Emergence of breath testing as a new non-invasive diagnostic modality for neurodegenerative diseases

Brain Research 1691 (2018) 75–86

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Review

Emergence of breath testing as a new non-invasive diagnostic modality for neurodegenerative diseases N. Siva Subramaniam a, C.S. Bawden b, H. Waldvogel d, R.M.L. Faull c, G.S. Howarth a,1, R.G. Snell d,⇑,1 a

School of Animal and Veterinary Sciences, The University of Adelaide, Roseworthy, South Australia 5371, Australia Livestock and Farming Systems, South Australian Research and Development Institute, Roseworthy, South Australia 5371, Australia c Centre for Brain Research and School of Biological Sciences, The University of Auckland, Auckland 1142, New Zealand d Centre for Brain Research and Department of Anatomy and Medical Imaging, The University of Auckland, Auckland 1142, New Zealand b

a r t i c l e

i n f o

Article history: Received 29 August 2017 Received in revised form 13 April 2018 Accepted 17 April 2018 Available online 22 April 2018 Keywords: Neurodegenerative diseases Dementia Mild cognitive impairment Diagnostic tools Biomarkers Breath analysis

a b s t r a c t Neurodegenerative diseases (NDDs) are incapacitating disorders that result in progressive motor and cognitive impairment. These diseases include Alzheimer’s disease, the most common cause of dementia, frontotemporal dementia, amyotrophic lateral sclerosis, dementia with Lewy bodies, Parkinson’s, Huntington’s, Friedreich’s ataxia, and prion disease. Dementia causing NDDs impose a high social and economic burden on communities around the world. Rapid growth in knowledge regarding the pathogenic mechanisms and disease-associated biomarkers of these diseases in the past few decades have accelerated the development of new diagnostic methods and therapeutic opportunities. Continuous effort is being applied to the development of more advanced, easy-to-apply and reliable methods of diagnosis, that are able to identify disease manifestation at its earliest stages and before clinical symptoms become apparent. Development of these diagnostic tools are essential in aiding effective disease management through accurate monitoring of disease progression, timely application of therapeutics and evaluation of treatment efficacy. Recently, several studies have identified novel biomarkers based on compounds in exhaled breath associated with specific NDDs. The use of breath testing, as a means of monitoring neurodegenerative disease onset and progression, has the potential to have a significant impact on augmenting the diagnosis of NDDs as the approach is non-invasive, relatively cost effective and straight forward to implement. This review highlights key features of current diagnostic methods utilised to identify NDDs, and describes the potential application and limitations associated with the use of breath analysis for disease diagnosis and progression monitoring. Ó 2018 Published by Elsevier B.V.

Contents 1. 2. 3.

4.

Introduction: Prevalence of neurodegenerative diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diagnostic methods and biomarkers of neurodegenerative diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Breath analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Breath analysis of neurodegenerative diseases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Advantages and limitations of breath analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Future prospect of breath analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

⇑ Corresponding author. E-mail addresses: [email protected] (N.S. Subramaniam), Simon. [email protected] (C.S. Bawden), [email protected] (H. Waldvogel), [email protected] (R.M.L. Faull), [email protected] (G.S. Howarth), [email protected] (R.G. Snell). 1 Joint last authorship. https://doi.org/10.1016/j.brainres.2018.04.017 0006-8993/Ó 2018 Published by Elsevier B.V.

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1. Introduction: Prevalence of neurodegenerative diseases The number of people diagnosed and living with neurodegenerative diseases (NDDs) is steadily increasing because of increasing lifespan. The chance of developing a neurodegenerative disease increases dramatically with advancing age, doubling every 5–10 years beyond the age of 65 (Castellani et al., 2010; Ferri et al., 2005; Forman et al., 2004). A systematic review and metaanalysis proposed that an estimate of 35.6 million people worldwide were living with dementia caused by neurodegenerative disorders in 2010 (Prince et al., 2013). The prevalence is projected to approximately double every 20 years, to 65.7 million in 2030 and 115.4 million in 2050 (Prince et al., 2013). An accurate prevalence of NDDs is difficult to estimate due to lack of large-scale epidemiological studies particularly in the developing world, and the use of non-standardised diagnosis criteria (Ferri et al., 2005). In addition to heightened social and mental burden, NDDs inflict a huge healthcare cost on society. The most common type of neurodegenerative disease, Alzheimer’s disease, is estimated to cost $172 billion per year in the United States of America alone (Reitz and Mayeux, 2014). Neurodegenerative diseases encompass a variety of debilitating, progressive disorders associated with neuronal degeneration (Ross and Pickart, 2004). The common types of NDDs include Alzheimer’s disease (AD), frontotemporal dementia (FTD), amyotrophic lateral sclerosis (ALS), dementia with Lewy bodies (DLB), Parkinson’s disease (PD), Huntington’s disease (HD), Friedreich’s ataxia (FRDA), and prion disease. Disease manifestation occurs predominantly in individuals above the age of 45 years old, (with the exception of FRDA where the common form results in onset between 5 and 15 years of age), although it is not uncommon for younger individuals to be affected (Bertram and Tanzi, 2005; Walker, 2007). General symptoms of NDDs include dementia, cognitive decline, motor impairment, behavioural transformation, psychosis and emotional disturbance (Bertram and Tanzi, 2005). Severity of symptoms gradually advances with disease development, resulting in deterioration of the capacity for independent living in affected individuals, and ultimately causing death (Brookmeyer et al., 2007; Helder et al., 2002). The typical disease course has a mean duration of 10–15 years from the onset of clinical symptoms, although there can be a large variability in disease duration amongst individuals, and there are currently no cures once symptoms have been established (Brookmeyer et al., 2007; Helder et al., 2002). Neurodegeneration is a gradual process and is known to start 20–30 years before clinical onset (Davies et al., 1988; Potter et al., 2013). Progression of NDDs can generally be categorised into three phases; preclinical, mild cognitive impairment (MCI) and clinical phases (Petersen, 2004; Sperling et al., 2011). Although noticeable clinical signs are absent during the preclinical phase, there are gradual physiological changes at the cellular level associated with disease pathogenesis. At the MCI phase, early nonclinical symptoms of cognitive impairment will start to manifest, where individuals who are at increased risk of developing dementia experience noticeable, but not severe, cognitive alterations. The MCI phase is a transitional state between normal ageing and clinical onset of NDDs, such as AD (Petersen, 2004). Due to the insidious nature of NDDs, treatment will be most beneficial if applied before clinical symptoms become apparent (Sperling et al., 2011). Therefore, a biomarker that is able to reliably identify disease development at the preclinical phase has high clinical value because it not only allows for early diagnosis, but also provides an opportunity for application of early preventative treatments. In general it is assumed that, early detection of NDDs is or will be important for timely application of preventative treatments and effective disease management. This review provides a comprehensive summary of diagnostic methods that are currently available

