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Tichelaar JG, Hezemans F, Bloem BR, Helmich RC, Cools R. Neural reinforcement learning signals predict recovery from impulse control disorder symptoms in Parkinson's disease. Biol Psychiatry 2024:S0006-3223(24)01434-3. [PMID: 39002875 DOI: 10.1016/j.biopsych.2024.06.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 05/26/2024] [Accepted: 06/20/2024] [Indexed: 07/15/2024]
Abstract
BACKGROUND Impulse control disorders (ICD) in Parkinson's disease (PD) are associated with a heavy burden on patients and caretakers. While recovery can occur, ICD persists in many patients despite optimal management. The basis for this inter-individual variability in recovery is unclear and poses a major challenge to personalized health care. METHODS We adopt a computational psychiatry approach and leverage the longitudinal, prospective Personalized Parkinson Project (N=136 persons with PD, within 5 years of diagnosis) to combine dopaminergic learning theory-informed fMRI with machine learning (at baseline) to predict ICD symptom recovery after two years of follow-up. We focused on a change in QUIP-rs across the entire cohort, regardless of an ICD diagnosis. RESULTS Greater reinforcement learning signals during gain trials but not loss trials at baseline, including those in the ventral striatum, medial prefrontal cortex and the behavioral accuracy score measured while ON medication were associated with greater recovery from impulse control symptoms two years later. These signals accounted for a unique proportion of the relevant variability over and above that explained by other known factors, such as decreases in dopamine agonist use. CONCLUSIONS Our results provide a proof of principle for combining generative model-based inference of latent learning processes with machine learning-based predictive modeling of variability in clinical symptom recovery trajectories. Hence, we showed that RL modelling parameters predict recovery from ICD symptoms in PD.
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Affiliation(s)
- Jorryt G Tichelaar
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, 6525EN, Nijmegen, The Netherlands; Radboud University Medical Center, Department of Neurology, Centre of Expertise for Parkinson and Movement Disorders, 6525GA, Nijmegen, The Netherlands.
| | - Frank Hezemans
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, 6525EN, Nijmegen, The Netherlands; Radboud University Medical Center, Department of Psychiatry, 6525GA, Nijmegen, The Netherlands
| | - Bastiaan R Bloem
- Radboud University Medical Center, Department of Neurology, Centre of Expertise for Parkinson and Movement Disorders, 6525GA, Nijmegen, The Netherlands
| | - Rick C Helmich
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, 6525EN, Nijmegen, The Netherlands; Radboud University Medical Center, Department of Neurology, Centre of Expertise for Parkinson and Movement Disorders, 6525GA, Nijmegen, The Netherlands
| | - Roshan Cools
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, 6525EN, Nijmegen, The Netherlands; Radboud University Medical Center, Department of Psychiatry, 6525GA, Nijmegen, The Netherlands
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2
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Janssen Daalen JM, van den Bergh R, Prins EM, Moghadam MSC, van den Heuvel R, Veen J, Mathur S, Meijerink H, Mirelman A, Darweesh SKL, Evers LJW, Bloem BR. Digital biomarkers for non-motor symptoms in Parkinson's disease: the state of the art. NPJ Digit Med 2024; 7:186. [PMID: 38992186 PMCID: PMC11239921 DOI: 10.1038/s41746-024-01144-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Accepted: 05/22/2024] [Indexed: 07/13/2024] Open
Abstract
Digital biomarkers that remotely monitor symptoms have the potential to revolutionize outcome assessments in future disease-modifying trials in Parkinson's disease (PD), by allowing objective and recurrent measurement of symptoms and signs collected in the participant's own living environment. This biomarker field is developing rapidly for assessing the motor features of PD, but the non-motor domain lags behind. Here, we systematically review and assess digital biomarkers under development for measuring non-motor symptoms of PD. We also consider relevant developments outside the PD field. We focus on technological readiness level and evaluate whether the identified digital non-motor biomarkers have potential for measuring disease progression, covering the spectrum from prodromal to advanced disease stages. Furthermore, we provide perspectives for future deployment of these biomarkers in trials. We found that various wearables show high promise for measuring autonomic function, constipation and sleep characteristics, including REM sleep behavior disorder. Biomarkers for neuropsychiatric symptoms are less well-developed, but show increasing accuracy in non-PD populations. Most biomarkers have not been validated for specific use in PD, and their sensitivity to capture disease progression remains untested for prodromal PD where the need for digital progression biomarkers is greatest. External validation in real-world environments and large longitudinal cohorts remains necessary for integrating non-motor biomarkers into research, and ultimately also into daily clinical practice.
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Affiliation(s)
- Jules M Janssen Daalen
- Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands.
| | - Robin van den Bergh
- Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
| | - Eva M Prins
- Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
| | - Mahshid Sadat Chenarani Moghadam
- Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
| | - Rudie van den Heuvel
- HAN University of Applied Sciences, School of Engineering and Automotive, Health Concept Lab, Arnhem, The Netherlands
| | - Jeroen Veen
- HAN University of Applied Sciences, School of Engineering and Automotive, Health Concept Lab, Arnhem, The Netherlands
| | | | - Hannie Meijerink
- ParkinsonNL, Parkinson Patient Association, Bunnik, The Netherlands
| | - Anat Mirelman
- Tel Aviv University, Sagol School of Neuroscience, Department of Neurology, Faculty of Medicine, Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition, and Mobility (CMCM), Tel Aviv, Israel
| | - Sirwan K L Darweesh
- Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
| | - Luc J W Evers
- Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
- Radboud University, Institute for Computing and Information Sciences, Nijmegen, The Netherlands
| | - Bastiaan R Bloem
- Radboud university medical center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands.
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3
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de Graaf D, de Vries NM, van de Zande T, Schimmel JJP, Shin S, Kowahl N, Barman P, Kapur R, Marks WJ, van 't Hul A, Bloem B. Measuring Physical Functioning Using Wearable Sensors in Parkinson Disease and Chronic Obstructive Pulmonary Disease (the Accuracy of Digital Assessment of Performance Trial Study): Protocol for a Prospective Observational Study. JMIR Res Protoc 2024; 13:e55452. [PMID: 38713508 PMCID: PMC11109858 DOI: 10.2196/55452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/07/2024] [Accepted: 03/11/2024] [Indexed: 05/08/2024] Open
Abstract
BACKGROUND Physical capacity and physical activity are important aspects of physical functioning and quality of life in people with a chronic disease such as Parkinson disease (PD) or chronic obstructive pulmonary disease (COPD). Both physical capacity and physical activity are currently measured in the clinic using standardized questionnaires and tests, such as the 6-minute walk test (6MWT) and the Timed Up and Go test (TUG). However, relying only on in-clinic tests is suboptimal since they offer limited information on how a person functions in daily life and how functioning fluctuates throughout the day. Wearable sensor technology may offer a solution that enables us to better understand true physical functioning in daily life. OBJECTIVE We aim to study whether device-assisted versions of 6MWT and TUG, such that the tests can be performed independently at home using a smartwatch, is a valid and reliable way to measure the performance compared to a supervised, in-clinic test. METHODS This is a decentralized, prospective, observational study including 100 people with PD and 100 with COPD. The inclusion criteria are broad: age ≥18 years, able to walk independently, and no co-occurrence of PD and COPD. Participants are followed for 15 weeks with 4 in-clinic visits, once every 5 weeks. Outcomes include several walking tests, cognitive tests, and disease-specific questionnaires accompanied by data collection using wearable devices (the Verily Study Watch and Modus StepWatch). Additionally, during the last 10 weeks of this study, participants will follow an aerobic exercise training program aiming to increase physical capacity, creating the opportunity to study the responsiveness of the remote 6MWT. RESULTS In total, 89 people with PD and 65 people with COPD were included in this study. Data analysis will start in April 2024. CONCLUSIONS The results of this study will provide information on the measurement properties of the device-assisted 6MWT and TUG in the clinic and at home. When reliable and valid, this can contribute to a better understanding of a person's physical capacity in real life, which makes it possible to personalize treatment options. TRIAL REGISTRATION ClinicalTrials.gov NCT05756075; https://clinicaltrials.gov/study/NCT05756075. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/55452.
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Affiliation(s)
- Debbie de Graaf
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
| | - Nienke M de Vries
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
| | - Tessa van de Zande
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
| | - Janneke J P Schimmel
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
| | - Sooyoon Shin
- Verily Life Sciences, South San Fransisco, CA, United States
| | - Nathan Kowahl
- Verily Life Sciences, South San Fransisco, CA, United States
| | - Poulami Barman
- Verily Life Sciences, South San Fransisco, CA, United States
| | - Ritu Kapur
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
- Verily Life Sciences, South San Fransisco, CA, United States
| | - William J Marks
- Verily Life Sciences, South San Fransisco, CA, United States
| | - Alex van 't Hul
- Radboud University Medical Center, Radboud Institute for Health Sciences, Department of Respiratory Diseases, Nijmegen, Netherlands
| | - Bastiaan Bloem
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, Netherlands
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van der Heide A, Dommershuijsen LJ, Puhlmann LMC, Kalisch R, Bloem BR, Speckens AEM, Helmich RC. Predictors of stress resilience in Parkinson's disease and associations with symptom progression. NPJ Parkinsons Dis 2024; 10:81. [PMID: 38605033 PMCID: PMC11009258 DOI: 10.1038/s41531-024-00692-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 03/21/2024] [Indexed: 04/13/2024] Open
Abstract
People with Parkinson's disease (PD) are sensitive to effects of long-term stress, but might differ in stress resilience, i.e. the ability to maintain mental health despite adversity. It is unclear whether stress resilience in PD is predominantly determined by dopamine deficiency, psychosocial factors, or both. In PD animal models, chronic stressors accelerate disease progression, but evidence in humans is lacking. Our objectives were to (1) distinguish stressor-reactive from resilient PD patients, (2) identify resilience factors, and (3) compare symptom progression between stressor-reactive and resilient patients. We conducted a longitudinal survey in Personalized Parkinson Project participants (N = 350 PD). We used the COVID-19 pandemic as a model of a stressor, aligned in time for the entire cohort. COVID-19-related stressors, perceived stress, and PD symptoms were assessed at 11 timepoints (April-October 2020). Both pre-COVID and in-COVID clinical assessments were available. We quantified stressor-reactivity as the residual between actual and predicted perceived stress relative to COVID-19-related stressors, and modeled trajectories of stressor-reactivity across timepoints. We explored pre-COVID predictors of 6-month average stressor-reactivity, and tested whether stressor-reactivity was prospectively associated with one-year clinical progression rates. Latent class trajectory models distinguished patients with high (N = 123) or low (N = 227) stressor-reactivity. Pre-existing anxiety, rumination and non-motor symptom severity predicted high stressor-reactivity (risk factors), whereas quality of life, social support, positive appraisal style and cognitive abilities predicted low stressor-reactivity (resilience factors). PD-specific factors, e.g. disease duration, motor severity, and levodopa use, did not predict stressor-reactivity. The COVID-19 pandemic did not accelerate disease progression, but worsened depressive symptoms in stressor-reactive PD patients.
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Affiliation(s)
- Anouk van der Heide
- Radboud University Medical Centre, Department of Neurology, Centre of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands.
- Radboud University, Donders Institute for Brain Cognition and Behavior, Centre for Cognitive Neuroimaging, Nijmegen, the Netherlands.
| | - Lisanne J Dommershuijsen
- Radboud University Medical Centre, Department of Neurology, Centre of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Lara M C Puhlmann
- Leibniz Institute for Resilience Research, Mainz, Germany
- Neuroimaging Center, Focus Program Translational Neuroscience, Johannes Gutenberg University Medical Center, Mainz, Germany
| | - Raffael Kalisch
- Leibniz Institute for Resilience Research, Mainz, Germany
- Neuroimaging Center, Focus Program Translational Neuroscience, Johannes Gutenberg University Medical Center, Mainz, Germany
| | - Bastiaan R Bloem
- Radboud University Medical Centre, Department of Neurology, Centre of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Anne E M Speckens
- Radboud University Medical Centre, Department of Psychiatry, Nijmegen, the Netherlands
| | - Rick C Helmich
- Radboud University Medical Centre, Department of Neurology, Centre of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
- Radboud University, Donders Institute for Brain Cognition and Behavior, Centre for Cognitive Neuroimaging, Nijmegen, the Netherlands
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de Graaf D, Araújo R, Derksen M, Zwinderman K, de Vries NM, IntHout J, Bloem BR. The sound of Parkinson's disease: A model of audible bradykinesia. Parkinsonism Relat Disord 2024; 120:106003. [PMID: 38219529 DOI: 10.1016/j.parkreldis.2024.106003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 01/08/2024] [Accepted: 01/08/2024] [Indexed: 01/16/2024]
Abstract
INTRODUCTION Evaluation of bradykinesia is based on five motor tasks from the MDS-UPDRS. Visually scoring these motor tasks is subjective, resulting in significant interrater variability. Recent observations suggest that it may be easier to hear the characteristic features of bradykinesia, such as the decrement in sound intensity or force of repetitive movements. The objective is to evaluate whether audio signals derived during four MDS-UPDRS tasks can be used to detect and grade bradykinesia, using two machine learning models. METHODS 54 patients with Parkinson's disease and 28 healthy controls were filmed while executing the bradykinesia motor tasks. Several features were extracted from the audio signal, including number of taps, speed, sound intensity, decrement and freezes. For each motor task, two supervised machine learning models were trained, Logistic Regression (LR) and Support Vector Machine (SVM). RESULTS Both classifiers were able to separate patients from controls reasonably well for the leg agility task, area under the receiver operating characteristic curve (AUC): 0.92 (95%CI: 0.78-0.99) for LR and 0.93 (0.81-1.00) for SVM. Also, models were able to differentiate less severe bradykinesia from severe bradykinesia, particularly for the pronation-supination motor task, with AUC: 0.90 (0.62-1.00) for LR and 0.82 (0.45-0.97) for SVM. CONCLUSION This audio-based approach discriminates PD from healthy controls with moderate-high accuracy and separated individuals with less severe bradykinesia from those with severe bradykinesia. Sound analysis may contribute to the identification and monitoring of bradykinesia.
