Digital biomarkers for precision diagnosis and monitoring in Parkinson’s disease

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Abstract

Parkinson’s disease (PD) is a multifactorial neurodegenerative disorder with high prevalence among the elderly, primarily manifested by progressive decline in motor function. The aging global demographic and increased life expectancy have led to a rapid surge in PD cases, imposing a significant societal burden. PD along with other neurodegenerative diseases has garnered increasing attention from the scientific community. In PD, motor symptoms are recognized when approximately 60% of dopaminergic neurons have been damaged. The irreversible feature of PD and benefits of early intervention underscore the importance of disease onset prediction and prompt diagnosis. The advent of digital health technology in recent years has elevated the role of digital biomarkers in precisely and sensitively detecting early PD clinical symptoms, evaluating treatment effectiveness, and guiding clinical medication, focusing especially on motor function, responsiveness and sleep quality assessments. This review examines prevalent digital biomarkers for PD and highlights the latest advancements.

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Parkinson's disease (PD) affects 1-2 per 1000 of the population at any time. PD prevalence is increasing with age and PD affects 1% of the population above 60 years. The main neuropathological finding is α-synuclein-containing Lewy bodies and loss of dopaminergic neurons in the substantia nigra, manifesting as reduced facilitation of voluntary movements. With progression of PD, Lewy body pathology spreads to neocortical and cortical regions. PD is regarded as a movement disorder with three cardinal signs: tremor, rigidity and bradykinesia. A recent revision of the diagnostic criteria excludes postural instability as a fourth hallmark and defines supportive criteria, absolute exclusion criteria and red flags. Non-motor symptoms in PD have gained increasing attention and both motor and non-motor signs are now included among the supportive criteria. The cause of PD is unknown in most cases. Genetic risk factors have been identified, including monogenetic causes that are rare in unselected populations. Some genetic factor can be identified in 5-10% of the patients. Several environmental factors are associated with increased risk of PD. Autopsy studies show that the clinical diagnosis of PD is not confirmed at autopsy in a significant proportion of patients. Revised diagnostic criteria are expected to improve the clinician´s accuracy in diagnosing PD. Increasing knowledge on genetic and environmental risk factors of PD will probably elucidate the cause of this disease within the near future.

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Parkinson disease

R. Balestrino, A.H.V. Schapira (2019)

Parkinson disease (PD) is the most common neurodegenerative movement disorder. In Europe, prevalence and incidence rates for PD are estimated at approximately 108-257/100 000 and 11-19/100 000 per year, respectively. Risk factors include age, male gender and some environmental factors. The aetiology of the disease in most patients is unknown, but different genetic causes have been identified. Although familial forms of PD account for only 5%-15% of cases, studies on these families provided interesting insight on the genetics and the pathogenesis of the disease allowing the identification of genes implicated in its pathogenesis and offering critical insights into the mechanisms of disease. The cardinal motor symptoms of PD are tremor, rigidity, bradykinesia/akinesia and postural instability, but the clinical picture includes other motor and non-motor symptoms. Its diagnosis is principally clinical, although specific investigations can help the differential diagnosis from other forms of parkinsonism. Pathologically, PD is characterized by the loss of dopaminergic neurons in the pars compacta of the substantia nigra and by accumulation of misfolded α-synuclein, which is found in intra-cytoplasmic inclusions called Lewy bodies. Currently available treatments offer good control of motor symptoms but do not modify the evolution of the disease. This article is intended to provide a comprehensive, general and practical review of PD for the general neurologist.

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Author and article information

Contributors

Chang-he Shi:

ORCID: http://orcid.org/0000-0002-4843-0072

shichanghe@gmail.com

Journal

Journal ID (nlm-ta): NPJ Digit Med

Journal ID (iso-abbrev): NPJ Digit Med

Title: NPJ Digital Medicine

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2398-6352

Publication date (Electronic): 21 August 2024

Publication date PMC-release: 21 August 2024

Publication date Collection: 2024

Volume: 7

Electronic Location Identifier: 218

Affiliations

[1 ]GRID grid.207374.5, ISNI 0000 0001 2189 3846, Department of Neurology, The First Affiliated Hospital of Zhengzhou University, , Zhengzhou University, ; Zhengzhou, 450000 Henan China

[2 ]GRID grid.207374.5, ISNI 0000 0001 2189 3846, Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, , Zhengzhou University, ; Zhengzhou, 450000 Henan China

[3 ]Institute of Neuroscience, Zhengzhou University, ( https://ror.org/04ypx8c21) Zhengzhou, 450000 Henan China

[4 ]GRID grid.207374.5, ISNI 0000 0001 2189 3846, NHC Key Laboratory of Prevention and treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, , Zhengzhou University, ; Zhengzhou, 450000 Henan China

Author information

Chang-he Shi http://orcid.org/0000-0002-4843-0072

Article

Publisher ID: 1217

DOI: 10.1038/s41746-024-01217-2

PMC ID: 11339454

PubMed ID: 39169258

SO-VID: c015acaa-0b0e-47ee-889a-0148a81c5c43

License:

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

History

Date received : 2 April 2024

Date accepted : 7 August 2024

Custom metadata

Keywords: parkinson's disease,mathematics and computing

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Keywords: parkinson's disease, mathematics and computing

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