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      Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease

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          Abstract

          Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions.

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          World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects.

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            Global, regional, and national burden of Parkinson's disease, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016

            Summary Background Neurological disorders are now the leading source of disability globally, and ageing is increasing the burden of neurodegenerative disorders, including Parkinson's disease. We aimed to determine the global burden of Parkinson's disease between 1990 and 2016 to identify trends and to enable appropriate public health, medical, and scientific responses. Methods Through a systematic analysis of epidemiological studies, we estimated global, regional, and country-specific prevalence and years of life lived with disability for Parkinson's disease from 1990 to 2016. We estimated the proportion of mild, moderate, and severe Parkinson's disease on the basis of studies that used the Hoehn and Yahr scale and assigned disability weights to each level. We jointly modelled prevalence and excess mortality risk in a natural history model to derive estimates of deaths due to Parkinson's disease. Death counts were multiplied by values from the Global Burden of Disease study's standard life expectancy to compute years of life lost. Disability-adjusted life-years (DALYs) were computed as the sum of years lived with disability and years of life lost. We also analysed results based on the Socio-demographic Index, a compound measure of income per capita, education, and fertility. Findings In 2016, 6·1 million (95% uncertainty interval [UI] 5·0–7·3) individuals had Parkinson's disease globally, compared with 2·5 million (2·0–3·0) in 1990. This increase was not solely due to increasing numbers of older people, because age-standardised prevalence rates increased by 21·7% (95% UI 18·1–25·3) over the same period (compared with an increase of 74·3%, 95% UI 69·2–79·6, for crude prevalence rates). Parkinson's disease caused 3·2 million (95% UI 2·6–4·0) DALYs and 211 296 deaths (95% UI 167 771–265 160) in 2016. The male-to-female ratios of age-standardised prevalence rates were similar in 2016 (1·40, 95% UI 1·36–1·43) and 1990 (1·37, 1·34–1·40). From 1990 to 2016, age-standardised prevalence, DALY rates, and death rates increased for all global burden of disease regions except for southern Latin America, eastern Europe, and Oceania. In addition, age-standardised DALY rates generally increased across the Socio-demographic Index. Interpretation Over the past generation, the global burden of Parkinson's disease has more than doubled as a result of increasing numbers of older people, with potential contributions from longer disease duration and environmental factors. Demographic and potentially other factors are poised to increase the future burden of Parkinson's disease substantially. Funding Bill & Melinda Gates Foundation.
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              Parkinson's disease: clinical features and diagnosis.

              Parkinson's disease (PD) is a progressive neurological disorder characterised by a large number of motor and non-motor features that can impact on function to a variable degree. This review describes the clinical characteristics of PD with emphasis on those features that differentiate the disease from other parkinsonian disorders. A MedLine search was performed to identify studies that assess the clinical characteristics of PD. Search terms included "Parkinson's disease", "diagnosis" and "signs and symptoms". Because there is no definitive test for the diagnosis of PD, the disease must be diagnosed based on clinical criteria. Rest tremor, bradykinesia, rigidity and loss of postural reflexes are generally considered the cardinal signs of PD. The presence and specific presentation of these features are used to differentiate PD from related parkinsonian disorders. Other clinical features include secondary motor symptoms (eg, hypomimia, dysarthria, dysphagia, sialorrhoea, micrographia, shuffling gait, festination, freezing, dystonia, glabellar reflexes), non-motor symptoms (eg, autonomic dysfunction, cognitive/neurobehavioral abnormalities, sleep disorders and sensory abnormalities such as anosmia, paresthesias and pain). Absence of rest tremor, early occurrence of gait difficulty, postural instability, dementia, hallucinations, and the presence of dysautonomia, ophthalmoparesis, ataxia and other atypical features, coupled with poor or no response to levodopa, suggest diagnoses other than PD. A thorough understanding of the broad spectrum of clinical manifestations of PD is essential to the proper diagnosis of the disease. Genetic mutations or variants, neuroimaging abnormalities and other tests are potential biomarkers that may improve diagnosis and allow the identification of persons at risk.
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                Author and article information

                Contributors
                Journal
                Front Neurol
                Front Neurol
                Front. Neurol.
                Frontiers in Neurology
                Frontiers Media S.A.
                1664-2295
                28 February 2022
                2022
                : 13
                : 788427
                Affiliations
                [1] 1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing , Sankt Augustin, Germany
                [2] 2Bonn-Aachen International Center for IT (b-it), University of Bonn , Bonn, Germany
                [3] 3Centre de Recherches Information, Droit et Societe, University of Namur , Namur, Belgium
                [4] 4Institut Polytechnique de Paris, Telecom SudParis , Evry, France
                [5] 5Luxembourg Center for Systems Medicine, University of Luxembourg , Esch, Luxembourg
                [6] 6Department of Artificial Intelligence in Biomedical Engineering, University of Erlangen Nuremberg , Erlangen, Germany
                [7] 7Asociacion Parkinson Madrid , Madrid, Spain
                [8] 8Institut du Cerveau (ICM) , Paris, France
                [9] 9Department of Neurology, University Hospital Erlangen , Erlangen, Germany
                Author notes

                Edited by: Panying Rong, University of Kansas, United States

                Reviewed by: Fahd Baig, University of Oxford, United Kingdom; Luca Marsili, University of Cincinnati, United States; Robert LeMoyne, Northern Arizona University, United States

                *Correspondence: Holger Fröhlich holger.froehlich@ 123456scai.fraunhofer.de

                This article was submitted to Dementia and Neurodegenerative Diseases, a section of the journal Frontiers in Neurology

                Article
                10.3389/fneur.2022.788427
                8918525
                35295840
                a88ccc8a-04f7-4982-bbd2-e7902ec10214
                Copyright © 2022 Fröhlich, Bontridder, Petrovska-Delacréta, Glaab, Kluge, Yacoubi, Marín Valero, Corvol, Eskofier, Van Gyseghem, Lehericy, Winkler and Klucken.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 02 October 2021
                : 31 January 2022
                Page count
                Figures: 2, Tables: 0, Equations: 0, References: 122, Pages: 13, Words: 11433
                Funding
                Funded by: European Commission, doi 10.13039/501100000780;
                Award ID: 01KU2110
                Categories
                Neurology
                Review

                Neurology
                digital biomarker,artificial intelligence,precision medicine,digital health,parkinson's disease

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