for diagnosis of neurodegenerative disorders, including their key features such as function, diagnostic efficiency, advantages and limitations. In general, the accuracy of diagnostics is highly influenced by the type and validity of features or biomarkers being assessed, the genetics, where available being the most accurate. The characteristics and significance the known biomarkers for diagnostic applications of NDDs are summarised in this review. We also highlight the attributes of recently discovered novel breath testing biomarkers, and their current application in diagnosis of neurodegenerative disorders through breath analysis. Furthermore, the present review also discusses the feasibility, challenges and future direction of breath analysis as a diagnostic method in the field of neuroscience.

2. Diagnostic methods and biomarkers of neurodegenerative diseases With the advancement of new potential treatments for NDDs, there is a fundamental necessity for the development of diagnostic methods that are able to objectively diagnose, measure and monitor changes related to disease pathogenesis and efficacy of therapeutics. Characteristic features of the most commonly used screening tools for diagnosis of NDDs, and their respective advantages and limitations are summarised in Table 1. Traditionally, neuropathology is considered as the most precise method of clinical diagnosis for NDDs, as it provides direct insight into the actual physical conditions of the brain (McKhann et al., 1984; Eskildsen et al., 2015). The major drawback to this diagnostic method is that it involves examination of brain tissue either from surgical biopsy intervention or whole-body autopsies after death, whilst surgery is a high-risk and invasive option for biopsy (Perl, 2010). Neuropsychological assessment primarily evaluates aspects of cognitive activities such as premorbid activity, memory, intellectual, language, visuoperceptual, spatial, executive and attention functions (Bokde et al., 2011). Although neuropsychological assessment is highly sensitive, it has low specificity due to its limited ability to provide quantitative evaluation on progression of a specific disease (Bokde et al., 2011). Neurophysiological assessment of NDDs generally refers to analysis of the brain’s electrical signals, usually by electroencephalogram (EEG). Typical neurophysiological assessment is susceptible to contamination of ‘‘noise” during data acquisition, and diagnosis is very subjective due to its dependence on evaluation of EEG data through visual inspection by a trained expert. However, EEG recordings can be reviewed to find epochs of artefactfree data, and only 60 s of artefact-free data is required for most quantitative EEG applications (Hargrove et al., 2010). In addition, automated tools based on mathematical algorithms are currently available for isolating artefacts to overcome problems associated with visual inspection and result interpretation (Delorme et al., 2007; Junghofer et al., 2000; Mognon et al., 2011; Nolan et al., 2010). Neuroimaging is the most commonly used in vivo assessment of brain structure and volume for diagnosis of NDDs in clinical applications (de Haen, 2001; Ferreira and Busatto, 2011; Higuchi et al., 2005). Neuroimaging can be divided into two categories; structural imaging and functional imaging. Structural neuroimaging provides detailed two- or three-dimensional brain topography, and includes techniques such as computed tomography (CT) scan, magnetic resonance imaging (MRI), diffusion-and-perfusion-weighted magnetic resonance imaging (DWI- and PWI-MRI), and diffusion tensor magnetic resonance imaging (DTI-MRI) (Bokde et al., 2011; Brenner and Hall 2007; Ferreira and Busatto, 2011). Functional imaging, however, provides information on the functionality of brain tissues by observing tissue metabolic activity (Bateman,

Table 1 Diagnostic methods for neurodegenerative diseases. Diagnostic method

Type

Description

Advantage

Limitation (cost/side effects)

References

Neuropathology

Biopsy or post-mortem examination

- Accurate - -Sensitive

Cognitive and behavioural test

Neurophysiological assessment

Electroencephalogram (EEG)

Computed tomography (CT) scan

Structural neuroimaging

Produces 3-dimensional image data for assessment of brain atrophy

- Invasive - Mostly conducted after patient has died - Difficult to distinguish different types of neurodegenerative diseases - - Low accuracy - Low spatial resolution - Not specific - Requires complex data processing due to poor signal-to-noise ratio - Expensive - Exposure to radiation - Not suitable for longitudinal studies

Perl (2010) Khachaturian (1985)

Neuropsychological assessment

Provides information on pathological changes taking place in the brain Provides information on cognitive function and premorbid behaviour Allows direct measurement of brain signals

Magnetic resonance imaging (MRI)

Structural neuroimaging

Depicts segmental and whole brain volumes

Diffusion-andperfusion-weighted imaging (DWI- and PWI-MRI) Diffusion tensor imaging (DTI-MRI)

Structural neuroimaging

Identifies acute regional cellular damage or neuronal cells at risk

Structural neuroimaging

Identifies interruption in brain pathways through analysis of white matter connectivity

- Better sensitivity compared to conventional MRI and DWI - Highly specific - Non-invasive (expect when a contrast agent is used)

Functional magnetic resonance imaging (fMRI)

Functional neuroimaging

Positron emission tomography (PET)

