Vestibular impairment in Multiple Chemical Sensitivity: Component analysis findings

Abstract

Multiple chemical sensitivity (MCS) is a common clinical diagnosis in western populations and its symptoms are thought to be mainly related to chemical compounds exposure. Although MCS subjects refer to complain from many central nervous system symptoms, including dizziness, no study to now deepened vestibular detriment nor to what extent such an impairment could worsen MCS. Thus, the purpose of present study was to objectively highlight those clinical/subclinical aspects of vestibular impairment that could be related to MCS symptoms cohorts. A principal component analysis within a wide battery of otoneurological test scores was employed in 18 right-handed MCS patients and 20 sex- and age-matched healthy individuals. A deranged dimensionality in near-optimal re-weighting within otoneurological variables was found in MCS as compared with healthy subjects. These data seem to support the idea that MCS physiopathological underpinnings could lead to a peripheral and higher vestibular decay that could be addressed as a further aspect to better follow MCS patients up along natural history of disease in clinical practice.

Keywords

Multiple chemical sensitivity vestibular visual dependency component analysis Fourier transform

1 Introduction

Multiple chemical sensitivity (MCS) is a relatively common clinical diagnosis in western populations [4]. With prevalence rates up to 6.3% [4] it constitutes a chronic polysymptomatic, multisystem condition characterized by self-reported susceptibility to illness from low levels of multiple environmental chemicals with a degree of uncertainty about accepted definition [4, 10]. Several studies in the last few years showed cerebral blood flow distribution abnormalities in patients with MCS, especially while processing odorous substances [5 , 25]. In particular, MCS sufferers demonstrated to peculiarly react to sensory stimuli, with activation of brain areas connected with motivational and emotional processing of the information, such as the amygdala and the hippocampus, encompassed in limbic system [5 , 25]. According with this perspective, limbic and mesolimbic pathways were postulated to mediate central nervous system (CNS) sensitization and their dysregulation to contribute to abnormalities of behaviour and mood, endocrine, autonomic and immune function [11].

Although the absence of consensus regarding the aetiology of MCS and diagnosed individuals may react to a wide range of everyday chemical compounds, central nervous system (CNS) symptoms such as difficulty concentrating, memory problems, fatigue, depression, daytime sleepiness, “spaciness” (derealization), tension, irritability and dizziness are commonly referred [4, 32]. Patients claim symptoms flaring and remitting during chemical exposures and avoidance, respectively.

Various proposals for possible MCS physiopathological mechanisms have been included over the time: (i) possible psychogenic factors such as odor conditioning or misattribution of psychiatric symptoms such as depression, anxiety/panic, or somatization, (ii) immune system dysfunction; (iii) nervous system dysfunction in the periphery and/or CNS [4 , 31] including sensitization, partial limbic kindling and time-dependent sensitization models [5, 40].

In turn, the same neural circuits have been shown to be pivotal in vestibular processing, given the fact that the limbic system – previously thought to be engaged in physiopathological underpinning of this disorder [10] – receives neural input from the vestibular cortex [6, 20].

To the best of our knowledge no research between intolerance to chemical compounds and peripheral and/or higher vestibular disorder was devised before, depicting a paucity of information about this topic. Thus, due to the peculiarity of MCS disorder, the purpose of this study was to objectively highlight peripheral and higher vestibular impairment that could be related to MCS symptoms cohort by employing a principal component analysis (PCA), an exploratory approach useful to disclose intercorrelation of clinical/subclinical variables possibly underpinning hidden phenomena.

2 Materials and methods

2.1 Participants and study design

We included in the study MCS patients admitted to the Regional Center for Diagnosis, Prevention and Treatment of MCS and evaluated at University of Rome Tor Vergata for symptoms related to Ear-Nose-Throat complaints. Diagnosis of MCS was achieved according to the US Consensus Criteria for MCS [8] and the revisions suggested by Lacour et al. [27].

We also enrolled as control group a population of gender- and age-matched healthy subjects (HC).

Both eligible MCS patients and HC were required to meet the following criteria: subjects with diabetes, oncologic or HIV history, neurological and psychiatric or mood disorders, history of surgery, radiation and brain trauma were excluded from the study. No patient showed liver or renal abnormalities, nor was pregnant or breastfeeding. The peripheral blood of MCS and HC was tested for the usual parameters. Neurological diseases were excluded with the Mini Mental State Examination and Magnetic Resonance Imaging (MRI). All those conditions that could potentially develop an otoneurological dysfunction were considered as exclusion criteria. Thus, patients with hearing/vestibular disorders, head trauma or surgery history, 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. Finally, we excluded all subjects taking drugs possibly impacting vestibular functions.

