55M (Table 1). Gene polymorphisms examined in our study are known to greatly affect antioxidant enzyme activities due to either resulting amino acid substitutions or the effects on gene transcription rate [53,54,55,56]. The resulting individual phenotype is characterized by a reduced antioxidant defense, potentially leading to increased susceptibility to oxidative stress. The observed increased prevalence of defects in antioxidant enzymes confirms previous results [10,40,51,52] and may provide a mechanistic explanation for reported MCS features of oxidative stress [48].
No significant differences were observed for the distribution of polymorphisms in genes coding for enzymes involved in DNA methylation and repair pathways, namely MTHFR and DNA repair enzyme 8-oxoguanine glycosylase 1 (OGG1) as well as for nitrosative stress-related enzyme NOS3 and immune response enzyme myeloperoxidase (MPO) (Table 2). The increased frequencies of NOS3 TT894, MTHFR TT677, and MTHFR AC1298 in MCS patients compared with the Caucasian general population are in agreement with previously published observations [40,45,51,57].
Multiple regression analysis was run in order to determine the results of LQrv in relation to the nine prognostic factors. The correlation was statistically significant only for the phase II class score, phase I class score, chemical compounds exposure, and previous surgery with partial correlation coefficient of 0.23, 0.28, 0.3, and 0.2, respectively (Table 3).
The multiple regression Equation (1) is as follows:
X = 0.87996 x 1 + 0.40192 x 2 + 1.09827 x 3 + 2.53639 x 4 + 1.25605 x 5 − 0.28221 x 6 + 1.89423 x 7 + 0.04117 x 8 + 0.81441 x 9 + 18.63568
where X is the predicted value of the LQrv; x1 is the MTHFR class score; x2 is the phase II class score; x3 is the phase I class score; x4 is the previous event of chemical compounds exposure (0 for absence and 1 for presence); x5 and x6 are the previous events of psychological and physical trauma (0 for absence and 1 for presence), respectively; x7 is the event of previous surgery (0 for absence and 1 for presence); x8 is the patient’s age and x9 is the patient’s gender (1 for female and 0 for male) (Table 3) for which t-values were contrasted on the significant p-value cut-off on a Pareto chart (Figure 1). The multiple correlation coefficient was 0.88 with a p value less than 10−4.
Finally, Table 4 and Figure 2 depict the desirability model results in partial and global desirability values for each prognostic factor, particularly depicting a cut-off value of the phase I class score, phase II class score, previous events of chemical compounds exposure, and surgery equal to 1.5, 5.84, 0.52, and 0.3, respectively in order to obtain a LQrv score at least equal to 28.67.

3. Discussion

The first interesting aspect of the present study resides in those partial coefficients depicting a multifactorial model contributing to one of the most complained symptoms in MCS, in other words, olfactory alterations which beyond a wide sensorial discomfort referred in this disorder [1,6,12,13,68,69,70] have been found to severely impact on the quality of life and routine activities of MCS patients [1,6,12,34]. In particular, when paying attention to both the regression model and the Pareto chart highlighting respective t-values accounting for significant impact on LQrv, such a multifactorial model was hierarchically contributed to by genetic factors (phase I and phase II classes scores), environmental (chemical compound exposures), and anamnestic characteristics (presence of previous surgery events) of the patients. This tends to confirm previous hypotheses suggesting the inherited and acquired dysfunction of the chemical defensive system as a molecular basis for MCS complaints [42,48]. Indeed, adequate body response to environmental toxicants presumably requires proper function of the xenobiotic detoxification pathways. Among those factors contributing to variability in human response to toxicants, it can be expected that inherited and acquired variations in the metabolism and excretion of xenobiotics play a major influence. Indeed, it is well known that fat soluble xenobiotics are typically absorbed from the digestive tract, oxidized to intermediates that may be highly reactive, conjugated to increase their solubility, then excreted either by the kidneys in urine, or by the liver in bile [71]. Specifically, elimination of renally conserved, nonpolar fat soluble compounds tends to be more troublesome to the human body and requires sequential metabolic steps. In particular, phase I metabolism of xenobiotics typically activates nonpolar toxic compounds to make them more reactive through oxidation mediated by the cytochrome P450 family of enzymes. On the other hand, phase II conjugation reactions take compounds with an active functional group including compounds activated in Phase I reactions and add an endogenous substrate to that group to make the compounds more soluble and/or reduce their toxicity. Phase II conjugates include glucuronic acid, sulfate, glutathione, acetyl, glycine, and methyl groups [71,72,73]. According to the general trend in literature, oxidative stress and diminished capacity in detoxifying mechanisms have been involved in the pathophysiology of MCS [9]. In particular, the pivotal point in the genesis of the MCS seems to reside in the strict connection existing between oxidative stress damage, neurogenic inflammation, and neural disorders [74,75], based on the vulnerability of the central nervous system to free radicals. In light of this, the present results further strengthen those studies [76,77], highlighting that neurogenic inflammation could be multi-factorially underpinned by genetic predisposition to the breakdown of oxidative stress mechanisms and exposure to environmental events. Following these assumptions, once neurogenic inflammation, together with central biochemical processes [9,78], has been established, according to the neural sensitization theory, MCS could be attributed to a pathological hyper-reactivity of neurons in some areas of the brain, mainly in the olfactory and limbic systems [79], inducing an over-reactivity to external stimuli in different end-organs [6], possibly relying on a olfactory-limbic kindling model [6,79].
Interestingly, the same pathways involved in the metabolism of xenobiotics have been previously linked both to impairment in the olfactory system and involvement in neurodegenerative as well as psychiatric diseases [80]. Previous studies focusing on MCS patients demonstrated altered olfactory sensitivity [1,13,34], although a direct correlation between detoxification pathways, chemical exposures, and/or anamnestic events has not been definitively established, possibly due to weaknesses related to MCS screening, relatively low accuracy of objective olfactory testing, and the absence of correlation between factors and specific tests investigating the quality of life in relation to olfactory disorders. However, many hypotheses have postulated that the chronic exposure (low-dose, overtime) of biogenic amines-based-pesticides (neonicotinoids and formamidines) may disrupt neuronal cholinergic and octopaminergic signaling and produce excessive reactive oxygen species and reactive nitrogen species [9,47,78,81]. These, in turn, may react with macromolecules and interfere with the mitochondrial respiratory chain and mitochondrial Ca2+ metabolism [9,47,78,81]. Oxidative stress has proven to impair cognitive behavior including olfactory learning and memory, especially in those conditions where detoxification pathways may not properly counterbalance the generation of damage [81,82]. This appears to be of much interest, considering that the effects of odors and chemical compounds are not only exerted by the stimulation of the olfactory system through inhalation, but they can also enter the body through absorption in the skin, nose, and mouth, and thereafter, enter the blood stream, thus reaching the brain due to their lipophilic characteristics and causing broad spectrum types of effects [83] such as breakdown in the subjective experience of odor [84].
We here demonstrated significant differences in the distribution of gene polymorphisms of enzymes involved in xenobiotic metabolism, oxidative stress, and DNA methylation/repair pathways between the MCS cohort here studied and the Caucasian general population, with a higher prevalence of gene defects in MCS patients than in healthy subjects (Table 2). Following the above-mentioned hypothesis that postulates the overlap between breakdown of detoxification pathways, neuropsychiatric disorders, and olfactory dysfunction, it is not surprising that the multiple regression and the desirability model demonstrated that phase I and II scores were increasingly associated with the risk of neuropsychological and quality of life consequences of the olfactory dysfunction in MCS (Table 3, Figure 1). By way of example, GSTs, accounting for phase II class score, are a family of multifunctional enzymes playing a central role in the detoxification of toxic and carcinogenic electrophiles [85], extensively indagated in MCS [52]. In fact, GST isoenzymes catalyze the conjugation of glutathione to a variety of electrophilic compounds including formaldehyde. If the lack of GSTM1 activity, which detoxifies the reactive metabolites of benzo[a]pyrene and other polycyclic aromatic hydrocarbons, is due to homozygous deletion of the gene [86,87], GSTT1 weakness has been found to negatively impact on the metabolism of various potential carcinogens such as monohalomethanes, which are widely used as methylating agents, pesticides, and solvents [86,87]. Furthermore, GST cytosolic activity in olfactory epithelium, the highest among extrahepatic tissues [39,86], is of particular interest in MCS, where the role of odorous triggers is important. In fact, acetaldehyde is one of the most important chemicals that induce sick house syndrome and MCS [88]. As further examples of such behavior, PON1—a high density lipoprotein (HDL)-associated enzyme which reacts with toxic organophosphorus compound including insecticides (malathion) and nerve agents (sarin, somon, and diazinon) [89] by cleaving the homocysteine-thiolactone ring [40,90,91]—is known to be polymorphic in humans, with two isoforms displaying distinct hydrolyzing activities. The Arg192 isoform hydrolyzes paraoxon rapidly, whereas the Gln192 isoform acts slowly [92]. PON1 genes were associated with Gulf War Syndrome whose complaints of olfactory dysfunction are extensively shared with those of MCS [39]. On the other hand, the overlapping symptoms among MCS spectrum disorders have been found to possibly share some common pathogenetic features such as increased nitric oxide/peroxynitrite levels [57] and its consequences on the olfactory pathways [93]. In light of this, SOD2 genetic polymorphisms, which may be considered genetic determinants of MCS risk [40,42,43,44,52], seem to be connected to the loss of efficiency of detoxification systems, disturbances of free radical/antioxidant homeostasis, and increased production of inflammatory cytokines [31,48,94]. Interestingly, it has also recently been reported that gene variants of NOS2 are associated with the alteration of NO levels in inflammatory bowel disorders, asthma, atopy, olfactory dysfunction, and migraine [57,93,95,96], all of which are comorbidities shared by environmental sensitivity illnesses [57].
In this vision, enzymes also involved in xenobiotic metabolism phase I demonstrated an interface between detoxification function, olfactory perception, and neurodegenerative and psychiatric disorders. In fact, animal models showed that the inhibition of rat CYP P450 monooxygenases increased the electro-olfactogram response amplitude, suggesting a role for these enzymes in signal termination [97], and that an odorant metabolite resulting from the cytochrome-dependent metabolism was able to activate an olfactory receptor [98,99]. CYP2D6, which is present not only in liver, but also at lower levels in brain and other tissues [40,100], metabolizes a wide variety of substances including therapeutic drugs, drugs of abuse, procarcinogens, and neurotoxins [40], and is genetically polymorphic [101]. Thus, the 5–10% of Caucasians who are CYP2D6 homozygous for two non-functional alleles display impaired metabolism of many centrally acting drugs and toxins such as tricyclic antidepressants, selective serotonin re-uptake inhibitors, monoamine oxidase inhibitors, amphetamines, codeine, neuroleptics, and neurotoxins [40]. The enzyme also metabolizes endogenous neurotransmitters [102], which may be related to the observation that poor metabolizers score higher on scales of neuropsychological disorders [103]. Recent literature has provided evidence for a link between the CYP2D6 genotype and many neurodegenerative disorders [40,100,104]. Table 5 summarizes the potential mechanisms involving the above-mentioned genes, their activity, and the polymorphisms affecting their function, possibly underpinning the physiopathological processes of MCS.
However, it is not surprising that the multiple regression and the desirability model highlighted two environmental and anamnestic factors impacting on the LQrv score, worsening the quality of life level of olfactory dysfunction. In particular, the desirability model clearly showed (Table 4, Figure 2)—in line with the Pareto chart built on the regression analysis (Table 3, Figure 1)—that chemical compounds exposure and previous surgery events may accrue, so that the LQrv score may increase over the level of 28.67. Thus, this aspect tends to agree with the literature vision suggesting that a) environmental and medical-surgical exposures may increase the possibility of an over-sensitivity toward external compounds and odorants [31,57] and that b) this may be evidenced in those subjects in which the risk is increased due to a genetic predisposition [31,38,57].
In conclusion, the present work, which implemented for the first time a genetic-acquired factors model on a regression analysis, further reinforces those theories positing MCS as a complex, multifactorial disease in which the genetic risk related to defects in xenobiotic detoxification phase I and II enzymes also involved in olfactory and neurodegenerative diseases plays a necessary, and probably at all not sufficient role in the pathophysiological route for which the combination of multiple aspects including acquired/environmental determinants induce an over-sensitivity to olfactory compounds, finally impacting on quality of life.