Functional/nuclearneuroimaging

- Does not require injection of contrast agents - Does not involve radioactive probes - Suitable for longitudinal studies - Highly sensitive - Real-time application - High spatial resolution

Single photon emission tomography (SPECT)

Functional/nuclear neuroimaging

Maps regional brain activity through the use of paramagnetic properties of deoxygenated haemoglobin and volume of perfusion Provides information on cell loss in the brain by indicating metabolic and blood flow changes in measurements of the whole brain Assesses regional brain perfusion

Molecular diagnosis

Analysis of molecular markers

Detects genetic predisposition to a disease

Biochemical analysis

Analysis of chemical markers

Assesses biochemical changes associated with disease pathogenesis

- Applicable before clinical symptoms manifest - Accurate - Highly sensitive - Highly specific - Highly sensitive - Applicable at pre-symptomatic disease phase

- Sensitive - Good source of patient history - Non-invasive - Detects changes milliseconds

over

- Does not require a local cyclotron - Sensitive - More economical than PET

Bonanni et al. (2018) Townley et al. (2018) Bokde et al. (2011) Brenner and Hall 2007 Fred (2004) Tidwell and Jones (1999)

- Costly - Not widely available in under-developed areas

Frisoni et al. (2010) McEvoy and Brewer (2010)

- Not specific for detection of restricted diffusion - Costly - Not widely available - Does not provide information on axonal connectivity - Unable to differentiate between afferent and efferent pathways of axonal tracts - Involves complex data interpretation - Costly - Not widely available

Svolos et al. (2014) Bokde et al. (2011)

Bokde et al. (2011) Alexander et al. (2007) Jellison et al. (2004) Stieltjes et al. (2001)

Mier and Mier (2015) Bokde et al. (2011) Mathis et al. (2005)

- Costly - Requires injection of radioactive probes - Requires a cyclotron - Limited to specialised centres

Ferreira and Busatto (2011) Eckert et al. (2005)

- Requires injection of radioactive probes - Costly - Lower sensitivity compared to PET - Dependent on pre-validated genetic markers - Requires prior understanding of disease aetiology - May involve invasive sample collection procedures - Requires thorough understanding of biochemical pathways associated with disease pathogenesis

Szymanski et al. (2010) Sawada et al. (1992)

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- Non-invasive - More economical than MRI - Suitable for patients with metal implants - Non-invasive (except when a contrast agent is used) - Virtually devoid of side-effects - Better resolution than CT - Suitable for longitudinal studies - Non-invasive (except when a contrast agent is used) Sensitive

Bokde et al. (2011) Khachaturian (1985)

Scherzer et al. (2007) Muller and Graeber (1996)

Bibl et al. (2012) Shaw et al. (2007) Lewczuk et al. (2003)

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2012). With the exception of functional magnetic resonance imaging (fMRI), other techniques such as positron emission tomography (PET) and single photon emission computed tomography (SPECT) use radioactively labelled tracers or ligands to aid diagnosis. Examples of commonly used tracers in functional neuroimaging include 18 F-fluorodeoxyglucose, 18F-florbetapir, 18F-flutametamol and 18Fflorbetaben (Morris et al., 2016; Richards and Sabbagh, 2014). Despite their many advantages, neuroimaging techniques lack the accuracy to definitively detect disease-specific brain atrophies in mild cognitive impairment and at the preclinical stage of disease development (Waldemar et al., 2007). Molecular diagnostic tests for NDDs are primarily based on analysis of DNA or RNA sequences through specific amplicon amplification or more recently exome (Ng et al., 2009) or genome sequencing (Gilissen et al., 2014; Scherzer et al., 2007; Snell et al., 1993). Unlike neuroimaging methods, molecular testing is capable of identifying individuals who are genetically predisposed and at a higher risk of developing NDDs before the disease manifests (Ross and Tabrizi, 2011). Furthermore, molecular analysis is able to provide a definitive indication of the presence or absence of a specific neurodegenerative disease, and thus has high specificity and sensitivity. Similarly, biochemical analysis is an effective and promising tool for diagnosing NDDs in their early stages before cognitive impairment occurs (Mapstone et al., 2014). Biochemical analysis is sensitive to subtle chemical alterations that are either directly or indirectly linked to disease pathogenesis (Heath and Shaw, 2002; Ishiguro et al., 1999; Lewczuk et al., 2003; Vandermeeren et al., 1993). For instance, cerebrospinal fluid (CSF) analysis of abeta, total-tau and phospho-tau has been shown to have increased diagnostic sensitivity and specificity to AD, especially when used collectively (Andreasen et al., 2003; Forlenza et al., 2015; Olsson et al., 2016). Nonetheless, sample acquisition for biochemical analysis mostly involves invasive procedures such as lumbar puncture and venepuncture (Otto et al., 2008), which can be unsuitable for longitudinal studies. Moreover, molecular and biochemical analyses are still nascent areas where NDDs are concerned. Further investigation is required to develop more reliable tests with a better understanding of the complex aetiology of heterogeneous disorders and their associated pathophysiological changes. For all diagnosis techniques, validation of biomarkers is essential as the accuracy and sensitivity of the tests are dependent on the validity of biomarkers analysed. In general, there are three classifications of biomarkers – structural, chemical and molecular, all of which are used for improving the diagnostic accuracy of test for NDDs. Each one of these categories can be further classified as invasive or non-invasive biomarkers based on the manner in which the diagnosis is performed. The most extensively characterised and studied biomarkers in AD, frontotemporal dementia, ALS, DLB, PD, HD, FRDA, and prion disease are summarised in Table 2. Although very reliable, structural biomarkers are suboptimal indicators for detecting disease manifestation before irreversible structural deterioration of brain tissue occurs. Contrastingly, molecular biomarkers are considered more beneficial in aiding diagnosis due to their ability to identify individuals with higher risk of developing NDDs years before structural changes can be detected in the brain (Reitz and Mayeux, 2009). Furthermore, the analysis of molecular biomarkers is less complex, has higher diagnostic accuracy and is more economical to implement. Although most efforts to identify chemical biomarkers for diagnosis of NDDs have been focused primarily within the brain or on metabolites in the central nervous system (CNS) (Andreasen et al., 2001; Wild et al., 2015), other peripheral biomarkers associated with NDDs have also been discovered (Baskin et al., 2000; Handley, 2014;