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Affiliation(s)
- Debbie de Graaf
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands.
| | - Rui Araújo
- Department of Neurology, Centro Hospitalar Universitário São João, Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto, Porto, Portugal
| | | | - Koos Zwinderman
- Academic Medical Center, Department of Cardiology, P.O. Box 22660, 1100 DD, Amsterdam, the Netherlands
| | - Nienke M de Vries
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Joanna IntHout
- Radboud University Medical Center, Department for Health Evidence Nijmegen, the Netherlands
| | - Bastiaan R Bloem
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
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Johansson ME, Toni I, Kessels RPC, Bloem BR, Helmich RC. Clinical severity in Parkinson's disease is determined by decline in cortical compensation. Brain 2024; 147:871-886. [PMID: 37757883 PMCID: PMC10907095 DOI: 10.1093/brain/awad325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 08/02/2023] [Accepted: 09/07/2023] [Indexed: 09/29/2023] Open
Abstract
Dopaminergic dysfunction in the basal ganglia, particularly in the posterior putamen, is often viewed as the primary pathological mechanism behind motor slowing (i.e. bradykinesia) in Parkinson's disease. However, striatal dopamine loss fails to account for interindividual differences in motor phenotype and rate of decline, implying that the expression of motor symptoms depends on additional mechanisms, some of which may be compensatory in nature. Building on observations of increased motor-related activity in the parieto-premotor cortex of Parkinson patients, we tested the hypothesis that interindividual differences in clinical severity are determined by compensatory cortical mechanisms and not just by basal ganglia dysfunction. Using functional MRI, we measured variability in motor- and selection-related brain activity during a visuomotor task in 353 patients with Parkinson's disease (≤5 years disease duration) and 60 healthy controls. In this task, we manipulated action selection demand by varying the number of possible actions that individuals could choose from. Clinical variability was characterized in two ways. First, patients were categorized into three previously validated, discrete clinical subtypes that are hypothesized to reflect distinct routes of α-synuclein propagation: diffuse-malignant (n = 42), intermediate (n = 128) or mild motor-predominant (n = 150). Second, we used the scores of bradykinesia severity and cognitive performance across the entire sample as continuous measures. Patients showed motor slowing (longer response times) and reduced motor-related activity in the basal ganglia compared with controls. However, basal ganglia activity did not differ between clinical subtypes and was not associated with clinical scores. This indicates a limited role for striatal dysfunction in shaping interindividual differences in clinical severity. Consistent with our hypothesis, we observed enhanced action selection-related activity in the parieto-premotor cortex of patients with a mild-motor predominant subtype, both compared to patients with a diffuse-malignant subtype and controls. Furthermore, increased parieto-premotor activity was related to lower bradykinesia severity and better cognitive performance, which points to a compensatory role. We conclude that parieto-premotor compensation, rather than basal ganglia dysfunction, shapes interindividual variability in symptom severity in Parkinson's disease. Future interventions may focus on maintaining and enhancing compensatory cortical mechanisms, rather than only attempting to normalize basal ganglia dysfunction.
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Affiliation(s)
- Martin E Johansson
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Centre of Expertise for Parkinson & Movement Disorders, 6525 EN Nijmegen, The Netherlands
| | - Ivan Toni
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, 6525 EN Nijmegen, The Netherlands
| | - Roy P C Kessels
- Department of Medical Psychology, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
- Radboudumc Alzheimer Center, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
- Vincent van Gogh Institute for Psychiatry, 5803 AC Venray, The Netherlands
| | - Bastiaan R Bloem
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Centre of Expertise for Parkinson & Movement Disorders, 6525 EN Nijmegen, The Netherlands
| | - Rick C Helmich
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Centre of Expertise for Parkinson & Movement Disorders, 6525 EN Nijmegen, The Netherlands
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7
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Maas BR, Göttgens I, Tijsse Klasen HPS, Kapelle WM, Radder DLM, Bloem BR, Post B, de Vries NM, Darweesh SKL. Age and gender differences in non-motor symptoms in people with Parkinson's disease. Front Neurol 2024; 15:1339716. [PMID: 38361642 PMCID: PMC10867965 DOI: 10.3389/fneur.2024.1339716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Accepted: 01/15/2024] [Indexed: 02/17/2024] Open
Abstract
Background Non-motor symptoms of Parkinson's disease (PD) are highly prevalent and heterogenic. Previous studies aimed to gain more insight on this heterogeneity by investigating age and gender differences in non-motor symptom severity, but findings were inconsistent. Furthermore, besides examining the single effects of age and gender, the interaction between them in relation to non-motor functioning has -as far as we know- not been investigated before. Objectives To investigate the association of age and gender identity -as well as the interaction between age and gender identity- with non-motor symptoms and their impact on quality of life. Methods We combined three large and independent studies. This approach resulted in a total number of unique participants of 1,509. We used linear regression models to assess the association of age and gender identity, and their interaction, with non-motor symptoms and their impact on quality of life. Results Older people with PD generally had worse cognitive functioning, worse autonomic functioning and worse quality of life. Women with PD generally experienced more anxiety, worse autonomic functioning and worse quality of life compared to men with PD, whereas men with PD generally had worse cognitive functioning. In interaction analyses by age and gender identity, depressive symptoms and anxiety were disproportionally worse with increasing age in women compared to men. Conclusion Our findings indicate that both age and gender -as well as their interaction- are differentially associated with non-motor symptoms of PD. Both research and clinical practice should pay more attention to demographic subgroups differences and possible different treatment approaches with respect to age and gender. We showed how combining datasets is of added value in this kind of analyses and encourage others to use similar approaches.
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Affiliation(s)
- Bart R. Maas
- Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, Netherlands
| | - Irene Göttgens
- Department of Primary and Community Care, Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Hermina P. S. Tijsse Klasen
- Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, Netherlands
| | - Willanka M. Kapelle
- Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, Netherlands
| | - Danique L. M. Radder
- Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, Netherlands
| | - Bastiaan R. Bloem
- Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, Netherlands
| | - Bart Post
- Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, Netherlands
| | - Nienke M. de Vries
- Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, Netherlands
| | - Sirwan K. L. Darweesh
- Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, Netherlands
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8
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Mirelman A, Volkov J, Salomon A, Gazit E, Nieuwboer A, Rochester L, Del Din S, Avanzino L, Pelosin E, Bloem BR, Della Croce U, Cereatti A, Thaler A, Roggen D, Mazza C, Shirvan J, Cedarbaum JM, Giladi N, Hausdorff JM. Digital Mobility Measures: A Window into Real-World Severity and Progression of Parkinson's Disease. Mov Disord 2024; 39:328-338. [PMID: 38151859 DOI: 10.1002/mds.29689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 11/20/2023] [Accepted: 11/27/2023] [Indexed: 12/29/2023] Open
Abstract
BACKGROUND Real-world monitoring using wearable sensors has enormous potential for assessing disease severity and symptoms among persons with Parkinson's disease (PD). Many distinct features can be extracted, reflecting multiple mobility domains. However, it is unclear which digital measures are related to PD severity and are sensitive to disease progression. OBJECTIVES The aim was to identify real-world mobility measures that reflect PD severity and show discriminant ability and sensitivity to disease progression, compared to the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) scale. METHODS Multicenter real-world continuous (24/7) digital mobility data from 587 persons with PD and 68 matched healthy controls were collected using an accelerometer adhered to the lower back. Machine learning feature selection and regression algorithms evaluated associations of the digital measures using the MDS-UPDRS (I-III). Binary logistic regression assessed discriminatory value using controls, and longitudinal observational data from a subgroup (n = 33) evaluated sensitivity to change over time. RESULTS Digital measures were only moderately correlated with the MDS-UPDRS (part II-r = 0.60 and parts I and III-r = 0.50). Most associated measures reflected activity quantity and distribution patterns. A model with 14 digital measures accurately distinguished recently diagnosed persons with PD from healthy controls (81.1%, area under the curve: 0.87); digital measures showed larger effect sizes (Cohen's d: [0.19-0.66]), for change over time than any of the MDS-UPDRS parts (Cohen's d: [0.04-0.12]). CONCLUSIONS Real-world mobility measures are moderately associated with clinical assessments, suggesting that they capture different aspects of motor capacity and function. Digital mobility measures are sensitive to early-stage disease and to disease progression, to a larger degree than conventional clinical assessments, demonstrating their utility, primarily for clinical trials but ultimately also for clinical care. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Anat Mirelman
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jana Volkov
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Amit Salomon
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Eran Gazit
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
| | - Alice Nieuwboer
- Department of Rehabilitation Science, KU Leuven, Neuromotor Rehabilitation Research Group, Leuven, Belgium
| | - Lynn Rochester
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Silvia Del Din
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, United Kingdom
- National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC), Newcastle University and The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
| | - Laura Avanzino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Department of Experimental Medicine, Section of Human Physiology, University of Genoa, Genoa, Italy
| | - Elisa Pelosin
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics and Maternal Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Policlinico San Martino Teaching Hospital, Genoa, Italy
| | - Bastiaan R Bloem
- Department of Neurology, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Nijmegen, The Netherlands
| | - Ugo Della Croce
- Department of Biomedical Sciences, University of Sassari, Sassari, Italy
| | - Andrea Cereatti
- Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy
| | - Avner Thaler
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | | | | | | | - Jesse M Cedarbaum
- Coeruleus Clinical Sciences, Woodbridge, Connecticut, USA
- Yale University School of Medicine, New Haven, Connecticut, USA
| | - Nir Giladi
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Jeffrey M Hausdorff
- Laboratory for Early Markers of Neurodegeneration (LEMON), Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv, Israel
- Faculty of Medicine and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Physical Therapy, Tel Aviv University, Tel Aviv, Israel
- Department of Orthopedic Surgery, Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois, USA
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9
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Bronte-Stewart H, Gala A, Wilkins K, Pettruci M, Kehnemouyi Y, Velisar A, Trager M. The digital signature of emergent tremor in Parkinson's disease. RESEARCH SQUARE 2023:rs.3.rs-3467667. [PMID: 37961117 PMCID: PMC10635351 DOI: 10.21203/rs.3.rs-3467667/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Background Emergent tremor in Parkinson's disease (PD) can occur during sustained postures or movement that is different from action tremor. Tremor can contaminate the clinical rating of bradykinesia during finger tapping. Currently, there is no reliable way of isolating emergent tremor and measuring the cardinal motor symptoms based on voluntary movements only. Objective Investigate whether emergent tremor during repetitive alternating finger tapping (RAFT) on a quantitative digitography (QDG) device can be reliably identified and distinguished from voluntary tapping. Methods Ninety-six individuals with PD and forty-two healthy controls performed a thirty-second QDG-RAFT task and the Movement Disorders Society - Unified Parkinson's Disease Rating Scale Part III (MDS-UPDRS III). Visual identification of tremor during QDG-RAFT was labelled by an experienced movement disorders specialist. Two methods of identifying tremor were investigated: 1) physiologically-informed temporal thresholds 2) XGBoost model using temporal and amplitude features of tapping. Results The XGBoost model showed high accuracy for identifying tremor (area under the precision-recall curve of 0.981) and outperformed temporal-based thresholds. Percent time duration of classifier-identified tremor showed significant correlations with MDS-UPDRS III tremor subscores (r = 0.50, P < 0.0001). There was a significant change in QDG metrics for bradykinesia, rigidity and arrhythmicity after tremor strikes were excluded (p < 0.01). Conclusions Emergent tremor during QDG-RAFT has a unique digital signature and the duration of tremor correlated with the MDS-UPDRS III tremor items. When involuntary tremor strikes were excluded, the QDG metrics of bradykinesia and rigidity were significantly worse, demonstrating the importance of distinguishing tremor from voluntary movement when rating bradykinesia.
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Affiliation(s)
| | | | | | | | | | | | - Megan Trager
- Columbia University College of Physicians and Surgeons
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10
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Schootemeijer S, de Vries NM, Macklin EA, Roes KCB, Joosten H, Omberg L, Ascherio A, Schwarzschild MA, Bloem BR. The STEPWISE study: study protocol for a smartphone-based exercise solution for people with Parkinson's Disease (randomized controlled trial). BMC Neurol 2023; 23:323. [PMID: 37700241 PMCID: PMC10496249 DOI: 10.1186/s12883-023-03355-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 08/02/2023] [Indexed: 09/14/2023] Open
Abstract
BACKGROUND Exercise has various health benefits for people with Parkinson's disease (PD). However, implementing exercise into daily life and long-term adherence remain challenging. To increase a sustainable engagement with physical activity of people with PD, interventions that are motivating, accessible, and scalable are needed. We primarily aim to investigate whether a smartphone app (STEPWISE app) can increase physical activity (i.e., step count) in people with PD over one year. Our second aim is to investigate the potential effects of the intervention on physical fitness, and motor- and non-motor function. Our third aim is to explore whether there is a dose-response relationship between volume of physical activity and our secondary endpoints. METHODS STEPWISE is a double-blind, randomized controlled trial. We aim to include 452 Dutch people with PD who can walk independently (Hoehn & Yahr stages 1-3) and who do not take more than 7,000 steps per day prior to inclusion. Physical activity levels are measured as step counts on the participant's own smartphone and scaled as percentage of each participant's baseline. Participants are randomly assigned to an active control group with an increase of 5-20% (active controls) or any of the three intervention arms with increases of 25-100% (intermediate dose), 50-200% (large dose), or 100-400% (very large dose). The primary endpoint is change in step count as measured by the STEPWISE smartphone app from baseline to 52 weeks. For our primary aim, we will evaluate the between-group difference in average daily step count change from baseline to 52 weeks. For our second aim, measures of physical fitness, and motor- and non-motor function are included. For our third aim, we will associate 52-week changes in step count with 52-week changes in secondary outcomes. DISCUSSION This trial evaluates the potential of a smartphone-based intervention to increase activity levels in people with PD. We envision that motivational apps will increase adherence to physical activity recommendations and could permit conduct of remote clinical trials of exercise for people with PD or those at risk of PD. TRIAL REGISTRATION ClinicalTrials.gov; NCT04848077; 19/04/2021. CLINICALTRIALS gov/ct2/show/NCT04848077.
