Accepted Manuscript Title: Volatile organic compounds (VOCs) fingerprint of Alzheimer’s disease Author: Andrea Mazzatenta Mieczyslaw Pokorski Ferdinando Sartucci Luciano Domenici Camillo Di Giulio PII: DOI: Reference:
S1569-9048(14)00263-8 http://dx.doi.org/doi:10.1016/j.resp.2014.10.001 RESPNB 2396
To appear in:
Respiratory Physiology & Neurobiology
Received date: Revised date: Accepted date:
25-9-2014 2-10-2014 2-10-2014
Please cite this article as: Mazzatenta, A., Pokorski, M., Sartucci, F., Domenici, L., Di Giulio, C.,Volatile organic compounds (VOCs) fingerprint of Alzheimer’s disease, Respiratory Physiology and Neurobiology (2014), http://dx.doi.org/10.1016/j.resp.2014.10.001 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
*Abstract
Volatile organic compounds (VOCs) fingerprint of Alzheimer’s disease
Abstract Alzheimer’s disease (AD) is a profoundly life changing condition and once diagnosis occurs, this is typically at a relatively late stage into the disease process. Therefore, a shift to earlier
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diagnosis , which means several decades before the onset of the typical manifestation of the disease, will be an important step forward for the patient.
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A promising diagnostic and screening tool to answer this purpose is represented by breath and exhaled volatile organic compounds (VOCs) analysis. In fact, human exhaled breath contains
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several thousand of VOCs that vary in abundance and number in correlation with the physiological status. The exhaled VOCs reflect the metabolism, including the neuronal ones, in healthy and
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pathological conditions. A growing number of studies clearly demonstrate the effectiveness of VOCs analysis in identifying pathologies, including neurodegenerative diseases. In the present study we recorded, in real time, breath parameters and exhaled VOCs. We were able to demonstrate
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a significant alteration in breath parameters induced by the pathology of AD. Further, we provide the putative VOCs fingerprint of AD. These vital findings are an important step towards the early
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diagnosis of AD.
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*Highlights (for review)
Highlight #1: Volatile Organic Compound (VOC) as a new biomarker of neurodegenerative disease
Highlight #2: Real time VOC analysis is a respiratory system physiological biomarker in Alzheimer disease
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Highlight #3: Real time VOC profile and fingerprint of Alzheimer disease patients are presented
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Title Page
Title: Volatile organic compounds (VOCs) fingerprint of Alzheimer’s disease
Authors: & Camillo Di Giulioa a
Physiology and Physiopathology Section, Department of Neuroscience, Imaging and Clinical
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Science, University of Chieti-Pescara „G. d‟Annunzio‟, Chieti, Italy Neuroscience Institute, CNR - Pisa
Department of Clinical and Experimental Medicine, Unit of Neurology, Pisa University Medical
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School, Pisa, Italy d
Public Higher Medical Professional School, Opole, Poland;
Institute of Psychology, Opole University, Opole, Poland
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e
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Andrea Mazzatentaa,b,c*, Mieczyslaw Pokorskid,e, Ferdinando Sartuccib,c, Luciano Domenicib,f
Department of Applied Clinical Sciences and Biotechnology (DUSCAB), School of Medicine
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*Corresponding author at:
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University of l‟Aquila, l‟Aquila, Italy
Physiology and Physiopathology Section, Department of Neuroscience, Imaging and Clinical Science, University of Chieti-Pescara „G. d‟Annunzio‟, Via dei Vestini 31, 66100, Chieti, Italy; phone +39 (0)871 355 4036.