Lanzrein et al., 1998; Licastro et al., 2000; Mapstone et al., 2014; Wurtman, 2015). It is worth noting that age-associated changes in some older individuals can be similar to AD biomarkers, making CSF and molecular PET result interpretation difficult (Ossenkoppele et al., 2015). Since not all patients with positive AD biomarkers have AD clinically, other biomarkers more closely related to brain function and clinical symptoms are required. In recent years, there has been an increasing focus on identifying novel biomarkers as an alternate avenue for diagnostic development in order to present more economical, non-invasive and easy-to-use diagnostics (Mazzatenta et al., 2015b; Stuwe et al., 2011, 2013; Tisch et al., 2012). The use of non-invasive procedure for biomarker analysis is particularly advantageous compared to invasive methods, especially for longitudinal studies both in laboratory and clinical applications. One such emerging avenue for biomarker discovery for NDDs is through the application of breath analysis.

3. Breath analysis Exhaled human breath mainly consists of nitrogen, oxygen, carbon dioxide, water vapour and inert gases. In addition, a portion of the breath also comprises thousands of endogenous and exogenous trace components which are produced through biochemical activities in the body and absorbed as contaminants from the environment (Miekisch and Schubert, 2006; Phillips, 1997). Detailed analysis of these trace components, especially endogenous compounds, can provide information about the cellular physiological conditions through which these molecules are produced. Therefore, breath testing provides an interesting avenue of diagnostic opportunity to assess pathophysiological changes associated with various conditions in the body. The most commonly used endogenous breath biomarkers for diagnostic applications include carbon disulphide, nitrogen containing substances such as ammonia and dimethyl/trimethylamine, hydrocarbons (for example ethane, pentane and isoprene), oxygen-containing compounds (including acetone, acetaldehyde, methanol, ethanol, and 2-propanol) and sulphur-containing compounds such as, dimethylsulfide, methyland ethyl- mercaptanes) (Miekisch and Schubert, 2006). Since its discovery more than 50 years ago (Pauling et al., 1971), the field of breath testing has progressed rapidly as a new cutting edge technology for medical diagnosis. Breath analysis is routinely implemented for clinical applications such as diagnosis of Helicobacter pylori infection (Kato et al., 2002), transplant organ rejection (Silkoff et al., 1998), evaluation of blood alcohol concentration (Jones and Andersson, 2003), monitoring of asthma (Alving et al., 1993), and monitoring of breath gases during anaesthesia and mechanical respiration (Ward and Yealy, 1998). The use of breath biomarkers for potential diagnostic applications has also been explored in various other complex diseases, for example cancer (Haick et al., 2014; Phillips et al., 1999), liver disease (Sehnert et al., 2002), coronary heart disease (Phillips et al., 2003), diabetes (Mazzatenta et al., 2013b; Phillips et al., 2004), cystic fibrosis (Barker et al., 2006), tuberculosis (Phillips et al., 2007), and chronic obstructive pulmonary disease (Mazzatenta et al., 2013a; Phillips et al., 2012). Additionally, breath analysis has been used to discriminate differences in breath properties between age-groups (Mazzatenta et al., 2015a), and to investigate the effect of sensory stimulation on breath parameters (Mazzatenta et al., 2016). Analysis of breath samples can be performed using mass spectrometry techniques such as Isotope Ratio Mass Spectrometry (IR-MS) (Stuwe et al., 2013), Selected Ion Flow Tube Mass Spectrometry (SIFT-MS) (Smith and Spanel, 1996) and Proton Transfer Reaction Mass Spectrometry (PTR-MS) (Jordan et al., 1995).

Table 2 Biomarkers of neurodegenerative diseases. Affected brain regions

Clinical symptoms

Structural biomarkers

Molecular biomarkers (genetic loci with causal genes)

Chemical biomarkers

References

Alzheimer’s Disease (AD)

Cortex, hippocampus, basal forebrain, brain stem

- Cognitive dysfunction such as impaired memory and judgment, decision-making, and orientation - Aphasia, apraxia, and agnosia

- Neuritic plaques - Neurofibrillary tangles

- Ab1-42 peptide - Phospho-tau - Tau protein

Ross and Pickart (2004) Blennow et al. (2006) Reitz and Mayeux (2009)

Frontotemporal dementia (FTD)

Frontal cortex, temporal cortex and hippocampus

- Behavioural changes - Primary progressive aphasia - Movement impairment

- Pick bodies

- Hyperphosphorylated tau protein

Ross and Pickart (2004) Arvanitakis (2010) Seelaar et al. (2010)

Amyotrophic Lateral Sclerosis (ALS)

Spinal motor neurons and motor cortex

- Degeneration of motor neurons - Muscular atrophy - Speech and swallowing difficulties

- Bonina bodies - Axonal spheroids

- 8-hydroxy-2- deoxyguanosine (8OHdG) - Cytokines - Glutathione - Glutamate metabotropic receptor 2 (mGLUR2) - Superoxide dismutase 1 (SOD1)

Ross and Pickart (2004) Kunst (2004) Kiernan (2009) DeJesus-Hernandez et al. (2011)

Dementia with Lewy bodies

Basal ganglia and substantia nigra

- Fluctuating cognitive impairment - Recurrent visual hallucinations - Tremor - Postural change - Gait abnormality