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Affiliation(s)
- Sabine Schootemeijer
- DisordersDonders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Nienke M de Vries
- DisordersDonders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Eric A Macklin
- Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Kit C B Roes
- Department of Health Evidence, Section Biostatistics, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, the Netherlands
| | - Hilde Joosten
- Department of Sports Medicine, Canisius Wilhelmina Hospital, Burgemeester Daleslaan 27, Nijmegen, 6532 CL, the Netherlands
| | | | - Alberto Ascherio
- Harvard Medical School, Boston, MA, USA
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Michael A Schwarzschild
- Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Mass General Institute for Neurodegenerative Disease, Massachusetts General Hospital, Boston, MA, USA
| | - Bastiaan R Bloem
- DisordersDonders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Radboud University Medical Center, Nijmegen, the Netherlands.
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11
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Tichelaar JG, Sayalı C, Helmich RC, Cools R. Impulse control disorder in Parkinson's disease is associated with abnormal frontal value signalling. Brain 2023; 146:3676-3689. [PMID: 37192341 PMCID: PMC10473575 DOI: 10.1093/brain/awad162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 04/18/2023] [Accepted: 04/26/2023] [Indexed: 05/18/2023] Open
Abstract
Dopaminergic medication is well established to boost reward- versus punishment-based learning in Parkinson's disease. However, there is tremendous variability in dopaminergic medication effects across different individuals, with some patients exhibiting much greater cognitive sensitivity to medication than others. We aimed to unravel the mechanisms underlying this individual variability in a large heterogeneous sample of early-stage patients with Parkinson's disease as a function of comorbid neuropsychiatric symptomatology, in particular impulse control disorders and depression. One hundred and ninety-nine patients with Parkinson's disease (138 ON medication and 61 OFF medication) and 59 healthy controls were scanned with functional MRI while they performed an established probabilistic instrumental learning task. Reinforcement learning model-based analyses revealed medication group differences in learning from gains versus losses, but only in patients with impulse control disorders. Furthermore, expected-value related brain signalling in the ventromedial prefrontal cortex was increased in patients with impulse control disorders ON medication compared with those OFF medication, while striatal reward prediction error signalling remained unaltered. These data substantiate the hypothesis that dopamine's effects on reinforcement learning in Parkinson's disease vary with individual differences in comorbid impulse control disorder and suggest they reflect deficient computation of value in medial frontal cortex, rather than deficient reward prediction error signalling in striatum. See Michael Browning (https://doi.org/10.1093/brain/awad248) for a scientific commentary on this article.
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Affiliation(s)
- Jorryt G Tichelaar
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, 6525EN Nijmegen, The Netherlands
- Radboud University Medical Center, Department of Neurology, Centre of Expertise for Parkinson and Movement Disorders, 6525GA Nijmegen, The Netherlands
| | - Ceyda Sayalı
- The Johns Hopkins University School of Medicine, Center for Psychedelic and Consciousness Research, Baltimore, MD 21224, USA
| | - Rick C Helmich
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, 6525EN Nijmegen, The Netherlands
- Radboud University Medical Center, Department of Neurology, Centre of Expertise for Parkinson and Movement Disorders, 6525GA Nijmegen, The Netherlands
| | - Roshan Cools
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, 6525EN Nijmegen, The Netherlands
- Radboud University Medical Center, Department of Psychiatry, 6525GA Nijmegen, The Netherlands
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12
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Wolters AF, Heijmans M, Priovoulos N, Jacobs HIL, Postma AA, Temel Y, Kuijf ML, Michielse S. Neuromelanin related ultra-high field signal intensity of the locus coeruleus differs between Parkinson's disease and controls. Neuroimage Clin 2023; 39:103479. [PMID: 37494758 PMCID: PMC10394012 DOI: 10.1016/j.nicl.2023.103479] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 07/05/2023] [Accepted: 07/18/2023] [Indexed: 07/28/2023]
Abstract
INTRODUCTION Neuromelanin related signal changes in catecholaminergic nuclei are considered as a promising MRI biomarker in Parkinson's disease (PD). Until now, most studies have investigated the substantia nigra (SN), while signal changes might be more prominent in the locus coeruleus (LC). Ultra-high field MRI improves the visualisation of these small brainstem regions and might support the development of imaging biomarkers in PD. OBJECTIVES To compare signal intensity of the SN and LC on Magnetization Transfer MRI between PD patients and healthy controls (HC) and to explore its association with cognitive performance in PD. METHODS This study was conducted using data from the TRACK-PD study, a longitudinal 7T MRI study. A total of 78 early-stage PD patients and 36 HC were included. A mask for the SN and LC was automatically segmented and manually corrected. Neuromelanin related signal intensity of the SN and LC was compared between PD and HC. RESULTS PD participants showed a lower contrast-to-noise ratio (CNR) in the right SN (p = 0.029) and left LC (p = 0.027). After adding age as a confounder, the CNR of the right SN did not significantly differ anymore between PD and HC (p = 0.055). Additionally, a significant positive correlation was found between the SN CNR and memory function. DISCUSSION This study confirms that neuromelanin related signal intensity of the LC differs between early-stage PD patients and HC. No significant difference was found in the SN. This supports the theory of bottom-up disease progression in PD. Furthermore, loss of SN integrity might influence working memory or learning capabilities in PD patients.
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Affiliation(s)
- Amée F Wolters
- Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands; School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands.
| | - Margot Heijmans
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Nikos Priovoulos
- Spinoza Centre for Neuroimaging, Amsterdam, The Netherlands; Computational Cognitive Neuroscience and Neuroimaging, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Heidi I L Jacobs
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands; Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Alida A Postma
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, The Netherlands
| | - Yasin Temel
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands; Department of Neurosurgery, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Mark L Kuijf
- Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands; School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Stijn Michielse
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
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13
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Mc Carthy M, Burrows K, Griffiths P, Black PM, Demanuele C, Karlsson N, Buenconsejo J, Patel N, Chen WH, Cappelleri JC. From Meaningful Outcomes to Meaningful Change Thresholds: A Path to Progress for Establishing Digital Endpoints. Ther Innov Regul Sci 2023; 57:629-645. [PMID: 37020160 DOI: 10.1007/s43441-023-00502-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 02/24/2023] [Indexed: 04/07/2023]
Abstract
This paper examines the use of digital endpoints (DEs) derived from digital health technologies (DHTs), focusing primarily on the specific considerations regarding the determination of meaningful change thresholds (MCT). Using DHTs in drug development is becoming more commonplace. There is general acceptance of the value of DHTs supporting patient-centric trial design, capturing data outside the traditional clinical trial setting, and generating DEs with the potential to be more sensitive to change than conventional assessments. However, the transition from exploratory endpoints to primary and secondary endpoints capable of supporting labeling claims requires these endpoints to be substantive with reproducible population-specific values. Meaningful change represents the amount of change in an endpoint measure perceived as important to patients and should be determined for each digital endpoint and given population under consideration. This paper examines existing approaches to determine meaningful change thresholds and explores examples of these methodologies and their use as part of DE development: emphasizing the importance of determining what aspects of health are important to patients and ensuring the DE captures these concepts of interest and aligns with the overarching endpoint strategy. Examples are drawn from published DE qualification documentation and responses to qualification submissions under review by the various regulatory authorities. It is the hope that these insights will inform and strengthen the development and validation of DEs as drug development tools, particularly for those new to the approaches to determine MCTs.
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14
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Meinders MJ, Marks WJ, van Zundert SBM, Kapur R, Bloem BR. Enhancing Participant Engagement in Clinical Studies: Strategies Applied in the Personalized Parkinson Project. JOURNAL OF PARKINSON'S DISEASE 2023:JPD225015. [PMID: 37092234 DOI: 10.3233/jpd-225015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Affiliation(s)
- Marjan J Meinders
- Radboud University Medical Center, Research Institute for Medical Innovation, Scientific Center for Quality of Healthcare, Nijmegen, the Netherlands
| | | | - Sabine B M van Zundert
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of neurology, Nijmegen, the Netherlands
| | - Ritu Kapur
- Verily Life Sciences, South San Francisco, CA, USA
| | - Bastiaan R Bloem
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of neurology, Nijmegen, the Netherlands
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15
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Bloem BR, Post E, Hall DA. An Apple a Day to Keep the Parkinson's Disease Doctor Away? Ann Neurol 2023; 93:681-685. [PMID: 36708048 DOI: 10.1002/ana.26612] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 01/26/2023] [Indexed: 01/29/2023]
Abstract
It is challenging to reliably assess the motor features of Parkinson's disease in real-time. This has motivated the search for new digital outcomes that can objectively and remotely measure the severity of parkinsonian motor impairments over an extended period of time. The United States Food and Drug Administration (FDA) has recently granted a 510(k) clearance to the Rune Labs Kinematics System, an ambulatory, smartwatch-based monitoring system to remotely track tremor and dyskinesias in persons with Parkinson's disease. The FDA clearance means that this new digital approach can be regarded as being safe for use in daily practice, with acceptable correlations to clinically based measures. However, the immediate implications for clinicians are limited, because it remains to be demonstrated whether the digital signals correlate well to clinically meaningful outcomes at patient level. The impact on research is also restricted for now, as more validation studies are needed before this new digital approach can be used as primary or secondary endpoint in clinical trials. ANN NEUROL 2023;93:681-685.
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Affiliation(s)
- Bastiaan R Bloem
- Department of Neurology, Centre of Expertise for Parkinson & Movement Disorders, Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Erik Post
- Department of Neurology, Centre of Expertise for Parkinson & Movement Disorders, Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Deborah A Hall
- Department of Neurological Sciences, Rush University, Chicago, IL, USA
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16
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Duque KR, Vizcarra JA, Hill EJ, Espay AJ. Disease-modifying vs symptomatic treatments: Splitting over lumping. HANDBOOK OF CLINICAL NEUROLOGY 2023; 193:187-209. [PMID: 36803811 DOI: 10.1016/b978-0-323-85555-6.00020-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Clinical trials of putative disease-modifying therapies in neurodegeneration have obeyed the century-old principle of convergence, or lumping, whereby any feature of a clinicopathologic disease entity is considered relevant to most of those affected. While this convergent approach has resulted in important successes in trials of symptomatic therapies, largely aimed at correcting common neurotransmitter deficiencies (e.g., cholinergic deficiency in Alzheimer's disease or dopaminergic deficiency in Parkinson's disease), it has been consistently futile in trials of neuroprotective or disease-modifying interventions. As individuals affected by the same neurodegenerative disorder do not share the same biological drivers, splitting such disease into small molecular/biological subtypes, to match people to therapies most likely to benefit them, is vital in the pursuit of disease modification. We here discuss three paths toward the splitting needed for future successes in precision medicine: (1) encourage the development of aging cohorts agnostic to phenotype in order to enact a biology-to-phenotype direction of biomarker development and validate divergence biomarkers (present in some, absent in most); (2) demand bioassay-based recruitment of subjects into disease-modifying trials of putative neuroprotective interventions in order to match the right therapies to the right recipients; and (3) evaluate promising epidemiologic leads of presumed pathogenetic potential using Mendelian randomization studies before designing the corresponding clinical trials. The reconfiguration of disease-modifying efforts for patients with neurodegenerative disorders will require a paradigm shift from lumping to splitting and from proteinopathy to proteinopenia.
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Affiliation(s)
- Kevin R Duque
- James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, Department of Neurology, University of Cincinnati, Cincinnati, OH, United States
| | - Joaquin A Vizcarra
- Department of Neurology, Emory University School of Medicine, Atlanta, GA, United States
| | - Emily J Hill
- James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, Department of Neurology, University of Cincinnati, Cincinnati, OH, United States
| | - Alberto J Espay
- James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, Department of Neurology, University of Cincinnati, Cincinnati, OH, United States.
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17
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Two-year clinical progression in focal and diffuse subtypes of Parkinson's disease. NPJ Parkinsons Dis 2023; 9:29. [PMID: 36806285 PMCID: PMC9937525 DOI: 10.1038/s41531-023-00466-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 01/06/2023] [Indexed: 02/19/2023] Open
Abstract
Heterogeneity in Parkinson's disease (PD) presents a barrier to understanding disease mechanisms and developing new treatments. This challenge may be partially overcome by stratifying patients into clinically meaningful subtypes. A recent subtyping scheme classifies de novo PD patients into three subtypes: mild-motor predominant, intermediate, or diffuse-malignant, based on motor impairment, cognitive function, rapid eye movement sleep behavior disorder (RBD) symptoms, and autonomic symptoms. We aimed to validate this approach in a large longitudinal cohort of early-to-moderate PD (n = 499) by assessing the influence of subtyping on clinical characteristics at baseline and on two-year progression. Compared to mild-motor predominant patients (42%), diffuse-malignant patients (12%) showed involvement of more clinical domains, more diffuse hypokinetic-rigid motor symptoms (decreased lateralization and hand/foot focality), and faster two-year progression. These findings extend the classification of diffuse-malignant and mild-motor predominant subtypes to early-to-moderate PD and suggest that different pathophysiological mechanisms (focal versus diffuse cerebral propagation) may underlie distinct subtype classifications.