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.

2.2 Otoneurological testing

After a thorough clinical otoneurological examination (including pure tone audiometry and impedance, binocular electrooculography analysis with positional manoeuvres, Head Shaking Test, clinical Head Impulse Test [cHIT] as well as limb coordination, gait observation and Romberg stance Test) all MCS and HC subjects underwent:

Video Head Impulse Testing (vHIT): For vHIT measurements the EyeSeeCam^TM System [35] and the technique proposed by Blodow et al. [12] were used. The vHIT results were classified as abnormal if two conditions were met: abnormal gain according to the calculated normative data and presence of refixation saccades, revealed by visual inspection, according to Blodow et al. [12]. As a robust characteristic to define a refixation saccade, we used peak acceleration. Refixation saccades were classified as covert, if they occurred before the end of the head impulse and classified as overt afterwards. Amplitudes (°/s) and latencies (ms) of the identified refixation saccades and physiologically induced saccades were evaluated at peak eye velocity (°/s). According to manufacturer software (OtoAccess ^TM), both side gain mean and median (med) values recorded at 60 ms as well as regression slope (reslo) were calculated and extracted on a.xls file.

Static Posturography Testing (SPT): Each patient was instructed to maintain an upright position on a standardized platform for static posturography (EDM Euroclinic^®). The recording period was 60 s for each test: eyes closed (CE) or open (OE) while standing on both the stiff platform (SP) or a 6 cm heightened foam carpet (FC) and the sampling frequency was 25 Hz [2, 3]. The center of pressure (CoP) was monitored, while performing the test. The posturographic parameters considered in our study were the trace length (length), the surface of the ellipse of confidence (surf), the mean velocity (avspe) of body sways with relative SD (stdspe) and Romberg index (RI) and the fast Fourier transform (FFT) elaboration of oscillations on both the X (right-left) and Y (forward-backwards) planes [2, 3]. Time-domain oscillations signals (X and Y) were extracted from original software manufacturer into.txt format and the FFT elaboration were gained through a function implemented in Matlab software. A main script (Appendix A) was realised to acquire the FFT for each patient related to four different conditions by accessing to a Matlab function cited above. The script returned for each condition a file containing the nomalised value of the FFT for X and Y oscillations. Spectral values (power spectra, PS) of body oscillations were quantified on an.xls file, for every frequency from 0.01 to 5.00 Hz [2, 3]. According to previous experiences, we subdivided the frequency spectrum into three groups: 0.01–0.70 Hz (low frequency interval); 0.70–1.00 Hz (middle frequency interval); 1.00–5.00 Hz (high frequency interval). Within each group, the spectral intensity was determined by adding the relative PS and the group mean PS (±SD) [2, 3].

Rod and Disc Test (RDT): According to Cousins et al. [18] visual dependence (VD) testing was measured with the RDT on a laptop computer, instructing subjects to align the rod to their perceived vertical (the subjective visual vertical) under three conditions. In condition 1, the collage of dots was stationary (N). In conditions 2 and 3, the collage rotated clockwise (CW) or counter-clockwise (CCW), respectively, at 30°/s. Subjects were given four trials in each condition, with conditions 2 and 3 presented in random order after condition 1. During each trial the rod was initially set randomly at±40° from vertical. The rod tilt for each trial was recorded as the difference in degrees between true vertical and the subjects’ final placement of the rod (Rod-and-Disc software is available online at: http://www.imperial.ac.uk/medicine/dizzinessandvertigo). Thus, there were 6 randomly presented conditions, each containing four trials. Errors were calculated as deviation from the gravitational vertical position of the rod.

Rod and Frame Test (RFT): A standard RFT device square frame was used, previously described in Fiori et al. [23]. The frame was tilted 33° CW, CCW, or not tilted (0°, N); the rod 11° or 22°, CW or CCW. Thus, there were 12 randomly presented conditions, each containing three trials [37]. Errors were calculated as deviation from the gravitational vertical position of the rod.

2.3 Data handling and statistical analysis

Mean and standard deviations (SDs) of instrumental otoneurological testing scores were calculated in both MCS and HC subjects.