- Neocortical and subcortical Lewy bodies

- Amyloid beta precursor protein (APP) gene mutations - Apolipoprotein E (ApoE) isoforms (major susceptibility loci) - Presenilin-1 (PSEN1) gene mutations - Presenilin-2 (PSEN2) gene mutations - Microtubule associated protein tau (MAPT) gene mutations - Granulin precursor (GRN) gene mutations - Charged multivesicular body protein 2B (CHMP2B) gene mutations causing inclusion body myopathy with Paget disease and FTD - Plus valosin containing protein (VCP), FUS RNA binding protein (FUS), TAR DNA binding protein (TARDB), and chromosome 9 open reading frame 72 (C9ORF72), which result in conditions including ALS referred to below - Superoxide dismutase 1 (SOD1) gene mutations - ALS2, alsin Rho guanine nucleotide exchange factor (ALS2) gene mutations (juvenile onset) - senataxin (SETX) gene mutations (juvenile onset) - Spatacsin vesicle trafficking associated (SPG11) gene mutations (juvenile onset) - FUS RNA binding protein (FUS) gene mutations - VAMP associated protein B and C (VAPB) gene mutations - Angiogenin (ANG) gene mutations - TAR DNA binding protein (TARDBP) gene mutations - FIG4 phosphoinositide 5-phosphatase (FIG4) gene mutations - Optineurin (OPTN) gene mutations - Valosin containing protein (VCP) gene mutations - Ubiquilin 2 (UBQLN2) gene mutations - Sigma non-opioid intracellular receptor 1 (SIGMAR1) gene mutations (juvenile onset) - Charged multivesicular body protein 2B (CHMP2B) gene mutations - Profilin 1 (PFN1) gene mutations - Erb-b2 receptor tyrosine kinase 4 (ERBB4) gene mutations - Heterogeneous nuclear ribonucleoprotein A1 (HNRNPA1) gene mutations - Matrin 3 (MATRX3) gene mutations - Tubulin alpha 4a (TUBA4A) gene mutations - Chromosome 9 open reading frame 72 gene mutation - neurofilament heavy (NEFH) gene mutations (susceptible gene) - Synuclein alpha (SNCA) gene mutations - Synuclein beta (SNCB) gene mutations - Apolipoprotein E (ApoE) and cytochrome P450 family 2 subfamily D member (6CYP2D6) gene mutations (genes with risk alleles)

- b-amyloid peptide - -synuclein protein - Dopamine transporter (DAT)

McKeith (2004) Ohtake et al. (2004) Zarranz et al. (2004) Halliday et al. (2011)

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Disease

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Table 2 (continued) Disease

Affected brain regions

Clinical symptoms

Structural biomarkers

Parkinson’s Disease (PD)

Substantia nigra, cortex, and locus ceruleus

-

- Lewy bodies - Lewy neurites

Bradykinesia Resting tremor Gait difficulties Postural instability rigidity

and/or

Molecular biomarkers (genetic loci with causal genes) - Synuclein alpha (SNCA) gene mutations - PTEN induced putative kinase 1 (PINK1) gene mutations - Parkinsonism associated deglycase (PARK7) gene mutations - Leucine rich repeat kinase 2 (LRRK2) gene mutations - ATP13A2 (PARK9) gene mutations - Phospholipase A2 group VI (PLA2G6) gene mutations - F-box protein 7 (FBXO7) gene mutations - VPS35, retromer complex component (VPS35) gene mutations Huntingtin (HTT) gene mutations

Striatum, other basal ganglia, cortex, other regions

- Motor, cognitive and psychiatric disturbances - Unintended weight loss - Sleep and circadian rhythm disturbances

- Intra-nuclear inclusions - Cytoplasmic aggregates - Astrocytosis

Friedreich ataxia (FRDA1)

Spinal cord, peripheral nerves and cerebellum

- Frataxin depletion

- Frataxin (FXN) gene mutations

Human prion disease

Cortex, thalamus, brain stem and cerebellum

- Progressive gait and limb ataxia - Sensory loss and muscle weakness - Scoliosis and foot deformity - Age of onset around puberty - Memory loss - Apathy - Disorientation - Involuntary jerky movements

- Spongiform degeneration - Amyloid plaques consisting of prion and other proteins

- Prion protein (PRNP) gene mutations

References

- -synuclein protein in Lewy body lesions - Dopamine transporter (DAT)

Ross and Pickart (2004) Hardy et al. (2006) Klein and Westenberger (2012)

- 8-hydroxy-2- deoxyguanosine (8OHdG) - Cytokines - Glutathione - Growth hormones - Glutamate metabotropic receptor 2 (mGLUR2) - Superoxide dismutase 1 (SOD1) - Glucose - Vitamin E

Helder et al. (2002) Rachakonda et al. (2004) Ross and Pickart (2004) Walker (2007)

- Prion protein

Ross and Pickart (2004) Mead (2006) Atarashi et al. (2011)

Pandolfo, 2002) Schulz et al. (2009) Marmolino (2011)

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Huntington’s Disease (HD)