4. Materials and Methods

4.1. Participants

We included in the study MCS patients admitted to the Lazio Regional Center for Diagnosis, Prevention and Treatment of MCS and evaluated at ‘‘Tor Vergata’’ University for those symptoms related to smell complaints. Diagnosis of MCS was achieved according to the US Consensus Criteria for MCS [105] and the revisions suggested by Lacour et al. [68,70,106], which were operationalized as follows: (1) symptoms present for at least six months; (2) symptoms occurred in response to exposure to at least two of 11 common volatile chemicals (many of which are reported in Table 6, according to previous studies [107]); (3) co-occurrence of at least one symptom from the CNS and one symptom from another organ system; (4) symptoms cause significant lifestyle changes; (5) symptoms occur when exposed and lessen or resolve when the symptom triggering agent is removed; and (6) symptoms triggered by exposure levels do not induce symptoms in other individuals who are exposed to the same levels. Diagnosis was supported by biochemical analyses, showing high levels of oxidative stress and inflammation (not reported here).
The Ethical Committee Board of IDI IRCCS (Rome, Italy) approved the protocol research (approval no. 121/CE/2008; approval date: 11/12/2008). The study adhered to the principles of the Declaration of Helsinki and all of the participants provided written informed consent after receiving a detailed explanation of the study.

4.2. Genotype Analysis

Eleven participants underwent genetic testing for the presence of functional polymorphisms in genes coding for detoxification phase I enzymes, namely CYP2C9, CYP2C19, and CYP2D6; detoxification phase II enzymes GSTP1, GSTM1, GSTT1, and UGT1A1; antioxidant enzymes SOD2, CAT, PON1, and NOS3, OGG1; immune response enzyme myeloperoxidase (MPO); and folate cycle/methylation enzyme 5,10-methylene-tetrahydrofolate reductase (MTHFR).
Genomic DNA (gDNA) was isolated from peripheral blood white cells using the PUREGENE-DNA purification system (GENTRA, QIAGEN, Milan), according to the manufacturer’s instructions. The gDNA was quantified by spectrophotometric measurement at 260 nm using a Biophotometer (Eppendorf). gDNA quality was considered acceptable for samples with a 260/280 ratio ≥ 1.6. DNA integrity and the presence of contaminant RNA was checked by electrophoresis on 0.8% agarose gel.
The screening for the presence of gene polymorphisms in the above cited genes was carried out by either real-time PCR-based allelic discrimination, direct DNA sequencing, and allele specific PCR.

4.2.1. Allelic Discrimination by Real-Time PCR

The following gene polymorphisms were analyzed by real-time PCR using predesigned TaqMan Single Nucleotide Polymorphism (SNP) genotyping assays available from Applied Biosystems (ThermoFisher, Monza, Italy): CYP2C9*2 (C430T, rs1799853, R144C; assay ID: C__25625805_10), CYP2C9*3 (A1074C, rs1057910; assay ID: C__27104892_10), CYP2C19*2 (G681A, rs4244285; assay ID: C__25986767_70), CYP2D6*2 (2850C > T, R296C, rs16947; assay ID: C__27102425_10), CYP2D6*2A (-C1584G, rs1080985; C__32407252_30), CYP2D6*4 (G1846A, rs3892097; assay ID: C_27102431_D0), CYP2D6*41 (G2988A, rs28371725; assay ID: C__34816116_20), GSTP1 (A313G, I105V, rs1695; assay ID: C___3237198_20), SOD2 Ala16Val (C48T, rs4880; assay ID: C___1202883_20), CAT –C262T (rs1001179; C__11468118_10), NOS3 Glu298Asp (G894T, rs1799983; C___3219460_20), PON1 L55M (C108T, rs705379; assay ID: C__11708905_109), PON1 Q192R (A575G, rs662; assay ID: C___2548962_20), MTHFR C677T (Ala222Val, rs1801133; assay ID: C___1202883_20), and MTHFR A1298C (Glu429Ala, rs1801131; assay ID: C___850486_20). PCR conditions used were those previously reported [43,44,108,109,110]. PCR reactions were run in a Fast Real-time PCR 7900 (Applied Biosystems, ThermoFisher, Monza, Italy).

4.2.2. Sanger Sequencing

The presence of gene polymorphisms CYP2C19*17, CYP2D6*10, MPO -G463A (rs2333227), OGG1 C315G (rs1052133, Ser326Cys), and UGT1A1*28 (-54-53insTA, rs8175347) was screened by DNA direct sequencing by Sanger methods.
Briefly, PCR reactions were performed in a total volume of 50 μL, containing 100 ng of genomic DNA, 10 × PCR buffer (20 mM Tris-HCl, pH 8.4; 50 mM KCl), 2.5 mM MgCl2, 25 pmoles of each primer, 0.1 mM dNTPs, 0.1 % Triton-X; and 2.5 U of Taq polymerase (EuroClone, Milan, Italy). Primer sequences used in this work were: OGG1 C315G were, respectively: CYP2C19*17 5′- AAGAAGCCTTAGTTTCTCAAG-3′ (fwd)/5′-AAACACCTTTACCATTTAACCC-3′ (rev); CYP2D6*10 5′- TCAACACAGCAGGTTCA -3′(fwd)/ 5′- CTGTGGTTTCACCCACC -3′(rev); UGT1A1*28 5′- GATTTGAGTATGAAATTCCAGCCAG -3′ (fwd)/5′- CCAGTGGCTGCCATCCACT -3′ (rev); MPO -G463A 5′- CGGTATAGGCACACAATGGTGAG -3′ (fwd)/5′- GCAATGGTTCAAGCGATTC -3′ (rev); OGG1 C315G 5′- GGAAGGTGCTTGGGGAAT -3′ (fwd)/ 5′- ACTGTCACTAGTCTCACCAG -3′ (rev). Primer sequences were designed by Primer 3 Input v. 0.4.0 software freely available online, except for those of UGTA1, which were derived by Massacesi et al. [111].
The PCR reactions were carried out in an Eppendorf Master Cycler Pro® (Vapo-protect) PCR instrument (Eppendorf, Germany). After 35 cycles of amplification (denaturation at 94 °C for 30 s, annealing at 60 °C for 1 min, and extension at 72 °C for 1 min), the amplification products were electrophoresed in 2% agarose gel, and visualized after staining with ethidium bromide.
PCR products were purified as previously reported [112]. DNA sequencing of PCR products for the MPO gene (289 bp), OGG1 gene (189 bp), and UGT1A1 gene (351 bp) was performed using the Sanger method employing the BigDye Terminator v1.1 Cycle Sequencing kit (Applied Biosystems, Life Technologies, Milan, Italy). The reaction was carried out in a final volume of 20 µL, containing 25 ng of an appropriate amount (20 ng/100 bp) of purified PCR product, 5 pmol of the relevant primer, 2.5_Ready Reaction mix, 1_Sequencing Buffer, and DNAse/RNAse-free water, in the Eppendorf Master Cycler Pro® (Vapo-protect) PCR instrument (Eppendorf, Germany). The thermal cycling conditions were: 96 °C for 30 s and 28 cycles, 30 s at 96 °C, 10 s at 50 °C, 4 min at 60 °C, and finally 5 min at 4 °C. The cycle sequencing products were purified and analyzed on an automated ABI PRISMs 310 Genetic Analyzer (Applied Biosystems, Applera Corp., Milan, Italy) as previously reported [112].
Genotyping calls were manually inspected and verified prior to release. MPO and OGG1 mutated genotypes were assigned on the basis of single nucleotide substitutions, when present. UGT1A1 genotypes were assigned based on the number of TA repeats for each allele (i.e., 6/6, 6/7, 7/7, or 7/8).

4.2.3. Multiplex PCR Analysis for Deletion Polymorphisms

The deletion polymorphisms for the GSTM1 and the GSTT1 genes were determined simultaneously in a single assay using a multiplex PCR approach with the amplification of the GSTM1 and the GSTT1 genes from genomic DNA and using β-globin as the internal control, as previously described [44].

4.3. Olfactory Study

A detailed case history investigating events beyond the education level and occupation of chemical compounds exposure, physical and psychological trauma, and previous surgery was performed in all MCS subjects who underwent ear–nose–throat examination with fiberoptic check of the upper airways.
For the study of olfactory complaints during daily activities, we used the “life quality statements” from QOD that expressed the patients’ complaints related to the smelling difficulties [1,12,113]. There were in total nineteen life quality statements: seventeen negative statements (QOD-NS) with 3 points assigned for checking the box “agree”, 2 points for “partly agree”, 1 point for “partly disagree”, and 0 points for “disagree”, and two positive statements (QOD-PS) with 0 points by checking the box “agree”, 1 point “partly agree”, 2 points “partly disagree” and 3 points “disagree”. The sum of the scores for the QOD-NS and QOD-PS produces the LQ raw score (LQrv); a maximum of 57 points can be reached. High scores indicate a strong impairment [113].
Subjects with diabetes, oncologic or HIV history, neurological, and psychiatric or mood disorders, radiation, and traumatic brain injury were excluded from the study. No patient showed liver or renal abnormalities nor was pregnant or breastfeeding. Neurological diseases were excluded with the mini mental state examination and magnetic resonance imaging (MRI). All conditions that could potentially develop an olfactory dysfunction were considered as exclusion criteria. Thus, patients with sino-nasal disorders; neuro-psychiatric disorders (Parkinson’s disease, Alzheimer’s disease, schizophrenia, multiple sclerosis, and depression); lower airways and/or lung diseases; active hepatitis, cirrhosis, chronic renal failure, vitamin B12 deficiency; alcohol, tobacco, or drug abuse; cerebral vascular accidents; insulin dependent diabetes mellitus; hypothyroidism; and Cushing syndrome were not included in the study [114].

