Techniques for Assessment of Parkinsonism for Diagnosis and Rehabilitation 9789811630552, 9789811630569

Table of contents :
Preface
Contents
Abbreviations
Parkinson’s Disease–An Introduction
1 History
2 Etiology and Pathogenesis
2.1 Environmental Factors
2.2 Genetic factors
2.3 Pathophysiology
2.4 Clinical Features
2.5 Motor Symptoms
2.6 Non-motor Symptoms
2.7 Cognitive Impairment
2.8 Neurobehavioral Abnormalities
2.9 Autonomic Dysfunction
2.10 Sensory Symptoms and Pain
2.11 Diagnosis
2.12 Doapmine Transporter (DaT) Scan
2.13 Differential Diagnosis
2.14 Essential Tremor
2.15 Dementia With Lewy Bodies
2.16 Multiple System Atrophy (MSA)
2.17 Cortico-Basal Degeneration
2.18 Progressive Supranuclear Palsy (PSP)
2.19 Other Neurodegenerative Disorders
2.20 Secondary Parkinsonism
2.21 Drugs
2.22 Toxins
2.23 Vascular Parkinsonism
2.24 Others Causes of Secondary Parkinsonism
2.25 Treatment
2.26 Treatment of Motor Symptoms
2.27 Anticholinergics
2.28 Antiglutamatergics
2.29 Monoamine Oxidase Inhibitors
2.30 Dopamine Agonists
2.31 Catechol-O-methyl Transferase Inhibitors
2.32 Treatment of Non-motor Symptoms
3 Surgical Treatment
3.1 Deep Brain Stimulation
4 Future Directions
4.1 Focused Ultrasound Therapy
4.2 Cell Replacement Therapies
References
Parkinson’s Disease and Its Symptoms
1 Pathophysiology of Parkinson’s Disease
2 Detecting and Monitoring Parkinson’s Disease
References
Epidemiology of Parkinson’s Disease—Current Understanding of Causation and Risk Factors
1 Background & Global Disease Burden
1.1 Prevalence
1.2 Age Trends
1.3 Incidence
2 Causes of Parkinsons Disease
2.1 Brak Hyptothesis
2.2 Gut Microbiome
2.3 A Life-Long and Exposure Approach to PD Etiological Research
2.4 Risk Factors for Parkinson’s Disease
3 Environmental Exposures
3.1 Pesticides
3.2 Antioxidants
3.3 Uprate (Uric Acid)
4 Lifestyle Factors
4.1 Smoking
4.2 Prospective
4.3 Meta-Analyses
4.4 Stress
4.5 Alcohol Case–control Studies
4.6 Physical Activity
4.7 Body Mass Index (BMI)
4.8 Diet
4.9 Coffee and Tea
4.10 Tea
4.11 Dairy Products
4.12 Macronutrients
4.13 Diabetes
4.14 Minerals
4.15 Well Water
4.16 Metals
4.17 Mild Traumatic Brain Injury
4.18 Hepatitis C
5 Organic Solvents
5.1 Positive
5.2 Magnetic Fields
5.3 REM Sleep Behavior Disorder
5.4 Inflammation
5.5 Mental Illness
5.6 Vascular Diseases
5.7 Estrogen
5.8 Mortality
References
Young Onset of Parkinson’s Disease
1 Definition
2 Historical Background
3 Prevalence
4 Etiology
5 Symptoms
6 Diagnosis
7 Disease Management
8 Quality of Life
9 Economic Burden
10 Perceived Preventive Measure
11 Juvenile Parkinson’s Disease
12 Perspectives of Young Onset Parkinson’s Disease Patients
13 Summary
References
Diagnosis of Parkinson Disease: Imaging and Non-Imaging Techniques
1 Introduction
1.1 Non-Imaging techniques used in the early diagnosis of PD
1.2 Imaging techniques used in the early diagnosis of PD
2 Positron Emission Tomography PET Scan Imaging 
2.1 Single-Photon Emission Computed Tomography (SPECT) Scan Imaging
3 Magnetic Resonance Imaging (MRI)
4 Transcranial B-Mode Sonography (TCS)
5 Thermal Imaging in the early diagnosis of PD
6 Conclusion
References
Inertial Measurement Units for Gait Analysis of Parkinson’s Disease Patients
1 Introduction
2 Materials and Methods
3 Results
4 Review of the Papers
4.1 Part I “IMU Sensors Used for Gait Analysis”
4.2 Part II “Measuring Gait Variability in Parkinson Disease Patients Using Other Sensors”
5 Discussion and Future Research
References
Treatment of Parkinson’s Disease
1 Introduction
2 Symptomatic Pharmacological Treatment
3 Deep Brain Stimulation
4 Treatments in Advanced Parkinson’s Disease
5 Summary
References
Voice Analysis for Diagnosis and Monitoring Parkinson’s Disease
1 Introduction
1.1 Dysarthria
1.2 Dysfluency
2 Voice Analysis for Parkinson’s Disease
3 Effect of Levodopa on Parkinsonian Voice Symptoms
4 Methods
4.1 Participants
4.2 Voice Recording
4.3 Feature Extraction
4.4 Statistical Analysis
4.5 Machine Learning Based Classification
5 Results
5.1 Statistical Analysis
5.2 Classification Analysis
6 Discussion
7 Summary of the Study
References
Tremors and Bradykinesia
1 Introduction
2 Types of Tremors
3 Tremor Characterization in Parkinsonian Disorders
4 Pathophysiology of Tremor
5 Cessation of Resting Tremor During Voluntary Movements in Parkinsonism
6 Managing Parkinson’s Tremor
7 Bradykinesia
8 Pathophysiology of Bradykinesia
9 Secondary Causes of Bradykinesia
10 Bradykinesia Test and Diagnosis
11 Bradykinesia Treatment
12 Surgical Procedures
13 Lifestyle Remedies for Symptom Management
References
Is There a Better Way to Assess Parkinsonian Motor Symptoms?—Experimental and Modelling Approach
1 Introduction
2 PD Symptoms
3 Diagnostics and Evaluation for Parkinsonian Symptoms
4 The Need for a More Quantitative Approach
5 Cognitive Impairments in PD
6 Therapeutic Interventions and Rehabilitation
7 Computational Modeling
8 Conclusions
References

Citation preview

Series in BioEngineering

Sridhar P. Arjunan Dinesh Kant Kumar   Editors

Techniques for Assessment of Parkinsonism for Diagnosis and Rehabilitation

Series in BioEngineering

The Series in Bioengineering serves as an information source for a professional audience in science and technology as well as for advanced students. It covers all applications of the physical sciences and technology to medicine and the life sciences. Its scope ranges from bioengineering, biomedical and clinical engineering to biophysics, biomechanics, biomaterials, and bioinformatics. Indexed by WTI Frankfurt eG, zbMATH.

More information about this series at http://www.springer.com/series/10358

Sridhar P. Arjunan · Dinesh Kant Kumar Editors

Techniques for Assessment of Parkinsonism for Diagnosis and Rehabilitation

Editors Sridhar P. Arjunan Department of Electronics and Instrumentation Engineering SRM Institute of Science and Technology Kattankulathur, Tamil Nadu, India

Dinesh Kant Kumar Biosignals lab School of Engineering RMIT University Melbourne, VIC, Australia

ISSN 2196-8861 ISSN 2196-887X (electronic) Series in BioEngineering ISBN 978-981-16-3055-2 ISBN 978-981-16-3056-9 (eBook) https://doi.org/10.1007/978-981-16-3056-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

Parkinson’s disease is a disorder of the central nervous system. It is the second most common neurodegenerative disorder, with over 0.5% of the population having this disease. The median age of people first diagnosed with Parkinson’s disease is around 65 years, and thus its prevalence is expected to increase with an aging population. With no blood tests, or easily available imaging tests, the presence of two or more motor symptoms of tremor, bradykinesia, rigidity, or postural impairment are considered as the basis for the diagnosis of the disease. Dopamine transporter scan can be performed using Positron Emission Tomography (PET) as confirmatory evidence, which however are yet only available in few places. The standard tools for the diagnosis and monitoring of PD uses Movement Disorder Society Unified Parkinson’s Disease Rating Scale Part III (MDS-UPDRSIII). However, this requires clinical observations and thus has the limitations of clinician bias and potential of missing some of the early symptoms. This results in a loss of sensitivity and specificity. Early stage diagnostics can be missed, and it is also difficult to monitor the effectiveness of treatment and disease progression. Parkinson’s disease is associated with the loss of habitual activity. Walking, speaking, and writing are three activities of people that are habitual to healthy people and have been found to be impaired among the people with Parkinson’s disease. PD patients often have dysarthria, or slurring in voice, and display micrographia or handwriting becoming small in the early stages of their disease. These can occur up to 5 years before the tremor. Thus, the use of gait analysis, handwriting analysis, and speech or voice analysis has been proposed for early diagnosis of the disease. The number of researchers have proposed computer-based techniques that can be used to quantify these symptoms and provide objective measures for the clinicians. This field is fast developing and there is an urgent need for technical solutions to get accurate and objective measures of the symptoms so that the disease can be identified in the early stages, and its progression can be monitored. Scheme for Promotion of Academic and Research Collaboration (SPARC) with the aim of supporting Indian researchers to solve global challenges, has provided a platform to collaborate with international experts for developing this book. The aim of this book is to provide a review along with expert opinions on this very needed issue. We, the authors have assembled this book with the aim of sharing with you v

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Preface

the current state of the art and identified potential research directions that will be useful. We are a team of clinicians, engineers, and scientists, and have provided the width of background and expertise through the book and attempted to provide you with the information regarding a large width of technologies. We do hope that you will find this useful, and we will soon have the methods to help reduce the burden of this disease in our society. Kattankulathur, India Melbourne, Australia

Sridhar P. Arjunan Dinesh Kant Kumar

Contents

Parkinson’s Disease–An Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Chandra Shekhar Rawat and Sanjay Pandey

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Parkinson’s Disease and Its Symptoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Dinesh Kant Kumar

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Epidemiology of Parkinson’s Disease—Current Understanding of Causation and Risk Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rajan R. Patil Young Onset of Parkinson’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Rajan R. Patil and Aiswarya Anilkumar Diagnosis of Parkinson Disease: Imaging and Non-Imaging Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A. Brindha, Karnam Anantha Sunitha, B. Venkatraman, M. Menaka, and Sridhar P. Arjunan Inertial Measurement Units for Gait Analysis of Parkinson’s Disease Patients . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sana M. Keloth, Sridhar P. Arjunan, Peter John Radcliffe, and Dinesh Kumar

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Treatment of Parkinson’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Sanjay Raghav and Laura D. Perju-Dumbrava Voice Analysis for Diagnosis and Monitoring Parkinson’s Disease . . . . . . 119 Nemuel D. Pah, M. A. Motin, and D. K. Kumar Tremors and Bradykinesia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135 K. Prabhavathi and Shantanu Patil Is There a Better Way to Assess Parkinsonian Motor Symptoms?—Experimental and Modelling Approach . . . . . . . . . . . . . . . . . 151 Sandeep Sathyanandan Nair, Vignayanandam Ravindernath Jayashree Muddapu, Meghna Sriram, R. Aditya, Reema Gupta, and Srinivasa Chakravarthy vii

Abbreviations

A-Syn AADC AD ApoE4 AR BDNF CBD COMT COMTI DaT scan DBS DLB ET FDA GBA GPi ICD MAOIs MAPT MCI MPTP MRI MSA NMDA PD PDD PET PSP PSP-P RBD REM

α-Synuclein L-Amino acid decarboxylase Autosomal dominant Apolipoprotein E4 Autosomal recessive Brain-derived neurotrophic factor Cortico-basal degeneration Catechol-O-methyltransferase Catechol-O-methyltransferase inhibitors Dopamine Transporter single-photon emission computed tomography Deep brain stimulation Dementia with Lewy bodies Essential tremor Food and drug administration Glucocerebrosidase Globus pallidus pars interna Impulse-control disorders Monoamine oxidase inhibitors Microtubule associated protein tau Mild cognitive impairment 1-Methyl-4-phenyl-1,2,5,6-tetrahydropyridine Magnetic resonance imaging Multiple systems atrophy N-Methyl-D-aspartate receptor Parkinson’s Disease Parkinson’s disease dementia Positron emission tomography Progressive supranuclear palsy PSP-Parkinsonism Rapid eye movement (REM) sleep behaviour disorder Rapid eye movement ix

x

SCA SN SSRI STN UKPDSBBCDC UPDRS VY-AADC01 YOPD

Abbreviations

Spinocerebellar Ataxia Substantia nigra Selective serotonin reuptake inhibitor Subthalamic nucleus United Kingdom Parkinson’s disease society brain bank clinical diagnostic criteria Unified Parkinson’s Disease Rating Scale Adeno-associated viral vector serotype-2 Young Onset Parkinson’s disease

Parkinson’s Disease–An Introduction Chandra Shekhar Rawat and Sanjay Pandey

Abstract Parkinson’s disease is the common neurodegenerative disorder that presents in a heterogeneous manner in terms of age at onset, natural history, clinical signs, genetic makeup, rate of disease progression, and response to treatment. Diagnosis of Parkinson’s disease relies on a good history with an inquiry about the sequence of emergence of symptoms and recognition of characteristic clinical features such as bradykinesia, rigidity, typical rest and reemergent tremor, and postural instability. These symptoms can also result from other neurodegenerative disorders as well as secondary to medications, metabolic causes, toxin exposures and all should be excluded in cases of suspicion, and historical review. It is necessary to recognize the non-motor features which may precede the motor manifestations such as olfactory dysfunction, constipation, rapid eye movement behaviour disorders that help in making an early diagnosis. Treatment of Parkinson’s disease requires an individualized approach based on clinical findings and complications. It involves pharmacologic approaches and surgical options such as deep brain stimulation. Keywords Parkinson’s disease · Substantia nigra · Alpha-synuclein · Lewy bodies · Deep brain stimulation

1 History The history of Parkinson’s disease (PD) can be traced back to 1817 when James Parkinson (1775–1824) published a monograph “an Essay on the Shaking Palsy”. He was a general medical practitioner who lived and worked in Shoreditch, as a British apothecary. Being a highly astute observer, James Parkinson described a disease of insidious onset and progressive disabling course [1]. He described rest tremor, flexed posture, and festination, whereas descriptions about rigidity or bradykinesia were not C. S. Rawat · S. Pandey (B) Department of Neurology, Govind Ballabh Pant Postgraduate Institute of Medical Education and Research, New Delhi 110002, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. P. Arjunan et al. (eds.), Techniques for Assessment of Parkinsonism for Diagnosis and Rehabilitation, Series in BioEngineering, https://doi.org/10.1007/978-981-16-3056-9_1

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found in his essay. Charcot later attributed the recognition of rigidity, as a characteristic sign of PD, to himself, stating that this phenomenon had been overlooked by James Parkinson. The term “Parkinson’s disease” was coined in 1865 by William Sanders and later popularized by French neurologist Jean-Martin Charcot [2]. Interestingly the first description of bradykinesia, which is one of the cardinal manifestations of PD was given by a patient, the German scholar Wilhelm von Humboldt. In a letter written to a lady friend in 1830, during his early 60 s, he responded to her remarks about his deteriorating handwriting [3]. Although firstly, PD was medically described as a neurological syndrome by James Parkinson, fragments of Parkinsonism can even be found in early descriptions. For examples, Sylvius de la Boë wrote of rest tremor, and Sauvages described festination [4, 5]. Much earlier, traditional Indian texts of approximately 1000 BC and ancient Chinese sources also provide descriptions about PD [6]. The anatomical substrate of PD remained controversial for over 100 years. In 1893 Blocq and Marinesco, two scientists who worked at Salpêtrière University Hospital, Paris, first alluded to a possible link between the substantia nigra and PD [7]. In 1912, Friedrich Heinrich Lewy first described the inclusion bodies, named after him and seen in paralysis agitans, but it was Constantin Tretiakoff who put these two (lewy body formation and neurodegeneration in substantia nigra) separate pathologies together in 1919, suggesting that both were found in most patients with PD [8]. The concept that, degeneration of the SN was central to the syndrome was cemented by two additional discoveries, the first by Arvid Carlsson on the role of dopamine in brain and the second by Oleh Hornykiewicz, who demonstrated that the largest group of dopaminergic neurons are found in the SN with their terminals in the caudate nucleus [9].

2 Etiology and Pathogenesis 2.1 Environmental Factors The environmental theory came into notice in the 1980s when MPTP (1-methyl-4phenyl-1,2,5,6-tetrahydropyridine) was identified to cause PD. It was found that drug addicts to MPTP developed PD like syndrome. Later it was concluded that MPTP induces mitochondrial toxicity through the pyridinium ion (MPP +), which is one of the mechanisms observed in PD. This observation supports the possibility that an environmental factor might cause PD. Most of the PD cases occur sporadically (85%-90%) and specific etiology linked with PD is not known. Although various epidemiologic studies showed that a number of factors may increase the risk of developing PD, but in many instances, their role of causation could not be identified. The most important risk factor identified so far for PD is aging, especially, the median age of 60 years. The prevalence of PD observed is more in men than women (1.3–2.0:1.0) but it appears to be related to differences in the lifestyle, work habits (drinking well water, farming, rural living) and many other factors such as, the risk of PD increases with exposure to industrial chemicals, pollutants, pesticides, solvents,

Parkinson’s Disease–An Introduction

3

and metals. While some of the factors proved to be associated with a decreased risk of PD like smoking, use of postmenopausal hormones, and caffeine intake [10]. Similarly, dozens of exogenous (trace metals, organic solvents, cyanide, carbon monoxide, and carbon disulfide) and endogenous toxins (tetrahydroisoquinolines and beta-carbolines) have been associated with the development of parkinsonism. However, no specific environmental factor has yet been proven to be a cause of PD [11].

2.2 Genetic factors Substantial progress has been made in the advancement of the genetics of PD. Approximately 5–10% of PD patients have a familial form of parkinsonism, and genetic research in PD has led to the identification of numerous genetic risk factors which increases the risk to develop PD, some of them are listed in Table 1. Over 20 common monogenic variants of PD have been described in the literature and over 100 loci have been identified. Recently with the advancement of whole-genome sequencing techniques, new monogenic variants are constantly added to comprehend the role of genetics in the pathogenesis of PD. The common variants associated with PD are SNCA, LRRK2, and MAPT as well as low-frequency coding variants in GBA [12]. The Table1 summarises some of the distinguishing features of more important monogenetic PD disorders. Over the last 15 years substantial progress has been made in the identification of other monogenic variants which usually present with atypical feature are as follows; autosomal dominant (VPS35, EIF4G1, DNAJC13, CHCHD2) and autosomal recessive (PINK1, DJ1, ATP13A2, GIGYF2, PLA2G6, FBXO7, DNJAC6, SYNJ1, VPS13C).

2.3 Pathophysiology The basic underlying pathology in PD is the gradual loss of cells in the substantia nigra par compacta, locus ceruleus, and other regions in the brain, as a part of the neurodegenerative process. The pattern of dopamine cell loss in the SN is distinctive for PD, with the most severe loss found in the ventrolateral region of the SN, whereas dopaminergic neurons in the nearby ventral tegmental area are nearly entirely spared [13]. The rapid advancement in research techniques during the past 20 years has determined that there is not a single cause but several causes, all leading to the common preferential early loss of dopaminergic neurons in the SN, in patients with clinical PD. The two most obvious culprits are the number of different genes involved (autosomal dominant, autosomal recessive, and risk genes) and some environmental factors (hydrocarbon exposure, less coffee intake, constipation, reduced physical activity). Braak et al. in 2003 provided a hypothesis that an unknown pathogen in the gut could be responsible for the initiation of the neurodegenerative process.

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Table 1 Distinguishing features of the important monogenetic Parkinson’s disease disorders Gene

Inheritance pattern

Clinical clues

PARK-SNCA (PARK1)

Autosomal dominant (AD)

Overexpression of Alpha-synuclein in transgenic mice can cause levodopa-responsive motor impairment and nigral degeneration. SNCA triplication is associated with early onset disease (compared with duplication carriers) and cognitive impairment suggesting a gene dosage effect

PARK-Parkin (PARK2)

Autosomal recessive (AR) Parkin (PRKN) is the most common autosomal recessive PD-related gene

The disease may present with dystonic gait, leg tremor at rest and on standing, cervical dystonia, dopa-responsive dystonia. Freezing, festination, retropulsion, marked sleep benefit, hyperreflexia, ataxia, peripheral neuropathy and dysautonomia Although usually symmetrical, it may be rarely present as hemiparkinsonism-hemiatrophy. Excellent levodopa response is typically complicated by early development of levodopa-induced dyskinesia

PARK-LRRK2 (PARK8) AD LRRK2 is the most common autosomal dominant PD-related gene and a common mutation (G2019S) have been identified in both familial (3–4%) and sporadic (1–3%) PD with age-dependent penetrance

Most LRRK2 carriers are of late onset simulating typical Parkinson’s disease, and clinically indistinguishable from non-carriers, but seem to have more benign course, manifested chiefly by postural instability and gait disorder phenotype, with less rapid eye movement behaviour disorder and relatively preserved olfaction Atypical features include orthostatic hypotension, dementia, hallucinations, corticobasal syndrome and primary progressive aphasia (continued)

Parkinson’s Disease–An Introduction

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Table 1 (continued) Gene

Inheritance pattern

Clinical clues

PARK-GBA

Glucocerebrosidase (GBA) gene, located on chromosome 1q21, encodes the lysosomal enzyme glucocerebrosidase Heterozygous, homozygous or compound heterozygous mutations of the GBA gene represent the single most important genetic risk factor of PD in the general population, conferring more than five times increased risk of PD

PARK-GBA has a younger age at onset, higher prevalence of cognitive impairment and of rapid eye movement behaviour disorder than in typical Parkinson’s disease (in non-carriers)

PARK -PINK1(PARK6)

AR

Often presents with psychiatric features

PARK-DJ1(PARK7)

AR

Clinically, Parkinson’s disease patients with DJ-1 mutations exhibit an early onset of dyskinesia, rigidity, and tremors, followed by later manifestation of psychiatric symptoms, such as psychotic disturbance, anxiety, and cognitive decline, and generally respond well to L-DOPA treatment

PARK-ATP13A2

AR

Kufor-Rakeb syndrome clinically exhibit parkinsonism and dystonia with supranuclear gaze palsy, spasticity, dementia, myoclonus, bulbar symptoms (dysphagia, dysarthria) and olfactory dysfunction

The pathology starts with the specific pattern of alpha synuclein (αSyn) spreading. According to their hypothesis, pathological changes first occur in the medulla oblongata and olfactory bulb i.e. Braak stages 1 and 2, followed by SN and midbrain i.e. Braak stages 3 and 4, and finally, the cortical regions eventually become affected corresponding to Braak stages 5 and 6 [14]. Above mentioned varied etiologies interact with each other and cause initiation of neurodegenerative processes in the form of dysfunction and death of specific dopaminergic neurons through the following pathways [15]: (i) (ii) (iii)

Protein (α synuclein) misfolding and aggregation and spread in a specific pattern as described by Braak (2003). Mitochondrial dysfunction. Disruption of autophagic catabolism (Impaired protein clearance via ubiquitin–proteasome and autophagy-lysosomal systems).

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Loss of calcium homeostasis (Neuroinflammation and oxidative stress).

The pathological hallmark of idiopathic PD is the presence of misfolded protein in the form of Lewy bodies. The major component protein of Lewy bodies is αSyn, although recently other molecules are also recognized in these pathological inclusions. The theory behind the production and deposition of these Lewy bodies demonstrated that, different manipulations leading to change in the solubility and binding affinities of the αSyn protein, cause its intracellular precipitation [16].

2.4 Clinical Features PD is an extremely heterogeneous movement disorder and patients differ in their clinical signs, natural history, genetic makeup, age at onset, rate of disease progression, and response to treatment [17]. The spectrum of clinical features and disease course manifested by individual patients varies greatly; some have an apparently benign disorder with a sustained response to levodopa and minimal nondopaminergic symptoms, whereas, others demonstrate a malignant course with an early predominance of nondopaminergic motor and non-motor features. The reasons for these clinical differences are poorly understood. Age and age of onset are the best-recognized influencing factors. Thus, the younger the onset, the longer the levodopa-responsive features predominates, albeit complicated by motor fluctuations. Independent of age of onset, older patients experience more levodopa-resistant motor signs, autonomic impairment, and cognitive decline [17, 18]. The symptoms of PD are predominantly motor-based such as tremor, rigidity, bradykinesia, postural instability, hypomimia, micrographia, festination, shuffling gait, dysarthria, and dystonia. The non-motor symptoms are autonomic dysfunction, cognitive abnormalities, dementia, sleep disorders, anosmia, and pain.

2.5 Motor Symptoms The motor symptoms in PD include four cardinal features bradykinesia, rest tremor, rigidity, and postural instability. Postural reflex disturbances generally occur later in the natural history of PD and are no longer considered essential diagnostic features [18]. The motor and non-motor features associated with PD are discussed in the following section (Table 2). 1.

Bradykinesia

Bradykinesia is defined as slowness of a performed movement with a progressive loss of amplitude or speed during attempted rapid alternating movements of body segments [19]. While examining bradykinesia, it is important to differentiate between true bradykinesia versus simple slowness, as simple slowness could be a part

Parkinson’s Disease–An Introduction

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Table 2 Motor and Non-Motor features of Parkinson’s disease Motor Features

Non-Motor Features

Cardinal features (i) Bradykinesia (ii) Rest tremor (iii) Rigidity (iv) Postural instability

Neuropsychiatric features (i) Hallucinations and psychosis (iii) Apathy (iv) Cognitive impairment

Other supportive motor features (i) Hypomimia (mask like facies) (ii) Reduced eye blinking (iii) Low volume monotonous soft voice (hypophonia) (iv) Dysphagia, drooling (v) Micrographia (vi) Gait; asymmetric decreased arm swing, shuffling gait, festination, turning difficulty, camptocormia, Pisa syndrome (reversible lateral bending of trunk with a tendency to lean to one side),freezing (vii) Glabellar reflex

Autonomic features (i) Urinary problems (ii) Constipation (iii) Light headedness on standing Sleep problems (i) Daytime sleepiness, (ii) REM sleep behaviour disorder, (iii) Insomnia, (iv) Restless leg syndrome Dopamine dysregulation syndrome (i) Addictive behaviour (ii) Excessive use of dopamine medications Sensory and other symptoms (i) Pain and other sensations, (ii) Olfactory dysfunction

of paresis, and spasticity. Furthermore, psychiatric patients often show generalized slowness which should be kept in mind too while examining patients. Assessment: Bradykinesia was clinically assessed by asking the patient to perform some repetitive movements as quickly and widely as possible, e.g., tapping thumb and index fingers, opening and closing the fist, or tapping foot on the ground. The clinician must pay attention to the emergence of progressive slowness and/or loss of amplitude, which might ultimately bring the movement to complete arrest. Bradykinesia can also be searched globally by observing the patient’s spontaneous movements while sitting, standing up from a chair, or walking. Other clinical displays of bradykinesia are hypomimia (decreased facial expression and eye blinking, termed “poker face” in milder stages, hypophonia (softer voice), micrographia (progressively smaller handwriting), and difficulty in swallowing. 2.

Rigidity

Rigidity is defined as, increased uniform resistance to passive limb movement throughout the range of motion. It does not vary with velocity, which distinguishes it from spasticity, owing to upper motor neuron lesions. It is examined by appreciating the passive movement of major joints with the patient in a relaxed position. When resting tremor coexists the classical “cogwheel rigidity” can be felt, especially in the wrist. As rigidity is not easily detectable in the early stage of the disease, reinforcing manoeuvres may be helpful to detect the rigidity, for which ask the patient to perform

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an activation manoeuvre e.g., tapping fingers, fist opening-closing, or heel tapping in a limb not being tested. [19] 3.

Rest tremor

The characteristic unilateral tremor that occurs in PD is a “resting tremor,” observed when the affected body part is relaxed and supported by a surface. In one study, Hughes and colleagues reported that 69% of patients with PD had rest tremor at disease onset and that 75% had tremors during their disease [20]. Others have mentioned that a small proportion of patients (11%) never had tremors [20]. Although a prospective study in patients with the autopsy proven disease found that 100% of patients had tremors at some point [21]. The most characteristic tremor in this disorder is the so-called unilateral “pill-rolling” type of hand tremors, described as supination–pronation tremors that spread from one hand to the other. Other forms of tremor movements can also be seen, such as finger flexion-extension or abductionadduction. PD tremor most commonly involves the fingers, followed by the hands, jaw, and feet in order, but, unlike essential tremor, rarely the neck/head or voice. Assessment: The rest tremor is a well-recognized cardinal feature of PD. Its frequency lies in the range of 3–5 Hz, whereas the amplitude can vary from less than 1 cm to >10 cm wide. Parkinsonian patients may also exhibit postural tremor. This postural tremor emerges after a latency of a few seconds or even minutes, the so-called re-emergent tremor. Re-emergent tremor differentiates this postural tremor from essential tremor, which occurs without any latency. This tremor shares many characteristics with the typical rest tremor, such as having same 3 to 5-Hz frequency, occasional supinating-pronating component, and relatively good response to dopaminergic therapy. It likely represents a variant of the more typical rest tremor [22]. An additional form of tremor, postural (e.g., occurs immediately on stretching out the arms), and faster (6–8 Hz), can be occasionally seen in PD, but it is noncontributory to the diagnosis. In clinical practice, tremors are best observed while the patient is focused on a particular mental task (e.g., countdown from 100 with eyes closed), which facilitates limb muscle relaxation. 4.

