Deep Phenotyping of Parkinson's Disease

E. Ray Dorsey, Larsson Omberg, Emma Waddell, Jamie L. Adams, Roy Adams, Mohammad Rafayet Ali, Katherine Amodeo, Abigail Arky, Erika F. Augustine, Karthik DInesh, Mohammed Ehsan Hoque, Alistair M. Glidden, Stella Jensen-Roberts, Zachary Kabelac, DIna Katabi, Karl Kieburtz, Daniel R. Kinel, Max A. Little, Karlo J. Lizarraga, Taylor MyersSara Riggare, Spencer Z. Rosero, Suchi Saria, Giovanni Schifitto, Ruth B. Schneider, Gaurav Sharma, Ira Shoulson, E. Anna Stevenson, Christopher G. Tarolli, Jiebo Luo, Michael P. McDermott

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)855-873
Number of pages19
JournalJournal of Parkinson's Disease
Volume10
Issue number3
DOIs
StatePublished - 2020

Keywords

  • Autonomic nervous system
  • Parkinson's disease
  • gait
  • natural history
  • observational study
  • phenotype
  • real-world data
  • sleep
  • smartphone
  • social behavior

ASJC Scopus subject areas

  • Clinical Neurology
  • Cellular and Molecular Neuroscience

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