4.4. Data Handling and Statistical Analysis

In order to carry out statistical analyses, all selected polymorphisms were sub-grouped according to their functional effects and the enzyme role [71,115]. The following three classes were obtained: MTHFR (including MTHFR C677T and MTHFR A1298C), phase I (including CYP2D6, CYP2C19, UGT1A1*28, CYP2C9 A1075T, and CYP2C9 C430T), and phase II (including MPO G463A, GSTP1 A313G, GSTM1, GSTT1, SOD2 RS4880, CAT C262T, OGG1 C315G, PON1 A575G, PON1 C108T, and eNOSAsp298Glu). Wild-type, heterozygote, and mutated homozygote genotypes of the MTHFR C677T, MTHFR A1298C, MPO G463A, GSTP1 A313G, SOD2 RS4880, CAT C262T, OGG1 C315G, PON1 A575G, PON1 C108T, eNOSAsp298Glu, UGT1A1*28, CYP2C9 A1075T, and CYP2C9 C430T were vectorially scored from 0 to 2, respectively. Wild-type and deleted genotype of both GSTM1 and GSTT1 were scored 0 and 1, respectively, and the ultra-rapid, extensive, intermediate and slow metabolizer phenotype of CYP2D6 and CYP2C19 was scored from −1 to 2. A composite score for each class was computed by adding the score of the single form of the enzymes. Absence and presence of events of chemical compounds exposure, physical and psychological trauma, and previous surgery were scored 0 and 1, respectively. Number and percentage of cases for each gene condition and clinical–anamnestic aspect was calculated and compared with the literature findings by means of the Fisher’s exact test. The X2 test was carried out to define associations between categorical factors and groups. To assess that data were of Gaussian distribution, D’Agostino K squared normality and Levene’s homoscedasticity test were applied (where the null hypothesis is that the data are normally and homogenously distributed) [116].
Given the exploratory nature of the study and previous biomedical approaches [117], correlations between the LQrv and nine prognostic factors (genotypic/phenotypic score of MTHFR, phase I and phase II classes, absence/presence of events of chemical compounds exposure, physical and psychological trauma and previous surgery, age, and gender (0 = male, 1 = female) were examined by a multiple regression analysis. P-values less than 0.05 were considered as statistically significant. A Pareto chart contrasting the t-values and p-values of each factor were generated. Finally, a two-sided desirability model was achieved for each prognostic factor, according to the mentioned regression model and setting upper and lower limit of LQrv at 22 and 40, representing the lowest and highest desirability values, respectively [117,118]. Subsequently, the partial desirability functions (di) were combined into a single composite global desirability function, defined as the geometric mean of the different di values, implying that all responses are in a desirable range simultaneously and the combination of the different criteria is therefore globally optimum, so the response values are near the target values.

5. Limitations of the Study

The results of this study have to be considered with caution, because of their preliminary nature and considering the following limitations. In fact, no correction for multiple comparisons was performed in the multiple regression model. If this exploratory—rather than confirmatory—approach is extensively adopted in similar studies [117,119,120] to reduce the likelihood of Type II statistical errors as much as possible, this choice may have induced the increase in likelihood of Type I statistical errors. However, given these assumptions, the present data have to be considered as preliminary and should be replicated in further cohorts of patients.

Author Contributions

Conceptualization, A.M. and M.A.; Methodology, A.M., D.C., M.A., and A.C.; Software, A.M. and D.C.; Validation, M.A., A.M., D.C., and A.C.; Formal Analysis, A.M., D.C., and A.C.; Investigation, M.A., A.M., A.C., and D.C.; Resources, M.A. and D.C.; Data Curation, A.M., D.C., A.C., and M.A.; Writing—Original Draft Preparation, A.M., D.C., M.A., and A.C.; Writing—Review & Editing, A.M., D.C., and M.A.; Visualization, A.M. and D.C.; Supervision, M.A., D.C., and A.M.; Project Administration, M.A., A.M., D.C., and A.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding

