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Abstract. Early diagnosis of Parkinson's disease (PD) is important because it affects ... ACS Paragon Plus Environment. ACS Chemical Neuroscience. 1. ...
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Sensors Array for Detection of Early Stage Parkinson’s Disease Before Medication John P. M. Finberg, Miguel Schwartz, Raneen Jeries, Samih Badarny, Morad K. Nakhleh, Enas Daoud, Yelena Ayubkhanov, Manal Aboud-Hawa, Yoav Y. Broza, and Hossam Haick ACS Chem. Neurosci., Just Accepted Manuscript • DOI: 10.1021/acschemneuro.8b00245 • Publication Date (Web): 10 Jul 2018 Downloaded from http://pubs.acs.org on July 11, 2018

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Sensors Array for Detection of Early Stage Parkinson’s Disease Before Medication John P.M. Finberg1†*, Miguel Schwartz2#, Raneen Jeries3†, Samih Badarny2, Morad K. Nakhleh3, Enas Daoud2, Yelena Ayubkhanov2, Manal Aboud-Hawa3, Yoav Y Broza3 and Hossam Haick3* 1

Neuroscience Department, Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel

2

Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel

3

Department of Chemical Engineering, Technion - Israel Institute of Technology, Haifa, Israel

#

Deceased



Equal Contribution

*

Corresponding authors

Abstract Early diagnosis of Parkinson’s disease (PD) is important because it affects the choice of therapy and is subject to a relatively high degree of error. In addition, early detection of PD can potentially enable the start of neuroprotective therapy before extensive loss of dopaminergic neurons of the substantia nigra occurs. However, until now, studies for early detection of PD using volatile biomarkers sampled only treated and medicated patients. Therefore, there is a great need to evaluate untreated patients for establishing a real world screening\diagnostic technology. Here we describe for the first time a clinical trial to distinguish between de novo PD and control subjects using an electronic system for detection of volatile molecules in exhaled breath (sensors array). We further determine for the first time the association to other common tests for PD diagnostics as smell, ultrasound and non-motor symptoms. The test group consisted of 29 PD patients after initial diagnosis by an experienced neurologist, compared with 19 control subjects of similar age. The sensitivity, specificity and accuracy values of the sensors array to detect PD from controls was 79%, 84% and 81% respectively, in comparison with mid-brain ultrasonography (93%, 90%, 92%) and smell detection (62%, 89%, 73%). The results confirm previous data showing the potential of sensors array to detect PD.

Keywords: Parkinson; volatile organic compounds; ultrasonography; UPSIT

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Early detection and diagnosis of Parkinson’s disease (PD), and differentiation between idiopathic Parkinson’s disease (iPD) and other Parkinsonian syndromes (e.g. atypical parkinsonism, essential tremor, progressive supranuclear palsy (PSP), multiple system atrophy (MSA), corticobasal ganglionic degeneration (CBGD), and iatrogenic disease), is highly important, because it affects the choice of therapy.(1) In addition, early detection could potentially assist in early neuroprotective therapy resulting in better clinical outcomes, because at the time of first clinical motor symptom detection a significant portion of substantia nigra dopaminergic neurons have already been lost, and the remainder compensate by increasing their firing rates,(2) leading to potentially damaging elevation of oxidative stress levels.(3) To date, despite advances in the field, PD diagnosis remains subject to a high degree of error.(1, 4, 5) In a recent study(6) only 26% of PD cases at first presentation were positively identified as iPD by subsequent neuropathological techniques. Previously, we have reported, both in preclinical and clinical studies, that the exhaled breath volatolome (the spectrum of exhaled volatile organic compounds, VOCs) is significantly altered in neurodegenerative diseases.(7-11) Moreover, sensors array that is based on organically functionalized random networks of single-walled carbon nanotubes or gold nanoparticles could be trained via machine learning methods to detect these changes.(7-13) In an animal model, the system successfully discriminated between serotonergic and dopaminergic denervations, as well as between transgenic rats bearing alpha-synuclein A53T mutation and wild-type controls.(14) In our previous clinical study, the classification of established PD patients and control subjects resulted in a sensitivity of 70 %, specificity of 100 % and accuracy of 79 %, based on the pattern response of the sensors.(7) More recently, using the same sensor array, we established the ability of the system to distinguish between iPD and parkinsonism of various etiologies with a sensitivity and accuracy of 88%.(15) In that study, comparison of ROC curves showed that there was no statistical difference between patients medicated with monoamine oxidase type B (MAOB) inhibitors or L-DOPA and those not treated with these drugs, however, we considered it of importance to try and entirely exclude this potential source of variability by also examining PD patients at first appearance of clinical symptoms, and before any anti-parkinsonian therapy was administered. Since our breath analysis technique depends on the production of a variety of metabolites from endogenous chemicals, the administration of drugs such as enzyme inhibitors or neurotransmitter precursors could potentially alter the production of endogenous VOCs.