Postural instability

Postural instability, due to loss of postural reflexes, is generally a manifestation of the late stages of PD, contributing to recurrent falls. It rarely occurs in the first two years after the onset of PD. The majority of PD patients experience falls only 5 to 10 years after disease onset, and the debut of falls is later when patients have a tremordominant phenotype of PD, as compared to the phenotype dominated by postural instability and gait disability [23]. The long latency to the onset of falls differentiates PD from other atypical parkinsonism, such as progressive supranuclear palsy (PSP) and multiple systems atrophy (MSA). Assessment Clinically, postural instability is assessed by observation of patient posture and gait while passing through narrow passages and during turning. In addition, a “pull test” is performed to assess postural stability; patients need to be informed that the clinician will pull their shoulders backward, and that they are allowed to take

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as many backward steps as needed to avoid falling. The patient should stand erect with eyes open and feet comfortably apart and parallel to each other. The clinician stands behind the patient sufficiently far away from the patient, allowing enough space for the patient to take several steps to recover balance independently. The patient is quickly pulled backward by the shoulders and is rated by counting the number of balances correcting steps. Taking more than two steps backward or the absence of any postural response indicates an abnormal postural response [24, 25]. Gait Parkinsonian gait is characterized by slowed ambulation (bradykinesia) with decreased or absent arm swing, longer double limb support (i.e. more time with both feet spent on the ground), stooped posture, and impaired postural control. One of the key observations to these gait problems is the inability of patients with PD to generate sufficient stride length (a problem that has been related to impaired scaling of amplitude) [26, 27].

2.6 Non-motor Symptoms PD is associated with a broad spectrum of non-motor symptoms. These include disorders of mood and affect with apathy, anhedonia and depression, cognitive dysfunction and hallucinosis, as well as complex behavioural disorders. Sensory dysfunction with hyposmia or pain is almost universal, as are disturbances of sleep–wake cycle regulation. Among them, some are often preceded by motor manifestations such as olfactory dysfunction, constipation, rapid eye movement (REM) behaviour disorder, and psychiatric features like depression and anxiety [28]. With the disease progression, patients may present with increasingly disabling non-motor features such as dementia, autonomic failure, psychiatric disturbances, and levodopa related complications.

2.7 Cognitive Impairment The most significant nonmotor symptom in PD is progressive cognitive impairment. Once thought to primarily affect executive abilities in a minority of patients, it is now known that a range of cognitive domains can be affected and that dementia (PDD) may affect 80% of patients in the long term [29]. Cognitive deficit has been reported in newly diagnosed and even prodromal PD patients. Diffused cortical Lewy-body disease pathology is the major contributing pathology to PDD, but about one-third of PDD patients also meet the criteria for comorbid Alzheimer’s disease. A range of neurotransmitter deficits (acetylcholine, dopamine, and norepinephrine) and genetic mutations (APOE E4, BDNF Val Met, COMT Val, MAPT, and glucocerebrosidase (GBA) polymorphisms) have been implicated [30–32].

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2.8 Neurobehavioral Abnormalities Prevalence rates for all depression subtypes in PD combined, range from 15 to 50%, and such disparity reflects in part somatic symptoms overlap between depression and PD. Among the disorders that affect, both anxiety and apathy in PD have received less attention than depression, despite their frequent occurrence (30–40% for each disorder). Anxiety can be present as generalized anxiety disorder, panic attacks, and social phobia. Psychosis was reported uncommonly prior to the introduction of levodopa, but now the cumulative prevalence of PD psychosis is 60% [33]. A recent study reported that minor hallucination is common even in newly diagnosed, and untreated patients [34]. Common subtypes of hallucinations observed in PD are visual, auditory, tactile, and olfactory hallucinations.

2.9 Autonomic Dysfunction Autonomic dysfunction manifesting as orthostatic hypotension, urogenital dysfunction, and constipation is present in a majority of PD patients. Whilst, overall nonmotor symptoms become increasingly prevalent with advancing disease, many of them can also antedate the first occurrence of motor signs, most notably depression, hyposmia, or REM sleep behaviour disorder (RBD) [35].

2.10 Sensory Symptoms and Pain Painful sensations, not explained by osteoarthritic conditions, neuropathy, or other causes of pain, commonly observed in elderly populations, have been reported in 40–50% of patients with PD, in different series. PD-related pain may be a presenting symptom when patients complain of aching shoulder initially affected by rigidity and loss of dexterity. Sensory symptoms and pain are also prominent in fluctuating PD, of which tingling or burning sensations, neuralgic pain, or diffused pain have been described as common off-period phenomena in one study [36]. The pathophysiology underlying painful sensations in PD are poorly understood but may include alterations in central pain-processing pathways, as suggested by a recent study, describing decreased heat pain thresholds in PD patients [37], which were more marked on the side initially and more severely affected by the disease. Defective odour detection and discrimination are sensory abnormalities that appear to affect some 90% of patients with PD [38] and have related to neuropathology affecting the olfactory bulbs [39]. Hyposmia is generally marked when formally tested, although many patients do not spontaneously complain of it. It helps in differentiating between vascular parkinsonism and PD and more frequently observed in parkin-related PD [33, 34].

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2.11 Diagnosis PD is a clinical diagnosis as there is no definitive test available to confirm the disease, except for gene testing in a few cases. Pathological findings in PD consists of loss of dopaminergic neurons in SN pars compacta and intraneuronal Lewy bodies. The first step for the diagnosis of PD is carefully taking the history with special emphasis on the sequence of symptoms that emerged and the correlation of the involvement of anatomical structures. History should include inquiry about the nonmotor symptoms e.g.; REM sleep behaviour, anosmia, and constipation as these can be present before the emergence of motor symptoms. Asking drug history is important if any atypical sign or symptom is observed especially due to drugs capable of inducing parkinsonian symptoms. Likewise, possible exposure to environmental toxins should also be searched for (e.g., manganese in welders). Past and present medical disorders should be systematically recorded. Family history is also an important stage and should include information regarding neurological disorders in other family members, as well as an inquiry about ethnic ancestry as, monogenic forms of PD, are more prevalent in some (e.g., Ashkenazi Jewish and North African Arabs who have a higher frequency of LRRK2 genetic PD). The United Kingdom Parkinson’s disease society brain bank clinical diagnostic criteria (UKPDSBBCDC) is more sensitive (90.8 vs 81.3%), but less specific (34%) compared to the expert clinical diagnosis (83.5%), and the most common mis-diagnoses includes other tremor disorders, atypical parkinsonian conditions, secondary parkinsonisms, and other dementias [40]. Recently, new clinical criteria for PD diagnosis have been published on behalf of the International PD and Movement Disorders Society that does not include dementia as an exclusion criterion, any longer [2, 41]. The new criteria accepts the diagnosis of PD, independent of when dementia arises, as long as the clinical criteria for PD is fulfilled. However, this proposal has triggered a considerable debate in the field and is presently open to further evaluation and discussion [41]. Although, improvement with levodopa is suggestive of PD, it does not definitively differentiate PD from other parkinsonian disorders [42]. One study found that only 77% of patients with pathologically proven PD had a “good” or “excellent” initial response to levodopa [43, 44]. There is no role of conventional MRI in the diagnosis of idiopathic PD. But it may be useful in doubtful settings especially when there is a dilemma regarding atypical parkinsonism and structural abnormalities. Special sequences like magnetization transfer imaging and diffusion tensor imaging may reveal a lower magnetization transfer ratio and fractional anisotropy in the SN in PD patients [45].

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2.12 Doapmine Transporter (DaT) Scan DaT scan is a tool used to confirm the diagnosis of PD. It is a specific type of singlephoton emission computed tomography (SPECT) imaging technique that helps visualize dopamine transporter levels in the brain. DaTscan is not necessary for every Parkinson’s patient. For most, the distinct clinical symptoms such as slowness of movement, tremors, and stiffness are sufficient to reach a diagnosis, and the information generated through the scan may not be new and will not alter their treatment plans. However, in patients with unclear symptoms that overlap with other neurological conditions and for those who do not respond to treatment, DaT scan may be required to reach a diagnosis. DaT scan in case of PD patients showed a decrease in uptake of radiotracer in the neostriatum with a predominantly early deficiency in the putamen and often an asymmetric distribution. Dopamine transporter uptake is more prominently reduced in the putamen than in the caudate. DaT scan may accurately differentiate between early PD and secondary parkinsonian conditions, namely vascular or drug-induced parkinsonism and ET [46].

2.13 Differential Diagnosis Other neurodegenerative disorders like dementia with Lewy bodies (DLB), corticobasal degeneration (CBD), multiple system atrophy (MSA), and progressive supranuclear palsy (PSP) also have sign and symptoms of parkinsonism (i.e., tremor, bradykinesia, rigidity, and postural instability) in various combinations. In addition, parkinsonism is seen in a wide variety of other conditions which are called secondary parkinsonism, caused by an identified structural, toxic, or metabolic mechanism [47]. Distinguishing PD from these parkinsonian syndromes can be difficult, particularly in the early stages of the disease. Essential tremor (ET) may also be confused with PD.

2.14 Essential Tremor ET is the most common neurologic cause of action tremor, with an estimated prevalence worldwide of up to 5% of the population. Distinguishing essential tremor from PD can be challenging, both in the early stages of these diseases when clinical signs are subtle and as they progress. Various tremor types (rest, postural and action) may be seen in both essential tremor and PD. Furthermore, with time, the two diseases may coexist within a single patient. One study observed that one-third of patients who were diagnosed as having ET was incorrect, with PD being the most common

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true diagnosis [48]. In a study that evaluated the risk of incident PD in 201 clinically diagnosed ET cases [49], it was found that 3% of ET cases developed PD as compared to 0.7% of controls, after a median follow-up of 3.3 years [49]. ET usually affects both hands and arms, and can also involve the head, voice, chin, trunk, and legs. An isolated tremor of the chin or lips is more likely to be a manifestation of PD. ET typically becomes immediately apparent in the arms when they are held outstretched or when they are engaged in activities such as writing or eating. ET is most often symmetric but can be asymmetric or, rarely, unilateral, particularly early in the course of the disease. Action tremor is the hallmark feature of ET while, the classic tremor type of PD is resting tremor, and differentiating them should be straightforward. However, patients with severe ET may present with rest tremor, conversely, action tremor may be found in patients with PD. Furthermore, some patients with PD may have a reemergent tremor: a postural tremor that manifests after a latency of several seconds with a frequency typical of the rest tremor in PD. This distinction is important, as patients with a re-emergent rest tremor may be mis-diagnosed as having ET [48]. The presence of subtle bradykinesia, rigidity, or micrographia in older adults with a diagnosis of ET may support the diagnosis of PD as well, although these signs may also be a non-specific accompaniment of aging.

2.15 Dementia With Lewy Bodies DLB is the second most common cause of neurodegenerative dementia after Alzheimer’s disease. Key features that might lead to a diagnosis of DLB are visual hallucinations, extreme and rapid fluctuations in alertness and attention span, and the presence of symptoms such as tremor, stiffness and slowness accompanied with repeated falls. The order of motor versus cognitive symptoms is different in both which helps us in making a diagnosis of DLB and PD dementia (PDD). In PDD motor features appear first followed by dementia usually a year later, however in DLB patient’s dementia usually occurs concomitantly with or before the development of parkinsonian signs [50]. The overlap between these two clinical entities and the uncertainty of the one-year rule continues to provoke debate about the validity of current nomenclature [51].

2.16 Multiple System Atrophy (MSA) Multiple system atrophy is a progressive neurodegenerative disorder characterized by a combination of symptoms that affect both the autonomic nervous system and movements which includes sign symptoms of autonomic failure, with orthostatic

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hypotension (OH), neurogenic bladder/erectile dysfunction, cerebellar ataxia, corticospinal dysfunction, along with poorly levodopa- responsive symmetric parkinsonism. However, early in the course, some cases of MSA may resemble typical PD in every way, including responsiveness to levodopa along with the presence of motor fluctuations and dyskinesia, only to evolve into the more typical profile of MSA, later. Cognitive function in MSA tends to be relatively well preserved as compared to PD and other parkinsonian syndromes, probably reflecting a lesser degree of cortical involvement. Aggregation of α-Syn is also the underlying pathology of MSA, except that it affects oligodendroglia instead of neurons [52]. In addition, subtle differences exist in the conformation of α-Syn aggregates in the two disorders, which may eventually be leveraged diagnostically [53].

2.17 Cortico-Basal Degeneration Cortico-basal degeneration is a progressive neurodegenerative disorder involving the cerebral cortex and basal ganglia. It is characterised by asymmetrical involvement of one limb with movement disorders, including various combinations of dystonia, akinesia and extreme rigidity, focal myoclonus, ideomotor apraxia, and difficulty in controlling the limbs on one side of the body, often known as ‘alien limb’ syndrome, as arms or legs may seem to move independently. Although originally considered a primary motor disorder, now it is also recognized as a cognitive disorder, usually presenting cognitive deficits before the onset of motor symptoms. Important cognitive features of CBD include executive dysfunction, aphasia, apraxia, behavioural change, and visuospatial dysfunction, with relatively preserved episodic memory. The distinctive clinical phenotype and the lack of clear response to an adequate trial of levodopa are typical of CBD and help to distinguish it from PD [54].

2.18 Progressive Supranuclear Palsy (PSP) Progressive Supranuclear Palsy (PSP) and PD, both can mimic each other, especially in their early phase. PSP has several distinct clinical phenotypes. The most common “classic” phenotype of PSP, known as Richardson syndrome, in which most patients present with gait disturbance, unsteadiness, along with a tendency to fall backward. Some of the patients present with early complaints of visual disturbance which are related to a typical disturbance of downward gaze and disruption of control of saccadic eye movements. Supranuclear vertical ophthalmoparesis or ophthalmoplegia is the hallmark of PSP. Dysarthria, dysphagia, rigidity, frontal cognitive abnormalities, and sleep disturbances are additional common clinical features. The second most common phenotype of PSP is PSP- parkinsonism predominant (PSP-P), which is observed in up to 35% of cases and is characterized by asymmetric onset of limb

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symptoms, tremor, and a moderate initial therapeutic response to levodopa, so it may be confused with idiopathic PD, early in the course, before characteristic vertical gaze abnormalities emerge [55].

2.19 Other Neurodegenerative Disorders Parkinsonism may also occur in several other disorders such as Huntington disease (in later stages, the patient becomes mute, dysarthric, dysphagic and develops hypokinesia, akinesia, and rigidity. So, non-dopa responsive parkinsonism occurs in the later stages of Huntington’s although parkinsonism could be a presenting feature of in early stages of choreic diseases too such as Westphal variant of Huntington’s disease [56], Frontotemporal dementia with parkinsonism linked to chromosome 17 [57], Spinocerebellar ataxias (many extrapyramidal symptoms including parkinsonism are also seen in diverse SCA subtypes such as SCA3, SCA2, SCA6, SCA8, and SCA17) and dentatorubral pallidoluysian atrophy [58].

2.20 Secondary Parkinsonism A number of secondary causes of parkinsonism should be considered before making a diagnosis of idiopathic PD.

2.21 Drugs Drug-induced parkinsonism is said to typically cause a symmetrical parkinsonism syndrome with prominent bradykinesia and rigidity. The symptoms typically resolve within weeks to months after the withdrawal of the offending drug. Sometimes the culprit drug may have unmasked early underlying PD; in these cases, where there is doubt regarding diagnosis, a DaT scan is useful, as it appears normal in drug-induced parkinsonism. Examples include. 1. 2. 3. 4. 5. 6.

Typical (e.g., haloperidol and chlorpromazine) and atypical antipsychotic agents (e.g., risperidone, olanzapine), Anti-emetics (e.g., metoclopramide and domperidone), Calcium channel blockers (e.g., flunarizine and cinnarizine), Anti-epileptics (e.g., sodium valproate and phenytoin), Dopamine depleting drugs (e.g., tetrabenazine), and Selective serotonin reuptake inhibitors (SSRI).

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2.22 Toxins As we know, that the causative link between PD and exposure to the following toxins has been previously described in literature, so it should be evaluated in history and examination before making a diagnosis of idiopathic PD. 1. 2. 3. 4. 5. 6.

Carbon disulfide, Carbon monoxide, Cyanide, MPTP, Manganese, Organic solvents.

2.23 Vascular Parkinsonism Vascular parkinsonism is an example of atypical parkinsonism caused by cerebrovascular disease. Its clinical phenotype is bilateral, and symmetrical parkinsonism typically affecting the lower limbs, in turn causing a predominant gait disorder. The patient gait is typically upright with a broad base, short steps, and normal armswing, compared to the stooped posture, narrow base, and reduced arm-swing of PD. Upper limbs are often not involved, and tremor is a rare presenting feature. The clue to the diagnosis is typically a stepwise progression based on supporting features, such as the presence of an extensor plantar response, brisk reflexes, a pseudobulbar palsy, and cognitive impairment on examination.

2.24 Others Causes of Secondary Parkinsonism It is vital to exclude other causes for instance, looking for head trauma (isolated or repeated (e.g., boxing), structural brain lesions that affect striatonigral circuits (eg, hydrocephalus, chronic subdural hematoma, tumor), metabolic and miscellaneous disorders (eg, Wilson’s disease, hypoparathyroidism and pseudohypoparathyroidism, chronic liver failure, extrapontine myelinolysis, neurodegeneration with brain iron accumulation, and neuroacanthocytosis), and infections (e.g., encephalitis lethargica or Economo’s encephalitis, HIV/AIDS, neurosyphilis, prion disease, progressive multifocal leukoencephalopathy, toxoplasmosis). It is important to evaluate history, associated features, and assessment of atypical features with laboratory or radiological findings to distinguish secondary parkinsonism and its underlying cause from PD [59].

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2.25 Treatment PD is a complex neurodegenerative disorder which shows heterogeneity in terms of clinical manifestations and disease course. Thus, PD patients require individualised therapeutic approach based on clinical findings and treatment related complications. The UPDRS score is used most widely in the clinical settings to assess the treatment response in PD patients [60]. drugs, their mechanisms of actions, and doses in motor symptoms of Parkinson’s disease.

2.26 Treatment of Motor Symptoms Levodopa, is the most effective and most commonly prescribed drug in the treatment of motor symptoms of PD. It is used to manage symptoms such as tremors, rigidity, and bradykinesia. It is almost always combined with peripheral decarboxylase inhibitors to prevent its peripheral metabolism, that markedly reduces the risk of nausea and vomiting. Examples include: (i) (ii)

(iii)

Immediate-release carbidopa-levodopa preparations (in pill form). This is generally the first line of levodopa treatment. Controlled release preparation, which allows for an immediate release dose of carbidopa-levodopa with an extended release over time. This results in a higher daily dosage of levodopa, but fewer pills needed throughout the day. Dudopa, a carbidopa-levodopa gel formulation that is administered through a surgically implanted tube in the intestine, and provides a continuous 16-h release of levodopa, directly at the site of absorption. This aims to significantly reduce the “off-time” periods and the incidence of dyskinesia.

Other adverse effects of levodopa include orthostatic hypotension, sedation, confusion, sleep disturbance, hallucinations and dyskinesias. Among the different types of dyskinesia, peak-dose dyskinesia and wearing off dystonia are most common dyskinesias associated with levodopa. Especially, the YOPD patients are more likely to develop levodopa related motor fluctuations and dyskinesias early in the course of treatment [61]. Although levodopa is the most efficacious drug used to control motor symptoms with a predictable therapeutic response which is used to support the diagnosis of PD. However, it does not have a disease-modifying effect because a recent multi-centre double-blind trial showed no significant change in the rate of progression between early or delayed-start groups [61]. Besides levodopa, there are many other types of medication available for the treatment of PD-related motor symptoms: anticholinergics, amantadine, MAOIs, COMTIs, dopamine agonists and istradefylline (Adenosine A2 receptor antagonist).

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2.27 Anticholinergics Anticholinergics, for example trihexyphenidyl and benztropine, are predominantly used to reduce tremor, while they have no effect on bradykinesia. These agents can be associated with a variety of adverse effects such as blurred vision, dry mouth, constipation, cognitive impairment, confusion, hallucination, and urinary retention. These side effects limit the usefulness of anticholinergics in the treatment of PD.

2.28 Antiglutamatergics Amantadine is currently the main drug among this group used in the treatment of levodopa related dyskinesias.

2.29 Monoamine Oxidase Inhibitors Examples include selegiline and rasagiline and these are indicated both in early mild PD and moderately advanced PD with levodopa-related motor complications. A new MAOI, named safinamide, has advantage over these two is that it increases mean ontime without producing troublesome dyskinesia. It also reduces daily and morning off-times [62].

2.30 Dopamine Agonists Non-ergot dopamine agonists used in clinical practice include pramipexole, ropinirole, rotigotine and apomorphine. These can be used as a monotherapy for motor symptoms or as an adjunct therapy when the symptoms are not sufficiently controlled by levodopa or when motor fluctuations are present. The side-effect profile of dopamine agonists includes orthostatic hypotension, sleepiness, hallucinations and leg oedema. These drugs have also been linked to cause behavioural problems that include pathological gambling, compulsive shopping and eating, hyper sexuality and other impulse-control disorders (ICD) [63].

2.31 Catechol-O-methyl Transferase Inhibitors COMTIs enhance or prolong the effectiveness of levodopa treatment, by preventing it from breaking down before it reaches the brain. This delays the “off-time” period.

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COMTIs are often used initially in lieu of increasing the levodopa dose. They are useful as adjunctive drugs for patients who experience levodopa-related motor fluctuations. Examples of drugs in this category include; opicapone, entacapone, and tolcapone. Opicapone, a novel COMTI, administered once-daily (50 mg), has been found to significantly reduce off-times [64]. These agents are generally well tolerated, but they may cause nausea, postural hypotension, diarrhoea and orange discoloration of urine, increasing levodopa-related dyskinesias.

2.32 Treatment of Non-motor Symptoms Non-motor symptoms described in parkinsonism include depression, anxiety, apathy, psychosis, impulse control dysfunction, cognitive impairment, dementia, autonomic dysfunction, sleep disturbances (insomnia, and RBD), olfactory dysfunction, pain and fatigue. Non-motor symptoms can affect the quality of life, even more than motor problems in patients of PD. Some of these like RBD, olfactory dysfunction and gastrointestinal dysfunction may precede motor symptoms. Cholinesterase inhibitors (Donepezil and rivastigmine) and NMDA receptor antagonist (memantine) improve global impression and cognitive function in PD patients. Atypical antipsychotics such as quetiapine and clozapine reported being useful in hallucinations. Pimavanserin (5-HT2A inverse agonist) has been approved by the FDA in 2016 for the treatment of hallucinations and delusions associated with PD. Sleep disturbances in PD could be managed by both pharmacological (hypnosedatives, tricyclic antidepressants, mirtazapine, trazodone, quetiapine, or night-time dopaminergic therapy) and nonpharmacological (physical exercise, and improving sleep hygiene) approaches. Excessive day-time sleepiness may respond to methylphenidate, modafinil, or armodafinil. Orthostatic hypotension can be managed conservatively with salt supplementation, fludrocortisone, midodrine and droxidopa. For urological complaints like urgency, frequency and incontinence, urological medication such as, migrabegron (beta-3 adrenergic agonist) and botulinum toxin injections into the bladder wall, may improve bladder dysfunction. Dietary changes along with medication such as linaclotide and lubiprostone may improve constipation. Finally, the important role of physical, occupational and speech/voice therapy coupled with a regular exercise program cannot be overemphasised [65].

3 Surgical Treatment 3.1 Deep Brain Stimulation In the early stage of the disease, dopamine replacement therapy (Levodopa and dopamine agonists) resulting in an effective relief of motor symptoms, can be helpful.

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However, despite optimal medical therapy, many patients with moderate to advanced disease develop complications like motor fluctuations, troublesome dyskinesia, or akinesias. Previously refractory symptoms of PD were managed by surgical lesional procedures such as pallidotomies and thalamotomies. These surgical procedures were reported to result in improvement of symptoms, but often at the risk of irreversible and severe side effects like dysarthria or hemiparesis. In addition, bilateral surgery dramatically increased complications and was therefore rarely performed [66]. During the last decade, deep brain stimulation (DBS) has replaced the ablative surgical approaches such as thalamotomy, and pallidotomy. Compared to surgical lesioning procedures, DBS use is associated with a very minimal tissue damage and is therefore largely reversible. Furthermore, another advantage with DBS is that the stimulation parameters can be changed as per the needs of the patient to get maximum benefits. STN or globus pallidus interna (GPi) are the most frequent targets for DBS treatment of patients with PD, with disabling tremor and levodopa-related motor complications. Several studies have shown significant improvement in motor function after bilateral STN-DBS or GPi-DBS in advanced PD in comparison to medical therapy. A randomised controlled trial comparing DBS and medical therapy done at seven Veterans Affairs and six university hospitals, demonstrated motor symptoms improvement (assessed by >5 points improvement in UPDRS score) in 71% of DBS and 32% of best medical therapy patients group, proving superiority of DBS over medical therapy [67, 68].

4 Future Directions 4.1 Focused Ultrasound Therapy It provides symptomatic relief by making thermal lesioning of the STN (in dyskinesia) or thalamus (in tremor-dominant forms of PD) that has been found to be beneficial in some patients, particularly if the symptoms are markedly asymmetric [69].

4.2 Cell Replacement Therapies Cell replacement therapy is an emerging strategy for regenerative medicine in PD. In the last three decades, clinical studies have accumulated evidence regarding the replacement of lost dopaminergic neurons (via human embryonic stem cell and somatic cells line) improving motor symptoms of PD patients [70]. Another emerging area of regenerative therapeutics is gene therapy in PD based on evidence that the enzyme, L-amino acid decarboxylase (AADC) which converts levodopa to dopamine, is lost with disease progression. In a study, 15 PD patients with medically

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refractive motor fluctuations underwent MRI-guided delivery of adeno-associated viral vector serotype-2 (encoding the complementary DNA for the enzyme, aromatic L-amino acid decarboxylase) VY-AADC01 into the putamen. This study proved that, there were increases in enzyme activity up to 79%, and improvement of clinical outcomes in terms of increases in ON-time without troublesome dyskinesia at 12 months follow-up [71].