Chemical biomarkers

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3.1. Breath analysis of neurodegenerative diseases Recently, breath analysis has been used to investigate hepatic mitochondrial dysfunction and for identification of NDDs such as Alzheimer’s, Parkinson’s, Huntington’s and Friedreich ataxia (Mazzatenta et al., 2015b; Stuwe et al., 2011, 2013; Tisch et al., 2012). Although the exact mechanisms of neurodegenerative disease pathogenesis are not fully understood, there is considerable evidence that implicate changes in mitochondrial function and potential dysfunction as a key contributor to neurodegeneration (Hroudova et al., 2014). Indeed, there is a generalised metabolic disruption associated with the development of NDDs including weight loss and a change in the cellular ATP: ADP ratio in the preclinical phase and/or MCI phase. (Besser et al., 2014; Cova et al., 2016; Mochel et al., 2007; Ramsey and Giasson, 2007; Robbinsa et al., 2006; Swerdlow, 2011). Several proposed pathways explain the mechanisms by which mutant pathogenic proteins cause mitochondrial dysfunction and influence pathogenesis of NDDs, for instance through abnormal mitochondrial gene expression (Reddy and Beal, 2008), atypical mitochondrial enzyme activities (Reddy, 2009), impaired axonal transport of mitochondria and inhibition of oxidative phosphorylation (OXPHOS) activities (Hroudova et al., 2014). Localisation of mutant proteins known to cause mitochondria dysfunctions attributing to neurodegeneration in peripheral tissues, for instance in the liver (Cersosimo and Benarroch, 2012; Choo et al., 2004; Koutnikova et al., 1997; Torres et al., 2011), presents an excellent opportunity for application of breath analysis to discover and evaluate biomarkers associated with cellular energy metabolism, mitochondrial dysfunction and oxidative stress. Breath testing can be performed using a stable isotope labelled compound to target a specific metabolic activity, or alternatively, through analysis of breath-sourced trace compounds to identify a disease-associated breath profile or ‘‘fingerprint” as proposed by Mazzatenta et al. (2015b). In one of the earliest studies, application of breath analysis to assess NDDs involved the use of methyl 13C-methionine to evaluate hepatic mitochondrial function of patients with FRDA (Stuwe et al., 2011). Methyl 13C-methioninie has been previously shown (Armuzzi et al., 2000) to be a suitable substrate to be used for breath analysis to indirectly estimate the oxidative capacity of liver mitochondria. Methionine is an essential amino acid necessary for the normal growth and development of mammals (Finkelstein, 1990). Methionine is metabolised exclusively in the liver through the transmethylation-transsulfuration pathway (Finkelstein, 1990), where excess methionine methyl groups are converted into CO2 via mitochondrial oxidation (Candelli et al., 2004; Mudd and Poole, 1975). In a more recent study, hepatic mitochondrial dysfunction in the liver of patients with pre-manifest and manifest HD was also investigated by the methyl 13C-methionine breath test (Stuwe et al., 2013). The test by Stuwe et al. (2013) revealed that patients with pre-manifest and manifest HD had less active liver mitochondria compared to the control group of patients without HD, despite demonstrating clinically normal liver function. Between the HD groups tested, differences in the amount of exhaled 13CO2 showed that the activity of liver mitochondria was lowest in HD patients with more advanced disease (Stuwe et al., 2013). In addition to the use of labelled substrates, disease progression can also be identified by measuring concentrations of volatile organic compounds (VOCs) and observing distribution patterns of the VOCs in exhaled breath samples (Lourenco and Turner, 2014). VOCs are a group of carbon-based molecules such as ethane, pentane, isoprene, and acetone, which have low boiling points and can easily form vapour at room temperature due to their high vapour pressure (Miekisch et al., 2004; Schmidt and Podmore,

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2015). Mitochondrial dysfunction in NDDs is often interlinked with increased production of reactive oxygen species (ROS) that cause cell damage, and results in generation and/or alteration of VOCs (Andreyev et al., 2005; Schmidt and Podmore, 2015). Tisch et al. (2012) was the first to investigate the feasibility of breath testing using the sensor- array concept for detection of NDDs. This study aimed to establish exhaled breath patterns and compounds in the breath of AD and PD patients using nanomaterial-based sensors and gas chromatography-mass spectrometry (GC-MS) respectively. Results indicated that the sensors used in this study could discriminate, with high accuracy, differences in breath patterns between disease (AD and PD) and control groups (Tisch et al., 2012). Aside from showing differences between disease and healthy states within a disease, the study also showed that breath patterns could adequately distinguish one disease (AD) from another (PD). Distinct breath patterns derived from the sensor array, were supported by identification of VOCs of differing abundance in exhaled breath between both AD and PD patients and healthy controls (Tisch et al., 2012). The GC-MS analysis identified 24 VOCs with significantly higher concentrations in the breath of AD patients compared with healthy subjects. Comparing the breath composition from PD patients and healthy controls, seven compounds were shown to be different in concentrations (Tisch et al., 2012). Studies were subsequently conducted by Bach et al. (2015) and Lau et al. (2017) to determine the difference in breath patterns and compounds also in AD and PD patients and healthy controls. These studies employed different combinations of analytical tools to achieve the same overall objective of applying breath testing for diagnosis of NDDs. Bach et al. (2015) used the combination of a Cyranose 320 electronic nose device (eNose) and ion mobility spectroscopy (IMS), whereas Lau et al. (2017) used a custom built exhaled breath sensor system based on nanostructure metal oxide gas sensors and GC-MS. Analysis of VOCs in the exhaled breath by Bach et al. (2015) resulted in identification of novel VOCs that could be used to differentiate AD and PD patients from healthy controls. In contrast to the previous findings by Tisch et al. (2012), a the study conducted by Lau et al. (2017) found no significant differences in alkane and benzene groups comparing AD, PD and control groups. However, they did find significant differences in abundance of other compounds such as acetamide and triphenyl phosphate (Lau et al., 2017). Although there were differences in VOC profiles, results of exhaled breath pattern analysis in both studies (Bach et al., 2015; Lau et al., 2017) were similar to the findings by Tisch et al. (2012). In another study, Mazzatenta et al. (2015b) used an iAQ-2000 sensor to characterise breath parameters and determine exhaled VOC profiles of AD patients. In AD subjects, increased frequency but reduced amplitude in breathing was observed. Furthermore, the average amount of VOCs was shown to decrease in AD, compared with healthy controls (Mazzatenta et al., 2015b). With the exception of sleep apnea syndrome studies in AD, this is the only study to date that has attempted to investigate the association between breath parameters and progression of AD (Mazzatenta et al., 2015b). Details of the NDD breath test studies described above are summarised in Table 3. Results from these studies indicate that breath analysis is able to discriminate between disease and healthy states, identify disease progression within a disorder, and distinguish different types of NDDs from one another (Bach et al., 2015; Lau et al., 2017; Mazzatenta et al., 2015b; Stuwe et al., 2011, 2013; Tisch et al., 2012). Currently, it is difficult to make a direct comparison of exhaled breath composition patterns within a disease or between NDDs, due to the use of different sensors and analytical software packages (Bach et al., 2015; Lau et al., 2017; Mazzatenta et al., 2015b; Tisch et al., 2012). Contrastingly, easier

Topic

Method

Analyte

Subjects

Study aims

Outcome

Conclusion

References

Friedreich ataxia (FRDA)

Nondispersive isotopeselective infrared spectroscopy (IRIS).

