Deep Phenotyping of Parkinson’s Disease

Abstract

Phenotype is the set of observable traits of an organism or condition. While advances in genetics, imaging, and molecular biology have improved our understanding of the underlying biology of Parkinson's disease (PD), clinical phenotyping of PD still relies primarily on history and physical examination. These subjective, episodic, categorical assessments are valuable for diagnosis and care but have left gaps in our understanding of the PD phenotype. Sensors can provide objective, continuous, real-world data about the PD clinical phenotype, increase our knowledge of its pathology, enhance evaluation of therapies, and ultimately, improve patient care. In this paper, we explore the concept of deep phenotyping-the comprehensive assessment of a condition using multiple clinical, biological, genetic, imaging, and sensor-based tools-for PD. We discuss the rationale for, outline current approaches to, identify benefits and limitations of, and consider future directions for deep clinical phenotyping.

Digital Twins for Multiple Sclerosis

“…Existing developments in precision medicine (44,(48)(49)(50) demonstrate that complex health-related big data of high quality are necessary, including lifestyle, nutrition, genetics, and environmental factors besides clinical, para-clinical, imaging and immunological or neurobiological parameters, which have to be analyzed and integrated in diagnosis, treatment and monitoring processes. To obtain big data and capture the bigger picture of a given individual on the way to precision medicine, Fagherazzi et al recommend the method of "deep digital phenotyping", which is a combination of deep phenotyping by collecting biomedical data in the real world and digital phenotyping by collecting digital biomarkers (42,44,(51)(52)(53).…”

Digital Technology in Movement Disorders: Updates, Applications, and Challenges

“…Smartphones and wearable devices may enable the earlier identification of individuals at risk for or with disease and may be more sensitive to disease progression, both of which may facilitate the identification of disease-modifying treatments. Devices can provide new insights into disability and progression that complement standard clinical assessments and enable deep clinical phenotyping of neurodegenerative diseases [ 113 ]. It is likely that some combination of clinical scales, imaging, biosamples, and digital tools will be the best and most comprehensive way to characterize and monitor disease.…”

Design of a virtual longitudinal observational study in Parkinson’s disease (AT‐HOME PD)

“…In order to meet this objective and also validate the remote assessment of the MoCA, we have initiated a sub‐study. For this sub‐study, we aim to recruit 50 PD participants from an on‐going, NINDS‐funded, University of Rochester study (Sensor Use to monitor Progression and Evaluate Symptoms Remotely in Parkinson’s Disease [SUPER‐PD]) 44 that is examining four different technologies (including mPower 2.0) for the assessment of PD disability and progression. Sub‐study participants are asked to complete a single video visit within 14 days of an in‐person study visit and permit the sharing of data from their in‐person study visit with the AT‐HOME PD study team.…”