References 1. Parkinson, J.: An Essay on the Shaking Palsy. Whittingham and Rowland Sherwood, Neely and Jones, London (1817) 2. Charcot, J.M.: Lectures on the Diseases of the Nervous System, Delivered at La Salpetriere. The New Sydenham Society, London, p 187 3. Horowski, R., Horowski, L., Phil, C., Vogel, S., Poewe, W., Kielhorn, F.W.: An essay on Wilhelm von Humboldt and the shaking palsy: first comprehensive description of Parkinson’s disease by a patient. Neurology 45(3), 565–568 (1995) 4. Sylvius de la Boë, F: Opera Medica. Danielem Elsevirium et Abrahamum Wolfgang, Amsterdam (1680) 5. Tyler, K.: A history of Parkinson’s disease. In: Koller, W.C. (ed.) Handbook of Parkinson’s disease, pp. 1–34. Marcel Dekker, New York (1992) 6. Manyam, B.V.: Paralysis agitans and levodopa in “Ayurveda”: Ancient Indian medical treatise. Mov. Disord. 5, 47–48 (1990) 7. Obeso, J.A., Stamelou, M., Goetz, C.G., Poewe, W., Lang, A.E., Weintraub, D., Burn, D., Halliday, G.M., Bezard, E., Przedborski, S., Lehericy, S.: Past, present, and future of Parkinson’s disease: a special essay on the 200th anniversary of the shaking palsy. Mov. Disord. 32(9), 1264–1310 (2017) 8. Holdorff, B.: Friedrich Heinrich Lewy (1885–1950) and his work. J. Hist. Neurosci. 11(1), 19–28 (2002) 9. Fahn, S.: The medical treatment of Parkinson disease from James Parkinson to George Cotzias. Mov. Disord. 30(1), 4–18 (2014) 10. Dorsey, E.R., Elbaz, A., Nichols, E., Abd-Allah, F., Abdelalim, A., Adsuar, J.C., Ansha, M.G., Brayne, C., Choi, J.Y., Collado-Mateo, D., Dahodwala, N.: Global, regional, and national burden of Parkinson’s disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 17(11), 939–953 (2018) 11. Salama, M., Arias-Carrión, O.: Natural toxins implicated in the development of Parkinson’s disease. Ther. Adv. Neurol. Disord. 4(6), 361–373 (2011) 12. Hernandez, D.G., Reed, X., Singleton, A.B.: Genetics in Parkinson disease: mendelian versus non-Mendelian inheritance. J. Neurochem. 139, 59–74 (2016) 13. Rodriguez, M., Morales, I., Rodriguez-Sabate, C., Sanchez, A., Castro, R., Brito, J.M., Sabate, M.: The degeneration and replacement of dopamine cells in Parkinson’s disease: the role of aging. Front. Neuroanat. 7(8), 80 (2014) 14. Rietdijk, C.D., Perez-Pardo, P., Garssen, J., van Wezel, R.J., Kraneveld, A.D.: Exploring Braak’s hypothesis of Parkinson’s disease. Front. Neurol. 13(8), 37 (2017) 15. Lehtonen, Š, Sonninen, T.M., Wojciechowski, S., Goldsteins, G., Koistinaho, J.: Dysfunction of cellular proteostasis in Parkinson’s disease. Front. Neurosci. 10(13), 457 (2019) 16. Forno, L.S.: The Lewy body in Parkinson’s disease. Adv. Neurol. 1(45), 35–43 (1987) 17. Jankovic, J.: Parkinson’s disease: clinical features and diagnosis. J. Neurol. Neurosurg. Psychiatry. 79, 368–376 (2008). https://doi.org/10.1136/jnnp.2007.131045 18. Hoehn, M.M., Yahr, M.D.: Parkinsonism: onset, progression and mortality. Neurology 17, 427–442 (1967). https://doi.org/10.1212/WNL.17.5.427

22

C. S. Rawat and S. Pandey

19. Blochberger, A., Jones, S.: Clinical Focus-Parkinson’s disease-Clinical features and diagnosis. Clin. Pharm. 3(11), 361 (2011) 20. Hughes, A.J., Daniel, S.E., Blankson, S., et al.: A clinicopathologic study of 100 cases of Parkinson’s disease. Arch. Neurol. 50, 140–148 (1993) 21. Martin, W.E., Loewenson, R.B., Resch, J.A., et al.: Parkinson’s disease: clinical analysis of 100 patients. Neurology 23, 783–790 (1973) 22. Dirkx, M.F., Zach, H., Bloem, B.R., Hallett, M., Helmich, R.C.: The nature of postural tremor in Parkinson disease. Neurology 90(13), e1095–e1103 (2018) 23. Wenning, G.K., Ebersbach, G., Verny, M., Chaudhuri, K.R., Jellinger, K., McKee, A., Poewe, W., Litvan, I.: Progression of falls in postmortem-confirmed parkinsonian disorders. Mov. Disord. 14, 947–950 (1999) 24. Nutt, J.G., Horak, F.B., Bloem, B.R.: Milestones in gait, balance, and falling. Mov. Disord. 26, 1166–1174 (2011). https://doi.org/10.1002/mds.23588 25. Goetz, C.G., Tilley, B.C., Shaftman, S.R., Stebbins, G.T., Fahn, S., Martinez-Martin, P., Poewe, W., Sampaio, C., Stern, M.B., Dodel, R., Dubois, B.: Movement Disorder Society-sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results. Mov. Disord. Off. J. Mov. Disord. Soc. 23(15), 2129–2170 (2008) 26. Massano, J., Bhatia, K.P.: Clinical approach to Parkinson’s disease: features, diagnosis, and principles of management. Cold Spring Harbor Perspect. Med. 2(6), a008870 (2012) 27. Hausdorff, J.M., Rios, D., Edelberg, H.K.: Gait variability and fall risk in community-living older adults: a 1-year prospective study. Arch. Phys. Med. Rehabil. 82, 1050 (2001).https:// doi.org/10.1053/apmr.2001.24893 28. Kempster, P.A., Williams, D.R., Selikhova, M., Holton, J., Revesz, T., Lees, A.J.: Patterns of levodopa response in Parkinson’s disease: a clinico-pathological study. Brain 130(8), 2123– 2128 (2007) 29. Litvan, I., Goldman, J.G., Tröster, A.I., et al.: Diagnostic criteria for mild cognitive impairment in Parkinson’s disease: Movement Disorder Society Task Force guidelines. Mov. Disord. 27(3), 349–356 (2012) 30. Smith, C., Malek, N., Grosset, K., Cullen, B., Gentleman, S., Grosset, D.G.: Neuropathology of dementia in patients with Parkinson’s disease: a systematic review of autopsy studies. J. Neurol. Neurosurg. Psychiatry 90(11), 1234–1243 (2019) 31. Fang, C., Lv, L., Mao, S., Dong, H., Liu, B.: Cognition deficits in Parkinson’s disease: mechanisms and treatment. Parkinson’s Dis. 24, 2020 (2020) 32. Caspell-Garcia, C., Simuni, T., Tosun-Turgut, D., Wu, I.W., Zhang, Y., Nalls, M., Singleton, A., Shaw, L.A., Kang, J.H., Trojanowski, J.Q., Siderowf, A.: Multiple modality biomarker prediction of cognitive impairment in prospectively followed de novo Parkinson disease. PloS one 12(5), e0175674 (2017) 33. Santamaria, J., Tolosa, E., Valles, A.: Parkinsons disease with depression: a possible subgroup of idiopathic parkinsonism. Neurology 36, 1130–1133 (1986) 34. Ponsen, M.M., Stoffers, D., Booij, J., van Eck-Smit, B.L., Wolters, E., Berendse, H.W.: Idiopathic hyposmia as a preclinical sign of Parkinsons disease. Ann. Neurol. 56, 173–181 (2004) 35. Goetz, C.G., Lutge, W., Tanner, C.M.: Autonomic dysfunction in Parkinson’s disease. Neurology 36(1), 73 (1986) 36. Hawkes, C.H., Shephard, B.C., Daniel, S.E.: Olfactory dysfunction in Parkinsons disease. J. Neurol. Neurosurg. Psychiatry 62, 436–446 (1997) 37. Pearce, R.K., Hawkes, C.H., Daniel, S.E.: The anterior olfactory nucleus in Parkinsons disease. Mov. Disord. 10, 283–287 (1995) 38. Katzenschlager, R., Zijlmans, J., Evans, A., Watt, H., Lees, A.J.: Olfactory function distinguishes vascular parkinsonism from Parkinsons disease. J. Neurol. Neurosurg. Psychiatry 75, 1749–1752 (2004) 39. Khan, N.L., Katzenschlager, R., Watt, H., et al.: Olfaction differentiates parkin disease from early-onset parkinsonism and Parkinson disease. Neurology 62, 1224–1226 (2004)

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40. Rizzo, G., Copetti, M., Arcuti, S., Martino, D., Fontana, A., Logroscino, G.: Accuracy of clinical diagnosis of Parkinson disease: a systematic review and meta-analysis. Neurology 86(6), 566–576 (2016) 41. Postuma, R.B., Berg, D., Stern, M., et al.: MDS clinical diagnostic criteria for Parkinson’s disease. Mov. Disord. 30(12), 1591–1601 (2015) 42. Boeve, B.F., Dickson, D.W., Duda, J.E., et al.: Arguing against the proposed definition changes of PD. Mov. Disord. 31(11), 1619–1622 (2016) 43. Parati, E.A., Fetoni, V., Geminiani, G.C. et al: Response to L-DOPA in multiple system atrophy. Clin. Neuropharmacol. 16, 139–44 (1993) 44. Clarke, C.E., Davies, P.: Systematic review of acute levodopa and apomorphine challenge tests in the diagnosis of idiopathic Parkinson’s disease. J. Neurol. Neurosurg. Psychiatry 69, 590–594 (2000) 45. Mascalchi, M., Vella, A., Ceravolo, R.: Movement disorders: role of imaging in diagnosis. J. Magn. Reson. Imaging. 35, 239–256 (2012) 46. Brigo, F., Matinella, A., Erro, R., Tinazzi, M.: [123 I]FP-CIT SPECT (DaTSCAN) may be a useful tool to differentiate between Parkinson’s disease and vascular or drug-induced parkinsonism: a meta-analysis. Eur J Neurol. 21, 1369–1376 (2014) 47. Tolosa, E., Wenning, G., Poewe, W.: The diagnosis of Parkinson’s disease. Lancet Neurol. 5, 75 (2006) 48. Jain, S., Lo, S.E., Louis, E.D.: Common misdiagnosis of a common neurological disorder: how are we misdiagnosing essential tremor? Arch. Neurol. 63(8), 1100–1104 (2006) 49. Benito-León, J., Louis, E.D., Bermejo-Pareja, F.: Neurological Disorders in Central Spain Study Group. Risk of incident Parkinson’s disease and parkinsonism in essential tremor: a population-based study. J. Neurol. Neurosurg. Psychiatr. 80(4), 423–425 (2009) 50. McKeith, I.G., Boeve, B.F., Dickson, D.W., et al.: Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium. Neurology 89, 88 (2017) 51. Boeve, B.F., Dickson, D.W., Duda, J.E., et al.: Arguing against the proposed definition changes of PD. Mov. Disord. 31, 1619 (2016) 52. Miki, Y., Foti, S.C., Asi, Y.T., et al.: Improving diagnostic accuracy of multiple system atrophy: a clinicopathological study. Brain 142, 2813 (2019) 53. Shahnawaz, M., Mukherjee, A., Pritzkow, S., et al.: Discriminating α-synuclein strains in Parkinson’s disease and multiple system atrophy. Nature 578, 273 (2020) 54. Parmera, J.B., Rodriguez, R.D., Studart Neto, A., Nitrini, R., Brucki, S.M.: Corticobasal syndrome: A diagnostic conundrum. Dementia Neuropsychologia. 10(4), 267–275 (2016) 55. Morris, H.R., Wood, N.W., Lees, A.J.: Progressive supranuclear palsy (Steele-RichardsonOlszewski disease). Postgrad. Med. J. 75(888), 579–584 (1999) 56. Reilmann, R.: Parkinsonism in Huntington’s disease. InInternational review of neurobiology 2019 Jan 1 (Vol. 149, pp. 299–306). Academic Press. 57. Naro˙za´nska, E., Jasi´nska-Myga, B., Sitek, E.J., Robowski, P., Brockhuis, B., Lass, P., Dubaniewicz, M., Wieczorek, D., Baker, M., Rademakers, R., Wszolek, Z.K.: Frontotemporal dementia and parkinsonism linked to chromosome 17–the first Polish family. Eur. J. Neurol. 18(3), 535–537 (2011) 58. Park, H., Kim, H.J., Jeon, B.S.: Parkinsonism in spinocerebellar ataxia. Biomed. Res. Int. 1, 2015 (2015) 59. Greenland, J.C., Barker, R.A.: The differential diagnosis of Parkinson’s disease. Exon Publ. 21, 109–128 (2018) 60. Tarakad, A.: Clinical rating scales and quantitative assessments of movement disorders. Neurol Clin. 38, 231–54 (2020) (In Press) 61. Mehanna, R., Jankovic, J.: Young-Onset Parkinson’s disease: its unique features and their impact on quality of life. Parkinsonism Relat. Disord. 65, 39–48 (2019) 62. Verschuur, C.V.M., Suwijn, S.R., Boel, J.A., et al.: Randomized Delayed-Start Trial of Levodopa in Parkinson’s Disease. N. Engl. J. Med. 380, 315–324 (2019) 63. Schapira, A.H.V., Fox, S.H., Hauser, R.A., et al.: Assessment of safety and efficacy ofsafinamide as a levodopa adjunct in patients with Parkinson disease and motor fluctuations. JAMA Neurol. 74, 216–224 (2017)

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C. S. Rawat and S. Pandey

64. Latt, M.D., Lewis, S., Zekry, O., et al.: Factors to consider in the selection of dopamine agonists for older persons with Parkinson’s Disease. Drugs Aging 36, 189–202 (2019) 65. Ferreira, J.J., Lees, A.J., Poewe, W., et al.: Effectiveness of opicapone and switching from entacapone in fluctuating Parkinson disease. Neurology 90, e1849–e1857 (2018) 66. Seppi, K., Ray Chaudhuri, K., Coelho, M., et al.: Update on treatments for nonmotor symptoms of Parkinson’s disease-an evidence-based medicine review. Mov. Disord. 34, 180–198 (2019) 67. Goetz, C.G., Poewe, W., Rascol, O., Sampaio, C.: Evidence-based medical review update: pharmacological and surgical treatments of Parkinson’s disease: 2001 to 2004. Mov. Disord. Off. J. Mov. Disord. Soc. 20(5), 523–539 (2005) 68. Weaver, F.M., Follett, K., Stern, M., et al.: Bilateral deep brain stimulation vs best medical therapy for patients with advanced Parkinson disease: a randomized controlled trial. JAMA 301, 63–73 (2009) 69. Follett, K.A., Weaver, F.M., Stern, M., et al.: Pallidal versus subthalamic deep-brain stimulation for Parkinson’s disease. N. Engl. J. Med. 362, 2077–2091 (2010) 70. Schlesinger, I., Sinai, A., Zaaroor, M.: MRI-guided focused ultrasound in Parkinson’s disease: a review. Parkinson’s Dis. 30, 2017 (2017) 71. Barker, R.A., Parmar, M., Studer, L., et al.: Human trials of stem cell-derived dopamine neurons for Parkinson’s disease: dawn of a new era. Cell Stem Cell 21, 569–573 (2017) 72. Christine, C.W., Bankiewicz, K.S., Van Laar, A.D., et al.: Magnetic resonance imaging-guided phase 1 trial of putaminal AADC gene therapy for Parkinson’s disease. Ann. Neurol. 85, 704–714 (2019)

Parkinson’s Disease and Its Symptoms Dinesh Kant Kumar

Parkinson’s disease (PD) is the second most common neurodegenerative disorder, and its prevalence is expected to increase with an aging population. About 0.5% of the global population have PD, with over 2 million diagnosed cases and significant number of undiagnosed ones. The median age of PD is 65 years, and the incidence of PD increases to 1% for people over the age of 60. In Australia, there are approximately 80,000 people living with Parkinson’s disease. At present, we do not know the cause of Parkinson’s disease with number of diverse theories that are being investigated. In most people there is no family history of the disease [2, 3]. Parkinson’s disease has a complex set of multiple symptoms and there are large variations in the manifestation of the symptoms of different people. People with Parkinson’s disease do not have exactly the same symptoms, and the change in symptoms with the progression of the disease is not uniform either. The type, number, severity and progression of Parkinson’s disease symptoms vary greatly. This makes it difficult to have an objective measure of the disease. Some of the more common symptoms are: • • • • • • • • • •

resting tremor muscle stiffness and rigidity bradykinesia (slowness of movement) freezing of an action; sudden inability to move) inability to perform multitasking dysarthria or speech posture imbalance weakness shuffling gait micrographia (small handwriting) apathy

D. K. Kumar (B) School of Engineering, RMIT UNIVERISTY, Melbourne, Australia e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. P. Arjunan et al. (eds.), Techniques for Assessment of Parkinsonism for Diagnosis and Rehabilitation, Series in BioEngineering, https://doi.org/10.1007/978-981-16-3056-9_2

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• • • • • •

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fatigue sleep disturbance loss of sense of smell emotional depression, and apathy blood pressure fluctuation constipation.

In addition to the physical symptoms, people with Parkinson’s disease for some time may experience non-motor symptoms. One such symptom is that of hallucinations; when they see, hear, feel or smell things or events that are not present. There are also reported experiences of paranoia; when they feel that someone wants to harm them, or that someone is present in their vicinity but who aren’t actually there. Some patients also have been found to be delusional, when they are convinced about something false or of a feeling which isn’t based on facts. Parkinson’s disease has the degeneration of the dopamine-producing neurons, the dopaminergic, in the substantia nigra region of the brain. This effects the basal ganglia and is the cause of the motor pathology. There are other pathological changes in other neuronal populations as well, which could explain the development of various non-motor impairments in people with PD. Parkinson’s disease symptoms are complex, and the manifestation of these are highly variable and this can result in some of the patients being undiagnosed. To overcome this, most diagnoses are based on clinical detection of motor signs where the different symptoms are evaluated and the presence of two or more of tremor, rigidity, bradykinesia, or postural impairment is considered to be the indicators of the disease. Confirmatory evidence can be obtained from dopamine transporter scans. There are no diagnostic tests for Parkinson’s and the disease is currently recognized based on the manifestation of multiple symptoms. Blood tests are used to rule out other conditions with similar symptoms. Similarly, imaging modalities such as MRI scans are used to check for other conditions that may have similar symptoms. Advances in Positron emission tomography (PET) such as single photon Positron emission tomography (SPET), commercially referred to as DaTscan has been used to confirm the disease, but it is an expensive, and requires the injection of nuclear radiopharmaceutical substance. These tests are only available in large hospitals and not widely available across the world. Other medical imaging modalities lack sensitivity. Studies have shown that general physicians, who are not trained in neurology and motor disorders are often unable to identify the symptoms in the early stage. Many of the tests are done based on what is reported by the patient, and that often has strong demographic biases. It has also been shown that there can be significant variation between different neurologists and to reduce this, motor disorder society have developed a set of guidelines and a scale, referred to as Motor Disorder SocietyUnified Parkinson’s Disease Rating Scale (MDS-UPDRS). This method divides the symptoms to motor and non-motor groups, and these are then sub-divided to individual symptoms. The UPDRS scale was first started in 1987, and has since been revised multiple times to update it based on the research observations. The current MDS-UPDRS scale has 42 sub-headings, and the net value of the scale is from 0

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for people without any symptoms, to 132, indicating the maximum presence of all symptoms. Typical value of greater than 5 is an indicator of the early stages of the disease. The Movement Disorder Society is the peak international body that helps oversee the management of people with Parkinson’s disease. They have developed the Unified Parkinson’s Disease Rating Scale, and the current version is Part III (MDS-UPDRSIII) which is the standard tool for objective measurement of parkinsonian motor disability. UPDRS provides a method to consider the different symptoms simultaneously, thereby reducing the individual clinician bias. It also allows a method using which a patient could be assessed by different clinicians and facilitates a method for observing and recording the progress of the disease. However, while it reduces the clinician bias, and ensures that all the symptoms are observed, these are visual observations or patient’s statements, and thus lacks objectivity. Compounding factors such as large natural differences in voice of people, or their handwriting could make it difficult for a clinician to observe the changes in the early stages of the disease. It also makes it very difficult to observe the progression of the disease, or to monitor the effect of medication or therapy. Yet another shortcoming in the use of UPDRS for detecting or monitoring Parkinson’s disease is that the entire test can take around 1 h, and hence many clinics would prefer to undertake a concise version. This however reduces the objectivity of the test. UPDRS scoring requires clinical observations and has the potential limitations of subjectivity, clinician bias, and inter-rater variability. Consequently, there is some loss of sensitivity for early stage diagnostics, for monitoring disease progression, and for assessing the effectiveness of medication or other therapies. The requirement for regular clinical visits can be burdensome in some circumstances, and there is a need for an objective measure of PD symptoms that is suitable for telehealth applications. Thus, there is a need for biomarkers that can, with high reliability, recognize PD before overt motor signs appear. For this purpose, researchers have been proposing the use of sensors to measure the Parkinson’s symptoms.

1 Pathophysiology of Parkinson’s Disease The fundamental pathological feature of Parkinson’s disease was obtained by the analysis of the brain specimens, the majority of which are from the Parkinson’s disease brain bank, based in United Kingdom. It has been found that progressive loss of the ascending dopaminergic projection in the basal ganglia is a fundamental pathological feature of Parkinson’s disease [1]. Animal models and human studies have identified functional territories in the basal ganglia that are spatially segregated and what appears to be most affected are those that are responsible for the control of goal-directed and habitual actions. In patients with Parkinson’s disease, the loss of dopamine has been found to be largely in the posterior putamen region. This is the region of the basal ganglia that is associated with the habitual behaviour, and which results in the energy economy when performing actions. The loss of the ability

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to perform habitual activity results in the inefficient actions by the patients. This indicates that people with Parkinson’s disease may be forced into a reliance on the goal-directed action control, which suggests why people with Parkinson’s disease are unable to simultaneously perform multiple tasks. Thus, they need their attention towards what are commonly considered to be habitual tasks such as walking. Many of their symptoms may reflect a loss of normal automatic control. Human speech, handwriting and level surface walking are overtrained and habitual responses. Speech and writing require fine-motor control, cognitive abilities, auditory feedback, and muscle strength. Walking, on level surface is also an overtrained response, though does not require fine motor control.

2 Detecting and Monitoring Parkinson’s Disease To improve the diagnosis and subsequent monitoring of Parkinson’s disease patients, number of computerized methods have been proposed. Some of the methods that show significant differences between healthy people and those with Parkinson’s disease and have the potential for being used for computerized and objective analysis are: • • • • •

Image analysis of handwriting Digital assessment of Handwriting and drawing Speech analysis Gait analysis Facial image analysis. A brief of these are provided below:

Handwriting: Parkinson disease is associated with movement disorder symptoms; tremor, rigidity, bradykinesia and postural instability. The manifestation of bradykinesia and rigidity are often in the early stages of the disease. These is a noticeable effect on the handwriting and sketching abilities of patients and micrographia has been used for early stage diagnosis of PD (Zham et al., 2019). While handwriting of a person is influenced by several factors such as language proficiency and education, sketching of a shape such as the spiral has been found to be non-invasive and independent measure. Parkinsonian gait: Parkinson’s disease patients are unable to optimise the energy for the actions [1]. They have resting tremor, slowness of movement and rigidity, and may have difficulty in initialisation of movements. The typical Parkinsonian gait features are small and shuffling steps where they appear to be dragging their feet. They also have highly reduced clearance from the floor, and this can be a major risk of falls. The changes to the gait can be continuous and it has been found that people with Parkinson’s disease have higher variability in their gait parameters [5]. However,

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there can also be episodic changes during walking. Freezing of gait (FOG) is an example of the episodic changes, and this can come without any warning, and suddenly. This makes it very difficult to predict. Poor rhythm, and increased variability are some of the other signs of the Parkinsonian gait. The difficulty in gait assessment of people with Parkinson’s disease is that the objective measures require purpose built gait laboratories which are expensive and located in large medical centres. More recently, there has been the development of wearable gait analysis using devices such as inertial movement units (IMU). IMU come with many challenges such as calibration, the direction of the axis, and relationship with gravity and research is being conducted to overcome many of these limitations. Dysarthria: The use of gait analysis [5] and handwriting have been proposed [4] as biomarkers for Parkinson’s disease and for early detection of the disease. However, these require specialized equipment and may not be suitable for applications such as telehealth or for monitoring patients in their own homes. One of the early symptoms of PD is the change to the voice of the patient, and this can precede other motor features. Voice testing has been proposed for early diagnosis of the disease, or to monitor its progression [6]. Human speech is an overtrained and habitual response that requires fine-motor control, cognitive abilities, auditory feedback, and muscle strength. Parkinsonian dysarthria can be characterized by reduced vocal tract loudness, reduced speech prosody, imprecise articulation, significantly narrower pitch range, longer pauses, vocal tremor, breathy vocal quality, harsh voice quality, and disfluency. Eye-gaze: Parkinson’s disease patients often have abnormal eye movement, and reduced ability to manage their gaze. They may exhibit saccadic eye movement, or slowness to follow a visual cue. Generally this is observed by clinicians by testing the ability of the patient to follow a small light, but research is being conducted to develop computerised assessment of the eye-gaze of the person. Management of PD: Parkinson’s disease is a neuro-degenerative disease. The disease is assessed based on the combination of the symptoms, often labelled from 0 to 4 which is largely based on their functionality. In the early stages of the disease, patients are able to perform most of their functions and are largely independent, which however, with the progression of the disease deteriorates and in the later stage, patients require regular care. There is currently no cure of the disease, nor a method to prevent its progression. However, there are number of effective methods that can be used to manage the symptoms of the disease. Appropriate management can significantly improve the quality of life of the patients and perhaps slow the progression of the disease. The most common and effective treatment is levodopa medication that provides synthetic dopamine to the brain. The dosage of the drug and the make-up is dependent

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on the condition of the patient and often requires repetitive visits by the patient to the neurologists. The drug has a combination of carbidopa which ensures that levodopa gets focused to the brain. Other methods that have been shown to be effective are. • • • •

Physiotherapy. Speech training. Yoga Deep brain stimulation

Early detection of Parkinson’s disease has been found to have significantly better prognosis. The treatment plan for a patient is based on multidisciplinary team that needs to assess the patient’s symptoms, needs and other situations.

References 1. Redgrave, P., Rodriguez, M., Smith, Y., Rodriguez-Oroz, M.C., Lehericy, S., Bergman, H., Agid, Y., DeLong, M.R., Obeso, J.A.: Goal-directed and habitual control in the basal ganglia: implications for Parkinson’s disease. Nat. Rev. Neurosci. 11(11), 760–772 (2010) 2. de Lau, L.M., Breteler, M.M.: Epidemiology of Parkinson’s disease. Lancet Neurol. 5(6), 525– 535 (2006) 3. Poewe, W., Seppi, K., Tanner, C.M., Halliday, G.M., Brundin, P., Volkmann, J., Schrag, A.-E., Lang, A.E.: Parkinson disease. Nat. Rev. Dis. Primers. 3(1), 1–21 (2017) 4. Zham, P., et al.: Effect of levodopa on handwriting tasks of different complexity in Parkinson’s disease: a kinematic study. J. Neurol. 266, 1376–1382 (2019) 5. Keloth, S.M., Arjunan, S.P., Kumar, D.K.: “Variance of the gait parameters and fraction of double-support interval for determining the severity of Parkinson’s disease”. Appl. Sci. 10(557), (2020) 6. Pah, N.D., Motin, M.A., Kempster, P., Kumar, D.K.: Detecting effect of levodopa in Parkinson?s disease patients using different sustained phonemes”. IEEE J. Transl. Eng. Health and Med. (2021) https://doi.org/10.1109/JTEHM.2021.3066800

Epidemiology of Parkinson’s Disease—Current Understanding of Causation and Risk Factors Rajan R. Patil

Abstract Parkinson’s disease is a global concern which appears to increase with advancing age. The disease is 1.5–2 times more common in males than females across the world. Parkinson’s diseases appears to be more common in western countries than in Asian countries. Except for few methodological nuances, the differences in prevalence could be reasoned and attributed to environmental and genetic risk factors. Even in same countries, differences in Incidence have been observed in different ethnic populations pointing to a lower occurrence of PD among population from Asians and Africans as compared to Hispanic Caucasians, Latinos. Such finding points to genetic influence on the risk of development of PD. At the same time the higher incidence of PD among Japanese and African in Americas when compared to similar populations in their native country, suggest an environmental role in PD. In the past two decades, scientists have identified several environmental factors associated. Examples include inverse associations with smoking, coffee drinking, vigorous exercise, ibuprofen use and plasma urate, as well as positive associations with overall pesticide exposure [use of specific pesticides and traumatic brain injury. However there discretion of caution is required in interpreting epidemiological observations, reverse causal relation could be potential explanation—that there could be changes lifestyle and behavior preceding PD development prior to clinical diagnosis. Consistently smoking has shown protective effect against Parkinson’s disease, decrease in prevalence of smoking is expected lead to an increase in the diseases burden of Parkinson’s disease across the world in the future. Some studies in the USA have indicated that declining smoking rates in the country might increase Parkinson’s disease by 10% in 2040.

R. R. Patil (B) Epidemiologist, SRM Institute of Science and Technology, Chennai, Tamilnadu, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. P. Arjunan et al. (eds.), Techniques for Assessment of Parkinsonism for Diagnosis and Rehabilitation, Series in BioEngineering, https://doi.org/10.1007/978-981-16-3056-9_3

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1 Background & Global Disease Burden Parkinson’s disease ranks second most common neurodegenerative disorder, which was described first by James Parkinson in 1817, in his classic “Essay on the Shaking Palsy” in which Parkinson’s disease (PD) was described as a neurodegenerative disorder of the central nervous system [1]. The classical symptoms of PD, such as rigidity, tremor, bradykinesia and impaired balance are witnessed after damage to 60–80% or more of the dopamine producing cells in the brain [2]. The number of individuals with Parkinson’s disease, Over the past generation, across the world has crossed more than 6 million. Parkinson’s disease was the fastest growing disorder of all the neurological disorders included in GBD 201. Since 1990, the crude prevalence rates was seen to increase by 74% globally. The reasons for increase in disease burden of Parkinson’s could be attributed to environmental factors following growing industrialization across the globe [3]. Generally, good health is positively correlated with better levels of socioeconomic status.in contrast the opposite holds true for Parkinson’s disease. PD is observed to be as the fastest growing neurological disease, with rapid growth of aging populations across the world, both in terms of morbidity and mortality [2–5]. Over the past a few decades an increasing trend has been observed in PD incidence [4, 6] this clearly points to a role for environmental factors. Between 1990 and 2016 the Increase in prevalence of Parkinson’s disease accross the globe could not be explained purely by an increasing proportion of older people. The age-standardized prevalence rates of Parkinsons disease had increased by 21·7% since late 1990s as compared with an increase of Parkinson’s disease burden by 74·3% between 1990 to 2016. Therefore, the prevalence of Parkinson’s disease is all set to increase [13, 14] across the world as industrialization and ageing population are set to increase globally and falling rates of smoking in many parts of world. Epidemiological studies suggest a strong role of environmental factors in the disease etiology. It was observed that the prevalence rates for African Americans as compared to native Africans in Nigeria even when studied applying the same methods [7]. Similar observations were made witnessing higher incidence rate and prevalence of disease compared to the Asian studies [8].