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In the current study, we compared the volatolomic changes associated with early Parkinson’s disease, in two groups (sites 1 and site 2, see Methods section) of Parkinsonian patients at first diagnosis, before onset of therapy (de novo PD), in comparison with healthy volunteers not suffering from Parkinsonian symptoms. The patient’s diagnosis was based on their clinical symptoms and was independently reinforced using transcranial sonometry of the mid-brain (TCS) and the University of Pennsylvania Smell Identification Test (UPSIT). Olfactory function is deficient (hyposmia) at an early stage of PD (16), but is not a definitive indicator for PD, because it occurs in a significant portion of the general public, especially in older subjects.(17) Patients also completed a non-motor symptom questionnaire (NMSQ)(18) which is an additional aid to PD diagnosis, although the symptoms in the questionnaire (e.g. constipation, sexual dysfunction, sleep disturbances) are common amongst healthy subjects. Hamilton score for affective disorders (depression) was also determined to further characterize the PD patient’s symptomatology, since clinical depression is known to be increased in PD and could also be associated with altered endogenous VOC production. The extent of motor symptoms was evaluated using the original part III of the Unified Parkinson’s Disease Rating Scale (UPDRS). In addition, clinical description of the patient’s state was added using the Hoehn and Yahr (HY) Staging Scale as currently used by PD expert clinicians (19).

Table 1: Descriptive statistics of the 48 participants in the study Type De Novo Parkinson’s disease patients

Healthy controls

Classification

No. patients

Total

Age

(Avg.

±

Gender

SD)

(M:F)

29

66.2±8.9

16:13

Site 1 (BZ)

18

67.0±8.0

12:6

Site 2 (CA)

11

65.0±10.6

4:7

Total

19

61.6±7.1

8:11

Site 1 (BZ)

14

62.6±5.8

5:9

Site 2 (CA)

5

58.8±10.1

3:2

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The descriptive statistics of the 48 participants in this study are shown in Table 1. The PD patients and control subjects were of similar mean age, but there was a slight preponderance of males in the PD group of site 1, and in the controls of site 2. Numbers of current smokers and exsmokers who were included in the PD group and the control group are very few and are shown in Table 1. The UPDRS scores of the PD patients show that they were nearly all in HY stages 1 to 2 (HY mean score = 1.42 ± 0.129), however three patients in site 2 had scores of 2.5-3. The mean UPSIT score in the control group was similar to that in other reports (20) and comparison with PD subjects showed a highly significant difference with lower values in the PD group, indicative of reduced olfactory ability (Table 2). The values of sensitivity, specificity and accuracy for distinction between the two groups by UPSIT test were 62, 89, and 73% respectively. Transcranial sonography values were normal in all but one of the control subjects, but in only one of the PD patients, producing values of 93.3, 89.5 and 91.8% respectively for sensitivity, specificity and accuracy (Table 2) between the two groups. NMSQ and Hamilton scores were also significantly different between HC and PD subjects, with higher values in PD patients as expected (Table 2).

Table 2: Motor and non-motor rating scores, and hyper-echogenic area of substantia nigra pars compacta by transcranial sonography (TCS) for Parkinson’s disease patients and healthy controls. Results (mean ± standard error of the mean) shown for all patients in sites 1 (Bnei Zion Hospital, Haifa) and 2 (Carmel Hospital, Haifa) combined. Unified Parkinson’s Disease Rating Scale (UPDRS; higher score = more disability), Non-Motor Score Questionnaire (NMSQ; higher score = increased symptom rating), Hamilton score for affective disorders (higher score = more depression), University of Pennsylvania Smell Identification Test (UPSIT; maximum score = 40), Transcranial Sonography (TCS) hyper-echogenic area. *** P