PCA with varimax rotation was used to detect – within the whole group of subjects – hidden phenomena mainly through reducing the variables to find the most important ones, finding patterns in the data and classifying any combination of variables. Otoneurological variables included in this approach were: i) med and mean of vHIT gain and reslo, ii) both classical posturography parameters (length, surf, avspe, stdspe, RI) and power spectra of body oscillation (quantified on X and Y planes and low, middle and high frequency interval) under CE and OE condition and while standing on SP or FC, iii) RDT parameters under CW, CCW and N dots collage rotation and±40° rod position and iv) RFT parameters under 33° CW, CCW or N frame tilt and±11° or±22° rod position (for detail see Materials and Methods chapter).

The number of factors in principle was determined by a combination of the Kaiser/Guttman criterion (only factors with eigenvalues larger than 1 are incorporated in the model) and interpretability. Also scree plots were used, in which the eigenvalue is plotted against component number, to evaluate if a factor is relevant or not. Finally, a component coefficient analysis was performed between variables and factors, grouping active cases by their relative group membership (STATISTICA 7 package for Windows).

3 Results

3.1 Subjects

23 consecutive MCS patients were enrolled. Among them, 3 were using antidepressant drugs, 1 reported history of alcohol abuse, 1 of hypothyroidism and were excluded.

Therefore, 18 right-handed MCS patients (11 women and 7 men, mean age 49.5±9.3 years) met the eligibility criteria and were included in the study. HC group consisted of 20 right-handed healthy individuals (12 women and 8 men; mean age 48.6±11.4 years).

3.2 Otoneurological data

MCS and HC subjects did not show any clinical otoneurological abnormality during examination. Mean and SDs of instrumental otoneurological testing scores are shown in Table 1.

3.3 PCA

From the analysis of the own values measuring the amount of variance due to each main component, there have been identified 37 factors (principal components; PC) in the whole group of subjects. In order to simplify the interpretation of the principal components we used the Varimax method and Kaiser/Guttman criterion. This indicated as significant those component that had coefficient higher than 1 [38], thus remaining 20 principal components for analysis in both groups (Fig. 1). The optimal factorial solution was the one with two extracted factors because they explained a maximal amount of variance. Proportion of variance explained by the first factor (PC1) was 17.6% as the second (PC2) explained the 10.01% (complete data are showed in Fig. 1).

Loadings function (component coefficient analysis) and PCA scree plots highlighted variable dimension and contribution: PC1 was positively correlated to RDT/CCW/–40° and RDT/CW/+40° and classical posturography parameters and negatively to vHIT parameters as well as PC2 was positively correlated to FFT posturography parameters and negatively to classical posturography parameters and vHIT parameters (component coefficients showed in Fig. 2 and Table 2).

Finally, PCA scores plot of the first two components (PC1 vs. PC2) has been shown in Fig. 3, which explained 27.61% of the total variance in the whole group of subjects. In the same figure active cases were labelled according to their membership so that MCS subjects tended to be clustered in the top right and HC in the top and bottom left quarters.

4 Discussion

The first interesting finding in the present study was the peculiar clustering behaviour demonstrated in subjects populations when labelled against scores plot of first two components (PC1 vs. PC2) explaining – together – the maximum percentage of variance (Fig. 1). Component correlation analysis and PCA scree plots highlighted that PC1 and PC2 were positively correlated to RDT (CCW/–40° and CW/+40° conditions) as well as classical posturography parameters and FFT results, respectively. In turn, PC1 and PC2 were found to negatively correlate with vHIT parameters and classical posturography as well as vHIT parameters, respectively (component coefficients are shown in scree plots in Fig. 2 and Table 2). Interestingly, when labelling active case on the factor plane, MCS cases placed in that plot zone (top right quarter) where variable dimension and contribution of PC1 and PC2 showed higher values (Fig. 3). On the other side, HC subjects arranged in that zone of the plot (top and bottom left quarters) where the same values of PC1 and PC2 are lower (Fig. 3).

This arrangement pattern demonstrated to grant a particular relief when compared with mean and SDs values of RDT, classical posturography, FFT parameters and vHIT that were found higher and lower in MCS than in HC subjects, respectively (Table 1). In line, increases in classical posturography, FFT parameters and VD behaviour (RDT/CCW/–40°, RDT/CW/+40°) mean values (Table 1) were highlighted by active MCS cases arrangement in that zone of the factor plane where PC1 and PC2 were found to positively correlate with RDT/CCW/–40° and RDT/CW/+40°, classical posturography and FFT parameters, respectively (Fig. 3). Accordingly, the relative decrease in vHIT mean scores (Table 1) was represented by the relative absence of MCS active cases in that quarter of the factor plane (bottom left) where PC1 and PC2 negatively correlated with vHIT parameters (Fig. 3).