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18
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Jansen JA, Tosserams A, Weerdesteyn VG, Bloem BR, Nonnekes J. The 'Pants-Sign': A Predictor for Falling in People with Parkinson's Disease? JOURNAL OF PARKINSON'S DISEASE 2023; 13:1321-1327. [PMID: 38108362 PMCID: PMC10741315 DOI: 10.3233/jpd-230353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/16/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND A history of falls is the most established predictor of future falls in people with Parkinson's disease (PD). However, predicting a first fall remains challenging. OBJECTIVE To assess whether experiencing difficulties putting on pants while standing is a viable predictor of future falling, and specifically a first fall, in persons with PD. We define this 'Pants-sign' as people who resort to putting on their pants only while seated. METHODS 264 persons with PD were included. Information on the Pants-sign, history of falls, disease severity (MDS-UPDRS part III), freezing of gait (N-FOGQ > 0), cognitive function (MoCA), self-reported disability (Schwab & England scale), health-related quality of life (SF-12), Timed-Up-and-Go, and one-legged stance were determined at baseline and after one-year follow-up. The association between the Pants-sign and future falling was examined by univariate logistic regression analysis. A multivariate step-wise logistic regression with forward selection was employed to identify the strongest associations in the entire cohort and a sub-cohort of people without falls in the year prior to baseline. RESULTS The Pants-sign was univariably associated with a future fall (OR = 2.406, 95% CI [1.313-4.409], p = 0.004]), but was not an independent predictor in the multivariate logistic regression; predictors were higher MDS-UPDRS part III scores (OR = 1.088, 95% CI [1.056-1.121], p < 0.001] and history of falls (OR = 5.696, 95% CI [2.650-12.243], p≤0.001]. For the sub-cohort of people without falls in the previous year (n = 189), the Pants-sign was not associated with future falls. CONCLUSIONS The Pants-sign is simple to assess and is associated with future falling in PD but is not an independent predictor.
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Affiliation(s)
- Jamie A.F. Jansen
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Rehabilitation, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
| | - Anouk Tosserams
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Rehabilitation, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
| | - Vivian G.M. Weerdesteyn
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Rehabilitation, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
- Department of Rehabilitation, Sint Maartenskliniek, Ubbergen, The Netherlands
| | - Bastiaan R. Bloem
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
| | - Jorik Nonnekes
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Rehabilitation, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
- Department of Rehabilitation, Sint Maartenskliniek, Ubbergen, The Netherlands
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19
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Hommel ALAJ, Krijthe JH, Darweesh S, Bloem BR. The association of comorbidity with Parkinson's disease-related hospitalizations. Parkinsonism Relat Disord 2022; 104:123-128. [PMID: 36333237 DOI: 10.1016/j.parkreldis.2022.10.012] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 09/19/2022] [Accepted: 10/09/2022] [Indexed: 11/12/2022]
Abstract
INTRODUCTION Unplanned hospital admissions associated with Parkinson's disease could be partly attributable to comorbidities. METHODS We studied nationwide claims databases and registries. Persons with newly diagnosed Parkinson's disease were identified based on the first Parkinson's disease-related reimbursement claim by a medical specialist. Comorbidities were classified based on the Charlson Comorbidity Index. We studied hospitalization admissions because of falls, psychiatric diseases, pneumonia and urinary tract infections, PD-related hospitalizations-not otherwise specified. The association between comorbidities and time-to-hospitalization was estimated using Cox proportional hazard modelling. To better understand pathways leading to hospitalizations, we performed multiple analyses on causes for hospitalizations. RESULTS We identified 18 586 people with newly diagnosed Parkinson's disease. The hazard of hospitalization was increased in persons with peptic ulcer disease (HR 2.20, p = 0.009), chronic obstructive pulmonary disease (HR 1.61, p < 0.001), stroke (HR 1.37, p = 0.002) and peripheral vascular disease (HR 1.31, p = 0.02). In the secondary analyses, the hazard of PD-related hospitalizations-not otherwise specified (HR 3.24, p = 0.02) and pneumonia-related hospitalization (HR 2.90, p = 0.03) was increased for those with comorbid peptic ulcer disease. The hazard of fall-related hospitalization (HR 1.57, p = 0.003) and pneumonia-related hospitalization (HR 2.91, p < 0.001) was increased in persons with chronic obstructive pulmonary disease. The hazard of pneumonia-related hospitalization was increased in those with stroke (HR 1.54, p = 0.03) or peripheral vascular disease (HR 1.60, p = 0.02). The population attributable risk of comorbidity was 8.4%. CONCLUSION Several comorbidities increase the risk of Parkinson's disease related-hospitalization indicating a need for intervention strategies targeting these comorbid disorders.
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Affiliation(s)
- Adrianus L A J Hommel
- Primary and Community Care, Radboud University Medical Center, Nijmegen, the Netherlands; Groenhuysen Organisation, Roosendaal, the Netherlands
| | - Jesse H Krijthe
- Delft University of Technology, Pattern Recognition & Bioinformatics, Delft, the Netherlands
| | - Sirwan Darweesh
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Bastiaan R Bloem
- Department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands.
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20
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Thomsen TH, Jørgensen LB, Kjær TW, Haahr A, Vogel A, Larsen IU, Winge K. Clinical Markers of 6 Pre-dominant Coping Behaviors in Living With Parkinson Disease: A Convergent Mixed Methods Study. INQUIRY : A JOURNAL OF MEDICAL CARE ORGANIZATION, PROVISION AND FINANCING 2022; 59:469580221129929. [PMID: 36314596 PMCID: PMC9629560 DOI: 10.1177/00469580221129929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
People with Parkinson's disease (PwP) experience a variety of symptoms and fluctuations in these, which they have to cope with every day. In tailoring a person-centered treatment to PwP there is a lack of knowledge about the association between pre-dominant coping behaviors and clinical markers among PwP. To describe and compare specific clinical markers between 6 suggested coping behaviors. Thirty-four PwP, who previously had been classified into 6 different pre-dominant coping behaviors, were included in this mixed methods study. Six primary variables were included in the descriptive analysis; motor function (UPDRS-III), non-motor symptoms score (NMS-Quest), change in bradykinesia score, apathy score (LARS), personality traits (NEO-FFI), and cognitive status (evaluated by a neuropsychologist). The merged results of this mixed methods study indicate that clinical markers as apathy, burden of non-motor symptoms, cognitive impairments and personality traits, have the potential to impact the coping behavior in PwP. In a clinical setting the markers; NMS-burden, degree of apathy, cognition, and personality traits may indicate specific coping behavior. Three of the six suggested typologies of coping behaviors differed from the other groups when comparing descriptive data. In order to improve patient care and guide the development of person-centered therapies, each PwP should be approached based on those typologies.
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Affiliation(s)
- Trine Hørmann Thomsen
- Rigshospitalet Glostrup, Glostrup, Capital Region, Denmark,Trine Hørmann Thomsen, Department of Neurology, Movement disorder Clinic, Rigshospitalet Glostrup, Valdemar Hansens Vej 6, opgang 7, Glostrup, Capital Region 2600, Denmark.
| | - Lene Bastrup Jørgensen
- Knowledge Centre for Neurorehabilitation of Western Denmark, Regional Hospital Viborg, Denmark,Department of Clinical Medicine, University of Aarhus, Aarhus, Denmark
| | - Troels Wesenberg Kjær
- Zealand University Hospital, Roskilde, Denmark,University of Copenhagen, København, Denmark
| | | | - Asmus Vogel
- Copenhagen University Hospital, Rigshospitalet, Denmark
| | | | - Kristian Winge
- Odense University Hospital, Odense, Denmark,University of Southern Denmark, Denmark
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21
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Xu Z, Shen B, Tang Y, Wu J, Wang J. Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:349-361. [PMID: 36939759 PMCID: PMC9590510 DOI: 10.1007/s43657-022-00051-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 03/12/2022] [Accepted: 03/28/2022] [Indexed: 11/27/2022]
Abstract
Despite recent advances in technology, clinical phenotyping of Parkinson's disease (PD) has remained relatively limited as current assessments are mainly based on empirical observation and subjective categorical judgment at the clinic. A lack of comprehensive, objective, and quantifiable clinical phenotyping data has hindered our capacity to diagnose, assess patients' conditions, discover pathogenesis, identify preclinical stages and clinical subtypes, and evaluate new therapies. Therefore, deep clinical phenotyping of PD patients is a necessary step towards understanding PD pathology and improving clinical care. In this review, we present a growing community consensus and perspective on how to clinically phenotype this disease, that is, to phenotype the entire course of disease progression by integrating capacity, performance, and perception approaches with state-of-the-art technology. We also explore the most studied aspects of PD deep clinical phenotypes, namely, bradykinesia, tremor, dyskinesia and motor fluctuation, gait impairment, speech impairment, and non-motor phenotypes.
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Affiliation(s)
- Zhiheng Xu
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Bo Shen
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Yilin Tang
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Jianjun Wu
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Jian Wang
- Department of Neurology and National Research Center for Aging and Medicine & National Center for Neurological Disorders, State Key Laboratory of Medical Neurobiology, Huashan Hospital, Fudan University, Shanghai, 200040 China
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22
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Janssen Daalen JM, Schootemeijer S, Richard E, Darweesh SKL, Bloem BR. Lifestyle Interventions for the Prevention of Parkinson Disease: A Recipe for Action. Neurology 2022; 99:42-51. [PMID: 35970584 DOI: 10.1212/wnl.0000000000200787] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 04/11/2022] [Indexed: 11/15/2022] Open
Abstract
The prevalence of Parkinson disease (PD) is growing fast, amplifying the quest for disease-modifying therapies in early disease phases where pathology is still limited. Lifestyle interventions offer a promising avenue for preventing progression from prodromal to manifest PD. We illustrate this primarily for 1 specific lifestyle intervention, namely aerobic exercise because the case for the other main lifestyle factor (dietary interventions) to modify the course of prodromal PD is currently less persuasive. Various observations have hinted at the disease-modifying potential of exercise. First, studies in rodents with experimental parkinsonism showed that exercise elicits adaptive neuroplasticity in basal ganglia circuitries. Second, exercise is associated with a reduced risk of developing PD, suggesting a disease-modifying potential. Third, 2 large trials in persons with manifest PD indicate that exercise can help to stabilize motor parkinsonism, although this could also reflect a symptomatic effect. In addition, exercise seems to be a feasible intervention, given its minimal risk of side effects. Theoretical risks include an increase in fall incidents and cardiovascular complications, but these concerns seem to be acceptably low. Innovative approaches using gamification elements indicate that adequate long-term compliance with regular exercise programs can be achieved, although more work remains necessary to demonstrate enduring adherence for multiple years. Advances in digital technology can be used to deliver the exercise intervention in the participant's own living environment and also to measure the outcomes remotely, which will help to further boost long-term compliance. When delivering exercise to prodromal participants, outcome measures should focus not just on phenoconversion to manifest PD (which may well take many years to occur) but also on measurable intermediate outcomes, such as physical fitness or prodromal nonmotor symptoms. Taken together, there seems to be sufficient evidence to advocate the first judicious attempt of investigating exercise as a disease-modifying treatment in prodromal PD.
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Affiliation(s)
- Jules M Janssen Daalen
- From the Department of Neurology (J.M.J.D., S.S., E.R., S.K.L.D., B.R.B.), Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior; Center of Expertise for Parkinson & Movement Disorders (J.M.J.D., S.S., S.K.L.D., B.R.B.); and Radboud University Medical Center Alzheimer Center (E.R.), the Netherlands
| | - Sabine Schootemeijer
- From the Department of Neurology (J.M.J.D., S.S., E.R., S.K.L.D., B.R.B.), Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior; Center of Expertise for Parkinson & Movement Disorders (J.M.J.D., S.S., S.K.L.D., B.R.B.); and Radboud University Medical Center Alzheimer Center (E.R.), the Netherlands
| | - Edo Richard
- From the Department of Neurology (J.M.J.D., S.S., E.R., S.K.L.D., B.R.B.), Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior; Center of Expertise for Parkinson & Movement Disorders (J.M.J.D., S.S., S.K.L.D., B.R.B.); and Radboud University Medical Center Alzheimer Center (E.R.), the Netherlands
| | - Sirwan K L Darweesh
- From the Department of Neurology (J.M.J.D., S.S., E.R., S.K.L.D., B.R.B.), Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior; Center of Expertise for Parkinson & Movement Disorders (J.M.J.D., S.S., S.K.L.D., B.R.B.); and Radboud University Medical Center Alzheimer Center (E.R.), the Netherlands
| | - Bastiaan R Bloem
- From the Department of Neurology (J.M.J.D., S.S., E.R., S.K.L.D., B.R.B.), Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior; Center of Expertise for Parkinson & Movement Disorders (J.M.J.D., S.S., S.K.L.D., B.R.B.); and Radboud University Medical Center Alzheimer Center (E.R.), the Netherlands.