1.1 Prevalence Wide global variations is seen in prevalence of Parkinsons disease across the world. Consistently higher prevalence was seen in America and Europe and also lower prevalence was seen in Africa and Asia. There is also wide variation in crude prevalence rate of PD ranging from 15 per 100,000 to 12,500 per 100,000 in different regions. Similar variation is seen the incidence of PD from 15 per 100,000 to 328 per 100,000, with Asian countries reporting lesser disease burden [9].

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European studies that were included in meta-analysis from 39 studies presented a prevalence rate of 108–257/100,000. 7 A prevalence of 101–439.4/100,000 and 61.4–141.1/100,000 was observed in a review of 11 studies conducted between 1990 to 2005 in Europe and America 15 [10]. The prevalence rate was 51.3–176.9/100,000 In a meta-analysis of 19 Asian studies from 1965 to 2008. However A recent meta-analysis of 15 Asian studies on Chinese population reported a prevalence of 16–440.3/100,000.8 [11]. In Africa, many studies reported lower prevalence of PD [12, 13]. The lower prevalence in Africa is explained by demographic Pyramid structure (with higher younger age group and shorter life expectancy [14]. The Incidence rates for Parkinsons disease In India 70 per 100,000 population, is lowest in the world. The exception being the Parsi community 16 in Mumbai with incidence of PD 328 per 100,000 population is world’s highest figures. [15] The north Indian population reported prevalence of PD, 67.71/105. The prevalence figures available from rural Kashmir 14.1, Bangalore of South India 27 and rural Bengal of Eastern India [16].

1.2 Age Trends It is estimated that about 0.3% of the global population which is about 10 million people in the world and about 1% of people in age group above 60 years are affected with PD.4 However, increasing incidence rates have been reported in several studies in population in the age group up to 85 years It is estimated that the lifetime risk of PD to be at 2% for Males & about 1.3% in females. Early and young onset of PD that is onset of PD before 50 years of ages is very rare, with about only 4% of patients aged any time before first five decades of their lives exhibiting PD clinical Features. In older age group it is relative common, with Approximately 1–2% of population above 65 years are affected by PD. A median prevalence of 950/100,000 was recorded in age groups over 65 years. The proportion of people affected by PD increases to 3 to 5% in population above 85 years Some countries documented a decline in prevalence in the oldest age group above 80 years or more) which could be explained by coexisting comorbidities and statistical reasons due to small numbers in higher old age groups [17–19].

1.3 Incidence Majority of incidence studies were reported from Europe, recording overall incidence rates in the range of 9 and 22 per 100,000 person-years [20]. with overall rates between 410 and 529 per 100,000 person-years [21]. Studies from Asia reported overall incidence rates in the range of 1.5 and 17 per 100,000 person-years [22, 23].

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There is contradictory time trends of PD across the globe. While Europe has recorded decreasing PD incidence over the past 20 years, [24] at the same time America reported increasing trend over past 30 years [25]. Studies from Japan have suggested that the PD incidence continue to be stable over past two and half decades [26]. In all, the variations in Incidence trends in different nations can also be explained by changes in exposures to environmental risk factors. The common example be being reduction in PD incidence that is hypothesized to corresponding increase in smoking, coffee intake, and increase in the use of certain statins.

2 Causes of Parkinsons Disease 2.1 Brak Hyptothesis A new hypothesis called Brak hypothesis adds an entirely new perspective to Parkinson’s a etiology by incriminating the olfactory pathway and digestive path way as the central to origins of PD evolution [27]. Incidentally, these are the two precise human anatomic sites that are directly exposed to the environment initiating the inflammation. The environmental toxicants such as pesticides, viruses, air pollutants or dietary contaminants generally gain entry into the human body via the mouth and/or through nose, and thus might trigger PD pathogenesis at the gut enteric nerves or at the olfactory bulb which over a time span the pathogenesis may progress to the brain via the vagus nerves or the olfactory bulbs, that may eventually lead to dopaminergic neuron deaths in the brain [28].

2.2 Gut Microbiome The Braak hypothesis further puts emphasis on microbiome in fore front of PD etiological research [29, 30] with gut enteric nerves as an originating site of PD pathology [27]. The gut microbiome play vital role in the uptake of nutrients, environmental toxicants or medications and thus could affect different aspects of neurological functioning and related behaviors [29]. In support of Braak hyptothesis, constipation is considered as one of the most prominent prodromal symptoms of PD, other symptoms related to olfactory or REM sleep behavior disorder (RBD) [31, 32] which could have developed in pre pathogenesis stage of 10-20 years preceding to PD diagnosis [33, 34]. May epidemiological studies have compared the characteristics of gut microbiomes among PD patients and in those of controls [35, 36].

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2.3 A Life-Long and Exposure Approach to PD Etiological Research Experimental studies have demonstrated that prenatal or early developmental exposures to select pesticides [37, 38] could initiate dopaminergic neuron degeneration much later in life. Life course approach emphasize on the importance of preventing repeated exposures during vulnerable periods early life.

2.4 Risk Factors for Parkinson’s Disease Current understanding suggest a combination of age, genetic and environmental factors to be involved in PD etiology. A meta-analysis of 17 incidence studies found that Male to FemaLE ratio for incident PD was 1.46. Female steroid hormones are hypothesized to be Neuroprotective. Other explanation could be gender differences in probability of exposure to environmental risk factors. Family members of PD patients are at 3 to 4-fold increased risk in developing the disease in contrast to the general population. A genetic a etiology of PD has been strongly hypothesized Due to this observation of aggregation of PD cases within families. A first-degree relative with PD cases has been demonstrated to be a strong risk factor for developing PD which is supported by A meta-analysis of studies with the Relative Risk of 2.9. The study also examined different types of relationships and found PD in a sibling was strongly associated with a higher risk as compared to PD in a parent or child supporting indication for recessive genetic or shared environmental [39–41].

3 Environmental Exposures 3.1 Pesticides Epidemiological studies have shown Increased risk for development of PD due exposure to Pesticides in case control studies [42–44]. The same is validated through longitudinal studies e.g. the largest Cohort in the US increased risk of PD to pesticide exposure with a RR of 1.7. The Honolulu-Asia Aging study in Hawaii, also reported an increased risk of PD with increasing years of work on a plantation with pesticide spray, with a RR of 1.9. However it is also important to recognize that few case control studies have reported no associations [45, 46] which are contested on grounds of bias and confounding.

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3.2 Antioxidants It is also hypothesized that oxidative stress may play role in the pathogenesis of PD development. However evidence on role of antioxidants have been conflicting. The role of lower serum levels of vitamins E and A have been studied [47] but most early case- control studies studying serum levels of vitamin C, vitamin E, or vitamin A found no associations [48, 49]. Vitamin D intake was not associated to PD risk. A German case- control study looked into folic acid and was associated with lower risk of PD (OR 0.51] [50].

3.3 Uprate (Uric Acid) Oxidative stress is thought to be a potential mechanism that underlies selective dopaminergic cell death in PD. 58 [51] Urate is a potent antioxidant and could be neuroprotective. A meta-analysis of 3 US cohorts obtained a pooled RR of 0.63. A meta-analysis 13 case-control studies found Serum uric acid was found lower in PD patients compared to controls 63 [52].

4 Lifestyle Factors 4.1 Smoking A meta-analysis analyzed 8 cohort and sixty one case- control studies conducted over five decades. A dose-response relationship was found with the pooled RR of PD found to be 0.59 [53]. The protective effect from smoking and PD was first reported from mortality studies in smokers, with standardized mortality ratio estimates ranging from 0.23 to 0.76 [54, 55]. However reliance on death certificates for PD diagnosis, had its own biases. Case-control studies with sample size ranging between 50-500 cases majority of them reported an inverse association between smoking and PD [56, 57].

4.2 Prospective Studies on smoking from 9 longitudinal studies were similar and all reported an protective effect of smoking with PD. In the 5 large cohort studies, that compared current smokers versus never smokers the RRs for PD in ranged between 0.27 and 0.56; and it was between 0.50 and 0.78 in past smokers versus never smokers [58, 59].

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4.3 Meta-Analyses Involving 44 case-control and 4 cohort studies reported a pooled RR of 0.59 [436& 438] [60, 61]. The protective association between smoking and PD was stronger in cohort studies [60] than in case-control studies, particularly when compared with never smokers.

4.4 Stress Oxidative stress is a risk factor of PD. Acute stress is known to increase in dopamine release while chronic stress such as emotionally stressful life, contributes in decrease in dopamine content. Depression is a major comorbidity in all stages of patients with PD.5 strongly associated increased PD severity and decline in motor function [62].

4.5 Alcohol Case–control Studies Epidemiological research have indicated that alcohol intake is associated with decreased risk of Parkinson’s disease. Alcohol is hypothesized to lowers PD risk through elevated urate levels. One meta-analysis and one review paper [63, 64], of 32 studies consistently reported ORs below unity, however the association was non- significant in most studies [65, 66]. These authors concluded that light to moderate drinkers may have lower risk of PD, while heavy drinkers have less pronounced effect.

4.6 Physical Activity Higher level of physical activities was found to have protective effect for PD development. A meta-analysis six cohort studies showed higher physical activity reduced risk of PD with RR 0.66 [67]. The findings suggested that higher physical activity during high school and college, or at in middle ages 35–39 years, [68] was beneficial with a reduced risk of subsequent old age development of PD. In summary, studies on physical activity in relation to PD, suggest that vigorous physical activity may lower the risk. However, the association is probably weak [69].

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4.7 Body Mass Index (BMI) Epidemiologic evidence does not support a strong relationship between adiposity and PD risk and often conflicting. The first meta-analysis of 10 longitudinal studies and did not find any increased risk for PD. Another meta-analysis 12 case–control studies had a significantly lower BMI compared to controls: OR 1.73 [70].

4.8 Diet There are few studies that have investigated Role of Dietary Cholesterol in risk of development of PD however they yielded contradictory results. One study from US showed decrease in the risk of PD, [71] while another study reported increase in the risk for PD. A study from Singapore showed protection was 0.53 while a study from Japan showed increased risk of PD [72]. In an Indian study, it was found vegetarian diet showed no risk of PD [73].

4.9 Coffee and Tea The epidemiologic evidence also suggest protective effect between coffee drinking and PD is very strong [60]. The experimental evidence suggests that the Caffeine acts as an adenosine receptor antagonist may exert a neuro-protective effect [74]. A meta-analysis of 26 studies yielded a relative risk of 0.75 [75]. A linear relationship was found between overall PD risk and caffeine consumption with an RR 0.83%. Another meta-analysis of 8 case-control studies and five cohort studies yield a pooled RR was 0.69 for coffee drinkers versus non-coffee drinkers [60].

4.10 Tea A recent meta-analysis which evaluated eight studies for tea consumption [76] obtained a summary relative risk of 0.63 across world 45 [74].

4.11 Dairy Products Available evidence supports a role for dairy products increases risk of PD, especially in men, however the underlying mechanism is unknown.

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A meta-analysis investigating consumption of dairy products and PD evaluated five prospective studies. 55 The pooled risk of PD was in the range of 1.40 and 1.66 [77]. A meta-analysis of all longitudinal studies on dairy products yielded a combined RR of 1.6 and RR 1.8 in male, and RR 1.3 in females [78].

4.12 Macronutrients The role of macronutrients total carbohydrates along with fat and protein [50, 79] in PD, in especially fat, are conflicting. Two studies reported comparatively higher energy intake among cases as compared to controls while other did not find such association. Especially carbohydrates and protein has not been associated with PD [80, 50].

4.13 Diabetes Epidemiological studies demonstrated Positive association [81] reporting increased PD risk among patients with diabetes however there was wide variation. Thus, evidence regarding association between diabetes and PD remains inconclusive. Two large longitudinal studies reported increased PD risk associated with diabetes [82], at the same time another reported lower risk of diabetes among PD cases. Yet another study demonstrated that diabetes was associated with deterioration in rigidity and gait however not associated with bradykinesia or tremor.

4.14 Minerals Dietary iron has been studied for a potential role in PD largely because of its role in oxidative stress however no association was found between dietary iron intake and PD [83, 84].

4.15 Well Water Investigation into association between Well water use and risk of development of PD have yielded conflicting results. Among the 34 case-control about nine of the them reported an increased risk [45, 85]. Most of these studies reported ORs ranging between 1.7 and 2.8. But a study by Johnson et al. reported a decrease in the risk of PD [86].

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However equally over whelming number of studies showed no association [66, 87].

4.16 Metals There is no convincing epidemiologic evidence that occupational exposure to specific heavy metals leads to PD. Only one study reported an increased risk (OR11.84, whereas the others found no association [88, 89].

4.17 Mild Traumatic Brain Injury Role of mild traumatic brain injury in causation of PD has been subject of one meta-analysis.in which 14 case-control studies and 1 cohort study which provided. A summary risk of OR 1 [4] traditional boxers from Thai were studied in boxers aged 50 years which correlated increased number of professional bouts increased risk, this observations suggesting that repetitive head injury is a potential risk factor for the development of PD [90].

4.18 Hepatitis C A UK study found an association between the Hepatitis B and C virus infection and PD [91]. Experimental studies have found The Hepatitis C virus induce neuronal toxicity in mouse models.

5 Organic Solvents 5.1 Positive Increased risk of PD was found with Occupational exposure to organic solvents in two case-control studies [92]. However Six other case-control studies, including European multicentre study [89] found no association between organic solvents and PD [46].

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5.2 Magnetic Fields Role of Magnetic field has been investigated in a study of workers in electric utility companies in North Carolina and also a Danish study [93] and found no association [94].

5.3 REM Sleep Behavior Disorder REM (rapid eye movement) sleep behavior disorder (RBD) is a condition characterized by enactment of dreams, most common in men, A study on RBD, where cases with PD were diagnosed about 13 years after the onset of RBD symptoms [95].

5.4 Inflammation There is limited evidence suggestive of protective effect of NSAIDs in PD. use of non-aspirin NSAIDs is associated with Lower risk of PD with over all risk RR 0.55, [96]. There was no dose-response relationship was observed between nonaspirin NSAIDs and PD [96]. In a meta-analysis of two longitudinal studies and 5 case-control studies over all RR for non-aspirin NSAID use was 0.85. However For aspirin and acetaminophen, no such associations. Similar association for ibuprofen RR 0.75 was found, but no association for aspirin [97].

5.5 Mental Illness Examining the relationship between different diseases may provide clues to etiology, as diseases are likely to share genetic or environmental risk factors. A shared etiologic constituent in PD and mental illness has been hypothesized because psychiatric diseases, as depression and anxiety disorders, are common in PD patients. Three casecontrol studies investigated increased PD risk for association to previous depression episodes and found ORs between 1.54 and 3.01 [56, 98].

5.6 Vascular Diseases There is no credible epidemiologic evidence that blood pressure, heart disease, or stroke have any etiological role in development of PD. A longitudinal study Nurses

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Follow-up Study [99]. Found a decline in systolic blood pressure following diagnosis of PD [634].

5.7 Estrogen A protective role of estrogen in PD has been hypothesized mainly based on the lower incidence and prevalence of PD in women than in men, but also on experimental evidence that estrogen has neuroprotective and antioxidant effects on dopaminergic neurons. Additionally, postmenopausal estrogen treatment has been reported to retard PD progression [100]. A small case-control study reported increased risk of PD associated with hysterectomy (OR 3.36, 95%) [101]. Increased PD risk was also reported (OR 2.19, 95% CI 1.22-3.91) [102].

5.8 Mortality These studies quite consistently reported about a two-fold increased mortality rate among patients with PD than in the general population, although the range was between 1.3 and 5.7 For PD patients diagnosed between age 25 and 39 mean life expectancy was 38 years versus 49 for the general population; for PD patents with onset between age 40 and 64, life expectancy was 21 years versus 31 years. Most mortality studies have been based on prevalent cases, and few adjusted for disease duration at the time of enrolment. Estimation of mortality from date of enrolment rather than date of diagnosis may overestimate relative mortality, as Other predictors of excess mortality in PD are disease severity [103] dementia [104, 105] and, as indicated, early age at enrolment [103]. Depression is a most common non-motor symptom, which is associated with PD. Poewe (2007) reported that 30–40% of PD patients have been associated with depressive disorders. Thus, Indian study have explored the depressive status in patients with PD among the north India setup. In present study, 40% subjects with minor depression and 15% subjects with major depression have been observed [106].

References 1. de Lau L.M., Breteler M.M.: Epidemiology of Parkinson’s disease. Lancet Neurol. 5, 525–535 (2006) 2. Gupta, B.M., Bala, A.: Parkinson’s disease in India: an analysis of publications output during 2002–2011. Int. J. Nutr. Pharmacol. Neurol. Dis. 3, 254–262 (2013)

Epidemiology of Parkinson’s Disease …

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3. GBD 2015 Neurological Disorders Collaborator Group.: Global, regional, and national burden of neurological disorders during 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet Neurol. 16, 877–897 (2017) 4. GBD 2016 Parkinson’s Disease Collaborators.: Global, regional, and national burden of Parkinson’s disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 17, 939–953 (2018) 5. Darweesh, S.K.L., Raphael, K.G., Brundin, P., Matthews, H., Wyse, K., Chen, H., Bloem, B.R.: Parkinson Matters. J. Parkinsons Dis. 8, 495–498 (2018) 6. Savica, R., Grossardt, B.R., Bower, J.H., Ahlskog, J.E., Rocca, W.A.: Time trends in the incidence of Parkinson’s disease: A 30-year study. JAMA Neurol. 32, 227–234 (2017) 7. Schoenberg, B.S., Osuntokun, B.O., Adeuja, A.O., et al.: Comparison of the prevalence of Parkinson’s disease in black populations in the rural United States and in rural Nigeria: door-to-door community studies. Neurology 38, 645–646 (1988) 8. Morens, D.M., Davis, J.W., Grandinetti, A., Ross, G.W., Popper, J.S., White, L.R.: Epidemiologic observations on Parkinson’s disease: incidence and mortality in a prospective study of middle-aged men. Neurology 46, 1044–1050 (1996) 9. Shin-Yuan Chen 1,2,3*, Sheng-Tzung Tsai 1,2,3.: The epidemiology of Parkinson’s disease. TZU CHI MED J 22(2) (2010, June) 10. Hirtz, D., Thurman, D.J., Gwinn-Hardy, K., Mohamed, M., Chaudhuri, A.R., Zalutsky, R.: How common are the “common” neurologic disorders? Neurology 68, 326–337 (2007) 11. Zou, Y.M., Liu, J., Tian, Z.Y., Lu, D., Zhou, Y.Y.: Systematic review of the prevalence and incidence of Parkinson’s disease in the people’s republic of China. Neuropsychiatr. Dis. Treat 11, 1467–1472 (2015) 12. Schoenberg, B.S., Osuntokun, B.O., Adeuja, A.O., Bademosi, O., Nottidge, V., Anderson, D.W., et al.: Comparison of the prevalence of Parkinson’s disease in black populations in the rural United States and in rural Nigeria: door-to-door community studies. Neurology 38, 645–646 (1988) 13. Mayeux, R., Marder, K., Cote, L.J., Denaro, J., Hemenegildo, N., Mejia, H., et al.: The frequency of idiopathic Parkinson’s disease by age, ethnic group, and sex in northern Manhattan, 1988–1993. Am. J. Epidemiol. 142, 820–827 (1995) 14. McInerney-Leo, A., Gwinn-Hardy, K., Nussbaum, R.L.: Prevalence of Parkinson’s disease in populations of African ancestry: a review. J. Natl. Med. Assoc. 96, 974–979 (2004) 15. Gupta, B.M., Bala, A.: Parkinson’s disease in India: an analysis of publications output during 2002–2011. Int. J. Nutr. Pharmacol. Neurol. Dis. 3, 245–262 (2013) 16. Muthane, U.B., Ragothaman, M., Gururaj, G.: Epidemiology of Parkinson’s disease and movement disorders in India: problems and possibilities. JAPI. 55, 719–724 (2007) 17. Driver, J.A., Logroscino, G., Gaziano, J.M., Kurth, T.: Incidence and remaining lifetime risk of Parkinson disease in advanced age. Neurology 72, 432–438 (2009) 18. Taylor, K.S., Counsell, C.E., Harris, C.E., Gordon, J.C., Smith, W.C.: Pilot study of the incidence and prognosis of degenerative Parkinsonian disorders in Aberdeen, United Kingdom: methods and preliminary results. Mov. Disord. 21, 976–982 (2006) 19. Van Den Eeden, S.K., Tanner, C.M., Bernstein, A.L., Fross, R.D., Leimpeter, A., Bloch, D.A., et al.: Incidence of Parkinson’s disease: variation by age, gender, and race/ethnicity. Am. J. Epidemiol. 157, 1015–1022 (2003) 20. Benito-Leon, J., Bermejo-Pareja, F., Morales-Gonzalez, J.M., Porta-Etessam, J., Trincado, R., Vega, S., et al.: Incidence of Parkinson disease and parkinsonism in three elderly populations of central Spain. Neurology 62, 734–741 (2004) 21. de Lau, L.M., Giesbergen, P.C., de Rijk, M.C., Hofman, A., Koudstaal, P.J., Breteler, M.M.: Incidence of parkinsonism and Parkinson disease in a general population: the Rotterdam Study. Neurology 63, 1240–1244 (2004) 22. Chen, R.C., Chang, S.F., Su, C.L., Chen, T.H., Yen, M.F., Wu, H.M., et al.: Prevalence, incidence, and mortality of PD: a door-to-door survey in Ilan county. Taiwan. Neurology. 57, 1679–1688 (2001)

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R. R. Patil

23. Morioka, S., Sakata, K., Yoshida, S., Nakai, E., Shiba, M., Yoshimura, N., et al.: Incidence of Parkinson disease in Wakayama, Japan. J. Epidemiol. 12, 403–407 (2002) 24. Darweesh, S.K., Koudstaal, P.J., Stricker, B.H., Hofman, A., Ikram, M.A.: Trends in the incidence of Parkinson disease in the general population: the Rotterdam Study. Am. J. Epidemiol. 183, 1018–1026 (2016) 25. Savica, R., Grossardt, B.R., Bower, J.H., Ahlskog, J.E., Rocca, W.A.: Time trends in the incidence of Parkinson disease. JAMA Neurol. 73, 981–989 (2016) 26. Yamawaki, M., Kusumi, M., Kowa, H., Nakashima, K.: Changes in prevalence and incidence of Parkinson’s disease in Japan during a quarter of a century. Neuroepidemiology 32, 263–269 (2009) 27. Braak, H., Bohl, J.R., M¨uller, C.M., R¨ub, U., de Vos, R.A.I., Tredici, K.D.: Stanley Fahn lecture 2005: the staging procedure for the inclusion body pathology associated with sporadic Parkinson’s disease reconsidered. Mov. Disord. 21, 2042–2051 (2006) 28. Reichmann, H.: View point: etiology in Parkinson’s disease. Dualhitor spreading intoxication. J. Neurol. Sci. 310, 9–11 (2011) 29. Tremlett, H., Bauer, K.C., Appel-Cresswell, S., Finlay, B.B., Waubant, E.: The gut microbiome in human neurological disease: a review. Ann. Neurol. 81, 369–382 (2017) 30. Parashar, A., Udayabanu, M.: Gutmicrobiota: implications in Parkinson’s disease. Parkinsonism Relat. Disord. 38, 1–7 (2017) 31. Postuma, R.B., Gagnon, J.F., Vendette, M., Fantini, M.L., Massicotte-Marquez, J., Montplaisir, J.: Quantifyingthe risk of neurodegenerative disease in idiopathic REM sleep behavior disorder. Neurology 72, 1296–1300 (2009) 32. Postuma, R.B., Iranzo, A., Hogl, B., Arnulf, I., Ferini-Strambi, L., Manni, R., Miyamoto, T., Oertel, W., Dauvilliers, Y., Ju, Y.E., Puligheddu, M., Sonka, K., Pelletier, A., Santamaria, J., Frauscher, B., Leu-Semenescu, S., Zucconi, M., Terzaghi, M., Miyamoto, M., Unger, M.M., Carlander, B., Fantini, M.L., Montplaisir, J.Y.: Risk factors for neurodegeneration in idiopathic rapid eye movement sleep behavior disorder: a multicenter study. Ann. Neurol. 77, 830–839 (2015) 33. Gao, X., Chen, H., Schwarzschild, M.A., Ascherio, A.: A prospective study of bowel movement frequency and risk of Parkinson’s disease. Am. J. Epidemiol. 174, 546–551 (2011) 34. Savica, R., Carlin, J.M., Grossardt, B.R., Bower, J.H., Ahlskog, J.E., Maraganore, D.M., Bharucha, A.E., Rocca, W.A.: Medical records documentation of constipation preceding Parkinson disease: a case-control study. Neurology 73, 1752–1758 (2009) 35. Unger, M.M., Spiegel, J., Dillmann, K.U., Grundmann, D., Philippeit, H., Burmann, J., Fassbender, K., Schwiertz, A., Schafer, K.H.: Short chain fatty acids and gut microbiota differ between patients with Parkinson’s disease and age-matched controls. Parkinsonism Relat Disord 32, 66–72 (2016) 36. Heintz-Buschart, A., Pandey, U., Wicke, T., Sixel-Doring, F., Janzen, A., Sittig-Wiegand, E., Trenkwalder, C., OertelWH, M.B., Wilmes, P.: The nasal and gut microbiome in Parkinson’s disease and idiopathic rapid eye movement sleep behavior disorder. Mov. Disord. 33, 88–98 (2018) 37. Cory-Slechta, D.A., Thiruchelvam, M., Barlow, B.K., Richfield, E.K.: Developmental pesticide models of the Parkinsondiseasephenotype. Environ. Health Perspect 113, 1263–1270 (2005) 38. LiY, SunY., YangJ, Wu.Y., YuJ, Li.B.: Age-dependent dopaminergic dysfunction following fetal exposure to atrazine in SD rats. Environ. Toxicol. Pharmacol. 37, 12751282 (2014) 39. Thacker, E.L., Ascherio, A.: Familial aggregation of Parkinson’s disease: a meta analysis. Mov. Disord. 23, 1174–1183 (2008) 40. Kuopio, A., Marttila, R.J., Helenius, H., Rinne, U.K.: Familial occurrence of Parkinson’s disease in a community-based case-control study. Parkinsonism Relat. Disord. 7, 297–303 (2001) 41. Sundquist, K., Li, X., Hemminki, K.: Familial risks of hospitalization for Parkinson’s disease in first-degree relatives: a nationwide follow-up study from Sweden. Neurogenetics 7, 231–237 (2006)