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Email address:
[email protected]
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Volatile organic compounds (VOCs) fingerprint of Alzheimer’s disease Andrea Mazzatentaa,b,c*, Mieczyslaw Pokorskid,e, Ferdinando Sartuccib,c, Luciano Domenicib,f
a
Physiology and Physiopathology Section, Department of Neuroscience, Imaging and Clinical
b
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Science, University of Chieti-Pescara „G. d‟Annunzio‟, Chieti, Italy Neuroscience Institute, CNR - Pisa
Department of Clinical and Experimental Medicine, Unit of Neurology, Pisa University Medical
School, Pisa, Italy e
Public Higher Medical Professional School, Opole, Poland;
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d
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c
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& Camillo Di Giulioa
Institute of Psychology, Opole University, Opole, Poland
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*
corresponding author
Andrea Mazzatenta
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University of l‟Aquila, l‟Aquila, Italy
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Department of Applied Clinical Sciences and Biotechnology (DUSCAB), School of Medicine
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Physiology and Physiopathology Sect., Department of Neuroscience, Imaging and Clinical Science, University of Chieti-Pescara „G. d‟Annunzio‟, Chieti, Italy
Abstract
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email
[email protected],
[email protected]
Alzheimer‟s disease (AD) is a profoundly life changing condition and once diagnosis occurs, this is typically at a relatively late stage into the disease process. Therefore, a shift to earlier diagnosis , which means several decades before the onset of the typical manifestation of the disease, will be an important step forward for the patient. A promising diagnostic and screening tool to answer this purpose is represented by breath and exhaled volatile organic compounds (VOCs) analysis. In fact, human exhaled breath contains several thousand of VOCs that vary in abundance and number in correlation with the physiological status. The exhaled VOCs reflect the metabolism, including the neuronal ones, in healthy and
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pathological conditions. A growing number of studies clearly demonstrate the effectiveness of VOCs analysis in identifying pathologies, including neurodegenerative diseases. In the present study we recorded, in real time, breath parameters and exhaled VOCs. We were able to demonstrate a significant alteration in breath parameters induced by the pathology of AD. Further, we provide the putative VOCs fingerprint of AD. These vital findings are an important step towards the early
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diagnosis of AD. Key words: Alzheimer’s disease, volatile organic compounds (VOCs), breath analysis,
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neurodegenerative disease, VOC fingerprint, VOC real time analysis
Introduction
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Alzheimer‟s disease (AD) is a progressive neurodegenerative disease of the central nervous system, which affects primarily limbic and neocortical structures, wherein patients suffer from sensory, cognitive, and motor loss (Bishop et al, 2010). In particular, among senses, olfactory
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function is extremely exposed in AD, deficits in odor threshold, detection, identification and recognition occur at an early stage of the disease as opposed to effects on other modalities, for
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instance, vision (for review see Wesson et al. 2011). AD is characterized at the molecular level by abnormal processing of amyloid precursor protein, hyperphosphorylation of tau protein, and apoptotic-like cell death (Troncoso et al, 1996). Consequently, progressive neurodegeneration
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occurs in the brain and deficiency in neurotransmitters, which are responsible for the milieu of pathological changes underlying the clinical syndrome (Francis et al, 1994). The presence of the apolipoprotein E4 allele (Roses, 1996) comes along with a neuroimmune response (McGeer and McGeer, 1996) support the AD pathology.
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Although there are characteristic signs for the clinical diagnosis of AD (McKhann et al, 1984), there is substantial clinical heterogeneity in their manifestations. Initial changes are often subtle and the diagnosis may occur too late when the degenerative changes result in neuronal dysfunction that exceeds the compensatory capacity of the brain (Blass, 1993). In fact, it has been suggested that the biological onset of the disease may occur several decades before physical and clinical manifestations are detected (Braak and Braak, 1991). Currently, there is a lack of a simple and feasible marker for early or timely (World Alzheimer Report, 2011) AD diagnosis. There is a link with reactive oxygen species (ROS) that play a role in the pathophysiological cascade leading to AD. ROS reactivity may be responsible for cellular and tissue damage and when ROS generation exceeds the endogenous ability to destroy them this cause oxidative stress
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(Christen, 2000; Risby et al, 2006). Furthermore, galaninergic systems undergo hypertrophy in the brain regions that mediate cognition and are prone to AD neuropathological damage. Galanin overexpression plays a role in the survival of select neuronal populations associated with cognitive decline in AD (Counts et al, 2008). In relation to the oxidative stress and the role of galanin, the neuropeptide has been identified in the human carotid body (Di Giulio et al, 2014), which has a key role in hypoxic respiration. In addition, galanin decreases in the carotid body with aging and
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dramatically so in drug addiction (Di Giulio et al, 2014).