Acknowledgments

The authors acknowledge the support of Association A.M.I.C.A.—Associazione per le Malattie da Intossicazione Cronica e Ambientale, Rome, Italy.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Alessandrini, M.; Micarelli, A.; Chiaravalloti, A.; Bruno, E.; Danieli, R.; Pierantozzi, M.; Genovesi, G.; Oberg, J.; Pagani, M.; Schillaci, O. Involvement of Subcortical Brain Structures During Olfactory Stimulation in Multiple Chemical Sensitivity. Brain Topogr. 2016, 29, 243–252. [Google Scholar] [CrossRef] [Green Version]
  2. Dantoft, T.M.; Elberling, J.; Brix, S.; Szecsi, P.B.; Vesterhauge, S.; Skovbjerg, S. An elevated pro-inflammatory cytokine profile in multiple chemical sensitivity. Psychoneuroendocrinology 2014, 40, 140–150. [Google Scholar] [CrossRef]
  3. Rossi, S.; Pitidis, A. Multiple Chemical Sensitivity: Review of the State of the Art in Epidemiology, Diagnosis, and Future Perspectives. J. Occup. Environ. Med. 2018, 60, 138–146. [Google Scholar] [CrossRef] [Green Version]
  4. Das-Munshi, J.; Rubin, G.J.; Wessely, S. Multiple chemical sensitivities: A systematic review of provocation studies. J. Allergy Clin. Immunol. 2006, 118, 1257–1264. [Google Scholar] [CrossRef]
  5. Karnekull, S.C.; Jonsson, F.U.; Larsson, M.; Olofsson, J.K. Affected by smells? Environmental chemical responsivity predicts odor perception. Chem. Senses 2011, 36, 641–648. [Google Scholar] [CrossRef]
  6. Viziano, A.; Micarelli, A.; Pasquantonio, G.; Della-Morte, D.; Alessandrini, M. Perspectives on multisensory perception disruption in idiopathic environmental intolerance: A systematic review. Int. Arch. Occup. Environ. Health 2018, 91, 923–935. [Google Scholar] [CrossRef]
  7. Bascom, R.; Meggs, W.J.; Frampton, M.; Hudnell, K.; Killburn, K.; Kobal, G.; Medinsky, M.; Rea, W. Neurogenic inflammation: With additional discussion of central and perceptual integration of nonneurogenic inflammation. Environ. Health Perspect. 1997, 105 (Suppl. 2), 531–537. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Winters, W.; Devriese, S.; Van Diest, I.; Nemery, B.; Veulemans, H.; Eelen, P.; Van de Woestijne, K.; Van den Bergh, O. Media warnings about environmental pollution facilitate the acquisition of symptoms in response to chemical substances. Psychosom. Med. 2003, 65, 332–338. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Pall, M.L. Elevated nitric oxide/peroxynitrite theory of multiple chemical sensitivity: Central role of N-methyl-D-aspartate receptors in the sensitivity mechanism. Environ. Health Perspect. 2003, 111, 1461–1464. [Google Scholar] [CrossRef]
  10. Wiesmuller, G.A.; Niggemann, H.; Weissbach, W.; Riley, F.; Maarouf, Z.; Dott, W.; Kunert, H.J.; Zerres, K.; Eggermann, T.; Blomeke, B. Sequence variations in subjects with self-reported multiple chemical sensitivity (sMCS): A case-control study. J. Toxicol. Environ. Health A 2008, 71, 786–794. [Google Scholar] [CrossRef] [PubMed]
  11. Bell, I.R.; Brooks, A.J.; Howerter, A.; Jackson, N.; Schwartz, G.E. Acute electroencephalographic effects from repeated olfactory administration of homeopathic remedies in individuals with self-reported chemical sensitivity. Altern. Ther. Health Med. 2013, 19, 46–57. [Google Scholar] [PubMed]
  12. Alessandrini, M.; Micarelli, A.; Bruno, E.; Ottaviani, F.; Conetta, M.; Cormano, A.; Genovesi, G. Intranasal administration of hyaluronan as a further resource in olfactory performance in multiple chemical sensitivity syndrome. Int. J. Immunopathol. Pharm. 2013, 26, 1019–1025. [Google Scholar] [CrossRef] [PubMed]
  13. Chiaravalloti, A.; Pagani, M.; Micarelli, A.; Di Pietro, B.; Genovesi, G.; Alessandrini, M.; Schillaci, O. Cortical activity during olfactory stimulation in multiple chemical sensitivity: A (18)F-FDG PET/CT study. Eur. J. Nucl. Med. Mol. Imaging 2015, 42, 733–740. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Papo, D.; Eberlein-Konig, B.; Berresheim, H.W.; Huss-Marp, J.; Grimm, V.; Ring, J.; Behrendt, H.; Winneke, G. Chemosensory function and psychological profile in patients with multiple chemical sensitivity: Comparison with odor-sensitive and asymptomatic controls. J. Psychosom. Res. 2006, 60, 199–209. [Google Scholar] [CrossRef] [PubMed]
  15. Andersson, L.; Claeson, A.S.; Nyberg, L.; Stenberg, B.; Nordin, S. Brain responses to olfactory and trigeminal exposure in idiopathic environmental illness (IEI) attributed to smells—An fMRI study. J. Psychosom. Res. 2014, 77, 401–408. [Google Scholar] [CrossRef]
  16. Zhang, J.X.; Liu, Y.J.; Zhang, J.H.; Sun, L. Dual role of preputial gland secretion and its major components in sex recognition of mice. Physiol. Behav. 2008, 95, 388–394. [Google Scholar] [CrossRef]
  17. Nodari, F.; Hsu, F.F.; Fu, X.; Holekamp, T.F.; Kao, L.F.; Turk, J.; Holy, T.E. Sulfated steroids as natural ligands of mouse pheromone-sensing neurons. J. Neurosci. 2008, 28, 6407–6418. [Google Scholar] [CrossRef]
  18. Haga, S.; Hattori, T.; Sato, T.; Sato, K.; Matsuda, S.; Kobayakawa, R.; Sakano, H.; Yoshihara, Y.; Kikusui, T.; Touhara, K. The male mouse pheromone ESP1 enhances female sexual receptive behaviour through a specific vomeronasal receptor. Nature 2010, 466, 118–122. [Google Scholar] [CrossRef]
  19. Leinders-Zufall, T.; Ishii, T.; Mombaerts, P.; Zufall, F.; Boehm, T. Structural requirements for the activation of vomeronasal sensory neurons by MHC peptides. Nat. Neurosci. 2009, 12, 1551–1558. [Google Scholar] [CrossRef]
  20. Touhara, K. Sexual communication via peptide and protein pheromones. Curr. Opin. Pharm. 2008, 8, 759–764. [Google Scholar] [CrossRef]
  21. Trotier, D. Vomeronasal organ and human pheromones. Eur. Ann. Otorhinolaryngol. Head Neck Dis. 2011, 128, 184–190. [Google Scholar] [CrossRef]
  22. Rodewald, A.; Mills, D.; Gebhart, V.M.; Jirikowski, G.F. Steroidal pheromones and their potential target sites in the vomeronasal organ. Steroids 2019, 142, 14–20. [Google Scholar] [CrossRef] [PubMed]
  23. Ploss, V.; Gebhart, V.M.; Dolz, W.; Jirikowski, G.F. Sex hormone binding globulin in the rat olfactory system. J. Chem. Neuroanat. 2014, 57–58, 10–14. [Google Scholar] [CrossRef] [PubMed]
  24. Ploss, V.M.; Gebhart, V.M.; Gisder, D.; Dolz, W.; Jirikowski, G.F. Localization of sex hormone binding globulin in the rat vomeronasal organ. J. Chem. Neuroanat. 2014, 61–62, 120–123. [Google Scholar] [CrossRef] [PubMed]
  25. Rodewald, A.; Gebhart, V.M.; Oehring, H.; Jirikowski, G.F. The rat vomeronasal organ is a vitamin D target. J. Chem. Neuroanat. 2017, 81, 42–47. [Google Scholar] [CrossRef] [PubMed]
  26. Li, R.; Cui, J.; Shen, Y. Brain sex matters: Estrogen in cognition and Alzheimer’s disease. Mol. Cell. Endocrinol. 2014, 389, 13–21. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Garcion, E.; Wion-Barbot, N.; Montero-Menei, C.N.; Berger, F.; Wion, D. New clues about vitamin D functions in the nervous system. Trends Endocrinol. Metab. 2002, 13, 100–105. [Google Scholar] [CrossRef]
  28. Ganji, V.; Milone, C.; Cody, M.M.; McCarty, F.; Wang, Y.T. Serum vitamin D concentrations are related to depression in young adult US population: The Third National Health and Nutrition Examination Survey. Int. Arch. Med. 2010, 3, 29. [Google Scholar] [CrossRef] [Green Version]
  29. Jorde, R.; Waterloo, K.; Saleh, F.; Haug, E.; Svartberg, J. Neuropsychological function in relation to serum parathyroid hormone and serum 25-hydroxyvitamin D levels. The Tromso study. J. Neurol. 2006, 253, 464–470. [Google Scholar] [CrossRef]
  30. Greene, G.J.; Kipen, H.M. The vomeronasal organ and chemical sensitivity: A hypothesis. Environ. Health Perspect. 2002, 110 (Suppl. 4), 655–661. [Google Scholar] [CrossRef] [Green Version]
  31. De Luca, C.; Raskovic, D.; Pacifico, V.; Thai, J.C.; Korkina, L. The search for reliable biomarkers of disease in multiple chemical sensitivity and other environmental intolerances. Int. J. Environ. Res. Public Health 2011, 8, 2770–2797. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Labarge, X.S.; McCaffrey, R.J. Multiple chemical sensitivity: A review of the theoretical and research literature. Neuropsychol. Rev. 2000, 10, 183–211. [Google Scholar] [CrossRef] [PubMed]
  33. Sorg, B.A. Multiple chemical sensitivity: Potential role for neural sensitization. Crit. Rev. Neurobiol. 1999, 13, 283–316. [Google Scholar] [CrossRef] [PubMed]
  34. Andersson, L.; Claeson, A.S.; Dantoft, T.M.; Skovbjerg, S.; Lind, N.; Nordin, S. Chemosensory perception, symptoms and autonomic responses during chemical exposure in multiple chemical sensitivity. Int. Arch. Occup. Environ. Health 2016, 89, 79–88. [Google Scholar] [CrossRef] [Green Version]
  35. Dantoft, T.M.; Andersson, L.; Nordin, S.; Skovbjerg, S. Chemical intolerance. Curr. Rheumatol. Rev. 2015, 11, 167–184. [Google Scholar] [CrossRef] [Green Version]
  36. Yunus, M.B. Editorial review: An update on central sensitivity syndromes and the issues of nosology and psychobiology. Curr. Rheumatol. Rev. 2015, 11, 70–85. [Google Scholar] [CrossRef]
  37. Light, A.R.; Bateman, L.; Jo, D.; Hughen, R.W.; Vanhaitsma, T.A.; White, A.T.; Light, K.C. Gene expression alterations at baseline and following moderate exercise in patients with Chronic Fatigue Syndrome and Fibromyalgia Syndrome. J. Intern. Med. 2012, 271, 64–81. [Google Scholar] [CrossRef]
  38. Dantoft, T.M.; Skovbjerg, S.; Andersson, L.; Claeson, A.S.; Engkilde, K.; Lind, N.; Nordin, S.; Hellgren, L.I. Gene expression profiling in persons with multiple chemical sensitivity before and after a controlled n-butanol exposure session. BMJ Open 2017, 7, 013879. [Google Scholar] [CrossRef]
