Wearable Sensor-Based Assessments for Remotely Screening Early-Stage Parkinson's Disease.

Prevalence estimates of Parkinson's disease (PD)-the fastest-growing neurodegenerative disease-are generally underestimated due to issues surrounding diagnostic accuracy, symptomatic undiagnosed cases, suboptimal prodromal monitoring, and limited screening access. Remotely monitored wearable devices and sensors provide precise, objective, and frequent measures of motor and non-motor symptoms. Here, we used consumer-grade wearable device and sensor data from the WATCH-PD study to develop a PD screening tool aimed at eliminating the gap between patient symptoms and diagnosis. Early-stage PD patients (n = 82) and age-matched comparison participants (n = 50) completed a multidomain assessment battery during a one-year longitudinal multicenter study. Using disease- and behavior-relevant feature engineering and multivariate machine learning modeling of early-stage PD status, we developed a highly accurate (92.3%), sensitive (90.0%), and specific (100%) random forest classification model (AUC = 0.92) that performed well across environmental and platform contexts. These findings provide robust support for further exploration of consumer-grade wearable devices and sensors for global population-wide PD screening and surveillance.

Artificial intelligence-enabled detection and assessment of Parkinson's disease using nocturnal breathing signals.

There are currently no effective biomarkers for diagnosing Parkinson's disease (PD) or tracking its progression. Here, we developed an artificial intelligence (AI) model to detect PD and track its progression from nocturnal breathing signals. The model was evaluated on a large dataset comprising 7,671 individuals, using data from several hospitals in the United States, as well as multiple public datasets. The AI model can detect PD with an area-under-the-curve of 0.90 and 0.85 on held-out and external test sets, respectively. The AI model can also estimate PD severity and progression in accordance with the Movement Disorder Society Unified Parkinson's Disease Rating Scale (R = 0.94, P = 3.6 × 10-25). The AI model uses an attention layer that allows for interpreting its predictions with respect to sleep and electroencephalogram. Moreover, the model can assess PD in the home setting in a touchless manner, by extracting breathing from radio waves that bounce off a person's body during sleep. Our study demonstrates the feasibility of objective, noninvasive, at-home assessment of PD, and also provides initial evidence that this AI model may be useful for risk assessment before clinical diagnosis.

Digital Endpoints: Definition, Benefits, and Current Barriers in Accelerating Development and Adoption.

The assessment of health and disease requires a set of criteria to define health status and progression. These health measures are referred to as "endpoints." A "digital endpoint" is defined by its use of sensor-generated data often collected outside of a clinical setting such as in a patient's free-living environment. Applicable sensors exist in an array of devices and can be applied in a diverse set of contexts. For example, a smartphone's microphone might be used to diagnose or predict mild cognitive impairment due to Alzheimer's disease or a wrist-worn activity monitor (such as those found in smartwatches) may be used to measure a drug's effect on the nocturnal activity of patients with sickle cell disease. Digital endpoints are generating considerable excitement because they permit a more authentic assessment of the patient's experience, reveal formerly untold realities of disease burden, and can cut drug discovery costs in half. However, before these benefits can be realized, effort must be applied not only to the technical creation of digital endpoints but also to the environment that allows for their development and application. The future of digital endpoints rests on meaningful interdisciplinary collaboration, sufficient evidence that digital endpoints can realize their promise, and the development of an ecosystem in which the vast quantities of data that digital endpoints generate can be analyzed. The fundamental nature of health care is changing. With coronavirus disease 2019 serving as a catalyst, there has been a rapid expansion of home care models, telehealth, and remote patient monitoring. The increasing adoption of these health-care innovations will expedite the requirement for a digital characterization of clinical status as current assessment tools often rely upon direct interaction with patients and thus are not fit for purpose to be administered remotely. With the ubiquity of relatively inexpensive sensors, digital endpoints are positioned to drive this consequential change. It is therefore not surprising that regulators, physicians, researchers, and consultants have each offered their assessment of these novel tools. However, as we further describe later, the broad adoption of digital endpoints will require a cooperative effort. In this article, we present an analysis of the current state of digital endpoints. We also attempt to unify the perspectives of the parties involved in the development and deployment of these tools. We conclude with an interdependent list of challenges that must be collaboratively addressed before these endpoints are widely adopted.

Quadruple Decision Making for Parkinson's Disease Patients: Combining Expert Opinion, Patient Preferences, Scientific Evidence, and Big Data Approaches to Reach Precision Medicine.

Clinical decision making for Parkinson's disease patients is supported by a combination of three distinct information resources: best available scientific evidence, professional expertise, and the personal needs and preferences of patients. All three sources have clear value but also share several important limitations, mainly regarding subjectivity, generalizability and variability. For example, current scientific evidence, especially from controlled clinical trials, is often based on selected study populations, making it difficult to translate the outcome to the care for individual patients in everyday clinical practice. Big data, including data from real-life unselected Parkinson populations, can help to bridge this information gap. Fine-grained patient profiles created from big data have the potential to aid in identifying therapeutic approaches that will be most effective given each patient's individual characteristics, which is particularly important for a disorder characterized by such tremendous interindividual variability as Parkinson's disease. In this viewpoint, we argue that big data approaches should be acknowledged and harnessed, not to replace existing information resources, but rather as a fourth and complimentary source of information in clinical decision making, helping to represent the full complexity of individual patients. We introduce the 'quadruple decision making' model and illustrate its mode of action by showing how this can be used to pursue precision medicine for persons living with Parkinson's disease.