Epidemiology of Parkinson’s Disease …

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42. Zorzon, M., Capus, L., Pellegrino, A., Cazzato, G., Zivadinov, R.: Familial and environmental risk factors in Parkinson’s disease: a case-control study in north-east Italy. Acta. Neurol. Scand. 105, 77–82 (2002) 43. Duzcan, F., Zencir, M., Ozdemir, F., Cetin, G.O., Bagci, H., Heutink, P., et al.: Familial influence on parkinsonism in a rural area of Turkey (Kizilcaboluk-Denizli): a communitybased case-control study. Mov. Disord. 18, 799–804 (2003) 44. Tanner, C.M., Ross, G.W., Jewell, S.A., Hauser, R.A., Jankovic, J., Factor, S.A., et al.: Occupation and risk of parkinsonism: a multicenter case-control study. Arch. Neurol. 66(1106–13), 305 (2009) 45. Wright, J.M., Keller-Byrne, J.: Environmental determinants of Parkinson’s disease. Arch. Environ. Occup. Health. 60, 32–38 (2005) 46. Firestone, J.A., Lundin, J.I., Powers, K.M., Smith-Weller, T., Franklin, G.M., Swanson, P.D., et al.: Occupational factors and risk of Parkinson’s disease: a population-based casecontrol study. Am. J. Ind. Med. 53, 217–223 (2010) 47. Abbott, R.A., Cox, M., Markus, H., Tomkins, A.: Diet, body size and micronutrient status in Parkinson’s disease. Eur. J. Clin. Nutr. 46, 879–884 (1992) 48. Fernandez-Calle, P., Jimenez-Jimenez, F.J., Molina, J.A., Cabrera-Valdivia, F., Vazquez, A., Garcia Urra, D., et al.: Serum levels of ascorbic acid (vitamin C) in patients with Parkinson’s disease. J. Neurol. Sci. 118, 25–28 (1993) 49. Morens, D.M., Grandinetti, A., Waslien, C.I., Park, C.B., Ross, G.W., White, L.R.: Casecontrol study of idiopathic Parkinson’s disease and dietary vitamin E intake. Neurology 46, 1270–1274 (1996) 50. Hellenbrand, W., Boeing, H., Robra, B.P., Seidler, A., Vieregge, P., Nischan, P., et al.: Diet and Parkinson’s disease. II: a possible role for the past intake of specific nutrients. Results from a self-administered food-frequency questionnaire in a case-control study. Neurology 47, 644–650 (1996) 51. Schlesinger, I., Schlesinger, N.: Uric acid in Parkinson’s disease. Mov. Disord. 23, 1653–1657 (2008) 52. Wen, M., Zhou, B., Chen, Y.H., et al.: Serum uric acid levels in patients with Parkinson’s disease: a meta-analysis. PLoS One 12, e0173731 (2017) 53. Breckenridge, C.B., Berry, C., Chang, E.T., Sielken, R.L. Jr., Mandel, J.S.: Association between Parkinson’s disease and cigarette smoking, rural living, well-water consumption, farming and pesticide use: systematic review and meta-analysis. PLoS One 11, e0151841 (2016) 54. Kahn, H.A.: The Dorn study of smoking and mortality among U.S. veterans: report on eight and one-half years of observation. Natl. Cancer Inst. Monogr. 19, 1125 (1966) 55. Doll, R., Peto, R.: Mortality in relation to smoking: 20 years’ observations on male British doctors. Br. Med. J. 2, 1525–1536 (1976) 56. Taylor, C.A., Saint-Hilaire, M.H., Cupples, L.A., Thomas, C.A., Burchard, A.E., Feldman, R.G., et al.: Environmental, medical, and family history risk factors for Parkinson’s disease: a New England-based case control study. Am. J. Med. Genet. 88, 742–749 (1999) 57. Preux, P.M., Condet, A., Anglade, C., Druet-Cabanac, M., Debrock, C., Macharia, W., et al.: Parkinson’s disease and environmental factors. Matched case-control study in the Limousin region, France. Neuroepidemiology 19, 333–337 (2000) 58. Chen, H., Huang, X., Guo, X., Mailman, R.B., Park, Y., Kamel, F., et al.: Smoking duration, intensity, and risk of Parkinson disease. Neurology 74, 878–884 (2010) 59. Tan, L.C., Koh, W.P., Yuan, J.M., Wang, R., Au, W.L., Tan, J.H., et al.: Differential effects of black versus green tea on risk of Parkinson’s disease in the Singapore Chinese health study. Am. J. Epidemiol. 167, 553–560 (2008) 60. Hernan, M.A., Takkouche, B., Caamano-Isorna, F., Gestal-Otero, J.J.: A meta-analysis of coffee drinking, cigarette smoking, and the risk of Parkinson’s disease. Ann. Neurol. 52, 276–284 (2002) 61. Allam, M.F., Campbell, M.J., Hofman, A., Del Castillo, A.S., Fernandez-Crehuet, N.R.: Smoking and Parkinson’s disease: systematic review of prospective studies. Mov. Disord. 19, 614–621 (2004)

46

R. R. Patil

62. Hemmerle, A.M., Herman, J.P., Seroogy, K.B.: Stress, depression and Parkinson’s disease. Exp. Neurol. 233, 79–86 (2012) 63. Zhang, D., Jiang, H., Xie, J.: Alcohol intake and risk of Parkinson’s disease: a meta-analysis of observational studies. Mov. Disord 29, 819–822 (2014) 64. Bettiol, S.S., Rose, T.C., Hughes, C.J., Smith, L.A.: Alcohol consumption and Parkinson’s disease risk: a review of recent findings. J. Parkinsons Dis. 5, 425–442 (2015) 65. Galanaud, J.P., Elbaz, A., Clavel, J., Vidal, J.S., Correze, J.R., Alperovitch, A., et al.: Cigarette smoking and Parkinson’s disease: a case-control study in a population characterized by a high prevalence of pesticide exposure. Mov. Disord. 20, 181–189 (2005) 66. Dong, J.Q., Zhang, Z.X., Zhang, K.L.: Parkinson’s disease and smoking: an integral part of PD’s etiological study. Biomed. Environ. Sci. 16, 173–179 (2003) 67. Yang, F., Trolle Lagerros, Y., Bellocco, R., et al.: Physical activity and risk of Parkinson’s disease in the Swedish National March Cohort. Brain 138(Pt 2), 269–272 (2015) 68. Xu, Q., Park, Y., Huang, X., et al.: Physical activities and future risk of Parkinson disease. Neurology 75, 341–348 (2010) 69. Chen, H., Zhang, S.M., Schwarzschild, M.A., Hernan, M.A., Ascherio, A.: Physical activity and the risk of Parkinson disease. Neurology 64, 664–669 (2005) 70. van der Marck, M.A., Dicke, H.C., Uc, E.Y., et al.: Body mass index in Parkinson’s disease: a meta-analysis. Parkinsonism Relat. Disord. 18, 263–267 (2012) 71. Powers, K.M., Smith-Weller, T., Franklin, G.M., Longstreth, W.T., Swanson, P.D., Checkoway, H.: Dietary fats, cholesterol and iron as risk factors for Parkinson’s disease. Parkinsonism Relat. Disord. 15, 47–52 (2009) 72. Miyake, Y., Sasaki, S., Tanaka, K., et al.: Dietary fat intake and risk of Parkinson’s disease: a case-control study in Japan. J. Neurol. Sci. 288, 117–122 (2010) 73. Behari, M., Srivastava, A.K., Das, R.R., Pandey, R.M.: Risk factors of Parkinson’s disease in Indian patients. J. Neurol. Sci. 190, 49–55 (2001) 74. Hernan, M.A., Takkouche, B., Caamano-Isorna, F., Gestal-Otero, J.J.: A meta-analysis of coffee drinking, cigarette smoking, and the risk of Parkinson’s disease. Ann. Neurol. 52, 276–284 (2002) 75. Schwarzschild, M.A., Agnati, L., Fuxe, K., Chen, J.F., Morelli, M.: Targeting adenosine A2A receptors in Parkinson’s disease. Trends Neurosci. 29, 647–654 (2006) 76. Costa, J., Lunet, N., Santos, C., Santos, J., Vaz-Carneiro, A.: Caffeine exposure and the risk of Parkinson’s disease: a systematic review and metaanalysis of observational studies. J. Alzheimers Dis. 20(Suppl 1), S221–S238 (2010) 77. Li, F.J., Ji, H.F., Shen, L.: A meta-analysis of tea drinking and risk of Parkinson’s disease. Sci. World J. 2012, 923464 (2012) 78. Qi, H., Li, S.: Dose-response meta-analysis on coffee, tea and caffeine consumption with risk of Parkinson’s disease. Geriatr. Gerontol. Int. 14, 430–439 (2014) 79. Jiang, W., Ju, C., Jiang, H., Zhang, D.: Dairy foods intake and risk of Parkinson’s disease: a dose-response meta-analysis of prospective cohort studies. Eur. J. Epidemiol. 29, 613–619 (2014) 80. Chen, H., O’Reilly, E., McCullough, M.L., Rodriguez, C., Schwarzschild, M.A., Calle, E.E., et al.: Consumption of dairy products and risk of Parkinson’s disease. Am. J. Epidemiol. 165, 998–1006 (2007) 81. Logroscino, G., Marder, K., Cote, L., Tang, M.X., Shea, S., Mayeux, R.: Dietary lipids and antioxidants in Parkinson’s disease: a population-based, case-control study. Ann. Neurol. 39, 89–94 (1996) 82. Powers, K.M., Smith-Weller, T., Franklin, G.M., Longstreth, W.T., Jr., Swanson, P.D., Checkoway, H.: Dietary fats, cholesterol and iron as risk factors for Parkinson’s disease. Parkinsonism Relat. Disord. 15, 47–52 (2009) 83. de Lau, L.M., Bornebroek, M., Witteman, J.C., Hofman, A., Koudstaal, P.J., Breteler, M.M.: Dietary fatty acids and the risk of Parkinson disease: the Rotterdam study. Neurology 64, 2040–2045 (2005)

Epidemiology of Parkinson’s Disease …

47

84. Pressley, J.C., Louis, E.D., Tang, M.X., Cote, L., Cohen, P.D., Glied, S., et al.: The impact of comorbid disease and injuries on resource use and expenditures in parkinsonism. Neurology 60, 87–93 (2003) 85. Hu, G., Jousilahti, P., Bidel, S., Antikainen, R., Tuomilehto, J.: Type 2 diabetes and the risk of Parkinson’s disease. Diabetes Care 30, 842–847 (2007) 86. Johnson, C.C., Gorell, J.M., Rybicki, B.A., Sanders, K., Peterson, E.L.: Adult nutrient intake as a risk factor for Parkinson’s disease. Int. J. Epidemiol. 28, 1102–1109 (1999) 87. Logroscino, G., Marder, K., Graziano, J., Freyer, G., Slavkovich, V., Lojacono, N., et al.: Dietary iron, animal fats, and risk of Parkinson’s disease. Mov. Disord. 13(Suppl 1), 13–16 (1998) 88. Wright, J.M., Keller-Byrne, J.: Environmental determinants of Parkinson’s disease. Arch Environ. Occup. Health. 60, 32–38 (2005) 89. Park, J., Yoo, C.I., Sim, C.S., Kim, H.K., Kim, J.W., Jeon, B.S., et al.: Occupations and Parkinson’s disease: a multi-center case-control study in South Korea. Neurotoxicology 26, 99–110 (2005) 90. McCann, S.J., LeCouteur, D.G., Green, A.C., Brayne, C., Johnson, A.G., Chan, D., et al.: The epidemiology of Parkinson’s disease in an Australian population. Neuroepidemiology 17, 310–317 (1998) 91. Dong, J.Q., Zhang, Z.X., Zhang, K.L.: Parkinson’s disease and smoking: an integral part of PD’s etiological study. Biomed. Environ. Sci. 16, 173–179 (2003) 92. Nuti, A., Ceravolo, R., Dell’Agnello, G., Gambaccini, G., Bellini, G., Kiferle, L., et al.: Environmental factors and Parkinson’s disease: a case-control study in the Tuscany region of Italy. Parkinsonism Relat. Disord. 10, 481–485 (2004) 93. Petersen, M.S., Halling, J., Bech, S., Wermuth, L., Weihe, P., Nielsen, F., et al.: Impact of dietary exposure to food contaminants on the risk of Parkinson’s disease. Neurotoxicology 29, 584–590 (2008) 94. Dick, F.D., De. Palma, G., Ahmadi, A., Scott, N.W., Prescott, G.J., Bennett, J., et al.: Environmental risk factors for Parkinson’s disease and parkinsonism: the Geoparkinson study. Occup. Environ. Med. 64, 666–672 (2007) 95. Lolekha, P., Phanthumchinda, K., Bhidayasiri, R.: Prevalence and risk factors of Parkinson’s disease in retired Thai traditional boxers. Mov. Disord. 25, 1895–1901 (2010) 96. Wu, W.Y., Kang, K.H., Chen, S.L., et al.: Hepatitis C virus infection: a risk factor for Parkinson’s disease. J. Viral. Hepat. 22, 784–791 (2015) 97. Smargiassi, A., Mutti, A., De. Rosa, A., De. Palma, G., Negrotti, A., Calzetti, S.: A case control study of occupational and environmental risk factors for Parkinson’s disease in the Emilia-Romagna region of Italy. Neurotoxicology 19, 709–712 (1998) 98. Dick, F.D., De Palma, G., Ahmadi, A., Scott, N.W., Prescott, G.J., Bennett, J., et al.: Environmental risk factors for Parkinson’s disease and parkinsonism: the Geoparkinson study. Occup. Environ. Med. 64, 666–672 (2007) 99. Firestone, J.A., Lundin, J.I., Powers, K.M., Smith-Weller, T., Franklin, G.M., Swanson, P.D., et al.: Occupational factors and risk of Parkinson’s disease: a population-based casecontrol study. Am. J. Ind. Med. 53, 217–223 (2010) 100. Savitz, D.A., Checkoway, H., Loomis, D.P.: Magnetic field exposure and neurodegenerative disease mortality among electric utility workers. Epidemiology 9, 398–404 (1998) 101. Johansen, C.: Exposure to electromagnetic fields and risk of central nervous system disease in utility workers. Epidemiology 11, 539–543 (2000) 102. Schenck, C.H., Bundlie, S.R., Mahowald, M.W.: Delayed emergence of a parkinsonian disorder in 38% of 29 older men initially diagnosed with idiopathic rapid eye movement sleep behaviour disorder. Neurology 46, 388–393 (1996) 103. Chen, H., Zhang, S.M., Hernan, M.A., Schwarzschild, M.A., Willett, W.C., Colditz, G.A., et al.: Nonsteroidal anti-inflammatory drugs and the risk of Parkinson disease. Arch Neurol. 60, 1059–1064 (2003) 104. Gagne, J.J., Power, M.C.: Anti-inflammatory drugs and risk of Parkinson disease: a metaanalysis. Neurology 74, 995–1002 (2010)

48

R. R. Patil

105. Taylor, C.A., Saint-Hilaire, M.H., Cupples, L.A., Thomas, C.A., Burchard, A.E., Feldman, R.G., et al.: Environmental, medical, and family history risk factors for Parkinson’s disease: a New England-based case control study. Am. J. Med. Genet. 88, 742–749 (1999) 106. Rajput, A.H., Offord, K.P., Beard, C.M., Kurland, L.T.: A case-control study of smoking habits, dementia, and other illnesses in idiopathic Parkinson’s disease. Neurology 37, 226–232 (1987) 107. Simon, K.C., Chen, H., Schwarzschild, M., Ascherio, A.: Hypertension, hypercholesterolemia, diabetes, and risk of Parkinson disease. Neurology 69, 1688–1695 (2007) 108. Saunders-Pullman, R., Gordon-Elliott, J., Parides, M., Fahn, S., Saunders, H.R., Bressman, S.: The effect of estrogen replacement on early Parkinson’s disease. Neurology 52, 1417–1421 (1999) 109. Benedetti, M.D., Maraganore, D.M., Bower, J.H., McDonnell, S.K., Peterson, B.J., Ahlskog, J.E., et al.: Hysterectomy, menopause, and estrogen use preceding Parkinson’s disease: an exploratory case-control study. Mov. Disord. 16, 830–837 (2001) 110. Ragonese, P., D’Amelio, M., Salemi, G., Aridon, P., Gammino, M., Epifanio, A., et al.: Risk of Parkinson disease in women: effect of reproductive characteristics. Neurology 62, 2010–2014 (2004) 111. D’Amelio, M., Ragonese, P., Morgante, L., Reggio, A., Callari, G., Salemi, G., et al.: Longterm survival of Parkinson’s disease: a population-based study. J. Neurol. 253, 33–37 (2006) 112. de Lau, L.M., Schipper, C.M., Hofman, A., Koudstaal, P.J., Breteler, M.M.: Prognosis of Parkinson disease: risk of dementia and mortality: the Rotterdam study. Arch. Neurol. 62, 1265–1269 (2005) 113. Herlofson, K., Lie, S.A., Arsland, D., Larsen, J.P.: Mortality and Parkinson disease: a community based study. Neurology 62, 937–942 (2004) 114. Marttila, R.J., Rinne, U.K.: Level of blood pressure in patients with Parkinson’s disease. A case–control study. Eur. Neurol. 16, 73–78 (1977)

Young Onset of Parkinson’s Disease Rajan R. Patil and Aiswarya Anilkumar

Abstract Early-onset of Parkinson’s disease is segmented into Young Onset Parkinson’s disease (YOPD) commonly defined as PD with an age onset of 21– 40 years and Juvenile Parkinson’s disease occurring in those aged less than 21 years. The prevalence of YOPD among those living in Europe is estimated to be 12–20 per 100,000, and up to 45 per 100,000 among those living in Asian countries. Though the exact etiology of YOPD is still unknown, the major risk factors attributed to the development of YOPD include family history, genetic mutations, and environmental exposure. YOPD begins with dystonia, an uncontrollable stiffness or cramping of a muscle group or limb, and is a very common initial symptom of young-onset PD. Currently, there are no definitive tests for young-onset Parkinson’s disease. Instead, there are a series of physical and cognitive assessments used to determine the diagnosis and rule out the existence of other conditions that may have symptoms similar to those of young-onset Parkinson’s disease. Treatment usually comprises the use of medications, surgical interventions, physical, occupational and speech therapies, and also lifestyle reforms such as diet and exercise. With the early onset of PD, its chronic characteristics, and progressive nature; the disease affects the quality of life of patients. As YOPD patients have longer disease duration, they may suffer from more significant physical, economic, and psychological consequences. YOPD patients live with this neurodegenerative disease for many years compared to their older PD counterparts. Nonetheless, with good treatment, support, and information, many patients can lead full productive lives. Early diagnosis and treatment-seeking behavior can improve the management of the disease at an early age and can enhance the health-related quality of life. YOPD research is in the preliminary phase and all the facets needs to be explored in detail for understanding the disease better.

R. R. Patil (B) · A. Anilkumar School of Public Health, SRM Institute of Science and Technology, Kattankulathur, Chennai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. P. Arjunan et al. (eds.), Techniques for Assessment of Parkinsonism for Diagnosis and Rehabilitation, Series in BioEngineering, https://doi.org/10.1007/978-981-16-3056-9_4

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1 Definition Early-onset of Parkinson’s disease (EOPD) or Early-onset Parkinsonism is a clinical phenotype of Parkinson’s disease (PD) occurring at an age below 40 years, or is defined as onset below 50 years. (1) The definition of early onset of Parkinson’s disease is ambiguous and no definite consensus has been reached concerning the definition of patients according to age at onset. (2) Early-onset of Parkinson’s disease is segmented into Young Onset Parkinson’s disease (YOPD) commonly defined as PD with an age onset of 21–40 years and Juvenile Parkinson’s disease occurring among those aged less than 21 years. (3) Various authors and research evidences define YOPD as PD with the onset of motor symptoms between the age 21–49 years as well. (4) The age at onset of PD is a variable of uncertain significance. For arbitrary reasons, the upper limit for early-onset parkinsonism is usually restricted to 40 years, though certain studies take in onset up to age 50–55 years (5).

2 Historical Background Dating back to 1875 the first case of young-onset Parkinson’s disease with akinetorigid symptomatology was reported by Huchard followed by Willigie in 1910, who published a series of early-onset cases [6]. Willigie coined the new term “paralysis agitans juvenilis familialis” emphasizing the necessity for nosological classification of PD cases based on their age of onset and family history. In the year 1979, Yokochi was the first to highlight the increasing prevalence of juvenile” and “young-onset” Parkinsonism in Japan in an all-inclusive report comprising patients with a mean age of onset of 26.1± 9.6 years [6].

3 Prevalence The prevalence of YOPD among those living in Europe is estimated to be 12–20 per 100,000, and up to 45 per 100,000 among those living in Asian countries [4]. It affects approximately 2–10% of one million people with PD in the US [7]. YOPD represents 5–7% of PD patients in the western hemisphere [3]. In the developed countries, the mean age of onset of PD is in the early-to-mid sixties but in 3–5% of cases, symptoms start decades earlier, before the age of 40. In Japan, higher proportions of early-onset PD have been reported, up to10–14% [8]. According to Joo Hyun Park et al., the incidence of PD increased in individuals under 50 years, from 1.2 cases per 100,000 individuals in 2010 to 1.7 cases per 10,000 individuals in 2015 [9]. The disease strikes at a time in life that for many are the most fruitful, fulfilling, and demanding. With the increasing prevalence of early onset of Parkinson’s disease, the disease poses its

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own set of clinical, social, and occupational challenges and stressors to patients and their families.

4 Etiology Although Parkinson’s disease was diagnosed in the early 1800s the etiology of the disease is still unidentified. Environmental factors, hereditary factors, or the culmination of both of these might trigger the young onset of Parkinson’s disease [10]. In younger people, especially those who have multiple family members with Parkinson’s disease, genetics may play a larger role. A higher incidence of familial cases has always been a distinguishing feature of Parkinsonism of early-onset [6]. Certain genetic mutations are associated with an increased risk of young-onset PD [10]. Several genes have been identified and their mutations associated with the early onset of PD are still being researched. Major genes involved in the pathogenesis of early onset of PD are PRKN (PARK 2), PINK1, SNCA (PARK1 and PARK4), and DJ-1 (PARK7) [1]. Mutations in these genes are presumed to be associated with EOPD etiology. Research studies show that the younger the age of onset of the disease higher is the risk of genetic predisposition. Family history of Parkinson’s disease is reported in 20% of YOPD patients and age-specific risk of PD is 7.8-times higher among lineages of patients with YOPD [3]. A genetic basis can be seen in > 10% of YOPD individuals and the proportion of genetically defined cases rises to greater than 40% if the onset of the disease is before 30 years [11]. The relationship between environmental factors and Parkinson’s disease is always an intriguing concern and the causation is not well established [12].Nevertheless, cumulative pieces of evidence have associated rural living, well water drinking, herbicides and pesticides exposure, and proximity to the industry as risk factors for the disease [13]. Substantial exposure to environmental toxins leads to rapid degeneration of dopamine neurons and is presumed to be responsible for the development of PD at an early age [14]. Though the risk factors are majorly attributed to family history, genetic mutations, and environmental exposure, the exact etiology of YOPD is still unknown.

5 Symptoms The conventional clinical appearance of PD consists of resting tremor, rigidity, bradykinesia, and gait instability [15]. The symptoms that appear in YOPD are similar to the classical symptoms of late-onset PD (LOPD), but several clinical features appear prominently in YOPD [16]. The YOPD begins with dystonia, an uncontrollable stiffness or cramping of a muscle group or limb, and is a very common initial symptom of young-onset PD [17]. Evidence comparing clinical symptoms of YOPD

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with LOPD established that YOPD patients generally present increased muscle stiffness, rigidity, cramps, and off-period dystonia [17, 18]. Young people tend to develop dyskinesias and fluctuations in movement while consuming levodopa [17]. Data also shows that those with YOPD are likely to have a more slowly progressive course than their older counterparts [6]. Jankovic et al. found that YOPD patients took considerably longer time to reach Hoehn and Yahr stage 1 from symptom onset compared to late-onset patients [19]. Rigidity, bradykinesia, tremor, fatigue, and postural instability are the other symptoms present among patients with early onset of Parkinson’s disease. Fatigue is an under-recognized symptom, but YOPD patients often quote unusual fatigue as an early symptom [19]. YOPD patients have less comorbidity, slower disease progression, less frequent gait disturbances as well as delayed falls, freezing, and delayed cognitive decline compared to those with LOPD [3]. Patients with early onset of Parkinson’s disease face occupational and life-style challenges, because of the emergence of relatively unique non-motor comorbidities as well. The non-motor symptoms include depression, restless leg syndrome, and loss of libido, sexual dysfunction, and future uncertainties [3]. Sleep disturbances, change in memory and thinking, constipation, and urinary problems are similar to the symptoms experienced by other patients with Parkinson’s disease [7]. They have also shown psychological symptoms such as psychosis, confusion, and hallucinations(16). The other commonly affected domains were memory, visuoperceptual, visuospatial, and executive function [20]. Evidence suggests that these non-motor symptoms develop initially among patients with YOPD and pave way for early diagnosis and timely treatment.

6 Diagnosis Though the clinical picture of YOPD usually bears a resemblance to that of olderonset PD, the differential diagnosis is different. Parkinson’s disease is uncommon among people younger than 50 and the disease is overlooked in younger people leading many to go undiagnosed or misdiagnosed for prolonged periods. Scans such as computerized tomography (CT), Magnetic resonance imaging (MRI), positron emission tomography (PET), and single-photon emission computerized tomography (SPECT) with a radiopharmaceutical-imaging agent (DaTSCAN), can help to confirm dopamine deficiency, but cannot be used to distinguish the different types of parkinsonism from each other [21]. Currently, there are no definitive tests for young-onset Parkinson’s disease. Instead, there are a series of physical and cognitive assessments used to determine the diagnosis and rule out the existence of other conditions that may have symptoms similar to those of young-onset Parkinson’s disease. The detection of definite genetic mutations in a significant number of cases of young-onset parkinsonism has greatly enabled the diagnostic process [6]. The symptoms those are less frequent, less severe, or delayed among YOPD compared to LOPD are disease progression gait disturbances, freezing of gait, falls, dementia,

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anxiety psychosis, double vision, gastrointestinal complaints, impairment of taste and smell, daytime sleepiness, REM sleep behavior disorders, and insomnia. Findings that are more frequent among YOPD compared to the age-matched population include depression, dysthymia, social anxiety disorder, psychosocial dysfunction, and sexual dysfunction [3]. These clinical manifestations can help in the differential diagnosis of YOPD from LOPD. Furthermore, lower serum ceruloplasmin levels have been correlated with the younger onset of PD [16]. The key to successful management of Parkinson’s disease is early testing and timely diagnosis; and research is being undertaken to find sustainable solutions.

7 Disease Management Currently, no treatment can definitively slow or stop the progression of the Youngonset of Parkinson’s disease. As an alternative, rehabilitation is directed at managing the symptoms of the disease. Treatment usually comprises the use of medications, surgical interventions, physical occupational and speech therapies, and also lifestyle reforms such as diet and exercise. Symptomatic treatment starts when the symptoms affect the motor or social functioning to the magnitude that the quality of life is compromised or when it’s difficult to maintain employment. Treatment is fixated on the most difficult symptoms, and medications often targeted to specific symptoms [17]. Levodopa is considered the most effective drug for controlling the symptoms of YOPD and helps in the production of the neurotransmitter called dopamine. Deficiency of dopamine adversely affects motor functioning and results in the manifestation of symptoms. YOPD patients may suffer from nausea or orthostatic hypotension as they begin dopamine agonist therapy; however, these can be minimized by taking drugs with food, maintaining adequate fluid intake, and avoiding excessive heat [19]. Extensive consumption of levodopa at high dosages often leads to movement difficulties that can be challenging to manage. Other medications used to treat the symptoms include MAO-B inhibitors, anti-cholinergic, amantadine, and dopamine receptor, agonists. Regular exercise, good sleep hygiene, and adjunct therapies are also used as early intervention measures to maintain flexibility, mobility, and general well-being. These can also help in gait and balance improvement. Despite these possibilities for treating YOPD, when medications do not continue to improve mobility or if PD medications cause significant side effects, surgical treatment may be considered. Deep Brain Stimulation is an FDA-approved surgical procedure whereby an electrical current is applied to numerous parts of the brain through implanted electrodes [16]. YOPD patients are characterized by slower disease progression rate, lower incidence of non-levodopa responsive symptoms, and more severe motor complications and also considering their age, surgical procedures are presumed to have a long-lasting effect. The availability of DBS represents a major milestone in the treatment options for YOPD [16, 17]. Management of disease for early onset of Parkinson’s disease is crucial as the disease poses a long-term challenge and timely treatment would be beneficial for their well-being.

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8 Quality of Life With the early onset of Parkinson’s disease, its chronic characteristics, and progressive nature the disease affects the quality of life of patients. As YOPD patients have longer disease duration they may suffer from more significant physical, economic, and psychological consequences [22]. Though initially, the physical dimensions seem to be affected the most, later the dimensions related to Quality of Life (QoL) are affected. This happens due to the progression of the disease and its impact on daily activities. Young adults are more likely to be employed and have younger children and, therefore, the disease impacts the overall productivity. Several cross-sectional studies advocate an important association between younger-onset and poor QoL. This is likely due to a range of factors including marital conflicts, difficulties with family life, social isolation, and loss of occupation [3]. Non-motor features also seem to have a particularly distressing impact on the QOL of patients with YOPD. Fatigue, depression, and sensory complaints are the non-motor symptoms contributing most to the reduction of health-related quality of life [23]. Study results have also shown that the life expectancy and anticipated age at death are reduced for all onset ages but the reduction is highest among individuals with young-onset of Parkinson’s disease [24]. Research evidence also suggests that YOPD patients experience greater perceived stigmatization and depression than patients with LOPD [25]. Career repercussions of disability for YOPD patients play a significant role in the quality of life, in terms of mental state, self-worth, and finances. They found that the severity of symptoms and lack of support in the workplace together with the need to take early retirement as other factors leading to reduced quality of life. YOPD patients also pose challenges in terms of relationships; many of them might be recently married or may have younger children. The inability to meet their expectations and perceived future uncertainties can strain the relationships [8, 26]. Therefore, exploring the quality of life becomes vital to understand the progression of the disease and to seek strategies to support the physical and psychological well-being of individuals, thus refining the development of therapeutic intervention directed towards them.

9 Economic Burden The study entitled “Economic Burden of Parkinson’s Disease”, published by the Michael J. Fox Foundation with the support from the Parkinson’s Foundation and other community organizations and industry partners, revealed that the economic burden of Parkinson’s disease (PD) is nearly $52 billion every year [27]. Families in which one of the breadwinners is diagnosed with YOPD, have to cope with medication expenses, health care procedures and income loss due to inability to progress in the place of work. Direct costs include the costs for rehabilitation, outpatient care, surgery, special equipment, transportation, and medications while the indirect cost

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was calculated according to the loss of income caused by premature retirement due to PD [28]. Studies have also shown that the economic and health burden of PD has resulted in increased healthcare costs and major reductions in the HR-QOL of both patients and their families. Economic outcomes associated with PD are of particular importance to patients, their families, and society. The advanced stages of PD are associated with poorer HR-QOL, decreased productivity, and greater consumption of healthcare resources, resulting in increased total direct and indirect costs [29]. With the increasing prevalence of Parkinson’s disease and growing expenditure for disease management, there is a heavy economic burden on patients, their families, and society. The economic burden due to YOPD in developed and developing countries have not been explored in detail to date and a clear picture of the cost implications remains undiscovered.