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23
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Demanuele C, Lokker C, Jhaveri K, Georgiev P, Sezgin E, Geoghegan C, Zou KH, Izmailova E, McCarthy M. Considerations for Conducting Bring Your Own “Device” (BYOD) Clinical Studies. Digit Biomark 2022; 6:47-60. [PMID: 35949223 PMCID: PMC9294934 DOI: 10.1159/000525080] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 04/07/2022] [Indexed: 12/21/2022] Open
Abstract
Background Digital health technologies are attracting attention as novel tools for data collection in clinical research. They present alternative methods compared to in-clinic data collection, which often yields snapshots of the participants' physiology, behavior, and function that may be prone to biases and artifacts, e.g., white coat hypertension, and not representative of the data in free-living conditions. Modern digital health technologies equipped with multi-modal sensors combine different data streams to derive comprehensive endpoints that are important to study participants and are clinically meaningful. Used for data collection in clinical trials, they can be deployed as provisioned products where technology is given at study start or in a bring your own “device” (BYOD) manner where participants use their technologies to generate study data. Summary The BYOD option has the potential to be more user-friendly, allowing participants to use technologies that they are familiar with, ensuring better participant compliance, and potentially reducing the bias that comes with introducing new technologies. However, this approach presents different technical, operational, regulatory, and ethical challenges to study teams. For example, BYOD data can be more heterogeneous, and recruiting historically underrepresented populations with limited access to technology and the internet can be challenging. Despite the rapid increase in digital health technologies for clinical and healthcare research, BYOD use in clinical trials is limited, and regulatory guidance is still evolving. Key Messages We offer considerations for academic researchers, drug developers, and patient advocacy organizations on the design and deployment of BYOD models in clinical research. These considerations address: (1) early identification and engagement with internal and external stakeholders; (2) study design including informed consent and recruitment strategies; (3) outcome, endpoint, and technology selection; (4) data management including compliance and data monitoring; (5) statistical considerations to meet regulatory requirements. We believe that this article acts as a primer, providing insights into study design and operational requirements to ensure the successful implementation of BYOD clinical studies.
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Affiliation(s)
| | | | - Krishna Jhaveri
- Philips Sleep and Respiratory Care, Monroeville, Pennsylvania, USA
| | | | - Emre Sezgin
- The Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, Ohio, USA
| | | | - Kelly H. Zou
- Global Medical Analytics and Real-World Evidence, Viatris Inc, Canonsburg, Pennsylvania, USA
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24
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Margiana R, Hammid AT, Ahmad I, Alsaikhan F, Turki Jalil A, Tursunbaev F, Umar F, Romero Parra RM, Fakri Mustafa Y. Current Progress in Aptasensor for Ultra-Low Level Monitoring of Parkinson's Disease Biomarkers. Crit Rev Anal Chem 2022; 54:617-632. [PMID: 35754381 DOI: 10.1080/10408347.2022.2091920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
In today's world, Parkinson's disease (PD) has been introduced as a long-term degenerative disorder of the central nervous system which mainly affects approximately more than ten million people worldwide. The vast majority of diagnostic methods for PD have operated based on conventional sensing platforms, while the traditional laboratory tests are not efficient for diagnosis of PD in the early stage due to symptoms of this common neurodegenerative syndrome starting slowly. The advent of the aptasensor has revolutionized the early-stage diagnosis of PD by measuring related biomarkers due to the myriad advantages of originating from aptamers which can be able to sensitive and selective capture various types of related biomarkers. The progress of numerous sensing platforms and methodologies in terms of biosensors based on aptamer application for PD diagnosis has revealed promising results. In this review, we present the latest developments in myriad types of aptasensors for the determination of related PD biomarkers. Working strategies, advantages and limitations of these sensing approaches are also mentioned, followed by prospects and challenges.
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Affiliation(s)
- Ria Margiana
- Department of Anatomy, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
- Master's Programme Biomedical Sciences, Faculty of Medicine, Universitas Indonesia, Jakarta, Indonesia
- Dr. Soetomo General Academic Hospital, Indonesia Surabaya
| | - Ali Thaeer Hammid
- Computer Engineering Techniques Department, Faculty of Information Technology, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq
| | - Irfan Ahmad
- Department of Clinical Laboratory Science, College of Applied Medical Sciences, King Khalid University, Abha, Saudi Arabia
| | - Fahad Alsaikhan
- College of Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj, Saudi Arabia
| | - Abduladheem Turki Jalil
- Medical Laboratories Techniques Department, Al-Mustaqbal University College, Babylon, Hilla, Iraq
| | - Farkhod Tursunbaev
- Independent Researcher, "Medcloud" Educational Centre, Tashkent, Uzbekistan
- Research Scholar, Department of Science and Innovation, Akfa University, Tashkent, Uzbekistan
| | - Fadilah Umar
- Department of Sports Science, Faculty of Sports, Sebelas Maret University, Surakarta, Indonesia
| | | | - Yasser Fakri Mustafa
- Department of Pharmaceutical Chemistry, College of Pharmacy, University of Mosul, Mosul, Iraq
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25
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Watching Parkinson's disease with wrist-based sensors. NPJ Digit Med 2022; 5:73. [PMID: 35697864 PMCID: PMC9192652 DOI: 10.1038/s41746-022-00619-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 05/24/2022] [Indexed: 11/08/2022] Open
Abstract
Parkinson's disease (PD) lacks sensitive, objective, and reliable measures for disease progression and response. This presents a challenge for clinical trials given the multifaceted and fluctuating nature of PD symptoms. Innovations in digital health and wearable sensors promise to more precisely measure aspects of patient function and well-being. Beyond research trials, digital biomarkers and clinical outcome assessments may someday support clinician-initiated or closed-loop treatment adjustments. A recent study from Verily Life Sciences presents results for a smartwatch-based motor exam intended to accelerate the development and evaluation of therapies for PD.
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26
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Reducing neuroinflammation via therapeutic compounds and lifestyle to prevent or delay progression of Parkinson's disease. Ageing Res Rev 2022; 78:101618. [PMID: 35395416 DOI: 10.1016/j.arr.2022.101618] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 03/08/2022] [Accepted: 04/01/2022] [Indexed: 02/06/2023]
Abstract
Parkinson's disease (PD) is the second most common age-associated neurodegenerative disorder and is characterised by progressive loss of dopamine neurons in the substantia nigra. Peripheral immune cell infiltration and activation of microglia and astrocytes are observed in PD, a process called neuroinflammation. Neuroinflammation is a fundamental response to protect the brain but, when chronic, it triggers neuronal damage. In the last decade, central and peripheral inflammation were suggested to occur at the prodromal stage of PD, sustained throughout disease progression, and may play a significant role in the pathology. Understanding the pathological mechanisms of PD has been a high priority in research, primarily to find effective treatments once symptoms are present. Evidence indicates that early life exposure to neuroinflammation as a consequence of life events, environmental or behaviour factors such as exposure to infections, pollution or a high fat diet increase the risk of developing PD. Many studies show healthy habits and products that decrease neuroinflammation also reduce the risk of PD. Here, we aim to stimulate discussion about the role of neuroinflammation in PD onset and progression. We highlight that reducing neuroinflammation throughout the lifespan is critical for preventing idiopathic PD, and present epidemiological studies that detail risk and protective factors. It is possible that introducing lifestyle changes that reduce neuroinflammation at the time of PD diagnosis may slow symptom progression. Finally, we discuss compounds and therapeutics to treat the neuroinflammation associated with PD.
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27
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Schalkamp AK, Rahman N, Monzón-Sandoval J, Sandor C. Deep phenotyping for precision medicine in Parkinson's disease. Dis Model Mech 2022; 15:dmm049376. [PMID: 35647913 PMCID: PMC9178512 DOI: 10.1242/dmm.049376] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
A major challenge in medical genomics is to understand why individuals with the same disorder have different clinical symptoms and why those who carry the same mutation may be affected by different disorders. In every complex disorder, identifying the contribution of different genetic and non-genetic risk factors is a key obstacle to understanding disease mechanisms. Genetic studies rely on precise phenotypes and are unable to uncover the genetic contributions to a disorder when phenotypes are imprecise. To address this challenge, deeply phenotyped cohorts have been developed for which detailed, fine-grained data have been collected. These cohorts help us to investigate the underlying biological pathways and risk factors to identify treatment targets, and thus to advance precision medicine. The neurodegenerative disorder Parkinson's disease has a diverse phenotypical presentation and modest heritability, and its underlying disease mechanisms are still being debated. As such, considerable efforts have been made to develop deeply phenotyped cohorts for this disorder. Here, we focus on Parkinson's disease and explore how deep phenotyping can help address the challenges raised by genetic and phenotypic heterogeneity. We also discuss recent methods for data collection and computation, as well as methodological challenges that have to be overcome.
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Affiliation(s)
| | | | | | - Cynthia Sandor
- UK Dementia Research Institute at Cardiff University,Division of Psychological Medicine and Clinical Neuroscience, Haydn Ellis Building, Maindy Road, Cardiff CF24 4HQ, UK
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28
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Burq M, Rainaldi E, Ho KC, Chen C, Bloem BR, Evers LJW, Helmich RC, Myers L, Marks WJ, Kapur R. Virtual exam for Parkinson's disease enables frequent and reliable remote measurements of motor function. NPJ Digit Med 2022; 5:65. [PMID: 35606508 PMCID: PMC9126938 DOI: 10.1038/s41746-022-00607-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 04/28/2022] [Indexed: 12/30/2022] Open
Abstract
Sensor-based remote monitoring could help better track Parkinson's disease (PD) progression, and measure patients' response to putative disease-modifying therapeutic interventions. To be useful, the remotely-collected measurements should be valid, reliable, and sensitive to change, and people with PD must engage with the technology. We developed a smartwatch-based active assessment that enables unsupervised measurement of motor signs of PD. Participants with early-stage PD (N = 388, 64% men, average age 63) wore a smartwatch for a median of 390 days. Participants performed unsupervised motor tasks both in-clinic (once) and remotely (twice weekly for one year). Dropout rate was 5.4%. Median wear-time was 21.1 h/day, and 59% of per-protocol remote assessments were completed. Analytical validation was established for in-clinic measurements, which showed moderate-to-strong correlations with consensus MDS-UPDRS Part III ratings for rest tremor (⍴ = 0.70), bradykinesia (⍴ = -0.62), and gait (⍴ = -0.46). Test-retest reliability of remote measurements, aggregated monthly, was good-to-excellent (ICC = 0.75-0.96). Remote measurements were sensitive to the known effects of dopaminergic medication (on vs off Cohen's d = 0.19-0.54). Of note, in-clinic assessments often did not reflect the patients' typical status at home. This demonstrates the feasibility of smartwatch-based unsupervised active tests, and establishes the analytical validity of associated digital measurements. Weekly measurements provide a real-life distribution of disease severity, as it fluctuates longitudinally. Sensitivity to medication-induced change and improved reliability imply that these methods could help reduce sample sizes needed to demonstrate a response to therapeutic interventions or disease progression.
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Affiliation(s)
- Maximilien Burq
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
| | - Erin Rainaldi
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
| | - King Chung Ho
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
| | - Chen Chen
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
| | - Bastiaan R. Bloem
- grid.5590.90000000122931605Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Luc J. W. Evers
- grid.5590.90000000122931605Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands ,grid.5590.90000000122931605Radboud University, Institute for Computing and Information Sciences, Nijmegen, the Netherlands
| | - Rick C. Helmich
- grid.5590.90000000122931605Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Nijmegen, the Netherlands
| | - Lance Myers
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
| | | | - Ritu Kapur
- grid.497059.6Verily Life Sciences, South San Francisco, CA USA
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29
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Schreglmann S, Cagnan H. Towards phenotype-specific, non-invasive therapeutic interventions for tremor. Clin Neurophysiol 2022; 140:169-170. [PMID: 35618566 DOI: 10.1016/j.clinph.2022.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 04/29/2022] [Indexed: 11/27/2022]
Affiliation(s)
- Sebastian Schreglmann
- Department of Neurology, University Hospital Würzburg, Josef-Schneider-Strasse 11, 97080 Würzburg, Germany.
| | - Hayriye Cagnan
- MRC Brain Network Dynamics Unit, University of Oxford, OX1 3TH, UK
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30
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Cools R, Tichelaar JG, Helmich RCG, Bloem BR, Esselink RAJ, Smulders K, Timmer MHM. Role of dopamine and clinical heterogeneity in cognitive dysfunction in Parkinson's disease. PROGRESS IN BRAIN RESEARCH 2022; 269:309-343. [PMID: 35248200 DOI: 10.1016/bs.pbr.2022.01.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Parkinson's disease (PD) is commonly treated with dopaminergic medication, which enhances some, while impairing other cognitive functions. It can even contribute to impulse control disorder and addiction. We describe the history of research supporting the dopamine overdose hypothesis, which accounts for the large within-patient variability in dopaminergic medication effects across different tasks by referring to the spatially non-uniform pattern of dopamine depletion in dorsal versus ventral striatum. However, there is tremendous variability in dopaminergic medication effects not just within patients across distinct tasks, but also across different patients. In the second part of this chapter we review recent studies addressing the large individual variability in the negative side effects of dopaminergic medication on functions that implicate dopamine, such as value-based learning and choice. These studies begin to unravel the mechanisms of dopamine overdosing, thus revising the strict version of the overdose hypothesis. For example, the work shows that the canonical boosting of reward-versus punishment-based choice by medication is greater in patients with depression and a non-tremor phenotype, which both implicate, among other pathology, more rather than less severe dysregulation of the mesolimbic dopamine system. Future longitudinal cohort studies are needed to identify how to optimally combine different clinical, personality, cognitive, neural, genetic and molecular predictors of detrimental medication effects in order to account for as much of the relevant variability as possible. This will provide a useful tool for precision neurology, allowing individual and contextual tailoring of (the dose of) dopaminergic medication in order to maximize its cognitive benefits, yet minimize its side effects.