  39. Fujimori, S.; Hiura, M.; Yi, C.X.; Xi, L.; Katoh, T. Factors in genetic susceptibility in a chemical sensitive population using QEESI. Environ. Health Prev. Med. 2012, 17, 357–363. [Google Scholar] [CrossRef] [Green Version]
  40. McKeown-Eyssen, G.; Baines, C.; Cole, D.E.; Riley, N.; Tyndale, R.F.; Marshall, L.; Jazmaji, V. Case-control study of genotypes in multiple chemical sensitivity: CYP2D6, NAT1, NAT2, PON1, PON2 and MTHFR. Int. J. Epidemiol. 2004, 33, 971–978. [Google Scholar] [CrossRef]
  41. Schnakenberg, E.; Fabig, K.R.; Stanulla, M.; Strobl, N.; Lustig, M.; Fabig, N.; Schloot, W. A cross-sectional study of self-reported chemical-related sensitivity is associated with gene variants of drug-metabolizing enzymes. Environ. Health 2007, 6, 6. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Korkina, L.; Scordo, M.G.; Deeva, I.; Cesareo, E.; De Luca, C. The chemical defensive system in the pathobiology of idiopathic environment-associated diseases. Curr. Drug Metab. 2009, 10, 914–931. [Google Scholar] [CrossRef]
  43. Caccamo, D.; Cesareo, E.; Mariani, S.; Raskovic, D.; Ientile, R.; Curro, M.; Korkina, L.; De Luca, C. Xenobiotic sensor- and metabolism-related gene variants in environmental sensitivity-related illnesses: A survey on the Italian population. Oxid. Med. Cell. Longev. 2013, 2013, 831969. [Google Scholar] [CrossRef] [PubMed]
  44. De Luca, C.; Thai, J.C.; Raskovic, D.; Cesareo, E.; Caccamo, D.; Trukhanov, A.; Korkina, L. Metabolic and genetic screening of electromagnetic hypersensitive subjects as a feasible tool for diagnostics and intervention. Mediat. Inflamm. 2014, 2014, 924184. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Loria-Kohen, V.; Marcos-Pasero, H.; de la Iglesia, R.; Aguilar-Aguilar, E.; Espinosa-Salinas, I.; Herranz, J.; Ramirez de Molina, A.; Reglero, G. Multiple chemical sensitivity: Genotypic characterization, nutritional status and quality of life in 52 patients. Med. Clin. (Barc) 2017, 149, 141–146. [Google Scholar] [CrossRef]
  46. D’Attis, S.; Massari, S.; Mazzei, F.; Maio, D.; Vergallo, I.; Mauro, S.; Minelli, M.; Bozzetti, M.P. Assessment of CYP2C9, CYP2C19, and CYP2D6 Polymorphisms in Allergic Patients with Chemical Sensitivity. Int. Arch. Allergy Immunol. 2019, 179, 173–186. [Google Scholar] [CrossRef] [PubMed]
  47. Pall, M.L. Common etiology of posttraumatic stress disorder, fibromyalgia, chronic fatigue syndrome and multiple chemical sensitivity via elevated nitric oxide/peroxynitrite. Med. Hypotheses 2001, 57, 139–145. [Google Scholar] [CrossRef] [PubMed]
  48. De Luca, C.; Scordo, M.G.; Cesareo, E.; Pastore, S.; Mariani, S.; Maiani, G.; Stancato, A.; Loreti, B.; Valacchi, G.; Lubrano, C.; et al. Biological definition of multiple chemical sensitivity from redox state and cytokine profiling and not from polymorphisms of xenobiotic-metabolizing enzymes. Toxicol. Appl. Pharm. 2010, 248, 285–292. [Google Scholar] [CrossRef]
  49. Del Tredici, A.L.; Malhotra, A.; Dedek, M.; Espin, F.; Roach, D.; Zhu, G.D.; Voland, J.; Moreno, T.A. Frequency of CYP2D6 Alleles Including Structural Variants in the United States. Front. Pharm. 2018, 9, 305. [Google Scholar] [CrossRef] [Green Version]
  50. Crews, K.R.; Gaedigk, A.; Dunnenberger, H.M.; Leeder, J.S.; Klein, T.E.; Caudle, K.E.; Haidar, C.E.; Shen, D.D.; Callaghan, J.T.; Sadhasivam, S.; et al. Clinical Pharmacogenetics Implementation Consortium guidelines for cytochrome P450 2D6 genotype and codeine therapy: 2014 update. Clin. Pharm. Ther. 2014, 95, 376–382. [Google Scholar] [CrossRef] [Green Version]
  51. Berg, N.D.; Rasmussen, H.B.; Linneberg, A.; Brasch-Andersen, C.; Fenger, M.; Dirksen, A.; Vesterhauge, S.; Werge, T.; Elberling, J. Genetic susceptibility factors for multiple chemical sensitivity revisited. Int. J. Hyg. Environ. Health 2010, 213, 131–139. [Google Scholar] [CrossRef] [PubMed]
  52. Cui, X.; Lu, X.; Hiura, M.; Oda, M.; Miyazaki, W.; Katoh, T. Evaluation of genetic polymorphisms in patients with multiple chemical sensitivity. PLoS ONE 2013, 8, e73708. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Antognelli, C.; Del Buono, C.; Ludovini, V.; Gori, S.; Talesa, V.N.; Crino, L.; Barberini, F.; Rulli, A. CYP17, GSTP1, PON1 and GLO1 gene polymorphisms as risk factors for breast cancer: An Italian case-control study. BMC Cancer 2009, 9, 115. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Crawford, A.; Fassett, R.G.; Geraghty, D.P.; Kunde, D.A.; Ball, M.J.; Robertson, I.K.; Coombes, J.S. Relationships between single nucleotide polymorphisms of antioxidant enzymes and disease. Gene 2012, 501, 89–103. [Google Scholar] [CrossRef] [PubMed]
  55. Palmirotta, R.; Barbanti, P.; De Marchis, M.L.; Egeo, G.; Aurilia, C.; Fofi, L.; Ialongo, C.; Valente, M.G.; Ferroni, P.; Della-Morte, D.; et al. Is SOD2 Ala16Val polymorphism associated with migraine with aura phenotype? Antioxid. Redox Signal. 2015, 22, 275–279. [Google Scholar] [CrossRef] [Green Version]
  56. Malinowska, K.; Kowalski, M.; Szaflik, J.; Szaflik, J.P.; Majsterek, I. The role of Cat -262C/T, GPX1 Pro198Leu and Sod1+35A/C gene polymorphisms in a development of primary open-angle glaucoma in a Polish population. Pol. J. Pathol. 2016, 67, 404–410. [Google Scholar] [CrossRef] [Green Version]
  57. De Luca, C.; Gugliandolo, A.; Calabro, C.; Curro, M.; Ientile, R.; Raskovic, D.; Korkina, L.; Caccamo, D. Role of polymorphisms of inducible nitric oxide synthase and endothelial nitric oxide synthase in idiopathic environmental intolerances. Mediat. Inflamm. 2015, 2015, 245308. [Google Scholar] [CrossRef]
  58. Serpe, L.; Canaparo, R.; Scordo, M.G.; Spina, E. Pharmacogenetics of drug-metabolizing enzymes in Italian populations. Drug Metab. Pers. Ther. 2015, 30, 107–120. [Google Scholar] [CrossRef]
  59. Martis, S.; Peter, I.; Hulot, J.S.; Kornreich, R.; Desnick, R.J.; Scott, S.A. Multi-ethnic distribution of clinically relevant CYP2C genotypes and haplotypes. Pharm. J. 2013, 13, 369–377. [Google Scholar] [CrossRef] [Green Version]
  60. Serrano, D.; Lazzeroni, M.; Zambon, C.F.; Macis, D.; Maisonneuve, P.; Johansson, H.; Guerrieri-Gonzaga, A.; Plebani, M.; Basso, D.; Gjerde, J.; et al. Efficacy of tamoxifen based on cytochrome P450 2D6, CYP2C19 and SULT1A1 genotype in the Italian Tamoxifen Prevention Trial. Pharm. J. 2011, 11, 100–107. [Google Scholar] [CrossRef] [Green Version]
  61. Boccia, S.; Sayed-Tabatabaei, F.A.; Persiani, R.; Gianfagna, F.; Rausei, S.; Arzani, D.; La Greca, A.; D’Ugo, D.; La Torre, G.; van Duijn, C.M.; et al. Polymorphisms in metabolic genes, their combination and interaction with tobacco smoke and alcohol consumption and risk of gastric cancer: A case-control study in an Italian population. BMC Cancer 2007, 7, 206. [Google Scholar] [CrossRef]
  62. Chen, S.; Laverdiere, I.; Tourancheau, A.; Jonker, D.; Couture, F.; Cecchin, E.; Villeneuve, L.; Harvey, M.; Court, M.H.; Innocenti, F.; et al. A novel UGT1 marker associated with better tolerance against irinotecan-induced severe neutropenia in metastatic colorectal cancer patients. Pharm. J. 2015, 15, 513–520. [Google Scholar] [CrossRef]
  63. Tetik Vardarli, A.; Harman, E.; Bozok Cetintas, V.; Kayikcioglu, M.; Vardarli, E.; Zengi, A.; Kucukaslan, A.S.; Eroglu, Z. Polymorphisms of lipid metabolism enzyme-coding genes in patients with diabetic dyslipidemia. Anatol. J. Cardiol. 2017, 17, 313–321. [Google Scholar] [CrossRef]
  64. Zakrzewski-Jakubiak, M.; de Denus, S.; Dube, M.P.; Belanger, F.; White, M.; Turgeon, J. Ten renin-angiotensin system-related gene polymorphisms in maximally treated Canadian Caucasian patients with heart failure. Br. J. Clin. Pharm. 2008, 65, 742–751. [Google Scholar] [CrossRef] [Green Version]
  65. Roszak, A.; Lutkowska, A.; Lianeri, M.; Sowinska, A.; Jagodzinski, P.P. Involvement of myeloperoxidase gene polymorphism 463G > A in development of cervical squamous cell carcinoma. Int. J. Biol. Markers 2016, 31, 440–445. [Google Scholar] [CrossRef]
  66. Mazzuca, F.; Borro, M.; Botticelli, A.; Aimati, L.; Gentile, G.; Capalbo, C.; Maddalena, C.; Mazzotti, E.; Simmaco, M.; Marchetti, P. Effect of MTHFR Polymorphisms on Gastrointestinal Cancer Risk in Italy. World J. Oncol. 2015, 6, 394–397. [Google Scholar] [CrossRef]
  67. Moreno, V.; Gemignani, F.; Landi, S.; Gioia-Patricola, L.; Chabrier, A.; Blanco, I.; Gonzalez, S.; Guino, E.; Capella, G.; Canzian, F. Polymorphisms in genes of nucleotide and base excision repair: Risk and prognosis of colorectal cancer. Clin. Cancer Res. 2006, 12, 2101–2108. [Google Scholar] [CrossRef] [Green Version]
  68. Micarelli, A.; Viziano, A.; Bruno, E.; Micarelli, E.; Alessandrini, M. Vestibular impairment in Multiple Chemical Sensitivity: Component analysis findings. J. Vestib. Res. 2016, 26, 459–468. [Google Scholar] [CrossRef]
  69. Micarelli, A.; Viziano, A.; Genovesi, G.; Bruno, E.; Ottaviani, F.; Alessandrini, M. Lack of contralateral suppression in transient-evoked otoacoustic emissions in multiple chemical sensitivity: A clinical correlation study. Noise Health 2016, 18, 143–149. [Google Scholar] [CrossRef]
  70. Viziano, A.; Micarelli, A.; Alessandrini, M. Noise sensitivity and hyperacusis in patients affected by multiple chemical sensitivity. Int. Arch. Occup. Environ. Health 2017, 90, 189–196. [Google Scholar] [CrossRef]
  71. Casarett, L.; Doull, J. Casarett and Doull’s Toxicology: The Basic Science of Poisons, 7th ed.; Klaassen, K.D., Ed.; McGraw-Hill—Medical Publishing Division: New York, NY, USA, 2008; p. 1309. [Google Scholar] [CrossRef]