Interconnected to these aspects is the novel diagnostic approach that relies on the
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identification of patterns of volatile organic compounds (VOCs) in exhaled breath (Risby, 2002; Phillips et al, 2003; Mazzatenta et al, 2013b; de Lacy Costello et al, 2014). In fact, disease-specific
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breath prints could be useful as robust and easily accessible biomarkers (Risby and Sehnert, 1999; Hakim et al, 2012; Mazzatenta et al, 2013b). The VOCs profile in exhaled breath reflects the
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biochemical alterations related to metabolic changes, organ failure, or neuronal dysfunction in disease, which are, at least in part, transmitted via the lung to the alveolar exhaled breath, even at the very onset of disease (Mazzatenta et al, 2013a,b,c). In several studies breath analysis has been
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applied to neurodegenerative diseases (Tisch et al, 2013; Ionescu et al, 2011). In the present study we investigated, in real time, the breath parameters and exhaled VOCs, and we present a putative
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Materials and methods
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VOCs fingerprint of AD.
The observational noninvasive and anonymous study included 59 volunteers, divided into neurodegenerative (N= 15, age range 59-95 years) and healthy (N= 44, age range 19-105 years) subjects. The volunteers, or their caregivers, provided written informed consent and the procedure
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was performed in agreement with the Ethical Standards of the Helsinki Declaration. Breath pattern and exhaled breath content of VOCs was continuously measured for 10 min in a standard controlled condition, in the morning between 10-11a.m. in a VOC free room monitored, before each subject, by a control recording of the environmental air. In addition, other physical parameters (T, brightness) that could affect the VOC recording were controlled. The recording system used in this experiments was an iAQ-2000 (Applied Sensor, Warren, NJ) equipped with a metal oxide semiconductor (MOS) having a sensing range of 450-2000 ppm CO2 equivalents, which is able to detect a broad range of volatile compounds (both organic and inorganic, e.g., alcohols, aldehydes, aliphatic hydrocarbons, amines, aromatic hydrocarbons, ketones, organic acids, and CO), while correlating directly with the CO2 levels (Mazzatenta et al, 2013a,b,c). Data
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treatment and statistical analysis was done by Excel, Origin and SPSS software, α is set at 0.05. Data normalization was made by Log10. Fit equation used was y=y0+(A/(wxsqrt(PI/2)))xexp(-2x((xxc)/w)2).
Results
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Average breath frequency in the AD subjects was compared with that in the healthy ones
frequency between the AD and control subjects.
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(Fig. 1). The results are summarized in Table 1. There was a significant difference in breath
In Fig. 2 the maximum average peak frequency in the AD subjects was compared with the
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healthy controls, and a significant difference was found (Table 1).
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Fig. 1 and Fig .2 about here
The comparison between grand averages of exhaled VOCs in the AD and healthy subjects is
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shown in Fig. 3. The statistical analyses showed a significant difference between the two groups
Fig. 3 and Tab. 1 about here
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(Table 1).
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The VOCs fingerprint of AD is shown in Fig. 4a. The VOCs fingerprint of AD is characterized by a cluster of signals generated by VOCs relative abundance significant different from the healthy one (Tab.1).
In Fig. 4b the frequency distribution of VOCs in both AD and control subjects is presented.
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The fit of the VOCs frequency distribution return two distinct Gaussian peaks for the AD (r2 = 0.56) and healthy (r2 = 0.98).
Fig. 4 about here
Discussion Neurodegenerative diseases, such Alzheimer‟s disease, are on the increase in the general public (Ballard et al, 2011). Because of the lack a viable tool for an early diagnosis (World Alzheimer Report, 2011), AD is recognized late when the neuropathology exists for several decades
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and cognitive declines are marked, with subjective impairment and disability (Braak and Braak, 1998). The exhaled VOCs embody a putative novel early diagnostic tool in AD (Risby, 2002; Solga and Risby, 2010; Mazzatenta et al, 2013b). In normal subjects, several thousands of different VOCs can be detected in exhaled breath, which is variable and reflects pathological conditions (Risby, 2002). A growing body of studies demonstrate the effectiveness of VOCs analysis as a diagnostic tool for a wide range of pathologies
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(for review see Risby, 2002; Solga and Risby, 2010; Mazzatenta et al, 2013b). Among these, few studies are recently devoted to the investigation of VOCs in neurodegenerative diseases (Tisch et al,
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2013; Ionescu et al, 2011).