10 Perceived Preventive Measure There are no proven ways to prevent Parkinson’s disease at an early stage, but perceived avoidance of risk factors and adopting a healthy lifestyle help reduce the risk of disease onset [30]. Genetic factors play an important role in YOPD and if there is a presence of familial aggregation, early testing can help prevent the disease onset. Studies have also reported diet high in antioxidants along with regular exercise as major factors in disease prevention [31].

11 Juvenile Parkinson’s Disease Of note YOPD and JPD have been used interchangeably in the literature, leading to confusion in classification. Juvenile Parkinsonism or Juvenile Parkinson’s disease (JPD) is relatively a rare syndrome with onset before age 21 years and is characterized by symptoms similar to Young-onset Parkinson’s disease. JPD affects the subcortical brain structures and shows symptoms usually during adolescence [32]. Since the disease is very rare the prevalence of Juvenile Parkinson’s disease is not known to date and the evidence has been in the form of case studies, making it difficult to understand the true population estimates of prevalence or occurrence [33]. JPD has higher rates of familial aggregation and is considered the major risk factor for the disease onset. Mutations in three genes namely parkin (PARK2, chromosome 6q), PTEN-induced putative kinase 1 (PINK1, previously known as PARK6, chromo- some 1p), and PARK7 (also known as DJ1, chromosome 1p) are known to cause Juvenile Parkinson’s disease. Another presumed genetic cause of Juvenile Parkinson’s disease is dopa-responsive dystonia, an autosomal dominant trait, which presents with limb dystonia and diurnal fluctuation of symptoms [32]. Unfortunately, due to the uncommon manifestation of JPD, it is difficult to make true predictions regarding the true causation. The clinical manifestations include

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atypical features, such as disproportionate severity of movement disorders (e.g. dystonia, ataxia, and spasticity), early cognitive decline, and severe behavioral disturbance [34]. The most common psychiatric symptoms comprise anxiety, depression, apathy, and hallucinations with preserved insight. There is no specific diagnostic test for JPD and patients with symptoms will be given specific care to physical symptoms of PD, such as tremor, rigidity, and dyskinesia. Patients also receive a series of symptom diagnosis, medical and neurologic tests, such as brain scans, blood tests, lumbar puncture, and x-rays to rule out other diseases. There is no definite treatment protocol for JPD and patients are managed following the idiopathic PD protocols. The major aim of the treatment is to control symptoms and manage the side effects as the disease has an early onset. The disease progression is slow and if undiagnosed and untreated will eventually lead to early total disability and deterioration of brain functions [33]. Evidence indicated that health-related quality of life is significantly worse in juvenile and young-onset versus older-onset Parkinson’s disease patients [33].

12 Perspectives of Young Onset Parkinson’s Disease Patients Blogs and webinars have become a major venue of information sharing and emotional release for people worldwide during the post-pandemic period [35]. A short note on different perspectives of the disease has been discussed by patients as a part of a webinar for YOPD patients. Few of these perspectives help us understand the reality of disease condition and patient viewpoint. Since the availability of qualitative studies and thematic analysis are negligible the narratives from the webinar organized by Parkinson’s Canada are included to understand the patient perspective on three domains [36]. Early Diagnosis of YOPD. A patient was in the process of completing her residency in family medicine and was expecting her first child when she was diagnosed with YOPD. The patient quoted “I had noticed a tremor in my right little finger but ignored it for a while. The first neurologist performed a motor examination and diagnosed it as YOPD. For the first 10 years I was “in denial,” but for the last decade there is more acceptance of the disease”. Impact of PD on family life. A female patient diagnosed with the disease quotes: “It’s been quite tough; I have a very supportive family but I do not work now. My husband works very hard and takes care of things around the house and I do less at times. At times, my children ask me questions and I know the answer in my head but find it difficult in figuring out how to explain it to them. All these are frustrating and is affecting my mental health.”

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Effect of PD medication. The patient quotes: “I am so grateful that we have levodopa, at least! It is very helpful. But this disease has affected so many and will affect so many more; it works for a few and does not for the rest. I am very optimistic about a lot of the drugs that are coming up.”

13 Summary Young-onset Parkinson’s disease patients live with this neurodegenerative disease for many years compared to their older PD counterparts. Nonetheless, with good treatment, support, and information, many patients can lead full productive lives. YOPD patients have altered treatment needs than older patients, and a multidisciplinary approach can be very effective in improving quality of life. Considering disease management, the initial step would be providing assurance, therapy, education, and information on resources for newly diagnosed YOPD patients. A multidisciplinary team of neurologists and therapists should guide patients in understanding the symptoms and their management and also help them understand the impact of non-motor symptoms. Family members should be given counseling and referrals to provide support and positive reinforcement to the patients. The patient should be sensitized about the benefits of seeking treatment and its impact on the quality of life. Early diagnosis and treatment-seeking behavior can improve the management of the disease at an early age and can enhance the health-related quality of life. YOPD research is in the preliminary phase and all the facets needs to be explored in detail for understanding the disease better.

References 1. Olusegun A, Makun HA, Ogara IM, Edema M, Idahor KO, Oluwabamiwo BF, et al. We are IntechOpen, the world ’ s leading publisher of Open Access books Built by scientists, for scientists TOP 1 %. Intech [Internet]. i(tourism): 38 (2012). http://dx.doi.org/10.1039/C7RA00 172J%0A; https://www.intechopen.com/books/advanced-biometric-technologies/liveness-det ection-in-biometrics%0A; http://dx.doi.org/10.1016/j.colsurfa.2011.12.014 2. Kasamo, S., Takeuchi, M., Ikuno, M., Kawasaki, Y., Tanaka, S., Takahashi, R., et al.: Realworld pharmacological treatment patterns of patients with young-onset Parkinson’s disease in Japan: a medical claims database analysis. J Neurol [Internet]. 266(8), 1944–1952 (2019) 3. Mehanna, R., Jankovic, J.: Young-onset Parkinson’s disease: its unique features and their impact on quality of life. Park Relat Disord [Internet]. 65(January), 39–48 (2019) 4. Manuscript, A.: Onset Parkinson ’ s Disease in the United States. 19(2), 202–206 (2014) 5. Kosti´c, V.S.: Treatment of young-onset Parkinson’s disease: role of dopamine receptor agonists. Park Relat Disord. 15(SUPPL. 4), 71–75 (2009) 6. Gershanik OS. Early onset parkinsonism. Front Biosci. 8(SUPPL.), (2003) 7. Parkinson YO, States U. Young Onset Parkinson ’ s How Is Young-Onset PD Different ? Why Is Distinguishing Young-Onset Parkinson ’ s Important ?, 1–8 (2020)

58

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8. Post, B., Van Den Heuvel, L., Van Prooije, T., Van Ruissen, X., Van De Warrenburg, B., Nonnekes, J.: Young onset Parkinson’s disease: a modern and tailored approach. J Parkinsons Dis. 10(s1), S29-36 (2020) 9. Park, J.H., Kim, D.H., Kwon, D.Y., Choi, M., Kim, S., Jung, J.H., et al.: Trends in the incidence and prevalence of Parkinson’s disease in Korea: a nationwide, population-based study. BMC Geriatr. 19(1), 1–10 (2019) 10. Parkinson YO, Menu D, Resource N, Guide P. Young onset Parkinson s disease. 1–8, (2020) 11. Davie, C., Davie, C.A.: A review of Parkinson ’ s disease A review of Parkinson ’ s disease. 2016, 1–15 (2008) 12. Anilkumar, A., Veliah, G., Kosalram, K.: Pesticide Exposure and Its Association to Parkinson ’ s Disease Development: An Intrinsic Case Study in India. 10(September), 232–235 (2020) 13. Tsai, C.H., Lo, S.K., See, L.C., Chen, H.Z., Chen, R.S., Weng, Y.H., et al.: Environmental risk factors of young onset Parkinson’s disease: a case-control study. Clin Neurol Neurosurg. 104(4), 328–333 (2002) 14. Stern M, Dulaney E, Gruber SB, Golbe L, Bergen M, Hurtig H, et al. Study of Parkinson ’ s Disease. (2015) 15. Aiswarya, A.: Mappilai Raja Bagvandas VG. Prediction of fall among patients with Parkinson’s disease: a cross sectional study, India. 4(9), 18–25 (2019) 16. Klepac, N., Habek, M., Adamec, I., Barusic, A.K., Bach, I., Margeti´c, E.: An update on the management of young-onset Parkinson’s disease. Degener Neurol Neuromuscul Dis. 3, 53–62 (2013) 17. Ali M, Corrado HN, Cuomo M, Learned MM, Gattullo VN, Levine R, et al. Young Parkinson’ s Handbook Regional Representatives. Memory. 18. Mehanna R, Moore S, Hou JG, Sarwar AI, Lai EC. Comparing clinical features of young onset, middle onset and late onset Parkinson’s disease. Park Relat Disord [Internet]. vol. 20(5), pp. 530–4 (2014). http://dx.doi.org/https://doi.org/10.1016/j.parkreldis.2014.02.013 19. Calne SM, Kumar A. Young onset Parkinson’s disease. Practical management of medical issues. Park Relat Disord. 14(2), 133–42 (2008) 20. Seubert-Ravelo, A.N., Yáñez-Téllez, M.G., Salgado-Ceballos, H., Escartín, R.E., Neri-Nani, G.A., Velázquez-Osuna, S.: Mild cognitive impairment in patients with early-onset Parkinson’s disease. Dement Geriatr Cogn Disord. 42(1–2), 17–30 (2016) 21. Shoulders, M.: Back to the future. Eng Syst. 16(10), 50 (1999) 22. Knipe, M.D.W., Wickremaratchi, M.M., Wyatt-Haines, E., Morris, H.R., Ben-, Y.: Quality of life in young- compared with late-onset Parkinson’s disease. Mov Disord. 26(11), 2011–2018 (2011) 23. Müller B, Assmus J, Herlofson K, Larsen JP, Tysnes OB. Importance of motor vs. non-motor symptoms for health-related quality of life in early Parkinson’s disease. Park Relat Disord [Internet]. 19(11), 1027–32 (2013). http://dx.doi.org/https://doi.org/10.1016/j.parkreldis.2013. 07.010 24. Ishihara, L.S., Cheesbrough, A., Brayne, C., Schrag, A.: Estimated life expectancy of Parkinson’s patients compared with the UK population. J Neurol Neurosurg Psychiatry. 78(12), 1304–1309 (2007) 25. Schrag, A., Hovris, A., Morley, D., Quinn, N., Jahanshahi, M.: Young- versus older-onset Parkinson’s disease: impact of disease and psychosocial consequences. Mov Disord. 18(11), 1250–1256 (2003) 26. Calne, S.M., Lidstone, S.C., Kumar, A.: Psychosocial issues in young-onset Parkinson’s disease: current research and challenges. Park Relat Disord. 14(2), 143–150 (2008) 27. Beck J. New study shows the economic burden of Parkinson’s disease double previous estimates | Parkinson’s foundation. 1–5 (2020). https://www.parkinson.org/about-us/Press-Room/PressReleases/Parkinsons-Economic-Burden 28. Yang JX, Chen L. Economic burden analysis of Parkinson’s disease patients in China. Parkinsons Dis. 2017 (2017) 29. Dowding, C.H., Shenton, C.L., Salek, S.S.: A review of the health-related quality of life and economic impact of Parkinson’s disease. Drugs Aging 23(9), 693–721 (2006)

Young Onset of Parkinson’s Disease

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30. Erin, B., Kelser, A.: What Is Parkinson ’ s Disease ? Symptoms, Causes, Diagnosis, Treatment, and Prevention 1, 1–21 (2020) 31. Factors R. Early onset Parkinson ’ s disease : symptoms, treatment, prevention. 1–18 (2020) 32. Thomsen, T.R., Rodnitzky, R.L.: Juvenile parkinsonism: epidemiology, diagnosis and treatment. CNS Drugs 24(6), 467–477 (2010) 33. Knowledge C, References C. Encyclopedia of clinical neuropsychology. Encycl Clin Neuropsychol. 2011, (2000) 34. Niemann, N., Jankovic, J.: Juvenile parkinsonism: differential diagnosis, genetics, and treatment. Park Relat Disord. 67(May), 74–89 (2019) 35. Shapira, A.L., Handzel, R., Korczyn, A.D.: The lived experience of parkinson’s disease: a content analysis of parkinson’s patients’ blogs. Isr Med Assoc J. 19(11), 685–690 (2017) 36. Canada P, Mathur S. Young onset Parkinson ’ s : a conversation on our unique needs –Webinar notes, 1–10 (2020)

Diagnosis of Parkinson Disease: Imaging and Non-Imaging Techniques A. Brindha, Karnam Anantha Sunitha, B. Venkatraman, M. Menaka, and Sridhar P. Arjunan

Abstract Parkinson’s disease (PD) is a disorder of the nervous system caused due to the consequence of dopaminergic neuron loss in the brain’s substantia nigra region. The neurological disorder leads to disability, and it is the second major cause of death worldwide. In 1990, the number of PD patients recorded was 2.5 million people, and in 2016, the count became 6.1 million people. This alarming prevalence rate shows that it has doubled in 17 years. About 276 million PD cases suffer from disability-adjusted life years (DALYs) [1]. Parkinson’s disease is an aberrantly increasing, progressive neurodegenerative disease that affects individuals, families, and society. PD is often termed as an idiopathic disease since the cause of the disease is unknown. The diagnosis of PD is clinical since no particular test can conclude the disease. There are various diagnostic tools used in combination to diagnose multiple symptoms of the disease. This paper comprehensively gives the different PD stages, the respective clinical tools (imaging and non-imaging techniques), and the research tools (imaging and non-imaging techniques) used to diagnose PD accurately. The sensitivity of current clinical diagnosis using gold standard techniques is just 23% for non-responsive PD subjects [2]. Thus, the risk of increasing PD subjects’ burden can A. Brindha · K. A. Sunitha (B) · S. P. Arjunan Department of Electronics and Instrumentation Engineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpet, India e-mail: [email protected] A. Brindha e-mail: [email protected] S. P. Arjunan e-mail: [email protected] B. Venkatraman Outstanding Scientist, Director, Health, Safety and Environment, Indira Gandhi Centre for Atomic Research, Kalpakkam, India e-mail: [email protected] M. Menaka Safety, Quality, and Resource Management Group, Indira Gandhi Centre for Atomic Research, Kalpakkam, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. P. Arjunan et al. (eds.), Techniques for Assessment of Parkinsonism for Diagnosis and Rehabilitation, Series in BioEngineering, https://doi.org/10.1007/978-981-16-3056-9_5

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be controlled by choosing appropriate diagnostic tools. The chapter aims to review various imaging and non-imaging tools used in multiple stages of the disease to identify the more accurate and sensitive diagnostic tool. Brain imaging using PET, SPECT, TCS, MRI, and thermal imaging for finding autonomic dysfunction are some of the non-invasive techniques that can diagnose the disease. In this article, various imaging techniques used for the diagnosis of early stages of PD is reviewed. Keywords PD diagnosis · Imaging techniques · PET scan · SPECT scan · Thermal imaging · TCS and MRI imaging

1 Introduction According to the estimation made by the Global Burden of Disease study, the number of PD subjects will get doubled to 13 million in 2040 from 7 million PD in 2015 [3]. Thus, creating a global PD pandemic. About 9.0 million people are dead due to neurological disorders globally [3]. Parkinson’s disease, a cause of progressive neurodegenerative disease, is triggered due to the development of Lewy bodies in brain cells, making the disease progress very quickly (Timothy et al. 2012). When abnormally high levels of α-synuclein accumulate, it results in the loss of dopaminergic neurons in PD (Richard et al. 2019). Variation of α-synuclein levels in the Cerebrospinal fluid and plasma is reported in PD patients [4]. Dopamine is the neurotransmitter that carries the signals to control movement, motivation, memory, and other functions [5]. Usually, PD symptoms and signs are developed after 70−80% of neurons diminished [6]. Thus, the process of molecular identification of patients between the appearance of symptomatology and the beginning of cellular dysfunction may be of significant importance in developing reliable treatment strategies to protect the brain [7]. There are different diagnostic tools available for achieving earlier intervention in PD, such as PD biomarkers and neuroimaging techniques [8]. Parkinson’s disease is a type of movement disorder which is characterised by tremor, rigidity, bradykinesia and postural instability. The non-motor symptoms of PD include quite a lot of people’s overall symptoms and appear earlier than the motor symptoms in Parkinson’s patients [9]. Non-motor symptoms have a substantial adverse effect on a person’s quality of life (QOL), and non-motor symptoms are likely to present 10 years before the emergence of motor symptoms [10]. Though motor symptoms being the major diagnostic tools, promising outcomes are arrived holding pre-motor symptoms as diagnostic standards in recent years [11]. Pre-motor symptoms manifestations in PD includes olfactory dysfunction (Hawkes et al. 1997) [12], gastrointestinal and urinary dysfunction [13], mood and sleep disturbances, malfunction in variety of cerebral activities such as reasoning, memory, attention, and language, etc. [14]. Table 1 summarises the signs and symptoms of PD in different stages of disease [1].

(0–30) % of dopamine loss

After 30% loss of dopamine

Stage 0

Stage 1

Stage 2

Patho physiologocal changes

Stages

Signs and symptoms in stage 1 affect both sides of the body Disability to do some work like dressing, bathing, eating, etc. Walking problems and gait

Resting Tremor symptoms on one side of the body Mild postural Change, difference in walking pattern and difference in facial expressions occur

Anxiety, mood disorders, Dysautonomia, REM sleep disorders, sensory dysfunction

Symptoms

UPDRS Rating Scale

MRI, SPECT SCAN, Sensors to find the DAT-PET SCAN, CT frequency of tremor SCAN, MIBG IMAGING, TCS scan, PET imaging using various tracers to study the brain chemicals CT SCAN

Parkinson’s disease sleep scale, Fatigue severity scale, Mini mental Parkinson test, PD quality of life questionnaire

Non-imaging tools

MRI, SPECT SCAN, DAT-PET SCAN, CT SCAN, MIBG IMAGING

Imaging tools

Non-imaging tools Parkinson’s disease sleep scale, Fatigue severity scale, Mini mental Parkinson test, PD quality of life questionnaire

Research tools

Clinical tools

(continued)

PET imaging using various tracers to study the brain chemicals CT SCAN, MRI, SPECT SCAN, DAT-PET SCAN, CT SCAN, MIBG IMAGING, Thermal imaging

MRI, SPECT SCAN, DAT-PET SCAN, PET imaging using various tracers to study the brain chemicals CT SCAN, MIBG IMAGING, Thermal imaging

Imaging tools

Table 1 Signs and symptoms of PD in different stages of disease (https://www.researchgate.net/publication/320629576_Current_understanding_of_the_mol ecular_mechanisms_in_Parkinson’s_disease_Targets_for_potential_treatments)

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symptoms become severe and limiting. Needs assistance to stand like walker to move

Stiffed legs, thus difficult to stand or walk. Mostly use walker

Stage 4

Stage 5

Symptoms

loss of balance slowness of movements frequent falls are common

Patho physiologocal changes

Stage 3

Stages

Table 1 (continued) Research tools Non-imaging tools

Non-imaging tools

Imaging tools

Clinical tools Imaging tools

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1.1 Non-Imaging techniques used in the early diagnosis of PD For the diagnosis of PD different Biomarkers are used as indicators in blood, urine, brain and spine fluid i.e., Cerebrospinal fluid (CSF) (Alves et al. 2010). The clinical manifestations of biomarkers show up similar symptoms in diseases similar to PD (Kansara et al. 2013). Also, the combination of various biomarkers will be required to get the early diagnosis of PD. This makes the diagnosis of PD more challenging. In recent years Genetic biomarkers are researched for the early and reliable diagnosis of PD (Molochnikov et al. 2012). For example, Orexin, also known as hypocretin, is a neuropeptide hormone that set how the body is supposed to behave, like the sleepwake cycle, affects how the body feels, regulates the body’s blood pressure, heart rate, and causes hypertension [15]. A study on orexin identified that the concentration of orexin-A in patients with PD is lower than in normal persons, and the rate of Orexin is associated with the prevalence of the illness [16].

1.2 Imaging techniques used in the early diagnosis of PD Neuroimaging techniques are basically used to figure out the abnormality in nervous symptoms. In this regard various imaging techniques are used to image the neural structure for the diagnosis of PD [17]. They are Transcranial sonography (TCS) [18], Magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT), and Positron emission tomography (PET) [19]. These imaging techniques are non-invasive in nature and thus are mostly suggested for the early intervention of PD. Also, these imaging techniques will be used as an effective tool in monitoring the disease progression [8]. Recent research using a gold standard technique for neurological observations revealed that the accuracy of clinical findings in medication non-responsive PD patients is 26% compared to medication responsive early PD patients is 53%. Also, these studies show that a clinical diagnosis of the disease has a sensitivity of 88% in detecting the disease (positive predictive value) and a specificity of 68%. Thus, the clinical diagnosis in the early stages is not precise enough and emphasizes the importance of brain imaging in the process of pathogenesis [2]. The early diagnosis of PD attracts promising clinical and economic benefits. Medication like L-dopa, dopamine agonists and Monoamine Oxidase Type-B are used to reduce the disease progression. These medications will reduce the symptoms of PD if there exists a reliable early diagnostic tool.

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2 Positron Emission Tomography PET Scan Imaging PET scan is used to find the functional information of a cell in the body [20]. The scan is done by injecting positively charged radiotracers into the vein [21]. These positively charged tracers combine with the electrons present in the vein [22]. The PET scan helps in understanding the pathophysiology of PD by imaging the brain [23]. It is a type of nuclear imaging that helps in the quantitative analysis of neurotransmitters. PET scan is highly sensitive, that allows differential and early diagnosis of PD and helps in monitoring the disease progression. The PET imaging that is used for the diagnosis of PD are dopaminergic and non-dopaminergic imaging (Table 2). a.

Dopaminergic Imaging

Synapse is a junction at which transmission of information from one neuron to another neuron will happen. The neuron that transmits information towards the synapse are known as presynaptic neurons and the neurons that transmit away from the synapse are called postsynaptic neurons. The functionality of presynaptic neuron is assisted by measuring the aromatic amino acid decarboxylase activity (AADC), dopamine transporter (DAT) and vescular monoamine transporter (VMAT2) density. i.

Presynaptic neuro imaging

Masayuki Miyamoto et al. [24] did the analysis of striatal aromatic I-amino acid activity in patients those who are suffering from idiopathic arapid eye movement sleep behaviour disorder (IRBD). The study is carried out using 6-[18 F]fluoro-metatyrosine brain positron emission tomography (FMT-PET). This study was used to analyse the risk for developing PD or dementia with Lewy bodies (DLB). The authors assessed 24 IRBD patients and 11 patients developed PD(n = 6) or DLB(n = 5) in the later stages. In another study both the DAT and VMAT2 activity in brain is studied using multi-tracer PET scan to identify the unique features (Fu, Jessie Fanglu et al. 2019). This study was carried out in 15 early PD patients. In this joint pattern analysis technique [11 C]-Dihydrotetrabenazine (DTBZ) is used for analysing VMAT2 and [11 C]-methylphenidate (MP) is used for the analysis of DAT. Whereas various other authors have used DAT tracers like 18 F-FE-PE2I [30], 11C-methylphenidate, 18 Fdopamine (18 F-dopa) [2], 18 F-LBT999 [31], and 18 F-b-CFT [32]. The dopaminergic cell loss and subsequent loss of VMAT2, can be deduced using radiolabeled dihydrotetrabenazine (DTBZ) PET signal [33], (Lin et al. 2013). There is another radiotracer that can track the level of acetylcholinesterase (AChE) activity is 11 C-MP4A [34]. The tracers can also be used to understand the disease progression and effect of medication. The joint pattern analysis proved more sensitive in the characteristic analysis of spatial and temporal pattern distribution. ii.

Postsynaptic neuro imaging

Author

Technique used

Fu, Jessie Fanglu et al. (2019)

Catafau, Ana et al. [25]

Rinne et al. [26], Turjanski et al. [27], Antonini et al. [28], Leenders et al. [29]

Cortical dopamine D1 receptor

Striatal D2 receptor

Postsynaptic dopaminergic imaging

Vescular monoamine transporter (VMAT2) density

Dopamine transporter (DAT)

Used to identify the density of dopamine in brain and to study the brain activity. The scan of 24 PD subjects attracted a Sensitivity 95.4% and Specifity is 100%. (Ibrahim et al. [2]

Idiopathic arapid eye movement sleep behaviour disorder is diagnosed.

Impact

11 C-raclopride

(RAC) tracer is used.

The accuracy of system will increase by 10-20% if RAC is used in combination.

Using [11 C] Used to analyse the NNC112(8-chloro-7-hydroxy-3-methyl-5-(7-benzofuranyl)-2,3,4,5-tetrahydro-IH-3- post synaptic benzazepine) tracer is used. dopamine in brain.

[11 C]-Dihydrotetrabenazine (DTBZ) tracer is used

[11 C]-methylphenidate (MP) tracer is used.

Aromatic amino acid Masayuki 6-[18 F]fluoro-meta-tyrosine tracer is used decarboxylase activity Miyamoto et al. [24] (AADC)

Presynaptic dopaminergic imaging

Type of dopaminergic degeneration

Table 2 Various Dopaminergic Imaging Techniques

Diagnosis of Parkinson Disease … 67

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In the post synaptic neuro imaging the cortical dopamine D1 receptors are analysed using [11 C] NNC112(8-chloro-7-hydroxy-3-methyl-5-(7-benzofuranyl)2,3,4,5-tetrahydro-IH-3- benzazepine) without significant 5-HT2A R contamination [25]. The 11 C-raclopride (RAC) tracer is used to assess striatal D2 receptors in early PD subjects. This study compared the analysis of D1 and D2 receptors using PET scan and identified an abnormal binding of D2 receptors in early PD [35]. In advanced PD cases RAC PET study has identified that, there is progress in bradykinesia and rigidity scores due to DA medication (Pavese et al. 1993). A rat-activity comparison utilizing PET in research among de novo cases of Parkinsonism demonstrated a 10 to 20% increase in D2 receptor level in the putamen contralateral. In contrast, the caudate nucleus manifests to remain relatively intact [26–29]. b.

Non-Dopaminergic Imaging

PD subjects suffer from non-dopaminergic degenerations such as serotonergic, cholinergic, noradrenergic and microglial activation [36]. Various techniques used to measure the activity of these degeneration is shown in the table (Tables 3 and 4).

2.1 Single-Photon Emission Computed Tomography (SPECT) Scan Imaging 123

I–ioflupane single–photon emission computed tomography [SPECT] (also known as DaTscan) is used to identify the density of presynaptic dopaminergic terminals in the striatum in PD [37]. This would help in differentiating PD from other similar illness that does not show presynaptic dopaminergic terminals [40]. [123 I] N–ω–fluoropropyl–2β–carbomethoxy–3β–(4–iodophenyl) nortropane (FP–CIT) is a dopamine transporter imaging [DAT] agent [41]. The Correlation of values obtained from FP–CIT SPECT and F–DOPA PET is an effective tool to differentiate the various stages of PD [42]. SPECT radiotracers also monitors the vesicular acetylcholine transporter (VAChT) to support early PD diagnosis [43]. The reduction of VAChT in PD subjects with dementia and without dementia is studied using SPECT radiotracers [17]. 123 I-iodobenzamide (123 I-IBZM) (Reiche et al. 1995), 123 I-IBF (Sasaki et al. 2003), 123 I-epidepride (Pirker et al. 1997) and 123 I-20-iodospiperone (20-ISP) are used to monitor the dopamine antagonists to distinguish PD and other parkinsonism disorders [44]. SPECT scan with sympathomimetic amine 123 Imetaiodobenzylguanidine (123 I-MIBG) injection will help to predict cardiac sympathetic innervation. This technique thus helps to distinguish Parkinson subjects from Multiple system atrophy (MSA) [38, 39]. Vasopressin (also known as antidiuretic hormone [ADH]) has analogues, including 123 I-metaiodobenzylguanidine (MIBG) that is present in nerves [45]. The imaging studies performed with [123I]-MIBG SPECT identified postganglionic sympathetic dysfunction in the myocardium [46]. Although the MIBG uptake in PD is observed normal. Conversely in atypical Parkinsonian disorders like MSA a

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Table 3 Summary of PET scan Imaging techniques Type of non-dopaminergic degeneration

Author

Technique used

Impact

Serotonergic system

Doder et al.

11 C-WAY100635

The analysis shows that 5-HT1A binding in the midbrain of PD subjects is reduced by 29% when compared to the healthy subjects. Sensitivity is 95.4%

tracer is used that binds to 5-HT1A

Cholinergic system

11 C-DASB

Politis et al. Kerenyi et al.

and tracers are used that specifically bind to SERT and 5-HTT

The studies showed that striatal serotonergic denervation is moderate when compared to striatal DA denervation in PD subjects

Bohnen et al.