  72. McFadden, S.A. Phenotypic variation in xenobiotic metabolism and adverse environmental response: Focus on sulfur-dependent detoxification pathways. Toxicology 1996, 111, 43–65. [Google Scholar] [CrossRef]
  73. Weber, W.W.; Hein, D.W. N-acetylation pharmacogenetics. Pharm. Rev. 1985, 37, 25–79. [Google Scholar]
  74. Bouayed, J.; Rammal, H.; Soulimani, R. Oxidative stress and anxiety: Relationship and cellular pathways. Oxid. Med. Cell. Longev. 2009, 2, 63–67. [Google Scholar] [CrossRef]
  75. Ng, F.; Berk, M.; Dean, O.; Bush, A.I. Oxidative stress in psychiatric disorders: Evidence base and therapeutic implications. Int. J. Neuropsychopharmacol. 2008, 11, 851–876. [Google Scholar] [CrossRef] [Green Version]
  76. Ross, P.M.; Whysner, J.; Covello, V.T.; Kuschner, M.; Rifkind, A.B.; Sedler, M.J.; Trichopoulos, D.; Williams, G.M. Olfaction and symptoms in the multiple chemical sensitivities syndrome. Prev. Med. 1999, 28, 467–480. [Google Scholar] [CrossRef]
  77. Lee, Y.L.; Pai, M.C.; Chen, J.H.; Guo, Y.L. Central neurological abnormalities and multiple chemical sensitivity caused by chronic toluene exposure. Occup. Med. (Lond) 2003, 53, 479–482. [Google Scholar] [CrossRef] [Green Version]
  78. Pall, M.L. NMDA sensitization and stimulation by peroxynitrite, nitric oxide, and organic solvents as the mechanism of chemical sensitivity in multiple chemical sensitivity. FASEB J. 2002, 16, 1407–1417. [Google Scholar] [CrossRef]
  79. Bell, I.R.; Miller, C.S.; Schwartz, G.E. An olfactory-limbic model of multiple chemical sensitivity syndrome: Possible relationships to kindling and affective spectrum disorders. Biol. Psychiatry 1992, 32, 218–242. [Google Scholar] [CrossRef]
  80. Cabungcal, J.H.; Preissmann, D.; Delseth, C.; Cuenod, M.; Do, K.Q.; Schenk, F. Transitory glutathione deficit during brain development induces cognitive impairment in juvenile and adult rats: Relevance to schizophrenia. Neurobiol. Dis. 2007, 26, 634–645. [Google Scholar] [CrossRef]
  81. Farooqui, T. A potential link among biogenic amines-based pesticides, learning and memory, and colony collapse disorder: A unique hypothesis. Neurochem. Int. 2013, 62, 122–136. [Google Scholar] [CrossRef]
  82. Gardiner, J.; Barton, D.; Overall, R.; Marc, J. Neurotrophic support and oxidative stress: Converging effects in the normal and diseased nervous system. Neuroscientist 2009, 15, 47–61. [Google Scholar] [CrossRef]
  83. Kako, H.; Fukumoto, S.; Kobayashi, Y.; Yokogoshi, H. Effects of direct exposure of green odour components on dopamine release from rat brain striatal slices and PC12 cells. Brain Res. Bull. 2008, 75, 706–712. [Google Scholar] [CrossRef]
  84. Angelucci, F.L.; Silva, V.V.; Dal Pizzol, C.; Spir, L.G.; Praes, C.E.; Maibach, H. Physiological effect of olfactory stimuli inhalation in humans: An overview. Int. J. Cosmet. Sci. 2014, 36, 117–123. [Google Scholar] [CrossRef]
  85. Hojo, S.; Sakabe, K.; Ishikawa, S.; Miyata, M.; Kumano, H. Evaluation of subjective symptoms of Japanese patients with multiple chemical sensitivity using QEESI(c). Environ. Health Prev. Med. 2009, 14, 267–275. [Google Scholar] [CrossRef] [Green Version]
  86. Robert-Hazotte, A.; Faure, P.; Neiers, F.; Potin, C.; Artur, Y.; Coureaud, G.; Heydel, J.M. Nasal mucus glutathione transferase activity and impact on olfactory perception and neonatal behavior. Sci. Rep. 2019, 9, 3104. [Google Scholar] [CrossRef]
  87. Hayes, J.D.; Pulford, D.J. The glutathione S-transferase supergene family: Regulation of GST and the contribution of the isoenzymes to cancer chemoprotection and drug resistance. Crit. Rev. Biochem. Mol. Biol. 1995, 30, 445–600. [Google Scholar] [CrossRef]
  88. Saito, M.; Kumano, H.; Yoshiuchi, K.; Kokubo, N.; Ohashi, K.; Yamamoto, Y.; Shinohara, N.; Yanagisawa, Y.; Sakabe, K.; Miyata, M.; et al. Symptom profile of multiple chemical sensitivity in actual life. Psychosom. Med. 2005, 67, 318–325. [Google Scholar] [CrossRef] [Green Version]
  89. La Du, B.N.; Aviram, M.; Billecke, S.; Navab, M.; Primo-Parmo, S.; Sorenson, R.C.; Standiford, T.J. On the physiological role(s) of the paraoxonases. Chem. Biol. Interact. 1999, 119–120, 379–388. [Google Scholar] [CrossRef]
  90. Billecke, S.; Draganov, D.; Counsell, R.; Stetson, P.; Watson, C.; Hsu, C.; La Du, B.N. Human serum paraoxonase (PON1) isozymes Q and R hydrolyze lactones and cyclic carbonate esters. Drug Metab. Dispos. 2000, 28, 1335–1342. [Google Scholar]
  91. Jakubowski, H. Calcium-dependent human serum homocysteine thiolactone hydrolase. A protective mechanism against protein N-homocysteinylation. J. Biol. Chem. 2000, 275, 3957–3962. [Google Scholar] [CrossRef] [Green Version]
  92. Smolen, A.; Eckerson, H.W.; Gan, K.N.; Hailat, N.; La Du, B.N. Characteristics of the genetically determined allozymic forms of human serum paraoxonase/arylesterase. Drug Metab. Dispos. 1991, 19, 107–112. [Google Scholar] [PubMed]
  93. Di Mauro, R.; Cantarella, G.; Bernardini, R.; Di Rosa, M.; Barbagallo, I.; Distefano, A.; Longhitano, L.; Vicario, N.; Nicolosi, D.; Lazzarino, G.; et al. The Biochemical and Pharmacological Properties of Ozone: The Smell of Protection in Acute and Chronic Diseases. Int. J. Mol. Sci. 2019, 20, 634. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  94. De Luca, C.; Scordo, G.; Cesareo, E.; Raskovic, D.; Genovesi, G.; Korkina, L. Idiopathic environmental intolerances (IEI): From molecular epidemiology to molecular medicine. Indian J. Exp. Biol. 2010, 48, 625–635. [Google Scholar] [PubMed]
  95. Holla, L.I.; Stejskalova, A.; Znojil, V.; Vasku, A. Analysis of the inducible nitric oxide synthase gene polymorphisms in Czech patients with atopic diseases. Clin. Exp. Allergy 2006, 36, 1592–1601. [Google Scholar] [CrossRef]
  96. Konno, S.; Hizawa, N.; Yamaguchi, E.; Jinushi, E.; Nishimura, M. (CCTTT)n repeat polymorphism in the NOS2 gene promoter is associated with atopy. J. Allergy Clin. Immunol. 2001, 108, 810–814. [Google Scholar] [CrossRef]
  97. Thiebaud, N.; Veloso Da Silva, S.; Jakob, I.; Sicard, G.; Chevalier, J.; Menetrier, F.; Berdeaux, O.; Artur, Y.; Heydel, J.M.; Le Bon, A.M. Odorant metabolism catalyzed by olfactory mucosal enzymes influences peripheral olfactory responses in rats. PLoS ONE 2013, 8, e59547. [Google Scholar] [CrossRef] [Green Version]
  98. Asakawa, M.; Fukutani, Y.; Savangsuksa, A.; Noguchi, K.; Matsunami, H.; Yohda, M. Modification of the response of olfactory receptors to acetophenone by CYP1a2. Sci. Rep. 2017, 7, 10167. [Google Scholar] [CrossRef] [Green Version]
  99. Heydel, J.M.; Menetrier, F.; Belloir, C.; Canon, F.; Faure, P.; Lirussi, F.; Chavanne, E.; Saliou, J.M.; Artur, Y.; Canivenc-Lavier, M.C.; et al. Characterization of rat glutathione transferases in olfactory epithelium and mucus. PLoS ONE 2019, 14, e0220259. [Google Scholar] [CrossRef] [Green Version]
  100. Miksys, S.; Rao, Y.; Hoffmann, E.; Mash, D.C.; Tyndale, R.F. Regional and cellular expression of CYP2D6 in human brain: Higher levels in alcoholics. J. Neurochem. 2002, 82, 1376–1387. [Google Scholar] [CrossRef]
  101. Gaedigk, A.; Gotschall, R.R.; Forbes, N.S.; Simon, S.D.; Kearns, G.L.; Leeder, J.S. Optimization of cytochrome P4502D6 (CYP2D6) phenotype assignment using a genotyping algorithm based on allele frequency data. Pharmacogenetics 1999, 9, 669–682. [Google Scholar] [CrossRef]
  102. Yu, A.M.; Idle, J.R.; Krausz, K.W.; Kupfer, A.; Gonzalez, F.J. Contribution of individual cytochrome P450 isozymes to the O-demethylation of the psychotropic beta-carboline alkaloids harmaline and harmine. J. Pharm. Exp. Ther. 2003, 305, 315–322. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  103. Llerena, A.; Edman, G.; Cobaleda, J.; Benitez, J.; Schalling, D.; Bertilsson, L. Relationship between personality and debrisoquine hydroxylation capacity. Suggestion of an endogenous neuroactive substrate or product of the cytochrome P4502D6. Acta Psychiatr. Scand. 1993, 87, 23–28. [Google Scholar] [CrossRef] [PubMed]
  104. Miksys, S.; Rao, Y.; Sellers, E.M.; Kwan, M.; Mendis, D.; Tyndale, R.F. Regional and cellular distribution of CYP2D subfamily members in rat brain. Xenobiotica 2000, 30, 547–564. [Google Scholar] [CrossRef] [PubMed]
  105. Multiple chemical sensitivity: A 1999 consensus. Arch. Environ. Health 1999, 54, 147–149. [CrossRef] [PubMed]
  106. Lacour, M.; Zunder, T.; Schmidtke, K.; Vaith, P.; Scheidt, C. Multiple chemical sensitivity syndrome (MCS)—suggestions for an extension of the U.S. MCS-case definition. Int. J. Hyg. Environ. Health 2005, 208, 141–151. [Google Scholar] [CrossRef] [PubMed]
  107. Tempere, S.; Hamtat, M.L.; Bougeant, J.C.; de Revel, G.; Sicard, G. Learning Odors: The Impact of Visual and Olfactory Mental Imagery Training on Odor Perception. J. Sens. Stud. 2014, 29, 435–449. [Google Scholar] [CrossRef]
  108. Ferlazzo, N.; Gorgone, G.; Caccamo, D.; Curro, M.; Condello, S.; Pisani, F.; Vernieri, F.; Rossini, P.M.; Ientile, R. The 894G > T (Glu298Asp) variant in the endothelial NOS gene and MTHFR polymorphisms influence homocysteine levels in patients with cognitive decline. Neuromol. Med. 2011, 13, 167–174. [Google Scholar] [CrossRef]
  109. Vecchio, M.; Curro, M.; Trimarchi, F.; Naccari, S.; Caccamo, D.; Ientile, R.; Barreca, D.; Di Mauro, D. The Oxidative Stress Response in Elite Water Polo Players: Effects of Genetic Background. Biomed. Res. Int. 2017, 2017, 7019694. [Google Scholar] [CrossRef]
  110. Di Mauro, D.; Curro, M.; Trimarchi, F.; Vecchio, M.; Rizzo, G.; Barreca, D.; Visalli, G.; Ientile, R.; Caccamo, D. Role of Genetic Background in Cardiovascular Risk Markers Changes in Water Polo Players. Int. J. Sports Med. 2018, 39, 390–396. [Google Scholar] [CrossRef]