Feasibility and safety of lumbar puncture in the Parkinson's disease research participants: Parkinson's Progression Marker Initiative (PPMI).

OBJECTIVE: To determine the feasibility, safety and tolerability of lumbar punctures (LPs) in research participants with early Parkinson disease (PD), subjects without evidence of dopaminergic deficiency (SWEDDs) and healthy volunteers (HC). BACKGROUND: Cerebrospinal fluid (CSF) analysis is becoming an essential part of the biomarkers discovery effort in PD with still limited data on safety and feasibility of serial LPs in PD participants. DESIGN/METHODS: Parkinson's Progression Marker Initiative (PPMI) is a longitudinal observation study designed to identify PD progression biomarkers. All PPMI participants undergo LP at baseline, 6, 12 months and yearly thereafter. CSF collection is performed by a trained investigator using predominantly atraumatic needles. Adverse events (AEs) are monitored by phone one week after LP completion. We analyzed safety data from baseline LPs. RESULTS: PPMI enrolled 683 participants (423 PD/196 HC/64 SWEDDs) from 23 study sites. CSF was collected at baseline in 97.5% of participants, of whom 5.4% underwent collection under fluoroscopy. 23% participants reported any related AEs, 68% of all AE were mild while 5.6% were severe. The most common AEs were headaches (13%) and low back pain (6.5%) and both occurred more commonly in HC and SWEDDs compared to PD participants. Factors associated with higher incidence of AEs across the cohorts included female gender, younger age and use of traumatic needles with larger diameter. AEs largely did not impact compliance with the future LPs. CONCLUSIONS: LPs are safe and feasible in PD research participants. Specific LP techniques (needle type and gauge) may reduce the overall incidence of AEs.

Multidisciplinary care for people with Parkinson's disease: the new kids on the block!

INTRODUCTION: Parkinson's disease (PD) is a chronic multisystem disorder that causes a wide variety of motor and non-motor symptoms. Over time, the progressive nature of the disease increases the risk of complications such as falls and loss of independence, having a profound impact on quality of life. The complexity and heterogeneity of symptoms therefore warrant a holistic, multidisciplinary approach. Specific healthcare professionals, e.g. the movement disorders neurologist and the PD nurse specialist, are considered essential members of this multidisciplinary team. However, with our increasing knowledge about different aspects of the disease, other disciplines are also being recognized as important contributors to the healthcare team. Areas covered: The authors describe a selection of these relatively newly-recognized disciplines, including the specialist in vascular medicine, gastroenterologist, pulmonologist, neuro-ophthalmologist, urologist, geriatrician/elderly care physician, palliative care specialist and the dentist. Furthermore, they share the view of a person with PD on how patients and caregivers should be involved in the multidisciplinary team. Finally, they have included a perspective on the new role of the movement disorder neurologist, with care delivery via 'tele-neurology'. Expert commentary: Increased awareness about the potential role of these 'new' professionals will further improve disease management and quality of life of PD patients.

Huntington disease.

Huntington disease is devastating to patients and their families - with autosomal dominant inheritance, onset typically in the prime of adult life, progressive course, and a combination of motor, cognitive and behavioural features. The disease is caused by an expanded CAG trinucleotide repeat (of variable length) in HTT, the gene that encodes the protein huntingtin. In mutation carriers, huntingtin is produced with abnormally long polyglutamine sequences that confer toxic gains of function and predispose the protein to fragmentation, resulting in neuronal dysfunction and death. In this Primer, we review the epidemiology of Huntington disease, noting that prevalence is higher than previously thought, geographically variable and increasing. We describe the relationship between CAG repeat length and clinical phenotype, as well as the concept of genetic modifiers of the disease. We discuss normal huntingtin protein function, evidence for differential toxicity of mutant huntingtin variants, theories of huntingtin aggregation and the many different mechanisms of Huntington disease pathogenesis. We describe the genetic and clinical diagnosis of the condition, its clinical assessment and the multidisciplinary management of symptoms, given the absence of effective disease-modifying therapies. We review past and present clinical trials and therapeutic strategies under investigation, including impending trials of targeted huntingtin-lowering drugs and the progress in development of biomarkers that will support the next generation of trials. For an illustrated summary of this Primer, visit: http://go.nature.com/hPMENh.

Use of Tetrabenazine in Huntington Disease Patients on Antidepressants or with Advanced Disease: Results from the TETRA-HD Study.

The safety and effectiveness of tetrabenazine in different sub-populations of Huntington disease (HD) is not known. In this study, we evaluated the safety of tetrabenazine in individuals on an antidepressant and its effectiveness in advanced HD. Tetrabenazine was not associated with an increased incidence of depressed mood among those taking antidepressants and was effective at reducing chorea in those with advanced HD.