11 C-PMP

and are radiotracers used to measure cholinergic systems through the measure of acetylcholinesterase (AChE) levels

The tracers are used to analyse the cholinergic system in dementia in PD

The 11 C-diprenorphine radiotracer is used to measure μ, κ and δ opioid sites and analyse the basal ganglia in PD

The analysis has proved that Opioid neuropeptides are abundant in the basal ganglia of PD subjects

The 11 CPK11195 (a selective marker of peripheral BDZ sites) is used to measure the microglial activation in PD

The analysis of microglial activation in PD will help in the study of motor and non-motor symptoms in PD

11 C-McN5652

11 C-MP4A

Opioid system

Koepp et al. Haber et al. Fernandez et al.

Microglial activation Ouchi, Yoshikawa in PD et al. Ouchi, Yagi et al. Gerhard et al.

normal or mild less sympathetic innervation in heart is observed. Though essential data obtained with MIBG SPECT, the technique has its drawbacks. The ratio of 123IMIBG cardiac to mediastinal activity is quantified, and observed that commercial SPECT cameras have comparatively less sensitivity and spatial resolution than PET cameras. A serotonergic transporter (SERT) study binding in the midbrain with 123I-betaCIT SPECT as a marker includes 45 PD subjects and 7 same age control subjects. The temporal patterns and cerebral activity of the midbrain had no significant difference in PD subjects than in controls. While the striatal tracer distribution V3, DAT binding observed to be reduced in all PD patients. Thus, in early PD, the midbrain 5-HT

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Table 4 Various SPECT imaging tools to diagnose PD Type of degeneration

Author

Technique used

Impact

DAT scan

Stoessl et al. [37]

123 I–ioflupane

The DAT is analysed to study the presynaptic dopaminergic terminals

Vesicular acetylcholine transporter (VAChT)

Niethammer et al. [17]

SPECT tracers are used

It is used in the early diagnosis of PD

Myocardial postganglionic sympathetic dysfunction

Pahuja et al. [38], Goldstein, David et al. [39]

123 I-metaiodobenzylguanidine

It is used to identify cardiac sympathetic denervation

tracer is used

(MIBG) tracer is used

transporter binding remains the same as in control subjects. For the presynaptic assessment of cholinergic function, the vesicular transporter binding is observed with IBVM SPECT in PD subjects. This study identified a reduction in IBVM binding among PD without dementia and more extensive in demented PD subjects [45].

3 Magnetic Resonance Imaging (MRI) Despite several limitations, the diagnostic accuracy of PD is enhanced over the past decades using Magnetic resonance imaging [47]. The brain MRI is mainly used to differentiate the PD from Parkinsonian diseases in the early stages [48, 49]. The MRI image is a technique in radiology used to obtain clear images of organs and tissues. Before the diagnosis of PD is established, 80% of the striatal dopamine and approximately 60−70% of nigrostriatal neurons would have been degenerated [50]. This is the stage when motor symptoms are not established [51]. There are advanced MRI techniques that can differentiate early PD from other similar diseases. They are diffusion weighted and diffusion tensor imaging, Magnetic resonance spectroscopy imaging and functional MRI. The functional MRI would also identify the abnormalities shown in the olfactory system in PD [52]. Also, the diffusion weighted imaging measures the amount of water through tissues and the diffusivity determines the death of cells [53]. This technique is used to categorize the PD from MSA [54] (Table 5).

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Table 5 Different MRI techniques to diagnose PD Type of information

Author

Technique used

Impact

Superior cerebellar peduncle volume

Paviour et al. (2005)

Structural MRI -T2 and T1 weighted sequences

19 PSP, 12 PD, 10MSA and 12 HC subjects are compared. Sensitivity of PSP compared with other disorder is 74% and specificity is 77%

rCBF

Huppertz et al. (2016)

Perfusion (Arterial spine labelling)

PSP 106, MSA-P 60,MSA-C 21, and PD 204 are compared. Obtained an accuracy level of ≥ 80% in classifying the groups

Abbreviations: rCBF—Regional cerebral blood flow, MSA-P—parkinsonian variant of MSA, MSAC—cerebellar variant of MSA, PD—Parkinson’s disease, PSP—progressive supranuclear palsy

4 Transcranial B-Mode Sonography (TCS) A means of measuring blood flow in brain vessels is B-mode sonography. The frequency of ultrasounds waves and their echoes quantified the blood flow velocity [55]. It is a reliable technique and cost-effective technique that shows good echogenicity of sympathetic neurons in PD subjects than in control subjects (Skoloudík et al. 2014). The increased iron level in PD can be due to either alternation or malfunction of the Blood Brain-Barrier. TCS images observed a marked decline in mesencephalic midline echogenicity of depressed vs. non—depressed PD patients; on the other hand, established no correlation between raphe signal intensity, T2 relaxation times, and TCS echogenicity and the severity of motor symptoms. The data showed that as brainstem midline structures (basal limbic structures) were comprised of fiber tracts and nuclei of the basal limbic system, this may support the hypothesis of an alteration in the basal limbic system (BLM) in mood disorders.

5 Thermal Imaging in the early diagnosis of PD The PD subjects suffer from autonomic denervation and experience abnormal vasoconstriction in the early stages of the disease [56]. The vasoconstriction in PD with cause cold limbs [57, 58] Autonomic denervation caused due α-synuclein deposits in PD subjects is proved by skin biopsies [59, 60]. The vasoconstriction in PD subjects is evaluable by performing cold stress test. A doppler flowmeter is used to measure this alteration in blood flow due to skin vasomotor reflex (SVR) [61]. In recent years this change in blood flow is measured using a non-invasive thermal imaging technique [62]. To quantify the sympathetic vasoconstriction and thermal recovery

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various authors have combined CST as a stimulus (Antonio-Rubio et al. 2015). The thermal recovery can be studied using a high-definition infrared camera (Ring et al. [63]. To validate the outcomes thermal responses of PD and control subjects are compared and that showed promising outcomes [64]. Thus, thermography would be a useful and precise technique to validate autonomic dysfunction in PD subjects [65].

6 Conclusion The complex pathology of PD includes genetics, epigenetics and environmental factors [66]. Early diagnosis of PD includes sleep disturbance, hyposmia, autonomic dysfunction and many other symptoms that are predominant in the initial stages of the disease [67]. Also, the dopamine medications are used to diagnose the disease. This can even worse the symptoms if the exact stage of PD is not recognised. When the disease has clear evident of motor symptoms i.e., in the stage when substantial neurophysiological damage has already taken place. In this stage achieving neuroprotection will be out of reach. Thus, detection of PD prior to the onset of significant motor symptoms would help preserve the damage of substantial neurons. Various imaging techniques are discussed which are cost-effective and helps in the early diagnosis of the disease. If the disease is diagnosed in an early stage then dopamine agonists would delay the motor phase, dyskinesia and will reduce the need of L-dopa [68]. Studies were done to understand the socio-economic burden of PD subjects and their family [69]. This study has proved that cost of living for a subject in advanced stages is high when compared to the cost of living of early onset PD subjects. [70] In order to dilute the disease progression, early PD diagnosis is required. Thus, in this article various non-invasive, effective and cost-effective techniques are shown for the early PD diagnosis. The accuracy of early diagnosis can be increased by using a combination of above-mentioned techniques.

References 1. Maiti, P., Manna, J., Dunbar, G.L.: Current understanding of the molecular mechanisms in Parkinson’s disease: Targets for potential treatments. Transl. Neurodegener. 6, 28 (2017) 2. Ibrahim, N., Kusmirek, J., Struck, A.F., Floberg, J.M., Perlman, S.B., Gallagher, C., Hall, L.T.: The sensitivity and specificity of F-DOPA PET in a movement disorder clinic. Am. J. Nucl. Med. Molec. Imag. 6(1), 102 (2016) 3. Jankovic, J., Tan, E.K.: Parkinson’s disease: etiopathogenesis and treatment. J. Neurol. Neuros. Psyc. 91(8), 795–808 (2020) 4. Hong, Z., Shi, M., Chung, K.A., Quinn, J.F., Peskind, E.R., Galasko, D., … Zhang, J.: DJ-1 and α-synuclein in human cerebrospinal fluid as biomarkers of Parkinson’s disease. Brain 133(3), 713 − 726 (2010) 5. Longhena, F., Faustini, G., Missale, C., Pizzi, M., Spano, P., Bellucci, A.: The contribution of α-synuclein spreading to Parkinson’s disease synaptopathy. Neur. Plast. (2017)

Diagnosis of Parkinson Disease …

73

6. El Agnaf, O.M., Salem, S.A., Paleologou, K.E., Curran, M.D., Gibson, M.J., Court, J.A., … Allsop, D.: Detection of oligomeric forms of α synuclein protein in human plasma as a potential biomarker for Parkinson’s disease. The FASEB J. 20(3), 419 − 425 (2006) 7. Berendse, H.W., Booij, J., Francot, C.M., Bergmans, P.L., Hijman, R., Stoof, J.C., Wolters, E.C.: Subclinical dopaminergic dysfunction in asymptomatic Parkinson’s disease patients’ relatives with a decreased sense of smell. Ann. Neurol. Official J. Am. Neurol. Assoc. Child Neurol. Soc. 50(1), 34–41 (2001) 8. Murman, D.L.: Early treatment of Parkinson’s disease: opportunities for managed care. Am. J. Manag. Care 18(7), S183 (2012) 9. Martinez-Martin, P., Rodriguez-Blazquez, C., Kurtis, M.M., Chaudhuri, K.R., NMSS Validation Group: The impact of non-motor symptoms on health-related quality of life of patients with Parkinson’s disease. Movem. Disord. 26(3), 399–406 (2011) 10. Prakash, K.M., Nadkarni, N.V., Lye, W.K., Yong, M.H., Tan, E.K.: The impact of non-motor symptoms on the quality of life of Parkinson’s disease patients: a longitudinal study. European J. Neurol. 23(5), 854–860 (2016) 11. Antonini, A., Barone, P., Marconi, R., Morgante, L., Zappulla, S., Pontieri, F.E., … Colosimo, C.: The progression of non-motor symptoms in Parkinson’s disease and their contribution to motor disability and quality of life. J. Neurol. 259(12), 2621 − 2631 (2012) 12. Busse, K., Heilmann, R., Kleinschmidt, S., Abu-Mugheisib, M., Höppner, J., Wunderlich, C., Walter, U.: Value of combined midbrain sonography, olfactory and motor function assessment in the differential diagnosis of early Parkinson’s disease. J. Neurol. Neurosurg. Psyc. 83(4), 441–447 (2012) 13. Lebouvier, T., Neunlist, M., des Varannes, S.B., Coron, E., Drouard, A., N’Guyen, J.M., … Derkinderen, P.: Colonic biopsies to assess the neuropathology of Parkinson’s disease and its relationship with symptoms. PloS One, 5(9), e12728 (2010) 14. Tolosa, E., Gaig, C., Santamaría, J., Compta, Y.: Diagnosis and the premotor phase of Parkinson disease. Neurology, 72(7 Supplement 2), S12 − S20 (2009) 15. Imperatore, R., Palomba, L., Cristino, L.: Role of Orexin-a in hypertension and obesity. Curr. Hypertens. Rep. 19, 34. https://doi.org/10.1007/s11906-0729-y 16. Wienecke, M., Werth, E., Poryazova, R., Baumann-Vogel, H., Bassetti, C.L., Weller, M., et al.: Progressive dopamine and hypocretin deficiencies in Parkinson’s disease: is there an impact on sleep and wakefulness? J. Sleep Res. 710–717 (2012). https://doi.org/10.1111/j. 1365-2869.2012.01027.x 17. Niethammer, M., Feigin, A., Eidelberg, D.: Functional neuroimaging in Parkinson’s disease. Cold Spring Harbor Persp. Med. 2(5), (2012) 18. Breen, D.P., Michell, A.W., Barker, R.A.: Parkinson’s disease–the continuing search for biomarkers. Clin. Chem. Laborat. Med. 49(3), 393–401 (2011) 19. Breen, D.P., Rowe, J.B., Barker, R.A.: Role of brain imaging in early parkinsonism. BMJ, 342 (2011) 20. Loane, C., Politis, M.: Positron emission tomography neuroimaging in Parkinson’s disease. Am. J. Transl. Res. 3(4), 323 (2011) 21. Peppard, R.F., Martin, W.W., Carr, G.D., Grochowski, E., Schulzer, M., Guttman, M., … Calne, D.B.: Cerebral glucose metabolism in Parkinson’s disease with and without dementia. Arch. Neurol. 49(12), 1262 − 1268 (1992) 22. Brück, A., Aalto, S., Rauhala, E., Bergman, J., Marttila, R., Rinne, J.O.: A follow-up study on 6-[18F] fluoro-L-dopa uptake in early Parkinson’s disease shows nonlinear progression in the putamen. Movem. Disord. 24(7), 1009–1015 (2009) 23. Pavese, N., Metta, V., Bose, S.K., Chaudhuri, K.R., Brooks, D.J.: Fatigue in Parkinson’s disease is linked to striatal and limbic serotonergic dysfunction. Brain 133(11), 3434–3443 (2010) 24. Miyamoto, M., Miyamoto, T., Saitou, J., Sato, T.: Longitudinal study of striatal aromatic l-amino acid decarboxylase activity in patients with idiopathic rapid eye movement sleep behavior disorder. Sleep Med. 68, 50–56 (2020)

74

A. Brindha et al.

25. Catafau, A.M., Searle, G.E., Bullich, S., Gunn, R.N., Rabiner, E.A., Herance, R., … Laruelle, M.: Imaging cortical dopamine D1 receptors using [11C] NNC112 and ketanserin blockade of the 5-HT2A receptors. J. Cerebral Blood Flow Metabol. 30(5), 985 − 993 (2010) 26. Rinne, J.O., Laihinen, A., Rinne, U.K., Någren, K., Bergman, J., Ruotsalainen, U.: PET study on striatal dopamine D2 receptor changes during the progression of early Parkinson’s disease. Movem. Disord. Off. J. Movem. Disord. Soc. 8(2), 134–138 (1993) 27. Turjanski, N., Lees, A.J., Brooks, D.J.: In vivo studies on striatal dopamine D1 and D2 site binding in L-dopa-treated Parkinson’s disease patients with and without dyskinesias. Neurology 49(3), 717–723 (1997) 28. Antonini, A., Schwarz, J., Oertel, W.H., Beer, H.F., Madeja, U.D., Leenders, K.L.: [11C]raclopride and positron emission tomography in previously untreated patients with Parkinson’s disease: influence of L-dopa and lisuride therapy on striatal dopamine D2receptors. Neurology 44(7), 1325–1329 (1994) 29. Leenders, K.L., Salmon, E.P., Tyrrell, P., Perani, D., Brooks, D.J., Sager, H., Jones, T., Marsden, C.D., Frackowiak, R.S.: The nigrostriatal dopaminergic system assessed in vivo by positron emission tomography in healthy volunteer subjects and patients with Parkinson’s disease. Arch. Neurol. 47(12), 1290–1298 (1990) 30. Fazio, P., Svenningsson, P., Forsberg, A., Jönsson, E.G., Amini, N., Nakao, R., … Varrone, A.: Quantitative analysis of 18F-(E)-N-(3-iodoprop-2-enyl)-2β-carbofluoroethoxy-3β-(4 methyl-phenyl) nortropane binding to the dopamine transporter in Parkinson disease. J. Nucl. Med. 56(5), 714 − 720 (2015) 31. Arlicot, N., Vercouillie, J., Malherbe, C., Bidault, R., Gissot, V., Maia, S., et al.: PET imaging of dopamine transporter with 18F-LBT999: first human exploration. J. Nucl. Med. 58, 1276 (2017). https://doi.org/10.1111/j.1527-3458.2007.00033.x 32. Rinne, J.O., Ruottinen, H., Bergman, J., Haaparanta, M., Sonninen, P., Solin, O.: Usefulness of a dopamine transporter PET ligand [18F] β-CFT in assessing disability in Parkinson’s disease. J. Neurol. Neurosurg. Psych. 67(6), 737–741 (1999) 33. Tong, J., Wilson, A.A., Boileau, I., Houle, S., Kish, S.J.: Dopamine modulating drugs influence striatal (+)-[11C] DTBZ binding in rats: VMAT2 binding is sensitive to changes in vesicular dopamine concentration. Synapse 62(11), 873–876 (2008) 34. Bohnen, N.I., Albm, R.L., Koeppe, R.A., Wernette, K.A., Kilbourn, M.R., Minoshima, S., Frey, K.A.: Positron emission tomography of monoaminergic vesicular binding in aging and Parkinson disease. J. Cerebral Blood Flow & Metabol. 26(9), 1198–1212 (2006) 35. Rinne, J.O., Laihinen, A., Ruottinen, H., Ruotsalainen, U., Någren, K., Lehikoinen, P., Oikonen, V., Rinne, U.K.: Increased density of dopamine D2 receptors in the putamen, but not in the caudate nucleus in early Parkinson’s disease: a PET study with [11C]raclopride. J. Neurol. Sci. 132(2), 156–161 (1995) 36. Brooks, D.J.: Imaging non-dopaminergic function in Parkinson’s disease. Molecul. Imag. Biol. 9(4), 217–222 (2007) 37. Stoessl, A.J., Lehericy, S., Strafella, A.P.: Imaging insights into basal ganglia function, Parkinson’s disease, and dystonia. The Lancet 384(9942), 532–544 (2014) 38. Pahuja, G., Nagabhushan, T.N., Prasad, B.: Early detection of Parkinson’s disease by using SPECT imaging and biomarkers. J. Intell. Syst. 29(1), 1329–1344 (2019) 39. Goldstein, David S.: Sympathetic neuroimaging. In Handbook of clinical neurology, vol. 117, pp. 365 − 370. Elsevier (2013) 40. Politis, M.: Neuroimaging in Parkinson disease: from research setting to clinical practice. Nat. Rev. Neurol. 10(12), 708 (2014) 41. Ishikawa, T., Dhawan, V., Kazumata, K., Chaly, T.: Comparative nigrostriatal dopaminergic imaging with iodine-123-betaCIT-FP/SPECT and fluorine-18-FDOPA/PET. J. Nucl. Med. 37(11), 1760 (1996) 42. Eshuis, S.A., Maguire, R.P., Leenders, K.L., Jonkman, S., Jager, P.L.: Comparison of FPCIT SPECT with F-DOPA PET in patients with de novo and advanced Parkinson’s disease. European J. Nucl. Med. Molecul. Imaging 33(2), 200–209 (2006)

Diagnosis of Parkinson Disease …

75

43. Yousaf, T., Wilson, H., Politis, M.: Imaging the nonmotor symptoms in Parkinson’s disease. In: International Review of Neurobiology, vol. 133, pp. 179 − 257. Academic Press (2017) 44. Kung, H.F., Kung, M.P., Choi, S.R.: Radiopharmaceuticals for single-photon emission computed tomography brain imaging. In Seminars in nuclear medicine, vol. 33, no. 1, pp. 2 − 13. WB Saunders (2003, January) 45. Brooks, D.J., Seppi, K., Neuroimaging Working Group on MSA.: Proposed neuroimaging criteria for the diagnosis of multiple system atrophy. Movem. Disord. 24(7), 949 − 964 (2009) 46. Yoshita, M.: Cardiac uptake of [123I]MIBG separates PD from multiple system atrophy. Neurology 54, 1877–1878 (2000) 47. Heim, B., Krismer, F., De Marzi, R., Seppi, K.: Magnetic resonance imaging for the diagnosis of Parkinson’s disease. J. Neural Trans. 124(8), 915–964 (2017) 48. Hotter, A., Esterhammer, R., Schocke, M.F., Seppi, K.: Potential of advanced MR imaging techniques in the differential diagnosis of parkinsonism. Mov. Disord. Off. J. Mov. Disord. Soc. 24(Suppl 2), S711–S720 (2009) 49. Mahlknecht, P., Hotter, A., Hussl, A., Esterhammer, R., Schocke, M., Seppi, K.: Significance of MRI in diagnosis and differential diagnosis of Parkinson’s disease. Neurodeg. Dis. 7(5), 300–318 (2010) 50. Riederer, P.: Time course of nigrostriatal degeneration in Parkinson’s disease. J. Neural. Transm. 38(3–4), 277–301 (1976) 51. Brooks, D.J.: Morphological and functional imaging studies on the diagnosis and progression of Parkinson’s disease. J. Neurol. 247(2), II11 − II18 (2000) 52. Marino, S., Ciurleo, R., Di Lorenzo, G., Barresi, M., De Salvo, S., Giacoppo, S., … Bramanti, P.: Magnetic resonance imaging markers for early diagnosis of Parkinson’s disease. Neural Regenerat. Res. 7(8), 611 (2012) 53. Seppi, K., Schocke, M.F.H., Esterhammer, R., Kremser, C., Brenneis, C., Mueller, J., … Wenning, G.K.: Diffusion-weighted imaging discriminates progressive supranuclear palsy from PD, but not from the parkinson variant of multiple system atrophy. Neurology 60(6), 922 − 927 (2003) 54. Chung, E.J., Kim, E.G., Bae, J.S., Eun, C.K., Lee, K.S., Oh, M., Kim, S.J.: Usefulness of diffusion-weighted MRI for differentiation between Parkinson’s disease and Parkinson variant of multiple system atrophy. J. Movem. Disord. 2(2), 64 (2009) 55. Borghammer, Per, Karoline Knudsen, Tatyana D. Fedorova, David J. Brooks.: Imaging Parkinson’s disease below the neck. npj Parkinson’s Dis. 3(1), 1 − 10 (2017) 56. Johnson, J.M., Kellogg Jr., D.L.: Local thermal control of the human cutaneous circulation. J. Appl. Physiol. 109(4), 1229–1238 (2010) 57. Nolano, M., Provitera, V., Estraneo, A., Selim, M. M., Caporaso, G., Stancanelli, A., … Santoro, L.:. Sensory deficit in Parkinson’s disease: evidence of a cutaneous denervation. Brain, 131(7), 1903 − 1911 (2008) 58. Shindo, K., Iida, H., Watanabe, H., Ohta, E., Nagasaka, T., Shiozawa, Z.: Sympathetic sudomotor and vasoconstrictive neural function in patients with Parkinson’s disease. Parkinsonism Relat. Disord. 14(7), 548–552 (2008) 59. Donadio, V., Incensi, A., Leta, V., Giannoccaro, M.P., Scaglione, C., Martinelli, P., … Liguori, R.: Skin nerve α-synuclein deposits: a biomarker for idiopathic Parkinson disease. Neurology 82(15), 1362 − 1369 (2014) 60. Wang, N., Gibbons, C.H., Lafo, J., Freeman, R.: α-Synuclein in cutaneous autonomic nerves. Neurology 81(18), 1604–1610 (2013) 61. Akaogi, Y., Asahina, M., Yamanaka, Y., Koyama, Y., Hattori, T.: Sudomotor, skin vasomotor, and cardiovascular reflexes in 3 clinical forms of Lewy body disease. Neurology 73(1), 59–65 (2009) 62. Pauling, J.D., Flower, V., Shipley, J.A., Harris, N.D., McHugh, N.J.: Influence of the cold challenge on the discriminatory capacity of the digital distal–dorsal difference in the thermographic assessment of Raynaud’s phenomenon. Microvasc. Res. 82(3), 364–368 (2011) 63. Ring, E.F.J., Ammer, K.: Infrared thermal imaging in medicine. Physiol. Measurem. 33(3), R33 (2012)

76

A. Brindha et al.

64. Asahina, M., Low, D.A., Mathias, C.J., Fujinuma, Y., Katagiri, A., Yamanaka, Y., …, Kuwabara, S.: Skin temperature of the hand in multiple system atrophy and Parkinson’s disease. Parkins. Rel. Disord. 19(5), 560 − 562 (2013) 65. Purup, M.M., Knudsen, K., Karlsson, P., Terkelsen, A.J., Borghammer, P.: Skin temperature in parkinson’s disease measured by infrared thermography. Parkinson’s Disease (2020) 66. Braak, H., Del Tredici, K., Rüb, U., De Vos, R.A., Steur, E.N.J., Braak, E.: Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol. Aging 24(2), 197–211 (2003) 67. Reichmann, H., Brandt, M.D., Klingelhoefer, L.: The nonmotor features of Parkinson’s disease: pathophysiology and management advances. Curr. Opin. Neurol. 29(4), 467–473 (2016) 68. Delea, T.E., Thomas, S.K., Hagiwara, M.: The association between Adherence to Levodopa/Carbidopa/Entacapone therapy and healthcare utilization and costs among patients with Parkinson’s disease. CNS Drugs 25(1), 53–66 (2011) 69. Davis, K.L., Edin, H.M., Allen, J.K.: Prevalence and cost of medication nonadherence in Parkinson’s disease: evidence from administrative claims data. Movem. Disord. 25(4), 474– 480 (2010) 70. Pagano, G., Niccolini, F., Politis, M.: Imaging in Parkinson’s disease. Clin. Med. 16(4), 371 (2016) 71. Mhyre, T.R., Boyd, J.T., Hamill, R.W., Maguire-Zeiss, K.A.: Parkinson’s disease. Sub-Cell. Biochem. 65, 389–455 (2012). https://doi.org/10.1007/978-94-007-5416-4_16 72. Meade, R.M., Fairlie, D.P., Mason, J.M.: Alpha-synuclein structure and Parkinson’s disease– lessons and emerging principles. Molec. Neurodegen. 14(1), 1–14 (2019) 73. Pagan, F.L.: Improving outcomes through early diagnosis of Parkinson’s disease. Am. J. Manag. Care 18(7), S176 (2012) 74. Lang, A.E.: A critical appraisal of the premotor symptoms of Parkinson’s disease: potential usefulness in early diagnosis and design of neuroprotective trials. Movem. Disord. 26(5), 775–783 (2011) 75. Hagan, J.J., Leslie, R.A., Patel, S., Evans, M.L., Wattam, T.A., Holmes, S., et al.: Orexin a activates locus coeruleus cell firing and increases arousal in the rat. Proc. Nat. Acad. Sci. U.S.A. 96, 10911–10916. (1999). https://doi.org/10.1073/pnas.96.19.10911 76. Jain, S.: Multi-organ autonomic dysfunction in Parkinson disease. Parkins. Relat. Disord. 17(2), 77–83 (2011) 77. Fahn, S., Oakes, D., Shoulson, I., Kieburtz, K., Rudolph, A., Lang, A., Olanow, C.W.: Levodopa and the progression of Parkinson’s disease. N. Engl. J. Med. 351 COI, 1, 2498 − 2508 (2004) 78. Stocchi, F., Rascol, O., Kieburtz, K., Poewe, W., Jankovic, J., Tolosa, E., … Olanow, C.W.: Initiating levodopa/carbidopa therapy with and without entacapone in early Parkinson disease: the STRIDE-PD study. Annal. Neurol. 68(1), 18 − 27 (2010) 79. Rascol, O., Brooks, D.J., Korczyn, A.D., De Deyn, P.P., Clarke, C.E., Lang, A.E.: A fiveyear study of the incidence of dyskinesia in patients with early Parkinson’s disease who were treated with ropinirole or levodopa. New Engl. J. Med. 342(20), 1484–1491 (2000) 80. Whone, A.L., Watts, R.L., Stoessl, A.J., Davis, M., Reske, S., Nahmias, C., …, REAL-PET study group.: Slower progression of Parkinson’s disease with ropinirole versus levodopa: the REAL-PET study. Ann. Neurol. 54(1), 93 − 101 (2003) 81. Parkinson Study Group: Pramipexole vs levodopa as initial treatment for Parkinson disease: a randomized controlled trial. JAMA 284(15), 1931–1938 (2000) 82. Parkinson Study Group: Dopamine transporter brain imaging to assess the effects of pramipexole vs levodopa on Parkinson disease progression. JAMA 287(13), 1653–1661 (2002) 83. Stowe, R., Ives, N., Clarke, C.E., Ferreira, J., Hawker, R.J., Shah, L., …, Gray, R.: Dopamine agonist therapy in early Parkinson’s disease. Cochrane Database Syst. Rev. (2) (2008) 84. Ives, N.J., Stowe, R.L., Marro, J., Counsell, C., Macleod, A., Clarke, C.E., …, Wheatley, K.: Monoamine oxidase type B inhibitors in early Parkinson’s disease: meta-analysis of 17 randomised trials involving 3525 patients. BMJ 329(7466), 593 (2004)