  111. Massacesi, C.; Terrazzino, S.; Marcucci, F.; Rocchi, M.B.; Lippe, P.; Bisonni, R.; Lombardo, M.; Pilone, A.; Mattioli, R.; Leon, A. Uridine diphosphate glucuronosyl transferase 1A1 promoter polymorphism predicts the risk of gastrointestinal toxicity and fatigue induced by irinotecan-based chemotherapy. Cancer 2006, 106, 1007–1016. [Google Scholar] [CrossRef]
  112. Pandolfo, G.; Gugliandolo, A.; Gangemi, C.; Arrigo, R.; Curro, M.; La Ciura, G.; Muscatello, M.R.; Bruno, A.; Zoccali, R.; Caccamo, D. Association of the COMT synonymous polymorphism Leu136Leu and missense variant Val158Met with mood disorders. J. Affect. Disord. 2015, 177, 108–113. [Google Scholar] [CrossRef] [PubMed]
  113. Simopoulos, E.; Katotomichelakis, M.; Gouveris, H.; Tripsianis, G.; Livaditis, M.; Danielides, V. Olfaction-associated quality of life in chronic rhinosinusitis: Adaptation and validation of an olfaction-specific questionnaire. Laryngoscope 2012, 122, 1450–1454. [Google Scholar] [CrossRef] [PubMed]
  114. Alessandrini, M.; Micarelli, A.; Chiaravalloti, A.; Candidi, M.; Bruno, E.; Di Pietro, B.; Oberg, J.; Schillaci, O.; Pagani, M. Cerebellar metabolic involvement and its correlations with clinical parameters in vestibular neuritis. J. Neurol. 2014, 261, 1976–1985. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  115. Funke, S.; Risch, A.; Nieters, A.; Hoffmeister, M.; Stegmaier, C.; Seiler, C.M.; Brenner, H.; Chang-Claude, J. Genetic Polymorphisms in Genes Related to Oxidative Stress (GSTP1, GSTM1, GSTT1, CAT, MnSOD, MPO, eNOS) and Survival of Rectal Cancer Patients after Radiotherapy. J. Cancer Epidemiol. 2009, 2009, 302047. [Google Scholar] [CrossRef] [Green Version]
  116. Micarelli, A.; Liguori, C.; Viziano, A.; Izzi, F.; Placidi, F.; Alessandrini, M. Integrating postural and vestibular dimensions to depict impairment in moderate-to-severe obstructive sleep apnea syndrome patients. J. Sleep Res. 2017, 26, 487–494. [Google Scholar] [CrossRef] [Green Version]
  117. Micarelli, A.; Viziano, A.; Panella, M.; Micarelli, E.; Alessandrini, M. Power spectra prognostic aspects of impulsive eye movement traces in superior vestibular neuritis. Med. Biol. Eng. Comput. 2019, 57, 1617–1627. [Google Scholar] [CrossRef]
  118. Park, S.H.; Park, J.O. Simultaneous Optimization of Multiple Responses Using a Weighted Desirability Function. In Quality Improvement Through Statistical Methods; Abraham, B., Ed.; Birkhäuser Boston: Boston, MA, USA, 1998; pp. 299–311. [Google Scholar] [CrossRef]
  119. Sakurai, A.; Ihara, S.; Tagami, R.; Yamaguchi, J.; Sugita, A.; Kuwana, T.; Sawada, N.; Hori, S.; Taniguch, T.; Kinoshita, K. Parameters Influencing Brain Oxygen Measurement by Regional Oxygen Saturation in Postcardiac Arrest Patients with Targeted Temperature Management. Ther. Hypothermia Temp. Manag. 2019. [Google Scholar] [CrossRef] [Green Version]
  120. Halili, L.; Liu, R.H.; Weeks, A.; Deonandan, R.; Adamo, K.B. High maternal self-efficacy is associated with meeting Institute of Medicine gestational weight gain recommendations. PLoS ONE 2019, 14, e0226301. [Google Scholar] [CrossRef] [Green Version]
Ijms 21 00156 g001 550
Figure 1. Pareto chart of t-values for all coefficients in the regression models. Pareto chart contrasting t-values of all coefficients accounting for the regression model with the significant p-value cut-off (vertical dotted red line).
Figure 1. Pareto chart of t-values for all coefficients in the regression models. Pareto chart contrasting t-values of all coefficients accounting for the regression model with the significant p-value cut-off (vertical dotted red line).
Ijms 21 00156 g001
Ijms 21 00156 g002 550
Figure 2. Profiles of predicted values for all coefficients in the desirability model. Plot depicting in MCS subjects the predicted values (bottom squares, grey highlighted) of the main prognostic factors when contrasted against the range included within the upper and lower limits of olfactory-related quality of life score (LQrv) (top squares) in the desirability model.
Figure 2. Profiles of predicted values for all coefficients in the desirability model. Plot depicting in MCS subjects the predicted values (bottom squares, grey highlighted) of the main prognostic factors when contrasted against the range included within the upper and lower limits of olfactory-related quality of life score (LQrv) (top squares) in the desirability model.
Ijms 21 00156 g002
Table
Table 1. Olfactory-related quality of life and clinical-anamnestic aspects of Multiple Chemical Sensitivity patients.
Table 1. Olfactory-related quality of life and clinical-anamnestic aspects of Multiple Chemical Sensitivity patients.
QOD NS23.8 ± 4.56
QOD PS4.86 ± 0.97
LQrv (NS + PS)28.67 ± 4.22
Age (years)47.23 ± 10.06
Gender27 females; 19 males
Compounds exposure24
Psychological trauma17
Physical trauma14
Previous surgery14
Legend: Olfactory-related quality of life and clinical-anamnestic aspects in 46 MCS patients. Questionnaire of Olfactory Disorders, QOD; negative statements, NS; positive statements, PS; sum of the scores for the QOD-NS and QOD-PS = quality of life raw score, LQrv. Where needed means ± standards deviations are given.
Table
Table 2. Distribution of gene polymorphisms of enzymes involved in xenobiotic metabolism, oxidative stress, and DNA methylation/repair pathways.
Table 2. Distribution of gene polymorphisms of enzymes involved in xenobiotic metabolism, oxidative stress, and DNA methylation/repair pathways.
Gene PolymorphismGenotype/AllelesGenotype and Allele Frequency in MCS PatientsGenotype and Allele Frequency in Caucasian Populationp
CYP2C9
C430T, *2 (R144C)
A1075T, *3 (I359L)		CC63.0%80% §10.0273
CT34.8%17.2% §10.0044
TT2.2%2.8% §10.9833
C(*1), T(*2)0.804, 0.1960.886, 0.124 §1
AA82.6%98.9% §1<0.00001
AT17.4%1.1% §1<0.00001
TT0%0% §1-
A(*1), T(*3)0.913, 0.0870.994, 0.056 §1
*2/*310.9%2.2% §10.0097
CYP2C19
G681A, *2
-C806T, *17		*1/*143.5%49.2% §20.252
*1/*221.7%16.4% §20.279
*2/*22.2%2.8% §21
*1/*1730.4%22.8% §20.179
*17/*172.2%2.8 §21
*1, *2, *170.695, 0.130, 0.1750.688, 0.110, 0.142 §2
CYP2D6
C2850T, *2 (R296C)
-C1584G, *2A
G1846A, *4
1707delT, *6
C100T, *10
G2988A, *41		PM #
(4*10/4*10)		6.5%6.7% §21.0
IM #
(*1/*4/*10, *2*2A/*4*10
*10/*4*10)		6.5%28.9% §20.006
EM #
(1*/*1, *2/*2, *2A/*2A, *2*41/*2*2A
*2*4*10/*2A, *4/*10, *1/*6)		80.5%62.2% §20.154
UM #
(*2/*2A*XN)		6.5%2.2% §20.617
GSTP1
A313G		AA41.3%23.5% §40.0074
AG50.0%62.5% §40.0943
GG8.7%14% §40.4888
A, G0.663, 0.3370.70, 0.30 §40.4573
GSTM1 DELINS, DEL0.413, 0.5870.527, 0.473, §50.4573
GSTT1 DELINS, DEL0.761, 0.2390.78, 0.22 §50.8263
GSTM1/GSTT1INS/DEL15%15% §30.96
GSTM1/GSTT1DEL/INS50.3%41% §30.25
GSTM1/GSTT1DEL/DEL9.0%1.5% §30.85
UGT1A1
(TA)7TAA, *28		*1/*132.6%48.5% §60.0532
*1/*2852.2%39.0% §60.135
*28/*2815.2%12.5% §60.631
*1, *280.587, 0.4130.701, 0.299 §6
Gene Polymorphism (Amino Acid Substitution)Genotype/AllelesGenotype and Allele Frequency in MCS PatientsGenotype and Allele Frequency in Caucasian Populationp
SOD2
C48T (A16V) 		CC19.6%40.2% §70.0076
CT47.8%45.2% §70.749
TT32.6%14.6% §70.0056
C, T0.435, 0.5650.628, 0.372 §7
CAT
-C262T		CC56.5%80% §80.0008
CT32.6%19% §80.0415
TT10.9%1% §80.0004
C, T0.728, 0.2720.895, 0.105 §8
PON1
A575G (Q192R)
C108T (L55M)		AA50%52.1% §90.4485
AG39.1%36.3% §90.7202
GG10.9%7.6% §90.4551
A, G0.696, 0.3040.742, 0.258 §9
CC28.3%19.9% §40.3868
CT47.8%54.4% §40.0002
TT23.9%26.7% §40.014
C, T0.522, 0.4780.461, 0.539 §4
AG/CT10.9%/
NOS3
G894T (D298E)		GG45.7%36.9% §100.3093
GT32.6%47.8% §100.0814
TT21.7%15.3% §100.3316
G, T0.619, 0.3810.608, 0.392 §10
MPO
G463A		GG43.5%64.9 §110.057
GA45.6%35% §110.1535
AA10.9%0.1% §110.4436
G, A0.337, 0.6630.76, 0.24 §11
MTHFR
C677T (A222V)
A1298C (E429A)		CC34.8%37.6% §120.7188
CT43.5%48.5% §120.3766
TT23.9%13.9% §120.091
C, T0.543, 0.4570.619, 0.381 §12
AA41.3%51% §120.256
AC47.8%39.6% §120.323
CC10.9%9.4% §120.783
A, C0.772, 0.2280.708, 0.292 §12
CT/AC23.9%18.4%§40.505
OGG1
C315G (S326C)		CC78.3%65.0% §130.094
CG19.6%32.2% §130.0893
GG2.2%2.8% §131
C, G0.804, 0.1960.811, 0.189 §13
Legend: §1 Serpe et al., 2015 [58]; §2 Martis et al., 2013 [59]; §3 Serrano et al., 2011 [60]; §4Antognelli et al., 2009 [53]; §5 Boccia et al., 2007 [61]; §6 Chen et al., 2015 [62]; §7 Palmirotta et al., 2015 [55]; §8 Malinowska et al., 2016 [56]; §9 Tetik Vardarli et al., 2017 [63]; §10 Zakrzewski-Jakubiak et al., 2008 [64]; §11 Roszak et al., 2016 [65]; §12 Mazzuca et al., 2015 [66]; §13 Moreno et al., 2006 [67]. # PM = Poor Metabolizer, IM = Intermediate Metabolizer, EM = Extensive Metabolizer, UM = Ultrametabolizer.
Table
Table 3. Multiple regression model of the olfactory-related life quality (LQrv) in relation to genetic and clinical-anamnestic factors.
Table 3. Multiple regression model of the olfactory-related life quality (LQrv) in relation to genetic and clinical-anamnestic factors.
Partial Regression CoefficientStd.Errtp-ValueCnf.Lmt
−95.00%		Cnf.Lmt
+95.00%		Partial Correlation Coefficient (ß)Std.Err. ßCnf.Lmt
−95.00%		Cnf.Lmt
+95.00%
Intercept18.635681.9530929.5416310.00000014.6746322.59673
Phase I enzymes score1.098270.3168173.4665810.0013820.455741.740810.2888950.0833370.1198790.457910
Compounds Exposure2.536390.9768632.5964620.0135520.555224.517560.3034150.1168570.0664180.540412
Previous Surgery1.894230.8129502.3300640.0255250.245493.542960.2087250.0895790.0270500.390400
Phase II enzymes score0.401920.1743342.3054730.0270120.048360.755490.2357370.1022510.0283620.443113