This pattern of cluster offered for the first time a possible perspective about defective central and peripheral vestibular processing insight into MCS sufferers. In particular, vHIT implementation provided a new dimension in vestibular diagnostics [16] allowing a quantitative assessment of the lateral semicircular canal function and providing specific informations about the VOR [12 , 35]. Bilaterally decrease of both gain and reslo in MCS cases (and their subsequent arrangement on the factor plane) could underpin a peripheral (although not clinically evident by means of a cHIT) vestibular disorder. When merging these data with classical otoneurological findings, the characteristic factor plane arrangement of MCS and HC depicted a congruent increase in correlation with classical posturography, FFT and VD. Many Authors effectively demonstrated that increases in body sways (mainly in the low frequency interval) are recognised to be under vestibular control [2 , 33] and patients suffering from peripheral vestibular deficits have increased body sways in low frequency range [2, 41]. On the other hand, a degraded vestibular input was revealed leading to an enhanced reliance on visual input (disclosed by VD scores increases) due to a process of sensory re-weighting [1 , 24], as it could possibly happen in MCS.

The ability to adapt sensory–motor control to challenging situations by selection and weighting of alternative frames of reference has been considered as one of the main issues for postural control [26, 29]. Recent models of multisensory combination based on neurophysiological [13], perceptual [22] and behavioural evidence [34] proposed that adaptation to environmental changes would depend upon sensory re-weighting to optimize the relevance of individual sensory inputs (maximize the reliability of our estimates). Such a phenomenon was considered to be determined by a maximum likelihood estimation so that the brain would appear as a near-optimal Bayesian estimator of object properties [21, 26].

In this context, PCA proved to be a useful technique in reducing information dimensionality often needed from the vast arrays of data obtained and relying on the fact that most of the descriptors are in some instances higlhly intercorrelated [9, 15]. Thus, present component model could suggest for the first time a rearrangement of variable dimensions, clusters and contribution within PC1 and PC2 in MCS as a possible aspect of the above-mentioned re-weighting due to the patophysiological neural cascade.

Speculative explanations of the mentioned rearrangement model could arise from those MCS patophysiological aspects proposed over the time by many Authors [4 , 40]. In this perspective, repeated exposure to multiple chemical sensitivity inducing agents have been thought to provide both time-dependent sensitization and kindling especially within hippocampal and limbic structures. In turn, these processes have been considered to cause, respectively, deterioration in stability of CNS and long-term potentiation [39]. Recent advances postulated that additional “higher” aspects of central vestibular functions and breakdowns could result from the integration of the vestibular network at the cortical level and within the hippocampal and limbic system [14]. These aspects comprised the internal representation of the body scheme and the internal model of the surrounding space as well as multisensory motion perception, attention, spatial memory, and navigation [14]. Even though a description of “higher vestibular function” disorders has not yet been elaborated [14], they are characterized by a complex integration of perceptual, sensorimotor, and behavioral deficits that exceed basic perceptions of head acceleration or motor responses [14, 36].

In the present study such phenomena were merged – by means of PCA – and a deranged dimensionality in near-optimal re-weighting within otoneurological variables was found in MCS suggesting possible reshuffles along vestibular networks.

In conclusion, present peculiar data supported the idea that MCS physiopathological underpinnings could probably lead to a peripheral and higher vestibular decay, the study of which could be employed as a further tool in order to better follow MCS patients up along natural history of disease in clinical and research practice.

5 Strengths and limitations of the study

Many uncertainties in the literature involving MCS otoneurological pathways could clearly be explained by different criteria for patient enrolment, different kinds of questionnaires employed in order to investigate symptoms spectrum, distortion related to the general incidence of personality traits in control subjects, and many nuisance variables biasing the general research in MCS [30].

As in previous works, we tried to reduce artifacts that could affect symptoms spectrum outcomes by enrolling and studying only MCS patients with commonly accepted criteria and regularly followed by the local center for diagnosis, treatment and prevention of MCS [30].

Conflicts of interest

The authors report no declarations of interest.

Footnotes

Appendix A

Core function implemented in Matlab in order to obtain fast Fourier transform of X and Y oscillations. The symbol ‘% ’ and ‘s’ represent a Matlab comment and X or Y oscillations, respectively:

L = length(s) % s vector of signal values

Fs = 25; % Sampling frequency

NFFT = 2ˆnextpow2(L); % Next power of 2 from length of s

f = Fs/2*linspace(0,1,NFFT/2+1);

S = fft(s,NFFT)/L; % FFT of a signal s

modS = 2*abs(S(1:NFFT/2+1));

ms = max(modS);

S_norm = modS/ms;

Acknowledgments

None.

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