In order to investigate the exhaled VOCs two strategies are applied, which are based on the
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principle of analytical chemistry or physiology (Risby and Solga, 2006; Solga and Risby, 2010; Mazzatenta et al, 2013a,b,c). The first approach requires to accumulate from 6 to 10 liters in volume
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of the exhaled breath air in a collection bag or in a chemical trap, followed by a post-collection VOCs analysis on the entire volume to identify the analytes. Physiological information could be lost because the sample is of too large a volume resulting from the sum of numerous breathing acts. In
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addition, forced breathing could introduce a contaminant consisting of analytes accumulating the lung dead space. Consequently, the fingerprint obtained would not be representative of the real
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breath activity or correlated with the breath act and the altered condition. The physiological approach, on the contrary, is based on a real time analysis of the exhaled breath air, which even if cannot return the singular analytes can give vital information on VOCs
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total amount, profile, and fingerprint during normal and spontaneous breathing activity. Afterward, the VOCs fingerprint is coupled with breathing activity and returns physiological time-related information.
In the present study, we used a real time breath and VOCs recording to evaluate breath
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activity in AD in comparison with the control healthy condition. We found significant differences in AD breath parameters in comparison with control healthy subjects. In AD, breathing was of increased frequency accordingly to reduced amplitude. In the literature, there are no studies on breathing pattern in AD, except for the sleep apnea syndrome in AD (Buratti et al, 2014). The breath modifications found in AD, we think, could hypothetically be related to neuronal death in the central and peripheral, the carotid body, respiratory areas, which overcomes the galanin neuroprotective function (Counts et al. 2008). That could also have to do with oxidative stress in AD, because neurons are extremely sensitive to free radicals formed during normal cellular metabolism. Free radical formation is enhanced within pathophysiological cascades as a consequence of low glutathione content, a high proportion of polyunsaturated fatty acids in cellular
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membranes, substantial quantities of oxygen required by brain metabolism and because neurons are post-mitotic cell (Grünblatt et al, 2005). Furthermore, breath modifications discovered could hardly be correlated with aging as we included senior and centenarian subjects in the control group, and they displayed normal breath parameters (Mazzatenta et al., other paper in this volume). Thus, respiration was affected by AD, which could be a useful diagnostic signal if coupled to VOC analysis.
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In a recent work, based on the analytical chemistry approach, by using a sensor designed ad hoc to identify specific VOCs, the authors could discriminate between patients with multiple
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sclerosis and control subjects with 85.3% sensitivity, 70.6% specificity, and 80.4% accuracy. The VOCs markers in exhaled breath of multiple sclerosis patients were hexanal and 5-methyl-undecane
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(Ionescu et al, 2011). Another chemical study identified a cluster of 24 VOCs found in the breath of both AD patients and healthy subjects; these VOCs distinctively increased in AD (Tisch et al,
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2013). However, this approach failed to identify a specific AD biomarker. Also, the information on breath parameters in relation to VOCs fingerprint characteristics is missing. In the present study, we clearly demonstrated that the average amount of VOCs decreases in
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AD, compared with healthy controls, and this is correlated with the breath parameters that also decrease in AD. These aspects are highly informative because the VOC recording was done in real
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time and each single breath act was monitored. Furthermore, we were able to produce, for the first time in literature, a real time putative fingerprint of VOCs in AD. The VOCs profiles show disaggregate and opposite fingerprint in AD compared with
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controls. It appears the disease altered the brain metabolism due to the death of neurons and their pathological state, which was reflected in the exhaled breath VOCs fingerprint. Furthermore, we found a different distribution of the VOC frequencies in AD and controls, fitted by two distinct Gaussian curves. The fit of the AD frequencies suggests once more the disaggregation of the
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exhaled VOCs distribution.
By comparing the study of Tisch et al. (2013) with our study we hypothesize that AD is characterized by a fingerprint of clustered VOCs rather than just few compounds. AD is categorized in different stages, from mild-to-moderate cognitive and executive functions impairment, progressively culminating into a complete deterioration of personality, which may underlie the heterogeneity of AD patients and the difficulties in formulating the early diagnosis. Our results allowed hypothesizing that discriminate among these stages is feasible by looking at the exhaled breath VOCs fingerprint. Because VOCs fingerprint will reasonably move gradually from the healthy pattern trough the progressive stages of the disease into the AD final pathological form.
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Thus, the VOCs fingerprint presented in this study is a key toward the development of a new tool for a early, or timely, screening and diagnosis of AD.
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Table 1. One-way ANOVA of the breath parameters and VOCs in Alzheimer‟s subjects in comparison with controls.
Figure Captions Fig. 1. Breathe frequency in AD compared to control subjects, p