MTHFR enzymes score0.879960.4540251.9381300.060481−0.040851.800760.1760580.090839−0.0081720.360289
Psychological Trauma1.256050.9997601.2563530.217080−0.771563.283660.1502550.119596−0.0922970.392807
Age0.041170.0345921.1900500.241816−0.028990.111320.0981150.082446−0.0690930.265323
Gender0.814410.7774641.0475190.301843−0.762362.391180.0960300.091673−0.0898930.281952
Physical Trauma−0.282210.952656−0.2962300.768757−2.214281.64987−0.0326210.110119−0.2559530.190712
Legend: Table depicting the multiple regression model of the olfactory-related life quality (LQrv) in relation to genetic and clinical-anamnestic factors. MTHFR, folate cycle/methylation enzyme; Std., standard; Err, error; Cnf., confidence; Lmt, limit. In bold significant p-values.
Table
Table 4. Partial desirability model of main factors predicting olfactory-related quality of life in MCS patients.
Table 4. Partial desirability model of main factors predicting olfactory-related quality of life in MCS patients.
Partial Desirability Values
Prognostic FactorFactor LevelPredicted LQrvDesirability Value−95% CI LQrv+95% CI LQrv
MTHFR enzymes score−0.36332327.187300.38862625.4946328.87997
0.481381927.930610.44431426.9058328.95539
1.32608728.673910.50000128.0066929.34114
2.17079229.417220.53281428.3924430.44200
3.01549730.160520.56562828.4678531.85319
Phase II enzymes score0.895297826.683380.35087324.8095228.55724
3.37156227.678650.42543726.5778628.77943
5.84782628.673910.50000128.0066929.34114
8.32409029.669180.54393728.5683930.76996
10.8003530.664440.58787428.7905932.53830
Phase I enzymes score−0.72111126.234530.31724624.6591227.80994
0.389444627.454220.40862326.4773028.43114
1.50000028.673910.50000128.0066929.34114
2.61055529.893600.55384528.9166930.87052
3.72111131.113290.60768929.5378932.68870
Compounds exposures−0.48835526.111920.30806024.0024628.22139
0.016692127.392920.40403126.1902728.59556
0.521739128.673910.50000128.0066929.34114
1.02678629.954910.55655128.7522631.15755
1.53183331.235900.61310229.1264333.34537
Psychological trauma−0.48835527.405180.40494925.2511629.55920
0.016692128.039550.45247526.8173229.26178
0.521739128.673910.50000128.0066929.34114
1.02678629.308280.52800528.0860530.53051
1.53183329.942640.55601027.7886232.09666
Physical Trauma−0.60647628.949360.51216026.9490130.94970
−0.11845628.811640.50608027.6565429.96673
0.369565228.673910.50000128.0066929.34114
0.857586028.536190.48968327.3811029.69128
1.34560728.398470.47936526.3981330.39881
Previous Surgery−0.62608226.911470.36796125.2386128.58433
−0.16086727.792690.43398126.7760828.80931
0.304347828.673910.50000128.0066929.34114
0.769563029.555140.53890328.5385230.57175
1.23477830.436360.57780528.7635032.10922
Age27.1142527.845450.43793326.2838529.40705
37.1766928.259680.46896727.2883229.23104
47.2391328.673910.50000128.0066929.34114
57.3015729.088150.51828728.1167930.05950
67.3640129.502380.53657427.9407831.06398
Gender−0.40868627.863050.43925226.1572529.56886
0.089135228.268480.46962727.2382729.29869
0.586956528.673910.50000128.0066929.34114
1.08477829.079340.51789928.0491330.10955
1.58259929.484770.53579727.7789731.19058
Legend: Partial desirability model depicting in MCS subjects the predicted values of main genetic and clinical-anamnestic factors when contrasted against olfactory-related quality of life (LQrv). MTHFR, folate cycle/methylation enzyme; CI, confidence interval.
Table
Table 5. Metabolic role of enzymes for which the occurrence of gene variants, affecting enzyme activity, has been observed in MCS patients.
Table 5. Metabolic role of enzymes for which the occurrence of gene variants, affecting enzyme activity, has been observed in MCS patients.
Enzyme NameGene SymbolCatalyzed ReactionDescription of Enzyme ActivityGene Polymorphisms Affecting Enzyme Function *
CatalaseCAT2 H2O2 ⇄ 2 H2O + O2Decomposition of hydrogen peroxide to water and oxygen, protecting the cells
from oxidative damage by ROS. Catalase reactions are a key part of body’s enzymatic antioxidant defense mechanisms.		CAT-262C > T
Cytochrome P450 monoxygenase isozymes
2C9, 2C19, 2D6		CYP2C9 CYP2C19 CYP2D6RH + O2 + NADPH + H+ →
ROH + H2O + NADP+		Insertion of one atom of oxygen (monoxygenation) into the aliphatic position of an organic substrate (RH), while the other oxygen atom is reduced to water. This biotransformation reaction is often referred to as “phase I detoxification” in xenobiotic metabolism. CYP2C9 *2, *3
CYP2C19 *2, *17
CYP2D6 *4, *6, *10, *41
Glutathione-S-transferase isozymes P1, M1, T1GSTP1 GSTM1 GSTT1GSH + X → GS-X + H+Conjugation of reduced glutathione (GSH) with toxic agents, carcinogens, drugs, leading to the formation of a detoxified complex more polar and more readily excreted from human body. This reaction is often referred to as “phase II detoxification” in xenobiotic metabolism.GSTP1 I105V, A114V,
GSTM1 null
GSTT1 null
Methylenetetrahydrofolate reductaseMTHFR5,10-MTHF + NADPH →
5-MTHF+ NADP+		Reduction of 5,10-methylenetetrahydrofolate (5,10-MTHF) to 5-methyltetrahydrofolate (5-MTHF), acting as methyl donor for homocysteine (Hcy) remethylation to methionine. Enzyme activity diminishment leads to Hcy accumulation that induces oxidative stress and endothelial dysfunction. MTHFR C677T (A222V)
MTHFR A1298C (E429V)
MyeloperoxidaseMPOH2O2 + X− ⇄ H2O + HOXMPO-mediated reaction of hydrogen peroxide with halide anions (X-),chloride, bromide, fluoride, iodide) generates hypohalous acids, that mediate the anti-microbial activity of neutrophil granulocytes, key cells of immune system.MPO-G463A
Nitric oxide synthase type IIINOS32 L-Arginine + 3 NADPH + 3 H+ + 4 O2 ⇄ 2 Citrulline + 2 NO + 4 H2O + 3 NADP+Production of nitric oxide (NO) from L-arginine in the presence of NADPH, as cofactor, and oxygen. NO is a potent mediator of vasodilation in blood vessels.NOS3 G894T (D298E)
8-Oxoguanine glycosylaseOGG18-oxoG excision → nucleotide gap in DNA sequenceExcision of 8-oxoguanine (8-oxoG), an oxidized deoxyribonucleotide, with mutagenic effects, resulting from DNA exposure to ROS. OGG1-mediated cleavage of glycosidic bond causes a strand break in the DNA backbone.OGG1 C315G (S326C)
Paraoxonase 1PON1OP + H2O → DEP + Phenol-XHydrolysis of pesticides organophosphates (OP) to diethylphosphate (DEP) and phenol compounds (Phenol-X: PNP, IMHP, TCP). PON1, as a HDL
component, also protects against atherosclerosis by preventing the oxidation of plasma lipoproteins and the accumulation of oxidized LDLs and HDLs.		PON1 C108T (L55M),
PON1 A575G (Q192R)
Superoxide dismutase 2SOD22O2− + 2H+ ⇄ O2 + H2O2Dismutation of superoxide radicals (O2−) to molecular oxygen (O2) and hydrogen peroxide (H2O2). SOD2 is a manganese-dependent enzyme located in mitochondria, providing to human cells a greatly effective defense against ROS.SOD2 C28T (A16V)
UDP-glucuronosyl transferase isozyme 1A1UGT1A1UDP-GA + X → UDP + GA-XTransfer of the glucuronic acid component of UDP-glucuronic acid (UDP-GA) to a small hydrophobic molecule (X) in microsomal compartment. Glucuronidation reaction takes place in phase II detoxification of xenobiotic metabolism.UGT1A1 (TA)7TAA, * 28
Legend: HDL, high density lipoprotein; LDL, low density lipoproteins; OP, organophosphates pesticides and insecticides such as paraoxon, diazoxon, and chlorpyrifos; Phenol-X: phenolic compounds, such as p-nitrophenol, isopropyl methyl pyrimidinol, and trichloropyridinol; X, xenobiotics, toxic, carcinogens, drugs. * Polymorphisms examined in this study.
Table
Table 6. List of odorants and respective chemical names.
Table 6. List of odorants and respective chemical names.
OdorantsChemical NamesOdorantsChemical Names
AlmondBenzaldehydeLavenderEssential oil
AniseAnetholLemonCitral
AppleEthyl 2-methylbutyrateMintEssential oil
BananaIsoamyl acetateMushroom1-Octen-3-one
Bell pepper2-Isobutyl-3-methoxypyrazineMuskOmega-pentadecalactone
ButterDiacetylOnionEthanethiol
CabbageMethionolOrange blossomMethyl anthranilate
CamembertS-methylthiobutyratePeachγ-Undecalactone
CaramelFuraneolPearHexyl acetate
CinnamonCinnamaldehydePineappleAllyl hexanoate
ClovesEugenolPlasticStyrene
CoconutWhiskey-lactoneRosePhenylethanol
CoffeeExtractRubberBenzothiazole
CorianderLinaloolSeaCalone
Cork taint2,4,6-TrichloroanisoleSmokey4-Ethylgaïacol
Crab stickDimethylsulfideSweat3-Sulfanylhexyle acetate
Earthy(±)-GeosminThymeThymol
Eucalyptus1,8-CineolToasted bread2-Acetylthiazole
FeetIsovaleric acidVanillaAroma
Fish2-ButylamineVinegarWhite vinegar
GrassCis-3-hexenolVioletβ-Ionone
Horse4-EthylphenolWashing powderAldehyde
KiwiEthyl butyrateWoodyCedryl acetate
Legend: List of common odorants (with grey background) and their chemical names (in italics).

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Micarelli, A.; Cormano, A.; Caccamo, D.; Alessandrini, M. Olfactory-Related Quality of Life in Multiple Chemical Sensitivity: A Genetic-Acquired Factors Model. Int. J. Mol. Sci. 2020, 21, 156. https://doi.org/10.3390/ijms21010156

AMA Style

Micarelli A, Cormano A, Caccamo D, Alessandrini M. Olfactory-Related Quality of Life in Multiple Chemical Sensitivity: A Genetic-Acquired Factors Model. International Journal of Molecular Sciences. 2020; 21(1):156. https://doi.org/10.3390/ijms21010156

Chicago/Turabian Style

Micarelli, Alessandro, Andrea Cormano, Daniela Caccamo, and Marco Alessandrini. 2020. "Olfactory-Related Quality of Life in Multiple Chemical Sensitivity: A Genetic-Acquired Factors Model" International Journal of Molecular Sciences 21, no. 1: 156. https://doi.org/10.3390/ijms21010156

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