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Affiliation(s)
- Roshan Cools
- Radboud university medical center, Department of Psychiatry, Nijmegen, The Netherlands; Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands.
| | - Jorryt G Tichelaar
- Radboud university medical center, Department of Neurology, Nijmegen, The Netherlands; Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Rick C G Helmich
- Radboud university medical center, Department of Neurology, Nijmegen, The Netherlands; Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Bastiaan R Bloem
- Radboud university medical center, Department of Neurology, Nijmegen, The Netherlands; Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Rianne A J Esselink
- Radboud university medical center, Department of Neurology, Nijmegen, The Netherlands; Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Katrijn Smulders
- Radboud university medical center, Department of Neurology, Nijmegen, The Netherlands; Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Monique H M Timmer
- Radboud university medical center, Department of Neurology, Nijmegen, The Netherlands; Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
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31
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Riggare S, Hägglund M, Bredenoord AL, de Groot M, Bloem BR. Ethical Aspects of Personal Science for Persons with Parkinson's Disease: What Happens When Self-Tracking Goes from Selfcare to Publication? JOURNAL OF PARKINSON'S DISEASE 2022; 11:1927-1933. [PMID: 34120915 PMCID: PMC8609698 DOI: 10.3233/jpd-212647] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Accepted: 05/21/2021] [Indexed: 11/29/2022]
Abstract
Using Parkinson's disease as an exemplary chronic condition, this Commentary discusses ethical aspects of using self-tracking for personal science, as compared to using self-tracking in the context of conducting clinical research on groups of study participants. Conventional group-based clinical research aims to find generalisable answers to clinical or public health questions. The aim of personal science is different: to find meaningful answers that matter first and foremost to an individual with a particular health challenge. In the case of personal science, the researcher and the participant are one and the same, which means that specific ethical issues may arise, such as the need to protect the participant against self-harm. To allow patient-led research in the form of personal science in the Parkinson field to evolve further, the development of a specific ethical framework for self-tracking for personal science is needed.
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Affiliation(s)
- Sara Riggare
- Uppsala University, Department of Women’s and Children’s Health, Healthcare Sciences and e-Health, Uppsala, Sweden
| | - Maria Hägglund
- Uppsala University, Department of Women’s and Children’s Health, Healthcare Sciences and e-Health, Uppsala, Sweden
| | - Annelien L. Bredenoord
- University Medical Center Utrecht, Utrecht University, Department of Medical Humanities, Utrecht, The Netherlands
| | - Martijn de Groot
- Radboud University Medical Centre, Health Innovation Labs, Nijmegen, The Netherlands
| | - Bastiaan R. Bloem
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Centre of Expertise for Parkinson & Movement Disorders, Nijmegen, The Netherlands
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New Recovery Strategies in Motor and Cognitive Functions, before, during and after Home-Confinement COVID-19, for Healthy Adults and Patients with Neurodegenerative Diseases: Review. J Clin Med 2022; 11:jcm11030597. [PMID: 35160048 PMCID: PMC8836374 DOI: 10.3390/jcm11030597] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/19/2022] [Accepted: 01/20/2022] [Indexed: 12/17/2022] Open
Abstract
Distancing and confinement at home during the Coronavirus Disease 2019 (COVID-19) outbreak has led to worsening of motor and cognitive functions, both for healthy adults and for patients with neurodegenerative diseases. The decrease in physical activity, the cessation of the intervention of the recovery and the social distance imposed by the lockdown, has had a negative impact on the physical and mental health, quality of life, daily activities, as well as on the behavioral attitudes of the diet. The purpose of this paper was to evaluate the impact of decreasing physical activity and the affected emotional status in healthy adults and patients with neurodegenerative diseases in conditions imposed by the stay at home mandate of COVID-19, along with new interventions, such as telemedicine and telerehabilitation. These interventions include online surveys carried out in multi-languages, semi-structured interviews, intervention smartphones and interventions through online platforms, for instance: Google, WhatsApp, Twitter, ResearchGate, Facebook and LinkedIn. For this study, we selected original papers that were intensively processed using characteristics co-related with physical activity, mental wellbeing, sleep quality, good eating behavior and healthy lifestyle. By searching the last two years of literature, our review presents and demonstrates the benefit of online technological interventions in lockdown, which promote physical exercise patterns and rehabilitation techniques, for healthy adults and patients with neurodegenerative diseases, and the need to develop new strategic directions and governmental measures, designed procedures and health services, which are expected to improve the quality of life, the progress of physical and cognitive functions, mental health and wellbeing for all.
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Morgan C, Tonkin EL, Craddock I, Whone AL. Acceptability of an In-Home Multimodal Sensor Platform in Parkinson’s Disease: A Qualitative Study (Preprint). JMIR Hum Factors 2022; 9:e36370. [PMID: 35797101 PMCID: PMC9305404 DOI: 10.2196/36370] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/07/2022] [Accepted: 05/23/2022] [Indexed: 12/28/2022] Open
Abstract
Background Parkinson disease (PD) symptoms are complex, gradually progressive, and fluctuate hour by hour. Home-based technological sensors are being investigated to measure symptoms and track disease progression. A smart home sensor platform, with cameras and wearable devices, could be a useful tool to use to get a fuller picture of what someone’s symptoms are like. High-resolution video can capture the ground truth of symptoms and activities. There is a paucity of information about the acceptability of such sensors in PD. Objective The primary objective of our study was to explore the acceptability of living with a multimodal sensor platform in a naturalistic setting in PD. Two subobjectives are to identify any suggested limitations and to explore the sensors’ impact on participant behaviors. Methods A qualitative study was conducted with an inductive approach using semistructured interviews with a cohort of PD and control participants who lived freely for several days in a home-like environment while continuously being sensed. Results This study of 24 participants (12 with PD) found that it is broadly acceptable to use multimodal sensors including wrist-worn wearables, cameras, and other ambient sensors passively in free-living in PD. The sensor that was found to be the least acceptable was the wearable device. Suggested limitations on the platform for home deployment included camera-free time and space. Behavior changes were noted by the study participants, which may have related to being passively sensed. Recording high-resolution video in the home setting for limited periods of time was felt to be acceptable to all participants. Conclusions The results broaden the knowledge of what types of sensors are acceptable for use in research in PD and what potential limitations on these sensors should be considered in future work. The participants’ reported behavior change in this study should inform future similar research design to take this factor into account. Collaborative research study design, involving people living with PD at every stage, is important to ensure that the technology is acceptable and that the data outcomes produced are ecologically valid and accurate. International Registered Report Identifier (IRRID) RR2-10.1136/bmjopen-2020-041303
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Affiliation(s)
- Catherine Morgan
- Translational Health Sciences, University of Bristol Medical School, Bristol, United Kingdom
- Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Bristol, United Kingdom
| | - Emma L Tonkin
- School of Computer Science, Electrical and Electronic Engineering, University of Bristol, Bristol, United Kingdom
| | - Ian Craddock
- School of Computer Science, Electrical and Electronic Engineering, University of Bristol, Bristol, United Kingdom
| | - Alan L Whone
- Translational Health Sciences, University of Bristol Medical School, Bristol, United Kingdom
- Movement Disorders Group, Bristol Brain Centre, North Bristol NHS Trust, Bristol, United Kingdom
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Drew CJG, Busse M. Considerations for clinical trial design and conduct in the evaluation of novel advanced therapeutics in neurodegenerative disease. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2022; 166:235-279. [PMID: 36424094 DOI: 10.1016/bs.irn.2022.09.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
The recent advances in the development of potentially disease modifying cell and gene therapies for neurodegenerative disease has resulted in the production of a number of promising novel therapies which are now moving forward to clinical evaluation. The robust evaluation of these therapies pose a significant number of challenges when compared to more traditional evaluations of pharmacotherapy, which is the current mainstay of neurodegenerative disease symptom management. Indeed, there is an inherent complexity in the design and conduct of these trials at multiple levels. Here we discuss specific aspects requiring consideration in the context of investigating novel cell and gene therapies for neurodegenerative disease. This extends to overarching trial designs that could be employed and the factors that underpin design choices such outcome assessments, participant selection and methods for delivery of cell and gene therapies. We explore methods of data collection that may improve efficiency in trials of cell and gene therapy to maximize data sharing and collaboration. Lastly, we explore some of the additional context beyond efficacy evaluations that should be considered to ensure implementation across relevant healthcare settings.
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Affiliation(s)
- Cheney J G Drew
- Centre For Trials Research, Cardiff University, Cardiff, United Kingdom; Brain Repair and Intracranial Neurotherapeutics Unit (BRAIN), College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom.
| | - Monica Busse
- Centre For Trials Research, Cardiff University, Cardiff, United Kingdom; Brain Repair and Intracranial Neurotherapeutics Unit (BRAIN), College of Biomedical and Life Sciences, Cardiff University, Cardiff, United Kingdom
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Park Y, Go TH, Hong SH, Kim SH, Han JH, Kang Y, Kang DR. Digital Biomarkers in Living Labs for Vulnerable and Susceptible Individuals: An Integrative Literature Review. Yonsei Med J 2022; 63:S43-S55. [PMID: 35040605 PMCID: PMC8790590 DOI: 10.3349/ymj.2022.63.s43] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 10/26/2021] [Accepted: 10/27/2021] [Indexed: 11/27/2022] Open
Abstract
PURPOSE The study aimed to identify which digital biomarkers are collected and which specific devices are used according to vulnerable and susceptible individual characteristics in a living-lab setting. MATERIALS AND METHODS A literature search, screening, and appraisal process was implemented using the Web of Science, Pubmed, and Embase databases. The search query included a combination of terms related to "digital biomarkers," "devices that collect digital biomarkers," and "vulnerable and susceptible groups." After the screening and appraisal process, a total of 37 relevant articles were obtained. RESULTS In elderly people, the main digital biomarkers measured were values related to physical activity. Most of the studies used sensors. The articles targeting children aimed to predict diseases, and most of them used devices that are simple and can induce some interest, such as wearable device-based smart toys. In those who were disabled, digital biomarkers that measured location-based movement for the purpose of diagnosing disabilities were widely used, and most were measured by easy-to-use devices that did not require detailed explanations. In the disadvantaged, digital biomarkers related to health promotion were measured, and various wearable devices, such as smart bands and headbands were used depending on the purpose and target. CONCLUSION As the digital biomarkers and devices that collect them vary depending on the characteristics of study subjects, researchers should pay attention not only to the purpose of the study but also the characteristics of study subjects when collecting and analyzing digital biomarkers from living labs.
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Affiliation(s)
- YouHyun Park
- Department of Biostatistics, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Tae-Hwa Go
- Department of Biostatistics, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Se Hwa Hong
- Department of Biostatistics, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Sung Hwa Kim
- Department of Biostatistics, Yonsei University Wonju College of Medicine, Wonju, Korea
| | - Jae Hun Han
- Department of Biostatistics, Yonsei University Wonju College of Medicine, Wonju, Korea
| | | | - Dae Ryong Kang
- Department of Biostatistics, Yonsei University Wonju College of Medicine, Wonju, Korea
- Department of Precision Medicine and Biostatistics, Yonsei University Wonju College of Medicine, Wonju, Korea.
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36
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Aarts E, Akkerman A, Altgassen M, Bartels R, Beckers D, Bevelander K, Bijleveld E, Blaney Davidson E, Boleij A, Bralten J, Cillessen T, Claassen J, Cools R, Cornelissen I, Dresler M, Eijsvogels T, Faber M, Fernández G, Figner B, Fritsche M, Füllbrunn S, Gayet S, van Gelder MMHJ, van Gerven M, Geurts S, Greven CU, Groefsema M, Haak K, Hagoort P, Hartman Y, van der Heijden B, Hermans E, Heuvelmans V, Hintz F, den Hollander J, Hulsman AM, Idesis S, Jaeger M, Janse E, Janzing J, Kessels RPC, Karremans JC, de Kleijn W, Klein M, Klumpers F, Kohn N, Korzilius H, Krahmer B, de Lange F, van Leeuwen J, Liu H, Luijten M, Manders P, Manevska K, Marques JP, Matthews J, McQueen JM, Medendorp P, Melis R, Meyer A, Oosterman J, Overbeek L, Peelen M, Popma J, Postma G, Roelofs K, van Rossenberg YGT, Schaap G, Scheepers P, Selen L, Starren M, Swinkels DW, Tendolkar I, Thijssen D, Timmerman H, Tutunji R, Tuladhar A, Veling H, Verhagen M, Verkroost J, Vink J, Vriezekolk V, Vrijsen J, Vyrastekova J, van der Wal S, Willems R, Willemsen A. Protocol of the Healthy Brain Study: An accessible resource for understanding the human brain and how it dynamically and individually operates in its bio-social context. PLoS One 2021; 16:e0260952. [PMID: 34965252 PMCID: PMC8716054 DOI: 10.1371/journal.pone.0260952] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 11/20/2021] [Indexed: 12/29/2022] Open
Abstract
The endeavor to understand the human brain has seen more progress in the last few decades than in the previous two millennia. Still, our understanding of how the human brain relates to behavior in the real world and how this link is modulated by biological, social, and environmental factors is limited. To address this, we designed the Healthy Brain Study (HBS), an interdisciplinary, longitudinal, cohort study based on multidimensional, dynamic assessments in both the laboratory and the real world. Here, we describe the rationale and design of the currently ongoing HBS. The HBS is examining a population-based sample of 1,000 healthy participants (age 30–39) who are thoroughly studied across an entire year. Data are collected through cognitive, affective, behavioral, and physiological testing, neuroimaging, bio-sampling, questionnaires, ecological momentary assessment, and real-world assessments using wearable devices. These data will become an accessible resource for the scientific community enabling the next step in understanding the human brain and how it dynamically and individually operates in its bio-social context. An access procedure to the collected data and bio-samples is in place and published on https://www.healthybrainstudy.nl/en/data-and-methods/access. Trail registration:https://www.trialregister.nl/trial/7955.