13

CO2 in exhaled breath

FRDA = 16 HCa = 16

Investigated hepatic mitochondrial function of FRDA patients. Determined feasibility of 13Cmethionine breath test to reflect severity of FRDA.

- Cumulative percentage dose recovery of 13C in exhaled breath of FRDA patients was lower than healthy controls. - No correlation between 13Cmethionine breath test and disease severity/genotype.

Stuwe et al. (2011)

Huntington’s disease (HD)

Nondispersive isotopeselective infrared spectroscopy (IRIS).

13

CO2 in exhaled breath

Pre-manifest HD mutation carriers = 30 Manifest HD = 21 HCa = 36 Total = 87

Evaluated hepatic mitochondrial dysfunction in manifest and/or premanifest HD.

Alzheimer’s disease (AD) and Parkinson’s disease (PD)

Gas chromatographymass spectrometry (GC-MS), and nanomaterial-based sensors.

Exhaled breath compounds

AD = 15 PD = 30 HCa = 12 Total = 57

Determined feasibility of the sensor-array concept as a diagnostic biomarker test for neurodegenerative diseases. Identified exhaled breath patterns and compounds in AD, PD and healthy controls.

Alzheimer’s disease (AD) and Parkinson’s disease (PD)

Cyranose 320 electronic nose device (eNose) and ion mobility spectroscopy (IMS).

Exhaled breath compounds

AD = 18 PD = 16 HCa = 19 Total = 53

Detected exhaled breath pattern of AD, PD and healthy controls. Discovered specific substances that contribute to different breath pattern in AD, PD and healthy controls.

Alzheimer’s Disease (AD)

Gas chromatographymass spectrometry (GC-MS), and a custom built exhaled breath sensor system based on nanostructure metal oxide gas sensors.

Exhaled breath compounds

AD = 20 PD = 20 HCa = 20 Total = 60

Studied chemical compounds in exhaled breath from AD and PD patients, and healthy controls using GC-MS.Examined the distribution pattern of breath compounds in AD and PD patients, and healthy controls using a breath sensor system.

Alzheimer’s disease (AD)

iAQ-2000 sensor.

Exhaled breath compounds

AD = 15 HCa = 44 Total = 59

Characterised breath parameters of AD patients versus controls. Determined exhaled VOCs profile of AD patients versus controls.

- Patients with pre-manifest and manifest HD had lower amounts of exhaled 13CO2 compared with healthy controls. - With the exception of motor assessment, results of 13Cmethionine breath test showed an association between functional and cognition assessment and caudate atrophy. - Identified 24 VOCs occurring in significantly higher concentrations in the breath of AD patients compared with healthy subjects. - Seven compounds were present at different concentrations between PD patients and healthy controls. - Found differences in breath patterns of AD and PD patients, and healthy controls. - Identified compounds to differentiate healthy controls form patients with AD and PD with an accuracy of 94%. - GC-MS analysis showed no significant difference in alkane and benzene groups between AD, PD and control groups. - Exhaled breath pattern of AD and PD patient groups could be distinguished from the control groups with 95% confidence. - Frequency of breathing was increased but had reduced amplitude in AD. - The average amount of VOCs decreased in AD, compared with healthy controls.

Observed significant decay of mitochondrial function in patients with FRDA compared to healthy subjects through 13Cmethionine breath test. Progression of HD was reflected by the 13Cmethionine breath test.

HC healthy control.

Stuwe et al. (2013)

Delivered proof-of concept that nanomaterial-based sensors could be used for breath testing in neurodegenerative diseases.

Tisch et al. (2012)

eNose and IMS are able to discriminate patients with AD, PD and healthy controls.

Bach et al. (2015)

Breath sensor system was able to classify patients with AD from PD and healthy individuals into distinctive clusters.

Lau et al. (2017)

AD was characterized by a fingerprint of clustered VOCs rather than just a few compounds.

Mazzatenta et al. (2015a,b)

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a

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Table 3 Summary of various studies using breath analysis to investigate neurodegenerative diseases.

