| Literature DB >> 32444562 |
E Ray Dorsey1,2, Larsson Omberg3, Emma Waddell1, Jamie L Adams1,2, Roy Adams4, Mohammad Rafayet Ali5, Katherine Amodeo2, Abigail Arky1, Erika F Augustine1,2, Karthik Dinesh6, Mohammed Ehsan Hoque5, Alistair M Glidden1, Stella Jensen-Roberts1, Zachary Kabelac7, Dina Katabi7, Karl Kieburtz1,2, Daniel R Kinel1,2, Max A Little8,9, Karlo J Lizarraga1,2, Taylor Myers1, Sara Riggare10, Spencer Z Rosero11, Suchi Saria4,12, Giovanni Schifitto2, Ruth B Schneider1,2, Gaurav Sharma6,13, Ira Shoulson1,2,14, E Anna Stevenson1, Christopher G Tarolli1,2, Jiebo Luo5, Michael P McDermott1,13.
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 created 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.Entities:
Keywords: Autonomic nervous system; Parkinson’s disease; gait; natural history; observational study; phenotype; real-world data; sleep; smartphone; social behavior
Year: 2020 PMID: 32444562 DOI: 10.3233/JPD-202006
Source DB: PubMed Journal: J Parkinsons Dis ISSN: 1877-7171 Impact factor: 5.568