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Affiliation(s)
- Healthy Brain Study consortium
- Radboud University, Nijmegen, The Netherlands
- Radboud University Medical Center, Nijmegen, The Netherlands
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Esther Aarts
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Agnes Akkerman
- Institute for Management Research, Radboud University, Nijmegen, The Netherlands
| | | | - Ronald Bartels
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Debby Beckers
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | | | - Erik Bijleveld
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | | | | | - Janita Bralten
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Toon Cillessen
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Jurgen Claassen
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Roshan Cools
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Martin Dresler
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Myrthe Faber
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Guillén Fernández
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
- * E-mail:
| | - Bernd Figner
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Matthias Fritsche
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Sascha Füllbrunn
- Institute for Management Research, Radboud University, Nijmegen, The Netherlands
| | - Surya Gayet
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | | | - Marcel van Gerven
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Sabine Geurts
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Corina U. Greven
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Martine Groefsema
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Koen Haak
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Peter Hagoort
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Yvonne Hartman
- Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Erno Hermans
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | | | - Florian Hintz
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | | | - Anneloes M. Hulsman
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Sebastian Idesis
- Center for Brain and Cognition, University Pompeu Fabra, Barcelona, Spain
| | - Martin Jaeger
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Esther Janse
- Centre for Language Studies, Radboud University, Nijmegen, The Netherlands
| | - Joost Janzing
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Roy P. C. Kessels
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Johan C. Karremans
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Willemien de Kleijn
- School of Psychology and Artificial Intelligence, Radboud University, Nijmegen, The Netherlands
| | - Marieke Klein
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Floris Klumpers
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Nils Kohn
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Hubert Korzilius
- Institute for Management Research, Radboud University, Nijmegen, The Netherlands
| | - Bas Krahmer
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Floris de Lange
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Judith van Leeuwen
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Huaiyu Liu
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Maartje Luijten
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Peggy Manders
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Katerina Manevska
- Institute for Management Research, Radboud University, Nijmegen, The Netherlands
| | - José P. Marques
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Jon Matthews
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - James M. McQueen
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Pieter Medendorp
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - René Melis
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Antje Meyer
- Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
| | - Joukje Oosterman
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Lucy Overbeek
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Marius Peelen
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Jean Popma
- Interdisciplinary Hub for Security, Privacy and Data Governance, Radboud University, Nijmegen, The Netherlands
| | - Geert Postma
- Institute for Molecules and Materials, Radboud University, Nijmegen, The Netherlands
| | - Karin Roelofs
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | | | - Gabi Schaap
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Paul Scheepers
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Luc Selen
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
| | - Marianne Starren
- Centre for Language Studies, Radboud University, Nijmegen, The Netherlands
| | | | - Indira Tendolkar
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Dick Thijssen
- Radboud University Medical Center, Nijmegen, The Netherlands
| | - Hans Timmerman
- University Medical Center Groningen, Groningen, The Netherlands
| | - Rayyan Tutunji
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Anil Tuladhar
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Harm Veling
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | - Maaike Verhagen
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | | | - Jacqueline Vink
- Behavioural Science Institute, Radboud University, Nijmegen, The Netherlands
| | | | - Janna Vrijsen
- Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Jana Vyrastekova
- Institute for Management Research, Radboud University, Nijmegen, The Netherlands
| | | | - Roel Willems
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen, The Netherlands
- Centre for Language Studies, Radboud University, Nijmegen, The Netherlands
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Di J, Demanuele C, Kettermann A, Karahanoglu FI, Cappelleri JC, Potter A, Bury D, Cedarbaum JM, Byrom B. Considerations to address missing data when deriving clinical trial endpoints from digital health technologies. Contemp Clin Trials 2021; 113:106661. [PMID: 34954098 DOI: 10.1016/j.cct.2021.106661] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 11/23/2021] [Accepted: 12/18/2021] [Indexed: 11/25/2022]
Abstract
Digital health technologies (DHTs) enable us to measure human physiology and behavior remotely, objectively and continuously. With the accelerated adoption of DHTs in clinical trials, there is an unmet need to identify statistical approaches to address missing data to ensure that the derived endpoints are valid, accurate, and reliable. It is not obvious how commonly used statistical methods to handle missing data in clinical trials can be directly applied to the complex data collected by DHTs. Meanwhile, current approaches used to address missing data from DHTs are of limited sophistication and focus on the exclusion of data where the quantity of missing data exceeds a given threshold. High-frequency time series data collected by DHTs are often summarized to derive epoch-level data, which are then processed to compute daily summary measures. In this article, we discuss characteristics of missing data collected by DHT, review emerging statistical approaches for addressing missingness in epoch-level data including within-patient imputations across common time periods, functional data analysis, and deep learning methods, as well as imputation approaches and robust modeling appropriate for handling missing data in daily summary measures. We discuss strategies for minimizing missing data by optimizing DHT deployment and by including the patients' perspective in the study design. We believe that these approaches provide more insight into preventing missing data when deriving digital endpoints. We hope this article can serve as a starting point for further discussion among clinical trial stakeholders.
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Affiliation(s)
- Junrui Di
- Pfizer Inc., United States of America.
| | | | | | | | | | | | | | - Jesse M Cedarbaum
- Yale University School of Medicine, United States of America; Coeruleus Clinical Sciences LLC, United States of America
| | - Bill Byrom
- Signant Health, United States of America
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38
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Grill F, Johansson J, Axelsson J, Brynolfsson P, Nyberg L, Rieckmann A. Dissecting Motor and Cognitive Component Processes of a Finger-Tapping Task With Hybrid Dopamine Positron Emission Tomography and Functional Magnetic Resonance Imaging. Front Hum Neurosci 2021; 15:733091. [PMID: 34912200 PMCID: PMC8667474 DOI: 10.3389/fnhum.2021.733091] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 11/02/2021] [Indexed: 11/19/2022] Open
Abstract
Striatal dopamine is involved in facilitation of motor action as well as various cognitive and emotional functions. Positron emission tomography (PET) is the primary imaging method used to investigate dopamine function in humans. Previous PET studies have shown striatal dopamine release during simple finger tapping in both the putamen and the caudate. It is likely that dopamine release in the putamen is related to motor processes while dopamine release in the caudate could signal sustained cognitive component processes of the task, but the poor temporal resolution of PET has hindered firm conclusions. In this study we simultaneously collected [11C]Raclopride PET and functional Magnetic Resonance Imaging (fMRI) data while participants performed finger tapping, with fMRI being able to isolate activations related to individual tapping events. The results revealed fMRI-PET overlap in the bilateral putamen, which is consistent with a motor component process. Selective PET responses in the caudate, ventral striatum, and right posterior putamen, were also observed but did not overlap with fMRI responses to tapping events, suggesting that these reflect non-motor component processes of finger tapping. Our findings suggest an interplay between motor and non-motor-related dopamine release during simple finger tapping and illustrate the potential of hybrid PET-fMRI in revealing distinct component processes of cognitive functions.
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Affiliation(s)
- Filip Grill
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.,Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
| | - Jarkko Johansson
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.,Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
| | - Jan Axelsson
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.,Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
| | - Patrik Brynolfsson
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.,Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden
| | - Lars Nyberg
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.,Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, Umeå, Sweden
| | - Anna Rieckmann
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.,Umeå Center for Functional Brain Imaging, Umeå University, Umeå, Sweden.,Department of Integrative Medical Biology, Umeå University, Umeå, Sweden.,The Munich Center for the Economics of Aging, Max-Planck-Institute for Social Law and Social Policy, Munich, Germany
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39
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Habets JGV, Herff C, Kubben PL, Kuijf ML, Temel Y, Evers LJW, Bloem BR, Starr PA, Gilron R, Little S. Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson's Disease Using a Wrist-Worn Accelerometer. SENSORS 2021; 21:s21237876. [PMID: 34883886 PMCID: PMC8659489 DOI: 10.3390/s21237876] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 11/19/2021] [Accepted: 11/22/2021] [Indexed: 01/07/2023]
Abstract
Motor fluctuations in Parkinson’s disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson’s patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson’s patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.
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Affiliation(s)
- Jeroen G. V. Habets
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
- Correspondence: ; Tel.: +31-433-876-052
| | - Christian Herff
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Pieter L. Kubben
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Mark L. Kuijf
- Department of Neurology, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands;
| | - Yasin Temel
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, 6229 ER Maastricht, The Netherlands; (C.H.); (P.L.K.); (Y.T.)
| | - Luc J. W. Evers
- Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 GC Nijmegen, The Netherlands; (L.J.W.E.); (B.R.B.)
| | - Bastiaan R. Bloem
- Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 GC Nijmegen, The Netherlands; (L.J.W.E.); (B.R.B.)
| | - Philip A. Starr
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
| | - Ro’ee Gilron
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
| | - Simon Little
- Department of Movement Disorders and Neuromodulation, University of California San Francisco, San Francisco, CA 94143, USA; (P.A.S.); (R.G.); (S.L.)
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Sharon T. From hostile worlds to multiple spheres: towards a normative pragmatics of justice for the Googlization of health. MEDICINE, HEALTH CARE, AND PHILOSOPHY 2021; 24:315-327. [PMID: 33721157 PMCID: PMC7957283 DOI: 10.1007/s11019-021-10006-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/22/2021] [Indexed: 05/03/2023]
Abstract
The datafication and digitalization of health and medicine has engendered a proliferation of new collaborations between public health institutions and data corporations like Google, Apple, Microsoft and Amazon. Critical perspectives on these new partnerships tend to frame them as an instance of market transgressions by tech giants into the sphere of health and medicine, in line with a "hostile worlds" doctrine that upholds that the borders between market and non-market spheres should be carefully policed. This article seeks to outline the limitations of this common framing for critically understanding the phenomenon of the Googlization of health. In particular, the mobilization of a diversity of non-market value statements in the justification work carried out by actors involved in the Googlization of health indicates the co-presence of additional worlds or spheres in this context, which are not captured by the market vs. non-market dichotomy. It then advances an alternative framework, based on a multiple-sphere ontology that draws on Boltanski and Thevenot's orders of worth and Michael Walzer's theory of justice, which I call a normative pragmatics of justice. This framework addresses both the normative deficit in Boltanski and Thevenot's work and provides an important emphasis on the empirical workings of justice. Finally, I discuss why this framework is better equipped to identify and to address the many risks raised by the Googlization of health and possibly other dimensions of the digitalization and datafication of society.
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Affiliation(s)
- Tamar Sharon
- Faculty of Philosophy, Theology and Religious Studies, Radboud University, PO Box 9103, 6500 HD, Nijmegen, The Netherlands.
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Wasmann JW, Pragt L, Eikelboom R, Swanepoel DW. Digital approaches to automated and machine learning assessments of hearing: a scoping review (Preprint). J Med Internet Res 2021; 24:e32581. [PMID: 34919056 PMCID: PMC8851345 DOI: 10.2196/32581] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 12/01/2021] [Accepted: 12/16/2021] [Indexed: 01/24/2023] Open
Abstract
Background Hearing loss affects 1 in 5 people worldwide and is estimated to affect 1 in 4 by 2050. Treatment relies on the accurate diagnosis of hearing loss; however, this first step is out of reach for >80% of those affected. Increasingly automated approaches are being developed for self-administered digital hearing assessments without the direct involvement of professionals. Objective This study aims to provide an overview of digital approaches in automated and machine learning assessments of hearing using pure-tone audiometry and to focus on the aspects related to accuracy, reliability, and time efficiency. This review is an extension of a 2013 systematic review. Methods A search across the electronic databases of PubMed, IEEE, and Web of Science was conducted to identify relevant reports from the peer-reviewed literature. Key information about each report’s scope and details was collected to assess the commonalities among the approaches. Results A total of 56 reports from 2012 to June 2021 were included. From this selection, 27 unique automated approaches were identified. Machine learning approaches require fewer trials than conventional threshold-seeking approaches, and personal digital devices make assessments more affordable and accessible. Validity can be enhanced using digital technologies for quality surveillance, including noise monitoring and detecting inconclusive results. Conclusions In the past 10 years, an increasing number of automated approaches have reported similar accuracy, reliability, and time efficiency as manual hearing assessments. New developments, including machine learning approaches, offer features, versatility, and cost-effectiveness beyond manual audiometry. Used within identified limitations, automated assessments using digital devices can support task-shifting, self-care, telehealth, and clinical care pathways.
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Affiliation(s)
- Jan-Willem Wasmann
- Department of Otorhinolaryngology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Leontien Pragt
- Department of Otorhinolaryngology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Robert Eikelboom
- Ear Science Institute Australia, Subiaco, Australia
- Ear Sciences Centre, Medical School, The University of Western Australia, Perth, Australia
- Department of Speech-Language Pathology and Audiology, University of Pretoria, Pretoria, South Africa
| | - De Wet Swanepoel
- Ear Science Institute Australia, Subiaco, Australia
- Ear Sciences Centre, Medical School, The University of Western Australia, Perth, Australia
- Department of Speech-Language Pathology and Audiology, University of Pretoria, Pretoria, South Africa
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van Wamelen DJ, Sringean J, Trivedi D, Carroll CB, Schrag AE, Odin P, Antonini A, Bloem BR, Bhidayasiri R, Chaudhuri KR. Digital health technology for non-motor symptoms in people with Parkinson's disease: Futile or future? Parkinsonism Relat Disord 2021; 89:186-194. [PMID: 34362670 DOI: 10.1016/j.parkreldis.2021.07.032] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 07/26/2021] [Accepted: 07/28/2021] [Indexed: 10/20/2022]
Abstract
INTRODUCTION There is an ongoing digital revolution in the field of Parkinson's disease (PD) for the objective measurement of motor aspects, to be used in clinical trials and possibly support therapeutic choices. The focus of remote technologies is now also slowly shifting towards the broad but more "hidden" spectrum of non-motor symptoms (NMS). METHODS A narrative review of digital health technologies for measuring NMS in people with PD was conducted. These digital technologies were defined as assessment tools for NMS offered remotely in the form of a wearable, downloadable as a mobile app, or any other objective measurement of NMS in PD that did not require a hospital visit and could be performed remotely. Searches were performed using peer-reviewed literature indexed databases (MEDLINE, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane CENTRAL Register of Controlled Trials), as well as Google and Google Scholar. RESULTS Eighteen studies deploying digital health technology in PD were identified, for example for the measurement of sleep disorders, cognitive dysfunction and orthostatic hypotension. In addition, we describe promising developments in other conditions that could be translated for use in PD. CONCLUSION Unlike motor symptoms, non-motor features of PD are difficult to measure directly using remote digital technologies. Nonetheless, it is currently possible to reliably measure several NMS and further digital technology developments are underway to offer further capture of often under-reported and under-recognised NMS.