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comparisons of results can be achieved for exhaled breath VOCs generated using different types of spectroscopy technologies, especially when the compounds contributing to the profiles are listed. Based on the outcomes of VOC breath profiling studies performed to date, it is highly likely that NDDs are characterised by clusters of VOCs instead of a specific disease-associated biomarker (Bach et al., 2015; Lau et al., 2017; Mazzatenta et al., 2015b; Tisch et al., 2012). While reactive oxygen species, oxidative stress and metabolic dysfunction have been proposed as the origin of some of the preliminarily identified VOCs, there is no definitive evidence of the mechanisms and source of most of the compounds identified in exhaled breath (Bach et al., 2015; Lau et al., 2017; Mazzatenta et al., 2015b; Tisch et al., 2012). This could be attributed to the limited knowledge in the general aetiology of NDDs and the mechanisms of neurodegeneration. Therefore, a better understanding of disease aetiology and pathophysiological changes is required to comprehend changes in 13C-methionine metabolism, VOC concentrations and VOC fingerprints in exhaled breath. 3.2. Advantages and limitations of breath analysis The most attractive feature of breath testing is the simplicity of the sampling procedure. Breath sampling is easy to perform, noninvasive and painless compared to blood or CSF sample collection. Moreover, the analysis is simpler because the matrix is less complex than blood, CSF or urine. In comparison to the neuropsychological assessment, neuroimaging, and neurophysiological test, breath analysis is less time consuming, is able to produce online and real-time outputs, is inexpensive, and likely to be more sensitive to subtle physiological changes. Analogous to other biochemical analyses and in contrast to neuroimaging, breath testing has the capacity to diagnose NDDs at the preclinical and MCI phases, long before clinical symptoms manifest. Although breath collection for VOC analysis is easy to perform, controlled sampling methodology is crucial for reliable analysis of breath biomarkers. Aspects such as mouth-exhaled versus noseexhaled breath, end-tidal versus alveolar breath, sampling of single breath versus multiple breaths, and cyclic breathing can significantly affect the results of analysis (Lourenco and Turner, 2014). Similarly, several other factors need to be considered when performing VOC analysis to minimise inaccuracies in result interpretations. These factors include baseline physiological levels of volatiles present in the breath of subjects, concentrations of exogenous VOCs in ambient air, presence of VOCs produced by bacteria in the oral cavity or the gut, and the level of exposure to pollution or air-contaminants such as cigarette smoke, air fresheners and industrial cleaning products (Lourenco and Turner, 2014). The on-line, real-time analytical techniques SIFT-MS and PTRMS are highly sensitive, and are able to detect concentrations of endogenously produced VOCs in the breath ranging from parts per million (ppmv), down to parts per billion (ppbv), and even parts per trillion (pptv) (Lourenco and Turner, 2014; Miekisch et al., 2004). Nonetheless, high concentrations of contaminants and exogenous VOCs can impact the sensitivity and specificity of breath analysis. Studies have shown that concentrations of exhaled VOCs cannot be confidently correlated with blood VOC levels when concentrations of inhaled (ambient) VOCs are greater than 5% of exhaled concentrations (Schubert et al., 2005; Spanel and Smith, 2013). In contrast to breath analysis through VOC profiling, breath analysis using labelled isotopes such as methyl 13C-methionine targets a particular metabolic pathway in the body and measures the rate of metabolism of clearance of that specific compound (Armuzzi et al., 2000; Stuwe et al., 2011). Therefore, sampling methodology and data interpretation of breath tests using compound-specific isotope are less complex because of fewer variables to influence the outcomes of the analysis. A primary limiting

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factor of isotope-based breath testing is that it requires a thorough prior understanding of the biochemical pathway being targeted. 3.3. Future prospect of breath analysis An interesting potential application of breath analysis is in understanding the relationship between the microbiome and NDDs. Although the role of the human microbiome has previously been known to extend beyond the gastrointestinal (GI) tract, and also influence the bidirectional communication between the GI tract and the CNS, the alteration in the microbiota have only recently been associated with aetiopathology of NDDs (Bhattacharjee and Lukiw, 2013; Borre et al., 2014; Cersosimo and Benarroch, 2012). Newly emerging studies suggest that the GI microbiome affects CNS neurochemistry and neurotransmission, through multiple neurochemical and neuro-metabolic pathways. These include the interactions of the GI microbiome in: (1) production of gamma-amino butyric acid (GABA); the major inhibitory neurotransmitter in the CNS (Mitew et al., 2013), (2) down-regulation of brain-derived neurotrophic factor (BDNF); a growth factor essential for neuronal development and synaptic plasticity (Carlino et al., 2013), and (3) up-regulation of b-Nmethylamino-l-alanine(BMAA); a neurotoxin that inhibits Nmethyl-d-aspartate (NMDA) glutamate receptor, which regulates synaptic plasticity and cognitive function (Brenner, 2013). The BMAA neurotoxin has even been linked with intra-neuronal protein misfolding, which is a hallmark feature of senile plaque lesions associated with AD and PD. However, more information regarding the underlying neuro-chemical and neuro-metabolic pathways is warranted before breath analysis can be applied to its full potential for the diagnosis of NDDs. 4. Conclusion With further developmental efforts to standardise sample collection and analysis, breath testing has the potential to emerge as a powerful pre-symptomatic diagnostic tool that determines whether more specific testing (e.g., imaging, CSF analysis, etc.) is required. Breath analysis could be used in combination with other diagnostic methods such as neuropsychological assessment, neuroimaging and molecular analysis to increase overall diagnosis sensitivity and specificity of NDDs. Effective application of breath analysis could also assist in the prediction of prodromal stages in individuals with higher risk of developing NDDs, and facilitate the testing and timely administration of preventive therapeutic interventions to suppress progression of the disease before irreversible brain damage or cognitive behaviour changes occur. Furthermore, the easy-to-use breath analysis can be applied to monitor efficacy of therapeutics and optimise treatments for NDDs in animal models prior to clinical trials, without subjecting experimental animals to ethically unacceptable invasive procedures. References Alexander, A.L., Lee, J.E., Lazar, M., Field, A.S., 2007. Diffusion tensor imaging of the brain. Neurotherapeutics 4, 316–329. Alving, K., Weitzberg, E., Lundberg, J., 1993. Increased amount of nitric oxide in exhaled air of asthmatics. Eur. Respir. J. 6, 1368–1370. Andreasen, N., Minthon, L., Davidsson, P., et al., 2001. Evaluation of csf-tau and csfab42 as diagnostic markers for alzheimer disease in clinical practice. Arch. Neurol. 58, 373–379. Andreasen, N., Sjogren, M., Blennow, K., 2003. CSF markers for Alzheimer’s disease: total tau, phospho-tau and Ab42. World J. Biol. Psychiatry 4, 147–155. Andreyev, A.Y., Kushnareva, Y.E., Starkov, A.A., 2005. Mitochondrial metabolism of reactive oxygen species. Biochemistry (Moscow) 70, 200–214. Armuzzi, A., Marcoccia, S., Zocco, M.A., De Lorenzo, A., Grieco, A., Tondi, P., Pola, P., Gasbarrini, G., Gasbarrini, A., 2000. Non-Invasive assessment of human hepatic mitochondrial function through the 13C-methionine breath test. Scand. J. Gastroenterol. 35, 650–653.

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