Wearable movement-tracking data identify Parkinson’s disease years before clinical diagnosis

Schalkamp, Ann-Kathrin; Peall, Kathryn J.; Harrison, Neil A.; Sandor, Cynthia

doi:10.1038/s41591-023-02440-2

Cited by 59 publications

(23 citation statements)

References 36 publications

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“…We have previously demonstrated the ability to identify those who will go on to receive a future a diagnosis of PD using a single week of accelerometer sensor data (Schalkamp et. al, 2023).…”

Section: Discussionmentioning

confidence: 99%

Leveraging long-term smartwatch data to inform Parkinson’s disease progression, subtypes, and risk

Schalkamp,

Peall,

Harrison

et al. 2023

Preprint

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Use of digital sensors to passively collect long-term, longitudinal data offers a step change in our ability to monitor Parkinson’s disease (PD). However, to date the evaluation of long-term digital sensor data has been neglected in favour of evaluating short-term data collected in controlled settings. To address this, we combined longitudinal clinical and biological assessment data from the Parkinson’s Progression Marker Initiative (PPMI) cohort with long-term (mean: 485 days) at-home digital monitoring data collected with the Verily Study Watch. We then derived digital timeseries components leveraging the long-term monitoring of the PPMI. We found three key findings: Firstly, that these digital timeseries components correlated with the rate of progression of motor (r = 0.23, p-value = 8.5×10-3, r = 0.26, p-value = 2.2×10-3) and autonomic symptoms (r = −0.23, p-value = 8.2×10-3), impairments in daily living (r = 0.26, p-value = 2.5×10-3), increase in medication requirements and complications (r = −0.25, p-value = 4.2×10-3), and rate of increase in cerebrospinal fluid (CSF) tau (ptau: r = 0.28, p-value = 2.6×10-3; ttau: r = 0.34, p-value = 1.2×10-4). Second, we derived digitally informed subtypes of PD and found higher similarity with CSF (0.35) and DaTscan (0.35) subtypes than has been found for previously published subtypes (CSF: 0.31±0.01, DaTscan: 0.31±0.02). Finally, we showed that long-term digital monitoring can inform PD risk and sensitively detect individuals with probable prodromal PD. Our findings highlight the wealth of application areas for digital sensors in PD research.

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Section: Discussionmentioning

confidence: 99%

Leveraging long-term smartwatch data to inform Parkinson’s disease progression, subtypes, and risk

Schalkamp,

Peall,

Harrison

et al. 2023

Preprint

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“…Parkinson’s disease (PD) is a chronic neurodegenerative disease that manifests as movement disorders, characterized by symptoms such as bradykinesia, paralysis, and resting tremors. , Unfortunately, there is currently no effective treatment available for PD . The pathological characteristics of PD include the loss of dopaminergic neurons in the substantia nigrapars compacta (SNpc) and the decrease of dopamine in striatum, , accompanied by neuroinflammation mediated by activated microglia cells.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Neuroprotection in Mouse Model of Parkinson’s Disease through Protein Nanosystem Conjugation with ApoE Peptide for miR-124 Delivery

Zhang,

Cui,

et al. 2024

ACS Appl. Mater. Interfaces

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Parkinson's disease (PD) affects millions of people's lives worldwide. The main pathogenesis of PD is dopaminergic neuron necrosis and neuroinflammation mediated by activated microglia cells. In recent years, the anti-inflammatory ability and neuroprotective effects of miR-124 in PD models were well proved, but the in vivo delivery of miR-124 remains challenging. Herein, we report a protein nanosystem modified with a brain-targeting peptide ApoE that could efficiently deliver miR-124 across the blood−brain barrier (BBB). This nanosystem showed good cell viability on brain endothelial cells and microglia cells, and administration of this nanosystem significantly decreased the neuroinflammation and dopaminergic neuron loss, as well as recovered parts of neurobehavioral deficits. This ApoE peptide-based protein nanosystem holds great promise for the delivery of RNA therapeutics to the brain and for realizing neuron protection in PD treatment.

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“…Proposed biomarkers include clinical, imaging, biofluidic-base, and inflammation-related biomarkers for preclinical, prodromal, and clinical stages [ 9 , 10 ]. Some of the proposed tools and methods for the early detection of PD are based on analysing voice disorders [ 11 , 12 ], handwriting [ 13 ], olfactory testing [ 14 ], and accelerometery data [ 15 ]. Other proposed solutions based on the use of Artificial Intelligence include convolutional neural networks for eye tracking and facial expression analysis [ 16 ], Machine Learning-assisted speech analysis [ 17 ], and deep learning models for various modalities such as brain analysis and motion symptoms [ 18 ].…”

Section: Introductionmentioning

confidence: 99%

A New Wrist-Worn Tool Supporting the Diagnosis of Parkinsonian Motor Syndromes

Battista,

Romaniello

2024

Sensors

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To date, clinical expert opinion is the gold standard diagnostic technique for Parkinson’s disease (PD), and continuous monitoring is a promising candidate marker. This study assesses the feasibility and performance of a new wearable tool for supporting the diagnosis of Parkinsonian motor syndromes. The proposed method is based on the use of a wrist-worn measuring system, the execution of a passive, continuous recording session, and a computation of two digital biomarkers (i.e., motor activity and rest tremor index). Based on the execution of some motor tests, a second step is provided for the confirmation of the results of passive recording. In this study, fifty-nine early PD patients and forty-one healthy controls were recruited. The results of this study show that: (a) motor activity was higher in controls than in PD with slight tremors at rest and did not significantly differ between controls and PD with mild-to-moderate tremor rest; (b) the tremor index was smaller in controls than in PD with mild-to-moderate tremor rest and did not significantly differ between controls and PD patients with slight tremor rest; (c) the combination of the said two motor parameters improved the performances in differentiating controls from PD. These preliminary findings demonstrate that the combination of said two digital biomarkers allowed us to differentiate controls from early PD.

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Wearable movement-tracking data identify Parkinson’s disease years before clinical diagnosis

Cited by 59 publications

References 36 publications

Leveraging long-term smartwatch data to inform Parkinson’s disease progression, subtypes, and risk

Leveraging long-term smartwatch data to inform Parkinson’s disease progression, subtypes, and risk

Enhancing Neuroprotection in Mouse Model of Parkinson’s Disease through Protein Nanosystem Conjugation with ApoE Peptide for miR-124 Delivery

A New Wrist-Worn Tool Supporting the Diagnosis of Parkinsonian Motor Syndromes

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