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Affiliation(s)
- Daniel J van Wamelen
- King's College London, Department of Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom; Parkinson's Foundation Centre of Excellence at King's College Hospital, Denmark Hill, London, United Kingdom; Radboud University Medical Centre; Donders Institute for Brain, Cognition and Behaviour; Department of Neurology, Nijmegen, the Netherlands.
| | - Jirada Sringean
- Chulalongkorn Centre of Excellence for Parkinson's Disease & Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
| | - Dhaval Trivedi
- King's College London, Department of Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom; Parkinson's Foundation Centre of Excellence at King's College Hospital, Denmark Hill, London, United Kingdom
| | - Camille B Carroll
- Faculty of Health, University of Plymouth, Plymouth, Devon, United Kingdom
| | - Anette E Schrag
- Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom
| | - Per Odin
- Division of Neurology, Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Angelo Antonini
- Movement Disorders Unit, Department of Neuroscience, University of Padua, Padua, Italy
| | - Bastiaan R Bloem
- Radboud University Medical Centre; Donders Institute for Brain, Cognition and Behaviour; Department of Neurology, Nijmegen, the Netherlands
| | - Roongroj Bhidayasiri
- Chulalongkorn Centre of Excellence for Parkinson's Disease & Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand; The Academy of Science, The Royal Society of Thailand, Bangkok, Thailand
| | - K Ray Chaudhuri
- King's College London, Department of Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom; Parkinson's Foundation Centre of Excellence at King's College Hospital, Denmark Hill, London, United Kingdom
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van Gastel BE, Jacobs B, Popma J. Data Protection Using Polymorphic Pseudonymisation in a Large-Scale Parkinson's Disease Study. JOURNAL OF PARKINSONS DISEASE 2021; 11:S19-S25. [PMID: 34092652 PMCID: PMC8385496 DOI: 10.3233/jpd-202431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This paper describes an advanced form of pseudonymisation in a large cohort study on Parkinson's disease, called Personalized Parkinson Project (PPP). The study collects various forms of biomedical data of study participants, including data from wearable devices with multiple sensors. The participants are all from the Netherlands, but the data will be usable by research groups worldwide on the basis of a suitable data use agreement. The data are pseudonymised, as required by Europe's General Data Protection Regulation (GDPR). The form of pseudonymisation that is used in this Parkinson project is based on cryptographic techniques and is 'polymorphic': it gives each participating research group its own 'local' pseudonyms. Still, the system is globally consistent, in the sense that if one research group adds data to PPP under its own local pseudonyms, the data become available for other groups under their pseudonyms. The paper gives an overview how this works, without going into the cryptographic details.
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Affiliation(s)
- Bernard E van Gastel
- Interdisciplinary Hub for Security, Privacy and Data Governance, Radboud University, Nijmegen, The Netherlands
| | - Bart Jacobs
- Interdisciplinary Hub for Security, Privacy and Data Governance, Radboud University, Nijmegen, The Netherlands
| | - Jean Popma
- Interdisciplinary Hub for Security, Privacy and Data Governance, Radboud University, Nijmegen, The Netherlands
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Stephenson D, Badawy R, Mathur S, Tome M, Rochester L. Digital Progression Biomarkers as Novel Endpoints in Clinical Trials: A Multistakeholder Perspective. JOURNAL OF PARKINSONS DISEASE 2021; 11:S103-S109. [PMID: 33579873 PMCID: PMC8385507 DOI: 10.3233/jpd-202428] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The burden of Parkinson's disease (PD) continues to grow at an unsustainable pace particularly given that it now represents the fastest growing brain disease. Despite seminal discoveries in genetics and pathogenesis, people living with PD oftentimes wait years to obtain an accurate diagnosis and have no way to know their own prognostic fate once they do learn they have the disease. Currently, there is no objective biomarker to measure the onset, progression, and severity of PD along the disease continuum. Without such tools, the effectiveness of any given treatment, experimental or conventional cannot be measured. Such tools are urgently needed now more than ever given the rich number of new candidate therapies in the pipeline. Over the last decade, millions of dollars have been directed to identify biomarkers to inform progression of PD typically using molecular, fluid or imaging modalities. These efforts have produced novel insights in our understanding of PD including mechanistic targets, disease subtypes and imaging biomarkers. While we have learned a lot along the way, implementation of robust disease progression biomarkers as tools for quantifying changes in disease status or severity remains elusive. Biomarkers have improved health outcomes and led to accelerated drug approvals in key areas of unmet need such as oncology. Quantitative biomarker measures such as HbA1c a standard test for the monitoring of diabetes has impacted patient care and management, both for the healthcare professionals and the patient community. Such advances accelerate opportunities for early intervention including prevention of disease in high-risk individuals. In PD, progression markers are needed at all stages of the disease in order to catalyze drug development-this allows interventions aimed to halt or slow disease progression (very early) but also facilitates symptomatic treatments at moderate stages of the disease. Recently, attention has turned to the role of digital health technologies to complement the traditional modalities as they are relatively low cost, objective and scalable. Success in this endeavor would be transformative for clinical research and therapeutic development. Consequently, significant investment has led to a number of collaborative efforts to identify and validate suitable digital biomarkers of disease progression.
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Affiliation(s)
| | | | | | - Maria Tome
- European Medicines Agency, Amsterdam, The Netherlands
| | - Lynn Rochester
- Institute of Translational and Clinical Research, Newcastle University, Newcastle, UK
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Personalized Medicine in Parkinson's Disease: New Options for Advanced Treatments. J Pers Med 2021; 11:jpm11070650. [PMID: 34357117 PMCID: PMC8303729 DOI: 10.3390/jpm11070650] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 06/29/2021] [Accepted: 07/07/2021] [Indexed: 12/11/2022] Open
Abstract
Parkinson’s disease (PD) presents varying motor and non-motor features in each patient owing to their different backgrounds, such as age, gender, genetics, and environmental factors. Furthermore, in the advanced stages, troublesome symptoms vary between patients due to motor and non-motor complications. The treatment of PD has made great progress over recent decades and has directly contributed to an improvement in patients’ quality of life, especially through the progression of advanced treatment. Deep brain stimulation, radiofrequency, MR–guided focused ultrasound, gamma knife, levodopa-carbidopa intestinal gel, and apomorphine are now used in the clinical setting for this disease. With multiple treatment options currently available for all stages of PD, we here discuss the most recent options for advanced treatment, including cell therapy in advanced PD, from the perspective of personalized medicine.
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Prasuhn J, Brüggemann N. Genotype-driven therapeutic developments in Parkinson's disease. Mol Med 2021; 27:42. [PMID: 33874883 PMCID: PMC8056568 DOI: 10.1186/s10020-021-00281-8] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 02/12/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Remarkable advances have been reached in the understanding of the genetic basis of Parkinson's disease (PD), with the identification of monogenic causes (mPD) and a plethora of gene loci leading to an increased risk for idiopathic PD. The expanding knowledge and subsequent identification of genetic contributions fosters the understanding of molecular mechanisms leading to disease development and progression. Distinct pathways involved in mitochondrial dysfunction, oxidative stress, and lysosomal function have been identified and open a unique window of opportunity for individualized treatment approaches. These genetic findings have led to an imminent progress towards pathophysiology-targeted clinical trials and potentially disease-modifying treatments in the future. MAIN BODY OF THE MANUSCRIPT In this review article we will summarize known genetic contributors to the pathophysiology of Parkinson's disease, the molecular mechanisms leading to disease development, and discuss challenges and opportunities in clinical trial designs. CONCLUSIONS The future success of clinical trials in PD is mainly dependent on reliable biomarker development and extensive genetic testing to identify genetic cases. Whether genotype-dependent stratification of study participants will extend the potential application of new drugs will be one major challenge in conceptualizing clinical trials. However, the latest developments in genotype-driven treatments will pave the road to individualized pathophysiology-based therapies in the future.
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Affiliation(s)
- Jannik Prasuhn
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
- Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany
| | - Norbert Brüggemann
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Lübeck, Lübeck, Germany.
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany.
- Center of Brain, Behavior and Metabolism, University of Lübeck, Lübeck, Germany.
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Williamson JR, Telfer B, Mullany R, Friedl KE. Detecting Parkinson's Disease from Wrist-Worn Accelerometry in the U.K. Biobank. SENSORS (BASEL, SWITZERLAND) 2021; 21:2047. [PMID: 33799420 PMCID: PMC7999802 DOI: 10.3390/s21062047] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 03/09/2021] [Accepted: 03/10/2021] [Indexed: 02/06/2023]
Abstract
Parkinson's disease (PD) is a chronic movement disorder that produces a variety of characteristic movement abnormalities. The ubiquity of wrist-worn accelerometry suggests a possible sensor modality for early detection of PD symptoms and subsequent tracking of PD symptom severity. As an initial proof of concept for this technological approach, we analyzed the U.K. Biobank data set, consisting of one week of wrist-worn accelerometry from a population with a PD primary diagnosis and an age-matched healthy control population. Measures of movement dispersion were extracted from automatically segmented gait data, and measures of movement dimensionality were extracted from automatically segmented low-movement data. Using machine learning classifiers applied to one week of data, PD was detected with an area under the curve (AUC) of 0.69 on gait data, AUC = 0.84 on low-movement data, and AUC = 0.85 on a fusion of both activities. It was also found that classification accuracy steadily improved across the one-week data collection, suggesting that higher accuracy could be achievable from a longer data collection. These results suggest the viability of using a low-cost and easy-to-use activity sensor for detecting movement abnormalities due to PD and motivate further research on early PD detection and tracking of PD symptom severity.
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Affiliation(s)
- James R. Williamson
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA 02421, USA; (B.T.); (R.M.)
| | - Brian Telfer
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA 02421, USA; (B.T.); (R.M.)
| | - Riley Mullany
- Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, MA 02421, USA; (B.T.); (R.M.)
| | - Karl E. Friedl
- U.S. Army Research Institute of Environmental Medicine, Natick, MA 01760, USA;
- Department of Neurology, University of California, San Francisco, CA 94143, USA
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A Long-Term, Real-Life Parkinson Monitoring Database Combining Unscripted Objective and Subjective Recordings. DATA 2021. [DOI: 10.3390/data6020022] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Accurate real-life monitoring of motor and non-motor symptoms is a challenge in Parkinson’s disease (PD). The unobtrusive capturing of symptoms and their naturalistic fluctuations within or between days can improve evaluation and titration of therapy. First-generation commercial PD motion sensors are promising to augment clinical decision-making in general neurological consultation, but concerns remain regarding their short-term validity, and long-term real-life usability. In addition, tools monitoring real-life subjective experiences of motor and non-motor symptoms are lacking. The dataset presented in this paper constitutes a combination of objective kinematic data and subjective experiential data, recorded parallel to each other in a naturalistic, long-term real-life setting. The objective data consists of accelerometer and gyroscope data, and the subjective data consists of data from ecological momentary assessments. Twenty PD patients were monitored without daily life restrictions for fourteen consecutive days. The two types of data can be used to address hypotheses on naturalistic motor and/or non-motor symptomatology in PD.
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Muddapu VR, Chakravarthy VS. Influence of energy deficiency on the subcellular processes of Substantia Nigra Pars Compacta cell for understanding Parkinsonian neurodegeneration. Sci Rep 2021; 11:1754. [PMID: 33462293 PMCID: PMC7814067 DOI: 10.1038/s41598-021-81185-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 12/23/2020] [Indexed: 01/29/2023] Open
Abstract
Parkinson's disease (PD) is the second most prominent neurodegenerative disease around the world. Although it is known that PD is caused by the loss of dopaminergic cells in substantia nigra pars compacta (SNc), the decisive cause of this inexorable cell loss is not clearly elucidated. We hypothesize that "Energy deficiency at a sub-cellular/cellular/systems level can be a common underlying cause for SNc cell loss in PD." Here, we propose a comprehensive computational model of SNc cell, which helps us to understand the pathophysiology of neurodegeneration at the subcellular level in PD. The aim of the study is to see how deficits in the supply of energy substrates (glucose and oxygen) lead to a deficit in adenosine triphosphate (ATP). The study also aims to show that deficits in ATP are the common factor underlying the molecular-level pathological changes, including alpha-synuclein aggregation, reactive oxygen species formation, calcium elevation, and dopamine dysfunction. The model suggests that hypoglycemia plays a more crucial role in leading to ATP deficits than hypoxia. We believe that the proposed model provides an integrated modeling framework to understand the neurodegenerative processes underlying PD.
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Affiliation(s)
- Vignayanandam Ravindernath Muddapu
- grid.417969.40000 0001 2315 1926Computational Neuroscience Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Sardar Patel Road, Chennai, 600036 Tamil Nadu India
| | - V. Srinivasa Chakravarthy
- grid.417969.40000 0001 2315 1926Computational Neuroscience Lab, Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Sardar Patel Road, Chennai, 600036 Tamil Nadu India
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