Diagnosis of Parkinson Disease …

77

85. Parkinson Study Group: Effects of tocopherol and deprenyl on the progression of disability in early Parkinson’s disease. New England J. Med. 328(3), 176–183 (1993) 86. Siderowf, Andrew., Stern, Matthew., Shoulson, Ira., Kieburtz, Karl., Oakes, David., Day, Denni., Shinaman, Aileen., et al.: A controlled trial of rasagiline in early Parkinson disease: the TEMPO study. Arch. Neurol. 59(12), 1937–1943 (2002) 87. Olanow, C.W., Rascol, O., Hauser, R., Feigin, P.D., Jankovic, J., Lang, A., …, Tolosa, E.: A double-blind, delayed-start trial of rasagiline in Parkinson’s disease. New England J. Med. 361(13), 1268 − 1278 (2009) 88. Rascol, O., Fitzer-Attas, C.J., Hauser, R., Jankovic, J., Lang, A., Langston, J.W., …, Olanow, C.W.: A double-blind, delayed-start trial of rasagiline in Parkinson’s disease (the ADAGIO study): prespecified and post-hoc analyses of the need for additional therapies, changes in UPDRS scores, and non-motor outcomes. The Lancet Neurol. 10(5), 415 − 423 (2011) 89. Hauser, R.A., Lew, M.F., Hurtig, H.I., Ondo, W.G., Wojcieszek, J., Fitzer-Attas, C.J., TEMPO Open-label Study Group.: Long-term outcome of early versus delayed rasagiline treatment in early Parkinson’s disease. Movem. Disord. 24(4), 564 − 573 (2009) 90. Fu, J.F., Klyuzhin, I., McKenzie, J., Neilson, N., Shahinfard, E., Dinelle, K., … Sossi, V.: Joint pattern analysis applied to PET DAT and VMAT2 imaging reveals new insights into Parkinson’s disease induced presynaptic alterations. NeuroImage Clin. 23, 101856 (2019) 91. Pavese, N., Evans, A.H., Tai, Y.F., Hotton, G., Brooks, D.J., Lees, A.J., Piccini, P.: Clinical correlates of levodopa-induced dopamine release in Parkinson disease: a PET study. Neurology 67(9), 1612–1617 (2006) 92. Sargent, P.A., Kjaer, K.H., Bench, C.J., Rabiner, E.A., Messa, C., Meyer, J., … Cowen, P.J.: Brain serotonin1A receptor binding measured by positron emission tomography with [11C] WAY-100635: effects of depression and antidepressant treatment. Arch. Gen. Psyc. 57(2), 174 − 180 (2000) 93. Politis, M., Wu, K., Loane, C., Kiferle, L., Molloy, S., Brooks, D.J., Piccini, P.: Staging of serotonergic dysfunction in Parkinson’s disease: an in vivo 11C-DASB PET study. Neurobiol. Dis. 40(1), 216–221 (2010) 94. Kerenyi, L., Ricaurte, G.A., Schretlen, D.J., McCann, U., Varga, J., Mathews, W.B., … Szabo, Z.: Positron emission tomography of striatal serotonin transporters in Parkinson disease. Arch. Neurol. 60(9), 1223 − 1229 (2003) 95. Koepp, M.J., Duncan, J.S.: PET: opiate neuroreceptor mapping. Adv. Neurol. 83, 145–156 (2000) 96. Haber, S.N., Watson, S.J.: The comparative distribution of enkephalin, dynorphin and substance P in the human globus pallidus and basal forebrain. Neuroscience 14(4), 1011–1024 (1985) 97. Fernandez, A., de Ceballos, M.L., Jenner, P., Marsden, C.D.: Neurotensin, substance P, delta and mu opioid receptors are decreased in basal ganglia of Parkinson’s disease patients. Neuroscience 61(1), 73–79 (1994) 98. Ouchi, Y., Yoshikawa, E., Sekine, Y., Futatsubashi, M., Kanno, T., Ogusu, T., Torizuka, T.: Microglial activation and dopamine terminal loss in early Parkinson’s disease. Ann. Neurol. 57(2), 168–175 (2005) 99. Ouchi, Y., Yagi, S., Yokokura, M., Sakamoto, M.: Neuroinflammation in the living brain of Parkinson’s disease. Parkinsonism Rel. Disord. 15(Suppl 3), S200–S204 (2009) 100. Gerhard, A., Pavese, N., Hotton, G., Turkheimer, F., Es, M., Hammers, A., Eggert, K., Oertel, W., Banati, R.B., Brooks, D.J.: In vivo imaging of microglial activation with [11C](R)-PK11195 PET in idiopathic Parkinson’s disease. Neurobiol. Dis. 21(2), 404–412 (2006) 101. Druschky, A., Hilz, M.J., Platsch, G., Radespiel-Troger, M., Druschky, K., Kuwert, T., Neundorfer, B.: Differentiation of Parkinson_s disease and multiple system atrophy in early disease stages by means of I-123- MIBG-SPECT. J. Neurol. Sci. 175, 3–12 (2000) 102. Reinhardt, M.J., Jungling, F.D., Krause, T.M., Braune, S.: Scintigraphic differentiation between two forms of primary dysautonomia early after onset of autonomic dysfunction: value of cardiac and pulmonary iodine-123 MIBG uptake. Eur. J. Nucl. Med. 27, 595–600 (2000)

78

A. Brindha et al.

103. Courbon, F., Brefel-Courbon, C., Thalamas, C., Alibelli, M.J., Berry, I., Montastruc, J.L., Rascol, O., Senard, J.M.: Cardiac MIBG scintigraphy is a sensitive tool for detecting cardiac sympathetic denervation in Parkinson_s disease. Mov. Disord. 18, 890–897 (2003) 104. Satoh, A., Serita, T., Seto, M., Tomita, I., Satoh, H., Iwanaga, K., Takashima, H., Tsujihata, M.: Loss of 123I-MIBG uptake by the heart in Parkinson_s disease: assessment of cardiac sympathetic denervation and diagnostic value. J. Nucl. Med. 40, 371–375 (1999) 105. Braune, S., Reinhardt, M., Schnitzer, R., Riedel, A., Lucking, C.H.: Cardiac uptake of [123I]MIBG separates Parkinson_s disease from multiple system atrophy. Neurology 53, 1020–1025 (1999) 106. Yoshita, M.: Differentiation of idiopathic Parkinson_s disease from striatonigral degeneration and progressive supranuclear palsy using iodine-123 meta-iodobenzylguanidine myocardial scintigraphy. J. Neurol. Sci. 155, 60–67 (1998) 107. Hirayama, M., Hakusui, S., Koike, Y., Ito, K., Kato, T., Ikeda, M., Hasegawa, Y., Takahashi, A.: A scintigraphical qualitative analysis of peripheral vascular sympathetic function with meta-[123I]iodobenzylguanidine in neurological patients with autonomic failure. J. Auton. Nerv. Syst. 53, 230–234 (1995) 108. Becker, G., Berg, D., Lesch, K.P., Becker, T.: Basal limbic system alteration in major depression: a hypothesis supported by transcranial sonography and MRI findings. Int. J. Neuropsychopharmacol. 4, 21–31 (2001) 109. Berg, D., Supprian, T., Hofmann, E., Zeiler, B., Jager, A., Lange, K.W., Reiners, K., Becker, T., Becker, G.: Depression in Parkinson_s disease: brainstem midline alteration on transcranial sonography and magnetic resonance imaging. J. Neurol. 246, 1186–1193 (1999) 110. Wijeratne, T., Grisold, W., Trenkwalder, C., Carroll, A.M.W.: World brain day 2020: move together to end Parkinson’s disease. J. Neurol. Sci. 416, 116996 (2020) 111. Krüger, R., Klucken, J., Weiss, D., Tönges, L., Kolber, P., Unterecker, S., … Riederer, P.: Classification of advanced stages of Parkinson’s disease: translation into stratified treatments. J. Neural Trans. 124(8), 1015 − 102 (2017) 112. Pfeiffer, R.F.: Non-motor symptoms in Parkinson’s disease. Parkinsonism Rel. Disord. 22, S119–S122 (2016) 113. Massano, J., Bhatia, K.P.: Clinical approach to Parkinson’s disease: features, diagnosis, and principles of management. Cold Spring Harbor Persp. Med. 2(6), (2012) 114. Pyatigorskaya, N., Gallea, C., Garcia-Lorenzo, D., Vidailhet, M., Lehericy, S.: A review of the use of magnetic resonance imaging in Parkinson’s disease. Therap. Adv. Neurol. Disord. 7(4), 206–220 (2014)

Inertial Measurement Units for Gait Analysis of Parkinson’s Disease Patients Sana M. Keloth, Sridhar P. Arjunan, Peter John Radcliffe, and Dinesh Kumar

Abstract Wearable and wireless sensors and technologies have been developed to monitor the gait of Parkinson’s Disease (PD) patients. While there is significant research in this field, however, these are yet not being routinely used by clinicians. A systematic, state-of-the-art literature review of the relevant literature published in the last 10 years (from 2009-July 2019) was conducted in this study. The results reveal that many researchers have not considered confounding factors such as age, gender, medication, and size of the patients. Another important observation is that sensor calibration, and methods of denoising have frequently not been reported. One common shortcoming is that often the studies have been conducted with a small number of participants. This review concludes that research needs to be conducted with a larger number of participants, and where the effects of the confounding factors, calibration methods, and denoising details are reported. Keywords IMU · Gait · Parkinson’s disease · Sensors

1 Introduction Parkinson’s disease (PD) patients have gait and postural impairment [1, 2] and these, along with handwriting [3, 4] and speech [5, 6], are one of the major symptoms that are used for diagnosis and monitoring of the disease [7–9]. Objective and detailed analysis of gait and posture is performed in gait laboratories which are equipped with multiple, high-speed cameras and pressure mats; which are large and highvalue facilities that are generally located in urban hospitals, limiting regular access for many PD patients [10, 11]. In most cases, however, gait and posture assessment is performed by trained neurologists, which requires the patients to visit their neurology S. M. Keloth · P. J. Radcliffe · D. Kumar Biosignals Lab, School of Engineering, RMIT University, Melbourne, VIC 3000, Australia S. P. Arjunan (B) Department of Electronics and Instrumentation Engineering, SRM Institute of Science and Technology, Chennai, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 S. P. Arjunan et al. (eds.), Techniques for Assessment of Parkinsonism for Diagnosis and Rehabilitation, Series in BioEngineering, https://doi.org/10.1007/978-981-16-3056-9_6

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clinics, and can result in their infrequent assessment [12, 13]. Timely identification of gait and posture disorder symptoms and subsequent monitoring of its progress in these patients can prevent falls and related injuries [14, 15]. It is well recognized that monitoring the true gait of the patients should best be done in their natural habitat and during their normal activity [16, 17]. However, the current gait laboratories are unsuitable for this purpose, as these require the patient to be in purpose-built facilities. To overcome this, it is necessary to have gait monitoring devices that are wireless and portable. The growth of micro-electro-mechanical systems (MEMS) and wireless technologies have led to miniaturized, portable, wireless inertial measurement unit (IMU) devices which are relatively inexpensive [18– 20]. One major application of MEMS IMUs is measuring human movement [21]. For example, MEMS IMUs have been used in applications such as indoor position localization and gait and posture analysis [22]. Due to the small and portable nature of these devices, they can easily be worn by the user while performing their regular activities [23–25] and hence provide parameters of their natural gait impairment. The gait of PD patients is often disturbed which is a major cause of their falls [26– 28] and its deterioration is an indicator of the progression of the disease [29, 30]. Gait is one of the measures for the Unified Parkinson’s Disease Rating Scale (UPDRS) [31] and is scored from 0–4 by a neurologist evaluating the patient disease severity [32–34]. One limitation of this approach is the subjectivity, sensitivity to scoring, and clinician bias in the evaluation [35]. Hence, there is a need for quantifiable and easy to use gait analysis techniques to study PD patients. Wearable sensors that can record the spatial and temporal parameters of gait can be useful for gait analysis of PD patients. In the selection of suitable sensors, it is essential to identify those that are suitable for monitoring the clinically relevant parameters. This review has evaluated the papers that report the use of IMU for performing gait analysis of PD patients. This review also reports the works that have selected the appropriate choice of gait features and suitable measurement devices for characterizing gait in PD patients. The first step of this review was to identify the research that has been conducted to identify the suitable features that can distinguish between PD patients and control subjects. The second step in this review was to identify the papers that report the use of IMU for gait analysis, with a focus on the work related to the identification of gait abnormalities of PD patients. This is a systematic, state-of-the-art literature review, which will help understand the use of the IMU sensor for gait analysis. We believe that the major impact of this review would be that it will help future researchers to facilitate the use of the IMU sensor for gait analysis of PD patients, identify the possible future research opportunities and determine the potential for translation of the technology.

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2 Materials and Methods This systematic and state-of-the-art review paper reports the outcome of the search for the development and use of IMU for gait analysis over the past 10 years (2009– July 2019). The terms and keywords used for the literature search in Pubmed were (“Parkinson” OR “Parkinson’s disease”) AND (“inertia” OR “wearable sensors” OR “body-fixed sensor” OR “accelerometer” OR “gyroscope” OR “gait” OR “walk”) located within the title and/or abstract. The review was conducted with a focus on the use of IMU for Parkinson’s application. The exclusion criteria were: (1) case studies, books, book chapters, conference articles, editorials, and letters (2) articles reporting results less than 10 subjects due to the low level of reliability and statistical validity that can be obtained from such results. Six recent review papers on wearable sensors technology in PD patients have also been considered in this review. The PRISMA flowchart representing the selection of the article is given in Fig. 1. The first section of the review provides the summary tables which briefly describe the aim, and outcomes reported in each of these papers along with our brief remarks. This has been presented to facilitate the reader to make a high-level comparison between these papers. The next section provides an in-depth discussion of the papers reviewed, followed by identifying the needs and opportunities for future research. This section examines the key issues that have been highlighted by the referenced works, gaps in the research, and thus the potential for future research. Finally, a conclusion summarizes the key findings and the generic issues observed during the review.

3 Results Table 1 summarizes the review for the IMU applications for PD gait analysis. It lists 38 papers that describe original research related to IMU for PD gait analysis and 4 review papers. Table 2 is a selection of 11 papers with the focus on gait variability of the spatiotemporal parameter in PD patients, which have used devices other than IMU sensors for measuring gait. This is a focused review of a very specific IMU features that have been highlighted in Table 1 as being the most promising analysis method. This review has brought together articles from the two disciplines and shows the available solutions and potential research opportunities. Figure 2 shows the details of the selected paper in terms of percentage based on the location of the IMU sensor, basic demographic details, medication details, and H&Y details reported in the paper.

S. M. Keloth et al.

Eligibility

Screening

Idenficaon

82

Records identified through database

Additional records identified through

searching

other sources

(n = 350)

(n = 0)

Records after duplicates removed (n = 322)

Records screened

Records excluded

(n = 322)

(n = 0)

Full-text articles assessed for

Full-text articles excluded,

eligibility

with reasons (Case studies,

(n = 42)

books, book chapters, conference articles, editorials and letters, articles reporting results less than 10 subjects,

Included

walking distance less than 5m, Studies included in qualitative

other than walking activities,

synthesis

robot assisted walking)

(n = 42)

(n = 280)

Fig. 1 PRISMA flowchart describing the selection of the article for the study

4 Review of the Papers This section reports the review of each paper and the gaps in the research reported in these papers. While this review, the papers have largely been presented chronologically.

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Table 1 Part I. Review of the publications on IMU sensors used for gait analysis in PD patients. Each reported study has been briefly summarized in Sect. 4 Sl. no Ref. no Aim of the reported study

Number and Gait parameter Our remarks of the location of IMU significantly paper sensor different between PD patients and control (or PD subtype or other disease types)

1

[36]

Differentiate the early-to-mid stage of PD patients

5, shank, foot, chest, wrist

Cadence

Variability was not able to differentiate

2

[37]

Spatiotemporal measurement in PD

1, LB

Stride length

Spatiotemporal can be used to assess gait in PD

3

[38]

Effect of cueing

1, LB

Without cueing walking velocity and stride length changed

Cueing can improve gait performance in PD patients

4

[39]

Timed up and go test

7, foot, wrist, shank, chest

Cadence, angular velocity of arm swing, turn time, time to turn to sit

Timed up and go test can differentiate between PD and control

5

[40]

Effect of cueing in 5, foot, wrist, PD patients chest

Step time variability

Cueing can improve gait performance in PD patients

6

[41]

Motor patterns of age-matched control and PD patients

1, LB

Temporal measures, jerk, and angular velocity

Characterize PD motor impairment

7

[42]

Phase plot variability of age-matched control and PD patients

1, LB

Angular velocity, the standard deviation of angular velocity

Characterize PD motor impairment

8

[43]

Gait and balance 1, LB in PD subtypePostural Instability Gait Disorder (PIGD), Tremor Dominant (TD)

Gait speed, shorter strides, increased stride variability

PD subtype classification may be useful

9

[44]

IMU for studying 6, foot, shank, the progress of the thigh disease

Stride classification on each segment of the leg

IMU attached to shank was able to differentiate (continued)

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Table 1 (continued) Sl. no Ref. no Aim of the reported study

Number and Gait parameter Our remarks of the location of IMU significantly paper sensor different between PD patients and control (or PD subtype or other disease types)

10

[45]

IMU attached to the head

1, head

Increased transverse plane head oscillations

Characterize PD motor impairment

11

[46]

Gait temporal parameters in many diseases

1, LB

Decreased accuracy in pathological groups

IMU attached to leg for highly impaired gait

12

[47]

Response to levodopa on six domains of balance and gait

6, wrist, foot, LB, chest

Arm swing and pace-related gait measures

Neural circuits control balance and gait is different

13

[48]

Accelerometer cut 1, hip points in PD patients

Optimal cut-points were obtained

Accelerometer cut points provided

14

[49]

Automated 1, LB Mechanical Peripheral Stimulation (AMPS) treatment in PD patients

Stride length, gait AMPS reduce velocity motor impairment in PD patients

15

[50]

Gait characteristics in PD patients

Variability of gait Variability was parameters able to differentiate

16

[51]

Implementation of 2, foot Kalman filter in IMU

Root means square difference was 2.9%

17

[52]

Effect of medication walking patterns

3, foot, LB

Gait velocity, step Dopaminergic duration, peak medication affects velocity PD

18

[53]

Gait characteristics in OFF/ ON medication of PD patients

6, shank, LB

Sway area, gait speed, and trunk motion

1, LB

Method of accurate gait analysis

Different parameter changes between OFF to ON (continued)

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Table 1 (continued) Sl. no Ref. no Aim of the reported study

Number and Gait parameter Our remarks of the location of IMU significantly paper sensor different between PD patients and control (or PD subtype or other disease types)

19

[54]

Gait comparison in Progressive Supranuclear Palsy (PSP) and PD patients

1, hip

20

[55]

21

Similar hypokinetic gait in PSP and PD

Reduced vertical displacement in PSP patients

Balance and gait 6, LB, foot in PD patients and the effect of medication

Turning speed, gait speed, and stride length

Off-medication state is more related to disease severity

[56]

Effect of walking distance in PD patients

1, LB

Gait parameter classification obtained

Short distance gait assessment is useful

22

[57]

Nordic Walking (NW) walking in PD patients

1, foot

Variance, gait speed, and cadence

NW improves gait parameters

23

[58]

Effect of cueing and prolonged walking in PD patients

7, foot, shank, wrist, LB

Less deviation in cadence

Cueing improves gait impairment

24

[59]

Effects of a 8, foot, shank, dual-task on the wrist, LB, chest gait of patients with freezing of gait (FOG + ) and without freezing of gait (FOG − )

FOG + shorter stride length, slower stride velocity

Dual-tasking affects FOG +

25

[60]

NW walking in PD patients

6, shank, LB, wrist, chest

Trunk frontal range of motion, peak velocity Cadence, gait speed and stride length

NW can improve postural stability

26

[61]

Association of kinematic gait parameters with quality of Life

3, shank, LB

Use of assistive gait equipment

Quality of life improves using assistive devices

27

[62]

Motor-cognitive profiles in PD patients

3, shank, hip

Gait speed and increased variability

Gait impairment related to cognitive decline (continued)

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Table 1 (continued) Sl. no Ref. no Aim of the reported study

Number and Gait parameter Our remarks of the location of IMU significantly paper sensor different between PD patients and control (or PD subtype or other disease types)

28

[63]

Gait characteristics in PD patient’s disease progression

2, foot

Stride length, gait Characterize PD speed, foot motor impairment clearance decreased, stride time, stance time, variability

29

[64]

Review on wearable sensors

–

Five main fields were studied

30

[65]

Gait parameters study in many diseases

1, foot

Gait speed, stride Characterized PD, length atypical parkinsonian disorders, progressive supranuclear palsy

31

[66]

Use of Trusted 2, shank Events and Acceleration Direct and Reverse Integration (TEADRIP)

Stride length means absolute errors on average 2%

Validated TEADRIP on 236 patients. large population

32

[67]

Calibration of IMU in PD patients ara>

2, foot

No difference in stride length, double support, step duration

IMU can be used for gait assessment

33

[68]

Review on walking biomechanics and falls

–

Spatiotemporal, kinematic and muscle activation pattern

Spatiotemporal and kinematic characterize fall in PD patients

34

[69]

Laboratory and clinical gait assessment

2, foot

Laboratory setting affected speed and stride length

Gait assessment in the same environment reduces error

35

[70]

Classify between PD and control

8, foot, shank, LB, chest

Range of motion The best result (RoM) variability from the knee range of motion

Overview of wearable sensors for studying PD patients

(continued)

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87

Table 1 (continued) Sl. no Ref. no Aim of the reported study

Number and Gait parameter Our remarks of the location of IMU significantly paper sensor different between PD patients and control (or PD subtype or other disease types)

36

[71]

IMU-based gait and balance assessment

3, shank, LB

Gait speed

37

[72]

Calibration of IMU in PD patients

2, knee

Classify between IMU can assess FOG + and FOG the freezing of gait (FOG) patients

38

[73]

Gait characteristics in PD patients walking pattern

4, shank

The variance of gait interval

IMU differentiate between PD and control

39

[74]

Review on the influence of dual tasking

–

Single task and dual-task gait

Dual tasking severely affects PD patients

40

[75]

Review on application of wearable sensors

–

Gait parameters used for analyzing PD patients

IMU can be used for gait assessment

41

[76]

Review of gait impairment in PD patients

–

Gait Gaps in gait quantification impairment in PD with multiple gait patients features

42

[77]

Review on – wearable cueing in PD patients

Auditory, visual, somatosensory cueing

Gait speed related to PD patients

Effectiveness of cueing

The abbreviation for LB is lower back

4.1 Part I “IMU Sensors Used for Gait Analysis” 4.1.1

Validation of IMU Sensor

IMU devices must be validated on PD patients to check the reliability, accuracy, and reproducibility of readings for gait analysis. This section describes the use of wearable sensors on PD patients to define the biomechanical range and comparison of error of the gait parameter measured using the IMU sensor and gold standard measurement during walking. The calculation of the gait parameter using IMU sensors is performed differently according to the choice of the author. The calibration and validation of the IMU sensor performed by Hakan et al. (2015) defined the accelerometer cut points for different walking speed in mild to moderate PD. The optimal cut points

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Table 2 Part II. Review of literature on the spatiotemporal parameter in the gait of PD patients. Each paper has been briefly summarized in Sect. 4 Sl. no

Ref. no

Aim of the reported study

2

[78]

3

Device used for measuring

Gait parameter significantly different between PD patients and control (or PD subtype or other disease types)

Remarks of the paper

Gait variability in Foot switch PD patients

Higher gait variability

Gait variability related to disease progression

[79]

Effect of levodopa and walking speed in PD patients

GAITRite

Variability of step time, double support time, stride length, stride velocity

Levodopa decreased gait variability in PD patients

4

[80]

Gait variability during the first two steps of gait initiation

GAITRite

Shorter steps, higher variability in step length, variability in swing time

High gait initiation variability in PD patients

5

[81]

Fractal scaling in Pressure sensor PD patients

Stride time fluctuation exponent

Reduced fractal scaling in PD patients

6

[82]

Reliability of gait GAITRite variability

More reliable Continuous during continuous walking and steps walking are not less than 30

7

[83]

Fractal scaling under clinical conditions

Pressure sensor

Fractal exponents by stitching short sequences

Stitching short sequences improved differentiating

8

[84]

Gait during treadmill and overground walking

Pressure sensor

Step length, step height, cadence, step width, and step width variability

Gait characteristics improve during treadmill walking

9

[85]

Effects of levodopa on gait variability

GAITRite

Variability of step time, swing time, stride length, stride velocity

Variability of double support time not affected by levodopa

10

[86]

Gait variability based on disease severity

GAITRite

Stance, swing interval, self-similarity parameter

Less rhythmic gait for PD patients (continued)

Inertial Measurement Units for Gait Analysis …

89

Table 2 (continued) Sl. no

Ref. no

Aim of the reported study

11

[87]

Reliability of gait GAITRite variability at slow and fast walking

a

Device used for measuring

Remarks of the paper

Step width variability

PD was reliable at normal and fast gait speeds

b

40 35

Selected article (%)

Selected article (%)

Gait parameter significantly different between PD patients and control (or PD subtype or other disease types)

30 25 20 15

10 5 0

LB

foot

chest

shank

100 90 80 70 60 50 40 30 20 10 0 Given

Location of sensors

c

d

70

Selected article (%)

Selected article (%)

80 60 50 40 30 20 10 0 Given

Not given

Medication details

Not given

Basic demographic details 90 80 70 60 50 40 30 20 10 0 Given

Not given

H & Y scale

Fig. 2 Bar chart representing the details of selected paper (%) to the a location of the sensor, b basic demographic details c medication details d H&Y scale of the patients

90

S. M. Keloth et al.

for walking speeds < 1.0 m/sec were < 328– < 470 counts/15 s, speed > 1.3 m/sec were < 730– < 851 counts/15 s [48]. In the latter years, Mico et al. (2017) performed a validation study on the short distance walking using the IMU sensor. They assessed 5-m walks of PD patients with a single IMU sensor and observed that the gait parameters could be classified against the disease conditions. It was concluded that the short distance walking measurements are informative, thus helping the clinical evaluation of gait [56]. Palmerini et al. (2013) and Esser et al. (2013) studied the reliability of IMU sensors to identify and quantify the gait of healthy and PD patients [41, 42]. Palmerini et al. (2013) observed that the temporal measures and angular velocity can characterize PD patients from healthy people using IMU sensors [41]. However, the latter reference showed that spatial parameters cannot be used as a measure for the gait parameters to differentiate the two groups. The paper found a non-significant difference between the two groups for the gait features-cadence and stride length [42]. Johannes et al. (2107) on the other hand, validated the use of wearable sensors on many patients in each group based on the Hoehn and Yahr (H & Y) scale for studying the disease progression. They observed that the gait parameters- stride length, gait speed, foot clearance decreased, stance time, and stride time and its variability increased with disease progression. They concluded that wearable sensors can be used effectively to measure the gait parameter and to monitor the disease progression in PD patients [63]. On the other hand, Djuric et al. (2014) performed a quantitative gait analysis in PD patients using the IMU sensor to identify the most suitable location of sensors. They observed that IMU sensors attached to the shank of the leg were able to identify freezing of gait events from normal strides and these can be used to study the progress of the disease [44]. IMU sensor used by Aich et al. (2018) for identifying and assessing FOG events in PD patients and the root mean square error between the estimated gait parameters from IMU when compared with the motion capture camera was found to be less than 10% and the authors proposed that IMU are suitable for monitoring the gait of PD patients [72]. Further, Ferrari et al. (2016) validated the IMU sensor on PD patients at three different walking speeds and found that the total mean square error on sub-interval of gait was low when compared to the gold standard method using high-speed cameras. They found the total mean square error on the gait feature- step length to be 2.9% [51]. The paper by Zago et al. (2018) compared the gait parameter measured using the IMU sensor with gold standard motion capture camera for checking its reliability and accuracy. They found that some of the spatiotemporal parameters i.e. stride length, double support, and step duration measured using IMU sensor, showed a non-significant difference when compared with motion capture camera. The mean the absolute error of the gait parameter measured using IMU when compared with the motion capture camera was 12%. The gait velocity showed a statistically significant difference between the IMU sensor and motion capture camera [67]. Further, Pau et al. (2018) studied the difference in the spatiotemporal parameters when walking was performed in a laboratory and clinical settings using the IMU sensor. They observed a decrease in gait speed and stride length (by 17% and 12% respectively) when passing from the clinical to the laboratory setting. They concluded that gait

Inertial Measurement Units for Gait Analysis …

91

assessment should always be performed in the same conditions to avoid the error which could lead to inaccurate patient evaluation [69]. The gait and balance study in PD patients using IMU sensor was studied by Felix et al. (2018) found that slower gait speed with no balance problem was observed in PD patients compared with age-matched control, suggesting some of the gait defects are due to the underlying disease progression and not as a result of the aging process [71].

4.1.2

Gait Impairment in Sub-Types of PD Patients and Comparison with Different Groups

IMU sensors have been used to investigate gait impairments in different patient groups and between subtypes of PD patients. The paper by Herman et al. (2014), studied the changes to the gait parameter in PD subtypes- Postural Instability Gait Disorder (PIGD) and Tremor Dominant (TD). Patients were classified as PIGD if the ratio of mean tremor score/mean PIGD score was less than or equal to 1 and TD were those whose ratio was greater than 1 [88]. They observed that the gait parameters which were studied by them- gait speed, stride length, and stride variability, did not have a significant difference between Postural Instability and other groups. They also found that the gait of purely PIGD (p-PIGD) significantly differed from purely TD group (p-TD) with a reduction in gait speed, shorter strides, and increased stride variability. These findings suggest that the classification into p-PIGD and pTD may be useful, which is based on the criteria that the PIGD or TD score